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Type 'q()' to quit R. > pkgname <- "quest" > source(file.path(R.home("share"), "R", "examples-header.R")) > options(warn = 1) > library('quest') Attaching package: ‘quest’ The following object is masked from ‘package:stats’: decompose > > base::assign(".oldSearch", base::search(), pos = 'CheckExEnv') > base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv') > cleanEx() > nameEx("agg") > ### * agg > > flush(stderr()); flush(stdout()) > > ### Name: agg > ### Title: Aggregate an Atomic Vector by Group > ### Aliases: agg > > ### ** Examples > > > # one grouping variable > agg(x = airquality$"Solar.R", grp = airquality$"Month", fun = mean) [1] NA NA NA NA NA NA NA NA [9] NA NA NA NA NA NA NA NA [17] NA NA NA NA NA NA NA NA [25] NA NA NA NA NA NA NA 190.1667 [33] 190.1667 190.1667 190.1667 190.1667 190.1667 190.1667 190.1667 190.1667 [41] 190.1667 190.1667 190.1667 190.1667 190.1667 190.1667 190.1667 190.1667 [49] 190.1667 190.1667 190.1667 190.1667 190.1667 190.1667 190.1667 190.1667 [57] 190.1667 190.1667 190.1667 190.1667 190.1667 216.4839 216.4839 216.4839 [65] 216.4839 216.4839 216.4839 216.4839 216.4839 216.4839 216.4839 216.4839 [73] 216.4839 216.4839 216.4839 216.4839 216.4839 216.4839 216.4839 216.4839 [81] 216.4839 216.4839 216.4839 216.4839 216.4839 216.4839 216.4839 216.4839 [89] 216.4839 216.4839 216.4839 216.4839 NA NA NA NA [97] NA NA NA NA NA NA NA NA [105] NA NA NA NA NA NA NA NA [113] NA NA NA NA NA NA NA NA [121] NA NA NA 167.4333 167.4333 167.4333 167.4333 167.4333 [129] 167.4333 167.4333 167.4333 167.4333 167.4333 167.4333 167.4333 167.4333 [137] 167.4333 167.4333 167.4333 167.4333 167.4333 167.4333 167.4333 167.4333 [145] 167.4333 167.4333 167.4333 167.4333 167.4333 167.4333 167.4333 167.4333 [153] 167.4333 > agg(x = airquality$"Solar.R", grp = airquality$"Month", fun = mean, + na.rm = TRUE) # ignoring missing values [1] 181.2963 181.2963 181.2963 181.2963 181.2963 181.2963 181.2963 181.2963 [9] 181.2963 181.2963 181.2963 181.2963 181.2963 181.2963 181.2963 181.2963 [17] 181.2963 181.2963 181.2963 181.2963 181.2963 181.2963 181.2963 181.2963 [25] 181.2963 181.2963 181.2963 181.2963 181.2963 181.2963 181.2963 190.1667 [33] 190.1667 190.1667 190.1667 190.1667 190.1667 190.1667 190.1667 190.1667 [41] 190.1667 190.1667 190.1667 190.1667 190.1667 190.1667 190.1667 190.1667 [49] 190.1667 190.1667 190.1667 190.1667 190.1667 190.1667 190.1667 190.1667 [57] 190.1667 190.1667 190.1667 190.1667 190.1667 216.4839 216.4839 216.4839 [65] 216.4839 216.4839 216.4839 216.4839 216.4839 216.4839 216.4839 216.4839 [73] 216.4839 216.4839 216.4839 216.4839 216.4839 216.4839 216.4839 216.4839 [81] 216.4839 216.4839 216.4839 216.4839 216.4839 216.4839 216.4839 216.4839 [89] 216.4839 216.4839 216.4839 216.4839 171.8571 171.8571 171.8571 171.8571 [97] 171.8571 171.8571 171.8571 171.8571 171.8571 171.8571 171.8571 171.8571 [105] 171.8571 171.8571 171.8571 171.8571 171.8571 171.8571 171.8571 171.8571 [113] 171.8571 171.8571 171.8571 171.8571 171.8571 171.8571 171.8571 171.8571 [121] 171.8571 171.8571 171.8571 167.4333 167.4333 167.4333 167.4333 167.4333 [129] 167.4333 167.4333 167.4333 167.4333 167.4333 167.4333 167.4333 167.4333 [137] 167.4333 167.4333 167.4333 167.4333 167.4333 167.4333 167.4333 167.4333 [145] 167.4333 167.4333 167.4333 167.4333 167.4333 167.4333 167.4333 167.4333 [153] 167.4333 > agg(x = setNames(airquality$"Solar.R", nm = row.names(airquality)), grp = airquality$"Month", + fun = mean, na.rm = TRUE) # keeps the names in the return object 1 2 3 4 5 6 7 8 181.2963 181.2963 181.2963 181.2963 181.2963 181.2963 181.2963 181.2963 9 10 11 12 13 14 15 16 181.2963 181.2963 181.2963 181.2963 181.2963 181.2963 181.2963 181.2963 17 18 19 20 21 22 23 24 181.2963 181.2963 181.2963 181.2963 181.2963 181.2963 181.2963 181.2963 25 26 27 28 29 30 31 32 181.2963 181.2963 181.2963 181.2963 181.2963 181.2963 181.2963 190.1667 33 34 35 36 37 38 39 40 190.1667 190.1667 190.1667 190.1667 190.1667 190.1667 190.1667 190.1667 41 42 43 44 45 46 47 48 190.1667 190.1667 190.1667 190.1667 190.1667 190.1667 190.1667 190.1667 49 50 51 52 53 54 55 56 190.1667 190.1667 190.1667 190.1667 190.1667 190.1667 190.1667 190.1667 57 58 59 60 61 62 63 64 190.1667 190.1667 190.1667 190.1667 190.1667 216.4839 216.4839 216.4839 65 66 67 68 69 70 71 72 216.4839 216.4839 216.4839 216.4839 216.4839 216.4839 216.4839 216.4839 73 74 75 76 77 78 79 80 216.4839 216.4839 216.4839 216.4839 216.4839 216.4839 216.4839 216.4839 81 82 83 84 85 86 87 88 216.4839 216.4839 216.4839 216.4839 216.4839 216.4839 216.4839 216.4839 89 90 91 92 93 94 95 96 216.4839 216.4839 216.4839 216.4839 171.8571 171.8571 171.8571 171.8571 97 98 99 100 101 102 103 104 171.8571 171.8571 171.8571 171.8571 171.8571 171.8571 171.8571 171.8571 105 106 107 108 109 110 111 112 171.8571 171.8571 171.8571 171.8571 171.8571 171.8571 171.8571 171.8571 113 114 115 116 117 118 119 120 171.8571 171.8571 171.8571 171.8571 171.8571 171.8571 171.8571 171.8571 121 122 123 124 125 126 127 128 171.8571 171.8571 171.8571 167.4333 167.4333 167.4333 167.4333 167.4333 129 130 131 132 133 134 135 136 167.4333 167.4333 167.4333 167.4333 167.4333 167.4333 167.4333 167.4333 137 138 139 140 141 142 143 144 167.4333 167.4333 167.4333 167.4333 167.4333 167.4333 167.4333 167.4333 145 146 147 148 149 150 151 152 167.4333 167.4333 167.4333 167.4333 167.4333 167.4333 167.4333 167.4333 153 167.4333 > agg(x = airquality$"Solar.R", grp = airquality$"Month", rep = FALSE, + fun = mean, na.rm = TRUE) # do NOT repeat aggregated values Group.1 x 1 5 181.2963 2 6 190.1667 3 7 216.4839 4 8 171.8571 5 9 167.4333 > agg(x = airquality$"Solar.R", grp = airquality$"Month", rep = FALSE, rtn.grp = FALSE, + fun = mean, na.rm = TRUE) # groups are the names of the returned atomic vector 5 6 7 8 9 181.2963 190.1667 216.4839 171.8571 167.4333 > > # two grouping variables > tmp_nm <- c("vs","am") # Roxygen2 doesn't like a c() within a [] > agg(x = mtcars$"mpg", grp = mtcars[tmp_nm], rep = TRUE, fun = sd) [1] 4.008865 4.008865 4.757701 2.471071 2.774396 2.471071 2.774396 2.471071 [9] 2.471071 2.471071 2.471071 2.774396 2.774396 2.774396 2.774396 2.774396 [17] 2.774396 4.757701 4.757701 4.757701 2.471071 2.774396 2.774396 2.774396 [25] 2.774396 4.757701 4.008865 4.757701 4.008865 4.008865 4.008865 4.757701 > agg(x = mtcars$"mpg", grp = mtcars[tmp_nm], rep = FALSE, + fun = sd) # do NOT repeat aggregated values vs am x 1 0 0 2.774396 2 1 0 2.471071 3 0 1 4.008865 4 1 1 4.757701 > agg(x = mtcars$"mpg", grp = mtcars[tmp_nm], rep = FALSE, rtn.grp = FALSE, + fun = sd) # groups are the names of the returned atomic vector 0_0 1_0 0_1 1_1 2.774396 2.471071 4.008865 4.757701 > agg(x = mtcars$"mpg", grp = mtcars[tmp_nm], rep = FALSE, rtn.grp = FALSE, + sep = ".", fun = sd) # change the separater for naming 0.0 1.0 0.1 1.1 2.774396 2.471071 4.008865 4.757701 > > # error messages > ## Not run: > ##D agg(x = airquality$"Solar.R", grp = mtcars[tmp_nm]) # error returned > ##D # b/c atomic vectors within \code{grp} not having the same length as \code{x} > ## End(Not run) > > > > > cleanEx() > nameEx("agg_dfm") > ### * agg_dfm > > flush(stderr()); flush(stdout()) > > ### Name: agg_dfm > ### Title: Data Information by Group > ### Aliases: agg_dfm > > ### ** Examples > > > ### one grouping variable > > ## by in base R > by(data = airquality[c("Ozone","Solar.R")], INDICES = airquality["Month"], + simplify = FALSE, FUN = function(dat) cor(dat, use = "complete")[1,2]) Month: 5 [1] 0.2428635 ------------------------------------------------------------ Month: 6 [1] 0.7177528 ------------------------------------------------------------ Month: 7 [1] 0.4293259 ------------------------------------------------------------ Month: 8 [1] 0.5296827 ------------------------------------------------------------ Month: 9 [1] 0.180373 > > ## rep = TRUE > > # rtn.group = TRUE > agg_dfm(data = airquality, vrb.nm = c("Ozone","Solar.R"), grp.nm = "Month", + rep = TRUE, rtn.grp = TRUE, fun = function(dat) cor(dat, use = "complete")[1,2]) Month result 1 5 0.2428635 2 5 0.2428635 3 5 0.2428635 4 5 0.2428635 5 5 0.2428635 6 5 0.2428635 7 5 0.2428635 8 5 0.2428635 9 5 0.2428635 10 5 0.2428635 11 5 0.2428635 12 5 0.2428635 13 5 0.2428635 14 5 0.2428635 15 5 0.2428635 16 5 0.2428635 17 5 0.2428635 18 5 0.2428635 19 5 0.2428635 20 5 0.2428635 21 5 0.2428635 22 5 0.2428635 23 5 0.2428635 24 5 0.2428635 25 5 0.2428635 26 5 0.2428635 27 5 0.2428635 28 5 0.2428635 29 5 0.2428635 30 5 0.2428635 31 5 0.2428635 32 6 0.7177528 33 6 0.7177528 34 6 0.7177528 35 6 0.7177528 36 6 0.7177528 37 6 0.7177528 38 6 0.7177528 39 6 0.7177528 40 6 0.7177528 41 6 0.7177528 42 6 0.7177528 43 6 0.7177528 44 6 0.7177528 45 6 0.7177528 46 6 0.7177528 47 6 0.7177528 48 6 0.7177528 49 6 0.7177528 50 6 0.7177528 51 6 0.7177528 52 6 0.7177528 53 6 0.7177528 54 6 0.7177528 55 6 0.7177528 56 6 0.7177528 57 6 0.7177528 58 6 0.7177528 59 6 0.7177528 60 6 0.7177528 61 6 0.7177528 62 7 0.4293259 63 7 0.4293259 64 7 0.4293259 65 7 0.4293259 66 7 0.4293259 67 7 0.4293259 68 7 0.4293259 69 7 0.4293259 70 7 0.4293259 71 7 0.4293259 72 7 0.4293259 73 7 0.4293259 74 7 0.4293259 75 7 0.4293259 76 7 0.4293259 77 7 0.4293259 78 7 0.4293259 79 7 0.4293259 80 7 0.4293259 81 7 0.4293259 82 7 0.4293259 83 7 0.4293259 84 7 0.4293259 85 7 0.4293259 86 7 0.4293259 87 7 0.4293259 88 7 0.4293259 89 7 0.4293259 90 7 0.4293259 91 7 0.4293259 92 7 0.4293259 93 8 0.5296827 94 8 0.5296827 95 8 0.5296827 96 8 0.5296827 97 8 0.5296827 98 8 0.5296827 99 8 0.5296827 100 8 0.5296827 101 8 0.5296827 102 8 0.5296827 103 8 0.5296827 104 8 0.5296827 105 8 0.5296827 106 8 0.5296827 107 8 0.5296827 108 8 0.5296827 109 8 0.5296827 110 8 0.5296827 111 8 0.5296827 112 8 0.5296827 113 8 0.5296827 114 8 0.5296827 115 8 0.5296827 116 8 0.5296827 117 8 0.5296827 118 8 0.5296827 119 8 0.5296827 120 8 0.5296827 121 8 0.5296827 122 8 0.5296827 123 8 0.5296827 124 9 0.1803730 125 9 0.1803730 126 9 0.1803730 127 9 0.1803730 128 9 0.1803730 129 9 0.1803730 130 9 0.1803730 131 9 0.1803730 132 9 0.1803730 133 9 0.1803730 134 9 0.1803730 135 9 0.1803730 136 9 0.1803730 137 9 0.1803730 138 9 0.1803730 139 9 0.1803730 140 9 0.1803730 141 9 0.1803730 142 9 0.1803730 143 9 0.1803730 144 9 0.1803730 145 9 0.1803730 146 9 0.1803730 147 9 0.1803730 148 9 0.1803730 149 9 0.1803730 150 9 0.1803730 151 9 0.1803730 152 9 0.1803730 153 9 0.1803730 > > # rtn.group = FALSE > agg_dfm(data = airquality, vrb.nm = c("Ozone","Solar.R"), grp.nm = "Month", + rep = TRUE, rtn.grp = FALSE, fun = function(dat) cor(dat, use = "complete")[1,2]) 1 2 3 4 5 6 7 8 0.2428635 0.2428635 0.2428635 0.2428635 0.2428635 0.2428635 0.2428635 0.2428635 9 10 11 12 13 14 15 16 0.2428635 0.2428635 0.2428635 0.2428635 0.2428635 0.2428635 0.2428635 0.2428635 17 18 19 20 21 22 23 24 0.2428635 0.2428635 0.2428635 0.2428635 0.2428635 0.2428635 0.2428635 0.2428635 25 26 27 28 29 30 31 32 0.2428635 0.2428635 0.2428635 0.2428635 0.2428635 0.2428635 0.2428635 0.7177528 33 34 35 36 37 38 39 40 0.7177528 0.7177528 0.7177528 0.7177528 0.7177528 0.7177528 0.7177528 0.7177528 41 42 43 44 45 46 47 48 0.7177528 0.7177528 0.7177528 0.7177528 0.7177528 0.7177528 0.7177528 0.7177528 49 50 51 52 53 54 55 56 0.7177528 0.7177528 0.7177528 0.7177528 0.7177528 0.7177528 0.7177528 0.7177528 57 58 59 60 61 62 63 64 0.7177528 0.7177528 0.7177528 0.7177528 0.7177528 0.4293259 0.4293259 0.4293259 65 66 67 68 69 70 71 72 0.4293259 0.4293259 0.4293259 0.4293259 0.4293259 0.4293259 0.4293259 0.4293259 73 74 75 76 77 78 79 80 0.4293259 0.4293259 0.4293259 0.4293259 0.4293259 0.4293259 0.4293259 0.4293259 81 82 83 84 85 86 87 88 0.4293259 0.4293259 0.4293259 0.4293259 0.4293259 0.4293259 0.4293259 0.4293259 89 90 91 92 93 94 95 96 0.4293259 0.4293259 0.4293259 0.4293259 0.5296827 0.5296827 0.5296827 0.5296827 97 98 99 100 101 102 103 104 0.5296827 0.5296827 0.5296827 0.5296827 0.5296827 0.5296827 0.5296827 0.5296827 105 106 107 108 109 110 111 112 0.5296827 0.5296827 0.5296827 0.5296827 0.5296827 0.5296827 0.5296827 0.5296827 113 114 115 116 117 118 119 120 0.5296827 0.5296827 0.5296827 0.5296827 0.5296827 0.5296827 0.5296827 0.5296827 121 122 123 124 125 126 127 128 0.5296827 0.5296827 0.5296827 0.1803730 0.1803730 0.1803730 0.1803730 0.1803730 129 130 131 132 133 134 135 136 0.1803730 0.1803730 0.1803730 0.1803730 0.1803730 0.1803730 0.1803730 0.1803730 137 138 139 140 141 142 143 144 0.1803730 0.1803730 0.1803730 0.1803730 0.1803730 0.1803730 0.1803730 0.1803730 145 146 147 148 149 150 151 152 0.1803730 0.1803730 0.1803730 0.1803730 0.1803730 0.1803730 0.1803730 0.1803730 153 0.1803730 > > ## rep = FALSE > > # rtn.group = TRUE > agg_dfm(data = airquality, vrb.nm = c("Ozone","Solar.R"), grp.nm = "Month", + rep = FALSE, rtn.grp = TRUE, fun = function(dat) cor(dat, use = "complete")[1,2]) Month result 1 5 0.2428635 2 6 0.7177528 3 7 0.4293259 4 8 0.5296827 5 9 0.1803730 > suppressWarnings(plyr::ddply(.data = airquality[c("Ozone","Solar.R","Month")], + .variables = "Month", .fun = function(dat) cor(dat, use = "complete")[1,2])) Month V1 1 5 0.2428635 2 6 0.7177528 3 7 0.4293259 4 8 0.5296827 5 9 0.1803730 > > # rtn.group = FALSE > agg_dfm(data = airquality, vrb.nm = c("Ozone","Solar.R"), grp.nm = "Month", + rep = FALSE, rtn.grp = FALSE, fun = function(dat) cor(dat, use = "complete")[1,2]) 5 6 7 8 9 0.2428635 0.7177528 0.4293259 0.5296827 0.1803730 > suppressWarnings(plyr::daply(.data = airquality[c("Ozone","Solar.R","Month")], + .variables = "Month", .fun = function(dat) cor(dat, use = "complete")[1,2])) 5 6 7 8 9 0.2428635 0.7177528 0.4293259 0.5296827 0.1803730 > > ### two grouping variables > > ## by in base R > by(data = mtcars[c("mpg","cyl","disp")], INDICES = mtcars[c("vs","am")], + FUN = nrow, simplify = FALSE) # with multiple group columns vs: 0 am: 0 [1] 12 ------------------------------------------------------------ vs: 1 am: 0 [1] 7 ------------------------------------------------------------ vs: 0 am: 1 [1] 6 ------------------------------------------------------------ vs: 1 am: 1 [1] 7 > > ## rep = TRUE > > # rtn.grp = TRUE > agg_dfm(data = mtcars, vrb.nm = c("mpg","cyl","disp"), grp.nm = c("vs","am"), + rep = TRUE, rtn.grp = TRUE, fun = nrow) vs am result Mazda RX4 0 1 6 Mazda RX4 Wag 0 1 6 Datsun 710 1 1 7 Hornet 4 Drive 1 0 7 Hornet Sportabout 0 0 12 Valiant 1 0 7 Duster 360 0 0 12 Merc 240D 1 0 7 Merc 230 1 0 7 Merc 280 1 0 7 Merc 280C 1 0 7 Merc 450SE 0 0 12 Merc 450SL 0 0 12 Merc 450SLC 0 0 12 Cadillac Fleetwood 0 0 12 Lincoln Continental 0 0 12 Chrysler Imperial 0 0 12 Fiat 128 1 1 7 Honda Civic 1 1 7 Toyota Corolla 1 1 7 Toyota Corona 1 0 7 Dodge Challenger 0 0 12 AMC Javelin 0 0 12 Camaro Z28 0 0 12 Pontiac Firebird 0 0 12 Fiat X1-9 1 1 7 Porsche 914-2 0 1 6 Lotus Europa 1 1 7 Ford Pantera L 0 1 6 Ferrari Dino 0 1 6 Maserati Bora 0 1 6 Volvo 142E 1 1 7 > > # rtn.grp = FALSE > agg_dfm(data = mtcars, vrb.nm = c("mpg","cyl","disp"), grp.nm = c("vs","am"), + rep = TRUE, rtn.grp = FALSE, fun = nrow) Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive 6 6 7 7 Hornet Sportabout Valiant Duster 360 Merc 240D 12 7 12 7 Merc 230 Merc 280 Merc 280C Merc 450SE 7 7 7 12 Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental 12 12 12 12 Chrysler Imperial Fiat 128 Honda Civic Toyota Corolla 12 7 7 7 Toyota Corona Dodge Challenger AMC Javelin Camaro Z28 7 12 12 12 Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa 12 7 6 7 Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E 6 6 6 7 > > ## rep = FALSE > > # rtn.grp = TRUE > agg_dfm(data = mtcars, vrb.nm = c("mpg","cyl","disp"), grp.nm = c("vs","am"), + rep = FALSE, rtn.grp = TRUE, fun = nrow) vs am result 1 0 0 12 2 1 0 7 3 0 1 6 4 1 1 7 > agg_dfm(data = mtcars, vrb.nm = c("mpg","cyl","disp"), grp.nm = c("vs","am"), + rep = FALSE, rtn.grp = TRUE, rtn.result.nm = "value", fun = nrow) vs am value 1 0 0 12 2 1 0 7 3 0 1 6 4 1 1 7 > > # rtn.grp = FALSE > agg_dfm(data = mtcars, vrb.nm = c("mpg","cyl","disp"), grp.nm = c("vs","am"), + rep = FALSE, rtn.grp = FALSE, fun = nrow) 0.0 1.0 0.1 1.1 12 7 6 7 > agg_dfm(data = mtcars, vrb.nm = c("mpg","cyl","disp"), grp.nm = c("vs","am"), + rep = FALSE, rtn.grp = FALSE, sep = "_", fun = nrow) 0_0 1_0 0_1 1_1 12 7 6 7 > > > > > cleanEx() > nameEx("aggs") > ### * aggs > > flush(stderr()); flush(stdout()) > > ### Name: aggs > ### Title: Aggregate Data by Group > ### Aliases: aggs > > ### ** Examples > > aggs(data = airquality, vrb.nm = c("Ozone","Solar.R"), grp.nm = "Month", + fun = mean, na.rm = TRUE) Ozone_a Solar.R_a 1 23.61538 181.2963 2 23.61538 181.2963 3 23.61538 181.2963 4 23.61538 181.2963 5 23.61538 181.2963 6 23.61538 181.2963 7 23.61538 181.2963 8 23.61538 181.2963 9 23.61538 181.2963 10 23.61538 181.2963 11 23.61538 181.2963 12 23.61538 181.2963 13 23.61538 181.2963 14 23.61538 181.2963 15 23.61538 181.2963 16 23.61538 181.2963 17 23.61538 181.2963 18 23.61538 181.2963 19 23.61538 181.2963 20 23.61538 181.2963 21 23.61538 181.2963 22 23.61538 181.2963 23 23.61538 181.2963 24 23.61538 181.2963 25 23.61538 181.2963 26 23.61538 181.2963 27 23.61538 181.2963 28 23.61538 181.2963 29 23.61538 181.2963 30 23.61538 181.2963 31 23.61538 181.2963 32 29.44444 190.1667 33 29.44444 190.1667 34 29.44444 190.1667 35 29.44444 190.1667 36 29.44444 190.1667 37 29.44444 190.1667 38 29.44444 190.1667 39 29.44444 190.1667 40 29.44444 190.1667 41 29.44444 190.1667 42 29.44444 190.1667 43 29.44444 190.1667 44 29.44444 190.1667 45 29.44444 190.1667 46 29.44444 190.1667 47 29.44444 190.1667 48 29.44444 190.1667 49 29.44444 190.1667 50 29.44444 190.1667 51 29.44444 190.1667 52 29.44444 190.1667 53 29.44444 190.1667 54 29.44444 190.1667 55 29.44444 190.1667 56 29.44444 190.1667 57 29.44444 190.1667 58 29.44444 190.1667 59 29.44444 190.1667 60 29.44444 190.1667 61 29.44444 190.1667 62 59.11538 216.4839 63 59.11538 216.4839 64 59.11538 216.4839 65 59.11538 216.4839 66 59.11538 216.4839 67 59.11538 216.4839 68 59.11538 216.4839 69 59.11538 216.4839 70 59.11538 216.4839 71 59.11538 216.4839 72 59.11538 216.4839 73 59.11538 216.4839 74 59.11538 216.4839 75 59.11538 216.4839 76 59.11538 216.4839 77 59.11538 216.4839 78 59.11538 216.4839 79 59.11538 216.4839 80 59.11538 216.4839 81 59.11538 216.4839 82 59.11538 216.4839 83 59.11538 216.4839 84 59.11538 216.4839 85 59.11538 216.4839 86 59.11538 216.4839 87 59.11538 216.4839 88 59.11538 216.4839 89 59.11538 216.4839 90 59.11538 216.4839 91 59.11538 216.4839 92 59.11538 216.4839 93 59.96154 171.8571 94 59.96154 171.8571 95 59.96154 171.8571 96 59.96154 171.8571 97 59.96154 171.8571 98 59.96154 171.8571 99 59.96154 171.8571 100 59.96154 171.8571 101 59.96154 171.8571 102 59.96154 171.8571 103 59.96154 171.8571 104 59.96154 171.8571 105 59.96154 171.8571 106 59.96154 171.8571 107 59.96154 171.8571 108 59.96154 171.8571 109 59.96154 171.8571 110 59.96154 171.8571 111 59.96154 171.8571 112 59.96154 171.8571 113 59.96154 171.8571 114 59.96154 171.8571 115 59.96154 171.8571 116 59.96154 171.8571 117 59.96154 171.8571 118 59.96154 171.8571 119 59.96154 171.8571 120 59.96154 171.8571 121 59.96154 171.8571 122 59.96154 171.8571 123 59.96154 171.8571 124 31.44828 167.4333 125 31.44828 167.4333 126 31.44828 167.4333 127 31.44828 167.4333 128 31.44828 167.4333 129 31.44828 167.4333 130 31.44828 167.4333 131 31.44828 167.4333 132 31.44828 167.4333 133 31.44828 167.4333 134 31.44828 167.4333 135 31.44828 167.4333 136 31.44828 167.4333 137 31.44828 167.4333 138 31.44828 167.4333 139 31.44828 167.4333 140 31.44828 167.4333 141 31.44828 167.4333 142 31.44828 167.4333 143 31.44828 167.4333 144 31.44828 167.4333 145 31.44828 167.4333 146 31.44828 167.4333 147 31.44828 167.4333 148 31.44828 167.4333 149 31.44828 167.4333 150 31.44828 167.4333 151 31.44828 167.4333 152 31.44828 167.4333 153 31.44828 167.4333 > aggs(data = airquality, vrb.nm = c("Ozone","Solar.R"), grp.nm = "Month", + rtn.grp = TRUE, fun = mean, na.rm = TRUE) # include the group columns Month Ozone_a Solar.R_a 1 5 23.61538 181.2963 2 5 23.61538 181.2963 3 5 23.61538 181.2963 4 5 23.61538 181.2963 5 5 23.61538 181.2963 6 5 23.61538 181.2963 7 5 23.61538 181.2963 8 5 23.61538 181.2963 9 5 23.61538 181.2963 10 5 23.61538 181.2963 11 5 23.61538 181.2963 12 5 23.61538 181.2963 13 5 23.61538 181.2963 14 5 23.61538 181.2963 15 5 23.61538 181.2963 16 5 23.61538 181.2963 17 5 23.61538 181.2963 18 5 23.61538 181.2963 19 5 23.61538 181.2963 20 5 23.61538 181.2963 21 5 23.61538 181.2963 22 5 23.61538 181.2963 23 5 23.61538 181.2963 24 5 23.61538 181.2963 25 5 23.61538 181.2963 26 5 23.61538 181.2963 27 5 23.61538 181.2963 28 5 23.61538 181.2963 29 5 23.61538 181.2963 30 5 23.61538 181.2963 31 5 23.61538 181.2963 32 6 29.44444 190.1667 33 6 29.44444 190.1667 34 6 29.44444 190.1667 35 6 29.44444 190.1667 36 6 29.44444 190.1667 37 6 29.44444 190.1667 38 6 29.44444 190.1667 39 6 29.44444 190.1667 40 6 29.44444 190.1667 41 6 29.44444 190.1667 42 6 29.44444 190.1667 43 6 29.44444 190.1667 44 6 29.44444 190.1667 45 6 29.44444 190.1667 46 6 29.44444 190.1667 47 6 29.44444 190.1667 48 6 29.44444 190.1667 49 6 29.44444 190.1667 50 6 29.44444 190.1667 51 6 29.44444 190.1667 52 6 29.44444 190.1667 53 6 29.44444 190.1667 54 6 29.44444 190.1667 55 6 29.44444 190.1667 56 6 29.44444 190.1667 57 6 29.44444 190.1667 58 6 29.44444 190.1667 59 6 29.44444 190.1667 60 6 29.44444 190.1667 61 6 29.44444 190.1667 62 7 59.11538 216.4839 63 7 59.11538 216.4839 64 7 59.11538 216.4839 65 7 59.11538 216.4839 66 7 59.11538 216.4839 67 7 59.11538 216.4839 68 7 59.11538 216.4839 69 7 59.11538 216.4839 70 7 59.11538 216.4839 71 7 59.11538 216.4839 72 7 59.11538 216.4839 73 7 59.11538 216.4839 74 7 59.11538 216.4839 75 7 59.11538 216.4839 76 7 59.11538 216.4839 77 7 59.11538 216.4839 78 7 59.11538 216.4839 79 7 59.11538 216.4839 80 7 59.11538 216.4839 81 7 59.11538 216.4839 82 7 59.11538 216.4839 83 7 59.11538 216.4839 84 7 59.11538 216.4839 85 7 59.11538 216.4839 86 7 59.11538 216.4839 87 7 59.11538 216.4839 88 7 59.11538 216.4839 89 7 59.11538 216.4839 90 7 59.11538 216.4839 91 7 59.11538 216.4839 92 7 59.11538 216.4839 93 8 59.96154 171.8571 94 8 59.96154 171.8571 95 8 59.96154 171.8571 96 8 59.96154 171.8571 97 8 59.96154 171.8571 98 8 59.96154 171.8571 99 8 59.96154 171.8571 100 8 59.96154 171.8571 101 8 59.96154 171.8571 102 8 59.96154 171.8571 103 8 59.96154 171.8571 104 8 59.96154 171.8571 105 8 59.96154 171.8571 106 8 59.96154 171.8571 107 8 59.96154 171.8571 108 8 59.96154 171.8571 109 8 59.96154 171.8571 110 8 59.96154 171.8571 111 8 59.96154 171.8571 112 8 59.96154 171.8571 113 8 59.96154 171.8571 114 8 59.96154 171.8571 115 8 59.96154 171.8571 116 8 59.96154 171.8571 117 8 59.96154 171.8571 118 8 59.96154 171.8571 119 8 59.96154 171.8571 120 8 59.96154 171.8571 121 8 59.96154 171.8571 122 8 59.96154 171.8571 123 8 59.96154 171.8571 124 9 31.44828 167.4333 125 9 31.44828 167.4333 126 9 31.44828 167.4333 127 9 31.44828 167.4333 128 9 31.44828 167.4333 129 9 31.44828 167.4333 130 9 31.44828 167.4333 131 9 31.44828 167.4333 132 9 31.44828 167.4333 133 9 31.44828 167.4333 134 9 31.44828 167.4333 135 9 31.44828 167.4333 136 9 31.44828 167.4333 137 9 31.44828 167.4333 138 9 31.44828 167.4333 139 9 31.44828 167.4333 140 9 31.44828 167.4333 141 9 31.44828 167.4333 142 9 31.44828 167.4333 143 9 31.44828 167.4333 144 9 31.44828 167.4333 145 9 31.44828 167.4333 146 9 31.44828 167.4333 147 9 31.44828 167.4333 148 9 31.44828 167.4333 149 9 31.44828 167.4333 150 9 31.44828 167.4333 151 9 31.44828 167.4333 152 9 31.44828 167.4333 153 9 31.44828 167.4333 > aggs(data = airquality, vrb.nm = c("Ozone","Solar.R"), grp.nm = "Month", + rep = FALSE, fun = mean, na.rm = TRUE) # do NOT repeat aggregated values Month Ozone_a Solar.R_a 1 5 23.61538 181.2963 2 6 29.44444 190.1667 3 7 59.11538 216.4839 4 8 59.96154 171.8571 5 9 31.44828 167.4333 > aggs(data = mtcars, vrb.nm = c("mpg","cyl","disp"), grp.nm = c("vs","am"), + rep = FALSE, fun = mean, na.rm = TRUE) # with multiple group columns vs am mpg_a cyl_a disp_a 1 0 0 15.05000 8.000000 357.6167 2 1 0 20.74286 5.142857 175.1143 3 0 1 19.75000 6.333333 206.2167 4 1 1 28.37143 4.000000 89.8000 > aggs(data = mtcars, vrb.nm = c("mpg","cyl","disp"), grp.nm = c("vs","am"), + rep = FALSE, rtn.grp = FALSE, fun = mean, na.rm = TRUE) # without returning groups mpg_a cyl_a disp_a 0_0 15.05000 8.000000 357.6167 1_0 20.74286 5.142857 175.1143 0_1 19.75000 6.333333 206.2167 1_1 28.37143 4.000000 89.8000 > > > > cleanEx() > nameEx("ave_dfm") > ### * ave_dfm > > flush(stderr()); flush(stdout()) > > ### Name: ave_dfm > ### Title: Repeated Group Statistics for a Data-Frame > ### Aliases: ave_dfm > > ### ** Examples > > > # one grouping variables > ave_dfm(data = airquality, vrb.nm = c("Ozone","Solar.R"), grp.nm = "Month", + fun = function(dat) cor(dat, use = "complete")[1,2]) 1 2 3 4 5 6 7 8 0.2428635 0.2428635 0.2428635 0.2428635 0.2428635 0.2428635 0.2428635 0.2428635 9 10 11 12 13 14 15 16 0.2428635 0.2428635 0.2428635 0.2428635 0.2428635 0.2428635 0.2428635 0.2428635 17 18 19 20 21 22 23 24 0.2428635 0.2428635 0.2428635 0.2428635 0.2428635 0.2428635 0.2428635 0.2428635 25 26 27 28 29 30 31 32 0.2428635 0.2428635 0.2428635 0.2428635 0.2428635 0.2428635 0.2428635 0.7177528 33 34 35 36 37 38 39 40 0.7177528 0.7177528 0.7177528 0.7177528 0.7177528 0.7177528 0.7177528 0.7177528 41 42 43 44 45 46 47 48 0.7177528 0.7177528 0.7177528 0.7177528 0.7177528 0.7177528 0.7177528 0.7177528 49 50 51 52 53 54 55 56 0.7177528 0.7177528 0.7177528 0.7177528 0.7177528 0.7177528 0.7177528 0.7177528 57 58 59 60 61 62 63 64 0.7177528 0.7177528 0.7177528 0.7177528 0.7177528 0.4293259 0.4293259 0.4293259 65 66 67 68 69 70 71 72 0.4293259 0.4293259 0.4293259 0.4293259 0.4293259 0.4293259 0.4293259 0.4293259 73 74 75 76 77 78 79 80 0.4293259 0.4293259 0.4293259 0.4293259 0.4293259 0.4293259 0.4293259 0.4293259 81 82 83 84 85 86 87 88 0.4293259 0.4293259 0.4293259 0.4293259 0.4293259 0.4293259 0.4293259 0.4293259 89 90 91 92 93 94 95 96 0.4293259 0.4293259 0.4293259 0.4293259 0.5296827 0.5296827 0.5296827 0.5296827 97 98 99 100 101 102 103 104 0.5296827 0.5296827 0.5296827 0.5296827 0.5296827 0.5296827 0.5296827 0.5296827 105 106 107 108 109 110 111 112 0.5296827 0.5296827 0.5296827 0.5296827 0.5296827 0.5296827 0.5296827 0.5296827 113 114 115 116 117 118 119 120 0.5296827 0.5296827 0.5296827 0.5296827 0.5296827 0.5296827 0.5296827 0.5296827 121 122 123 124 125 126 127 128 0.5296827 0.5296827 0.5296827 0.1803730 0.1803730 0.1803730 0.1803730 0.1803730 129 130 131 132 133 134 135 136 0.1803730 0.1803730 0.1803730 0.1803730 0.1803730 0.1803730 0.1803730 0.1803730 137 138 139 140 141 142 143 144 0.1803730 0.1803730 0.1803730 0.1803730 0.1803730 0.1803730 0.1803730 0.1803730 145 146 147 148 149 150 151 152 0.1803730 0.1803730 0.1803730 0.1803730 0.1803730 0.1803730 0.1803730 0.1803730 153 0.1803730 > > # two grouping variables > ave_dfm(data = mtcars, vrb.nm = c("mpg","cyl","disp"), grp.nm = c("vs","am"), + fun = nrow) # with multiple group columns Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive 6 6 7 7 Hornet Sportabout Valiant Duster 360 Merc 240D 12 7 12 7 Merc 230 Merc 280 Merc 280C Merc 450SE 7 7 7 12 Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental 12 12 12 12 Chrysler Imperial Fiat 128 Honda Civic Toyota Corolla 12 7 7 7 Toyota Corona Dodge Challenger AMC Javelin Camaro Z28 7 12 12 12 Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa 12 7 6 7 Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E 6 6 6 7 > > > > > cleanEx() > nameEx("by2") > ### * by2 > > flush(stderr()); flush(stdout()) > > ### Name: by2 > ### Title: Apply a Function to Data by Group > ### Aliases: by2 > > ### ** Examples > > > # one grouping variable > by2(mtcars, .vrb.nm = c("mpg","cyl","disp"), .grp.nm = "vs", + .fun = cov, use = "complete.obs") $`0` mpg cyl disp mpg 14.905000 -3.601961 -334.77912 cyl -3.601961 1.320261 97.66471 disp -334.779118 97.664706 11398.81206 $`1` mpg cyl disp mpg 28.933407 -3.3428571 -231.61582 cyl -3.342857 0.8791209 44.36484 disp -231.615824 44.3648352 3236.84110 > > # two grouping variables > x <- by2(mtcars, .vrb.nm = c("mpg","cyl","disp"), .grp.nm = c("vs","am"), + .fun = cov, use = "complete.obs") > print(x) $`0.0` mpg cyl disp mpg 7.697273 0 -106.6682 cyl 0.000000 0 0.0000 disp -106.668182 0 5158.6142 $`1.0` mpg cyl disp mpg 6.106190 -2.157143 -43.30405 cyl -2.157143 1.142857 39.24762 disp -43.304048 39.247619 2413.82810 $`0.1` mpg cyl disp mpg 16.071 -5.980000 -334.3150 cyl -5.980 2.266667 130.1933 disp -334.315 130.193333 9069.4417 $`1.1` mpg cyl disp mpg 22.63571 0 -78.88167 cyl 0.00000 0 0.00000 disp -78.88167 0 353.52000 > str(x) List of 4 $ 0.0: num [1:3, 1:3] 7.7 0 -106.7 0 0 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:3] "mpg" "cyl" "disp" .. ..$ : chr [1:3] "mpg" "cyl" "disp" $ 1.0: num [1:3, 1:3] 6.11 -2.16 -43.3 -2.16 1.14 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:3] "mpg" "cyl" "disp" .. ..$ : chr [1:3] "mpg" "cyl" "disp" $ 0.1: num [1:3, 1:3] 16.07 -5.98 -334.31 -5.98 2.27 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:3] "mpg" "cyl" "disp" .. ..$ : chr [1:3] "mpg" "cyl" "disp" $ 1.1: num [1:3, 1:3] 22.6 0 -78.9 0 0 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:3] "mpg" "cyl" "disp" .. ..$ : chr [1:3] "mpg" "cyl" "disp" > > # compare to by > vrb_nm <- c("mpg","cyl","disp") # Roxygen runs the whole script if I put a c() in a [] > grp_nm <- c("vs","am") # Roxygen runs the whole script if I put a c() in a [] > y <- by(mtcars[vrb_nm], INDICES = mtcars[grp_nm], + FUN = cov, use = "complete.obs", simplify = FALSE) > str(y) # has dimnames rather than names List of 4 $ : num [1:3, 1:3] 7.7 0 -106.7 0 0 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:3] "mpg" "cyl" "disp" .. ..$ : chr [1:3] "mpg" "cyl" "disp" $ : num [1:3, 1:3] 6.11 -2.16 -43.3 -2.16 1.14 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:3] "mpg" "cyl" "disp" .. ..$ : chr [1:3] "mpg" "cyl" "disp" $ : num [1:3, 1:3] 16.07 -5.98 -334.31 -5.98 2.27 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:3] "mpg" "cyl" "disp" .. ..$ : chr [1:3] "mpg" "cyl" "disp" $ : num [1:3, 1:3] 22.6 0 -78.9 0 0 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:3] "mpg" "cyl" "disp" .. ..$ : chr [1:3] "mpg" "cyl" "disp" - attr(*, "dim")= int [1:2] 2 2 - attr(*, "dimnames")=List of 2 ..$ vs: chr [1:2] "0" "1" ..$ am: chr [1:2] "0" "1" - attr(*, "call")= language by.data.frame(data = mtcars[vrb_nm], INDICES = mtcars[grp_nm], FUN = cov, use = "complete.obs", simplify = FALSE) - attr(*, "class")= chr "by" > > > > cleanEx() > nameEx("center") > ### * center > > flush(stderr()); flush(stdout()) > > ### Name: center > ### Title: Centering and/or Standardizing a Numeric Vector > ### Aliases: center > > ### ** Examples > > center(x = mtcars$"disp") [1] -70.721875 -70.721875 -122.721875 27.278125 129.278125 -5.721875 [7] 129.278125 -84.021875 -89.921875 -63.121875 -63.121875 45.078125 [13] 45.078125 45.078125 241.278125 229.278125 209.278125 -152.021875 [19] -155.021875 -159.621875 -110.621875 87.278125 73.278125 119.278125 [25] 169.278125 -151.721875 -110.421875 -135.621875 120.278125 -85.721875 [31] 70.278125 -109.721875 > center(x = mtcars$"disp", scale = TRUE) [1] -0.57061982 -0.57061982 -0.99018209 0.22009369 1.04308123 -0.04616698 [7] 1.04308123 -0.67793094 -0.72553512 -0.50929918 -0.50929918 0.36371309 [13] 0.36371309 0.36371309 1.94675381 1.84993175 1.68856165 -1.22658929 [19] -1.25079481 -1.28790993 -0.89255318 0.70420401 0.59124494 0.96239618 [25] 1.36582144 -1.22416874 -0.89093948 -1.09426581 0.97046468 -0.69164740 [31] 0.56703942 -0.88529152 > center(x = mtcars$"disp", center = FALSE, scale = TRUE) [1] 0.6034061 0.6034061 0.4072991 0.9729923 1.3576637 0.8485398 1.3576637 [8] 0.5532480 0.5309974 0.6320679 0.6320679 1.0401213 1.0401213 1.0401213 [15] 1.7800480 1.7347925 1.6593668 0.2968004 0.2854865 0.2681386 0.4529317 [22] 1.1992696 1.1464716 1.3199508 1.5085152 0.2979318 0.4536860 0.3586495 [29] 1.3237221 0.5468368 1.1351577 0.4563259 > center(x = setNames(mtcars$"disp", nm = row.names(mtcars))) Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive -70.721875 -70.721875 -122.721875 27.278125 Hornet Sportabout Valiant Duster 360 Merc 240D 129.278125 -5.721875 129.278125 -84.021875 Merc 230 Merc 280 Merc 280C Merc 450SE -89.921875 -63.121875 -63.121875 45.078125 Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental 45.078125 45.078125 241.278125 229.278125 Chrysler Imperial Fiat 128 Honda Civic Toyota Corolla 209.278125 -152.021875 -155.021875 -159.621875 Toyota Corona Dodge Challenger AMC Javelin Camaro Z28 -110.621875 87.278125 73.278125 119.278125 Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa 169.278125 -151.721875 -110.421875 -135.621875 Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E 120.278125 -85.721875 70.278125 -109.721875 > > > > cleanEx() > nameEx("center_by") > ### * center_by > > flush(stderr()); flush(stdout()) > > ### Name: center_by > ### Title: Centering and/or Standardizing a Numeric Vector by Group > ### Aliases: center_by > > ### ** Examples > > chick_data <- as.data.frame(ChickWeight) # because the "groupedData" class calls > # `[.groupedData`, which is different than `[.data.frame` > center_by(x = ChickWeight[["weight"]], grp = ChickWeight[["Chick"]]) [1] -69.6666667 -60.6666667 -52.6666667 -47.6666667 -35.6666667 [6] -18.6666667 -5.6666667 13.3333333 37.3333333 59.3333333 [11] 87.3333333 93.3333333 -79.9166667 -70.9166667 -61.9166667 [16] -47.9166667 -35.9166667 -16.9166667 2.0833333 18.0833333 [21] 42.0833333 67.0833333 89.0833333 95.0833333 -72.8333333 [26] -76.8333333 -60.8333333 -48.8333333 -31.8333333 -16.8333333 [31] -0.8333333 22.1666667 47.1666667 71.1666667 82.1666667 [36] 86.1666667 -57.3333333 -50.3333333 -43.3333333 -32.3333333 [41] -25.3333333 -12.3333333 2.6666667 8.6666667 36.6666667 [46] 54.6666667 60.6666667 57.6666667 -85.6666667 -84.6666667 [51] -78.6666667 -66.6666667 -47.6666667 -20.6666667 14.3333333 [56] 37.3333333 70.3333333 72.3333333 93.3333333 96.3333333 [61] -72.7500000 -64.7500000 -54.7500000 -39.7500000 -16.7500000 [66] 10.2500000 27.2500000 34.2500000 41.2500000 46.2500000 [71] 46.2500000 43.2500000 -109.0000000 -101.0000000 -93.0000000 [76] -79.0000000 -61.0000000 -38.0000000 -4.0000000 24.0000000 [81] 68.0000000 100.0000000 138.0000000 155.0000000 -50.0000000 [86] -42.0000000 -31.0000000 -21.0000000 -8.0000000 1.0000000 [91] 18.0000000 24.0000000 34.0000000 42.0000000 33.0000000 [96] -39.1666667 -30.1666667 -22.1666667 -13.1666667 3.8333333 [101] 14.8333333 8.8333333 10.8333333 11.8333333 18.8333333 [106] 18.8333333 16.8333333 -42.0833333 -39.0833333 -31.0833333 [111] -20.0833333 -9.0833333 -2.0833333 5.9166667 12.9166667 [116] 17.9166667 28.9166667 36.9166667 40.9166667 -86.9166667 [121] -78.9166667 -66.9166667 -45.9166667 -17.9166667 9.0833333 [126] 38.0833333 47.0833333 52.0833333 54.0833333 51.0833333 [131] 45.0833333 -73.0833333 -65.0833333 -58.0833333 -52.0833333 [136] -42.0833333 -26.0833333 4.9166667 20.9166667 47.9166667 [141] 70.9166667 80.9166667 90.9166667 -26.8333333 -19.8333333 [146] -14.8333333 -7.8333333 -2.8333333 -0.8333333 3.1666667 [151] 2.1666667 3.1666667 13.1666667 23.1666667 28.1666667 [156] -110.3333333 -102.3333333 -89.3333333 -72.3333333 -50.3333333 [161] -23.3333333 12.6666667 40.6666667 75.6666667 96.6666667 [166] 107.6666667 114.6666667 -19.1250000 -11.1250000 -4.1250000 [171] 3.8750000 7.8750000 7.8750000 6.8750000 7.8750000 [176] -8.7142857 -4.7142857 -0.7142857 1.2857143 7.2857143 [181] 1.2857143 4.2857143 -50.5000000 -41.5000000 -31.5000000 [186] -20.5000000 -9.5000000 -3.5000000 5.5000000 10.5000000 [191] 20.5000000 30.5000000 40.5000000 49.5000000 2.0000000 [196] -2.0000000 -43.7500000 -38.7500000 -31.7500000 -24.7500000 [201] -21.7500000 -15.7500000 -4.7500000 1.2500000 19.2500000 [206] 33.2500000 57.2500000 70.2500000 -37.4166667 -31.4166667 [211] -24.4166667 -20.4166667 -13.4166667 -5.4166667 -1.4166667 [216] 10.5833333 19.5833333 28.5833333 36.5833333 38.5833333 [221] -144.5000000 -134.5000000 -122.5000000 -98.5000000 -59.5000000 [226] -21.5000000 32.5000000 55.5000000 90.5000000 122.5000000 [231] 133.5000000 146.5000000 -63.2500000 -49.2500000 -40.2500000 [236] -27.2500000 -14.2500000 -9.2500000 3.7500000 6.7500000 [241] 26.7500000 43.7500000 59.7500000 62.7500000 -68.4166667 [246] -59.4166667 -50.4166667 -38.4166667 -21.4166667 -8.4166667 [251] 15.5833333 23.5833333 33.5833333 51.5833333 58.5833333 [256] 63.5833333 -24.2500000 -14.2500000 -8.2500000 7.7500000 [261] -0.2500000 1.7500000 3.7500000 4.7500000 5.7500000 [266] 5.7500000 9.7500000 7.7500000 -103.0833333 -94.0833333 [271] -81.0833333 -65.0833333 -41.0833333 -19.0833333 2.9166667 [276] 20.9166667 53.9166667 87.9166667 115.9166667 121.9166667 [281] -89.0000000 -83.0000000 -74.0000000 -57.0000000 -38.0000000 [286] -17.0000000 5.0000000 16.0000000 38.0000000 74.0000000 [291] 105.0000000 120.0000000 -71.4166667 -64.4166667 -52.4166667 [296] -37.4166667 -23.4166667 -10.4166667 4.5833333 12.5833333 [301] 33.5833333 52.5833333 74.5833333 81.5833333 -90.9166667 [306] -83.9166667 -71.9166667 -56.9166667 -37.9166667 -15.9166667 [311] 15.0833333 26.0833333 54.0833333 77.0833333 82.0833333 [316] 103.0833333 -102.8333333 -93.8333333 -82.8333333 -67.8333333 [321] -54.8333333 -35.8333333 -7.8333333 8.1666667 45.1666667 [326] 88.1666667 137.1666667 167.1666667 -61.5000000 -55.5000000 [331] -44.5000000 -31.5000000 -18.5000000 -5.5000000 11.5000000 [336] 18.5000000 39.5000000 47.5000000 53.5000000 46.5000000 [341] -86.5833333 -75.5833333 -66.5833333 -55.5833333 -43.5833333 [346] -26.5833333 -5.5833333 9.4166667 41.4166667 75.4166667 [351] 106.4166667 127.4166667 -116.5833333 -108.5833333 -92.5833333 [356] -75.5833333 -50.5833333 -28.5833333 1.4166667 21.4166667 [361] 63.4166667 105.4166667 133.4166667 147.4166667 -70.7500000 [366] -59.7500000 -46.7500000 -32.7500000 -13.7500000 1.2500000 [371] 27.2500000 34.2500000 41.2500000 36.2500000 46.2500000 [376] 37.2500000 -127.8333333 -119.8333333 -105.8333333 -83.8333333 [381] -61.8333333 -34.8333333 -4.8333333 17.1666667 66.1666667 [386] 125.1666667 158.1666667 172.1666667 -152.1666667 -140.1666667 [391] -129.1666667 -106.1666667 -70.1666667 -35.1666667 7.8333333 [396] 44.8333333 93.8333333 138.8333333 167.8333333 179.8333333 [401] -95.9166667 -86.9166667 -73.9166667 -58.9166667 -36.9166667 [406] -18.9166667 10.0833333 31.0833333 63.0833333 92.0833333 [411] 90.0833333 85.0833333 -61.5000000 -54.5000000 -46.5000000 [416] -34.5000000 -22.5000000 -19.5000000 0.5000000 9.5000000 [421] 32.5000000 54.5000000 66.5000000 75.5000000 -101.3333333 [426] -93.3333333 -81.3333333 -68.3333333 -44.3333333 -33.3333333 [431] -14.3333333 11.6666667 49.6666667 89.6666667 137.6666667 [436] 147.6666667 -92.2500000 -84.2500000 -73.2500000 -56.2500000 [441] -45.2500000 -25.2500000 -4.2500000 11.7500000 35.7500000 [446] 79.7500000 115.7500000 137.7500000 -116.5833333 -102.5833333 [451] -91.5833333 -78.5833333 -56.5833333 -37.5833333 -3.5833333 [456] 24.4166667 57.4166667 104.4166667 137.4166667 163.4166667 [461] -86.4166667 -77.4166667 -62.4166667 -43.4166667 -25.4166667 [466] -4.4166667 26.5833333 24.5833333 46.5833333 55.5833333 [471] 70.5833333 75.5833333 -107.0833333 -100.0833333 -86.0833333 [476] -65.0833333 -46.0833333 -23.0833333 10.9166667 24.9166667 [481] 54.9166667 84.9166667 119.9166667 131.9166667 -101.0000000 [486] -88.0000000 -74.0000000 -47.0000000 -12.0000000 14.0000000 [491] 41.0000000 45.0000000 54.0000000 55.0000000 56.0000000 [496] 57.0000000 -60.1000000 -51.1000000 -37.1000000 -16.1000000 [501] 0.9000000 15.9000000 24.9000000 35.9000000 42.9000000 [506] 43.9000000 -78.5833333 -69.5833333 -58.5833333 -41.5833333 [511] -21.5833333 -2.5833333 15.4166667 21.4166667 27.4166667 [516] 54.4166667 77.4166667 76.4166667 -94.0833333 -82.0833333 [521] -72.0833333 -52.0833333 -33.0833333 -14.0833333 9.9166667 [526] 21.9166667 38.9166667 75.9166667 96.9166667 103.9166667 [531] -86.9166667 -74.9166667 -61.9166667 -48.9166667 -27.9166667 [536] -4.9166667 20.0833333 29.0833333 40.0833333 57.0833333 [541] 82.0833333 77.0833333 -118.6666667 -107.6666667 -95.6666667 [546] -77.6666667 -53.6666667 -32.6666667 -3.6666667 12.3333333 [551] 64.3333333 103.3333333 145.3333333 164.3333333 -97.7500000 [556] -84.7500000 -73.7500000 -52.7500000 -29.7500000 -9.7500000 [561] 14.2500000 28.2500000 46.2500000 65.2500000 95.2500000 [566] 99.2500000 -106.5000000 -93.5000000 -80.5000000 -63.5000000 [571] -42.5000000 -25.5000000 7.5000000 27.5000000 57.5000000 [576] 86.5000000 116.5000000 116.5000000 > center_by(x = setNames(obj = ChickWeight[["weight"]], nm = row.names(ChickWeight)), + grp = ChickWeight[["Chick"]]) # with names 1 2 3 4 5 6 -69.6666667 -60.6666667 -52.6666667 -47.6666667 -35.6666667 -18.6666667 7 8 9 10 11 12 -5.6666667 13.3333333 37.3333333 59.3333333 87.3333333 93.3333333 13 14 15 16 17 18 -79.9166667 -70.9166667 -61.9166667 -47.9166667 -35.9166667 -16.9166667 19 20 21 22 23 24 2.0833333 18.0833333 42.0833333 67.0833333 89.0833333 95.0833333 25 26 27 28 29 30 -72.8333333 -76.8333333 -60.8333333 -48.8333333 -31.8333333 -16.8333333 31 32 33 34 35 36 -0.8333333 22.1666667 47.1666667 71.1666667 82.1666667 86.1666667 37 38 39 40 41 42 -57.3333333 -50.3333333 -43.3333333 -32.3333333 -25.3333333 -12.3333333 43 44 45 46 47 48 2.6666667 8.6666667 36.6666667 54.6666667 60.6666667 57.6666667 49 50 51 52 53 54 -85.6666667 -84.6666667 -78.6666667 -66.6666667 -47.6666667 -20.6666667 55 56 57 58 59 60 14.3333333 37.3333333 70.3333333 72.3333333 93.3333333 96.3333333 61 62 63 64 65 66 -72.7500000 -64.7500000 -54.7500000 -39.7500000 -16.7500000 10.2500000 67 68 69 70 71 72 27.2500000 34.2500000 41.2500000 46.2500000 46.2500000 43.2500000 73 74 75 76 77 78 -109.0000000 -101.0000000 -93.0000000 -79.0000000 -61.0000000 -38.0000000 79 80 81 82 83 84 -4.0000000 24.0000000 68.0000000 100.0000000 138.0000000 155.0000000 85 86 87 88 89 90 -50.0000000 -42.0000000 -31.0000000 -21.0000000 -8.0000000 1.0000000 91 92 93 94 95 96 18.0000000 24.0000000 34.0000000 42.0000000 33.0000000 -39.1666667 97 98 99 100 101 102 -30.1666667 -22.1666667 -13.1666667 3.8333333 14.8333333 8.8333333 103 104 105 106 107 108 10.8333333 11.8333333 18.8333333 18.8333333 16.8333333 -42.0833333 109 110 111 112 113 114 -39.0833333 -31.0833333 -20.0833333 -9.0833333 -2.0833333 5.9166667 115 116 117 118 119 120 12.9166667 17.9166667 28.9166667 36.9166667 40.9166667 -86.9166667 121 122 123 124 125 126 -78.9166667 -66.9166667 -45.9166667 -17.9166667 9.0833333 38.0833333 127 128 129 130 131 132 47.0833333 52.0833333 54.0833333 51.0833333 45.0833333 -73.0833333 133 134 135 136 137 138 -65.0833333 -58.0833333 -52.0833333 -42.0833333 -26.0833333 4.9166667 139 140 141 142 143 144 20.9166667 47.9166667 70.9166667 80.9166667 90.9166667 -26.8333333 145 146 147 148 149 150 -19.8333333 -14.8333333 -7.8333333 -2.8333333 -0.8333333 3.1666667 151 152 153 154 155 156 2.1666667 3.1666667 13.1666667 23.1666667 28.1666667 -110.3333333 157 158 159 160 161 162 -102.3333333 -89.3333333 -72.3333333 -50.3333333 -23.3333333 12.6666667 163 164 165 166 167 168 40.6666667 75.6666667 96.6666667 107.6666667 114.6666667 -19.1250000 169 170 171 172 173 174 -11.1250000 -4.1250000 3.8750000 7.8750000 7.8750000 6.8750000 175 176 177 178 179 180 7.8750000 -8.7142857 -4.7142857 -0.7142857 1.2857143 7.2857143 181 182 183 184 185 186 1.2857143 4.2857143 -50.5000000 -41.5000000 -31.5000000 -20.5000000 187 188 189 190 191 192 -9.5000000 -3.5000000 5.5000000 10.5000000 20.5000000 30.5000000 193 194 195 196 197 198 40.5000000 49.5000000 2.0000000 -2.0000000 -43.7500000 -38.7500000 199 200 201 202 203 204 -31.7500000 -24.7500000 -21.7500000 -15.7500000 -4.7500000 1.2500000 205 206 207 208 209 210 19.2500000 33.2500000 57.2500000 70.2500000 -37.4166667 -31.4166667 211 212 213 214 215 216 -24.4166667 -20.4166667 -13.4166667 -5.4166667 -1.4166667 10.5833333 217 218 219 220 221 222 19.5833333 28.5833333 36.5833333 38.5833333 -144.5000000 -134.5000000 223 224 225 226 227 228 -122.5000000 -98.5000000 -59.5000000 -21.5000000 32.5000000 55.5000000 229 230 231 232 233 234 90.5000000 122.5000000 133.5000000 146.5000000 -63.2500000 -49.2500000 235 236 237 238 239 240 -40.2500000 -27.2500000 -14.2500000 -9.2500000 3.7500000 6.7500000 241 242 243 244 245 246 26.7500000 43.7500000 59.7500000 62.7500000 -68.4166667 -59.4166667 247 248 249 250 251 252 -50.4166667 -38.4166667 -21.4166667 -8.4166667 15.5833333 23.5833333 253 254 255 256 257 258 33.5833333 51.5833333 58.5833333 63.5833333 -24.2500000 -14.2500000 259 260 261 262 263 264 -8.2500000 7.7500000 -0.2500000 1.7500000 3.7500000 4.7500000 265 266 267 268 269 270 5.7500000 5.7500000 9.7500000 7.7500000 -103.0833333 -94.0833333 271 272 273 274 275 276 -81.0833333 -65.0833333 -41.0833333 -19.0833333 2.9166667 20.9166667 277 278 279 280 281 282 53.9166667 87.9166667 115.9166667 121.9166667 -89.0000000 -83.0000000 283 284 285 286 287 288 -74.0000000 -57.0000000 -38.0000000 -17.0000000 5.0000000 16.0000000 289 290 291 292 293 294 38.0000000 74.0000000 105.0000000 120.0000000 -71.4166667 -64.4166667 295 296 297 298 299 300 -52.4166667 -37.4166667 -23.4166667 -10.4166667 4.5833333 12.5833333 301 302 303 304 305 306 33.5833333 52.5833333 74.5833333 81.5833333 -90.9166667 -83.9166667 307 308 309 310 311 312 -71.9166667 -56.9166667 -37.9166667 -15.9166667 15.0833333 26.0833333 313 314 315 316 317 318 54.0833333 77.0833333 82.0833333 103.0833333 -102.8333333 -93.8333333 319 320 321 322 323 324 -82.8333333 -67.8333333 -54.8333333 -35.8333333 -7.8333333 8.1666667 325 326 327 328 329 330 45.1666667 88.1666667 137.1666667 167.1666667 -61.5000000 -55.5000000 331 332 333 334 335 336 -44.5000000 -31.5000000 -18.5000000 -5.5000000 11.5000000 18.5000000 337 338 339 340 341 342 39.5000000 47.5000000 53.5000000 46.5000000 -86.5833333 -75.5833333 343 344 345 346 347 348 -66.5833333 -55.5833333 -43.5833333 -26.5833333 -5.5833333 9.4166667 349 350 351 352 353 354 41.4166667 75.4166667 106.4166667 127.4166667 -116.5833333 -108.5833333 355 356 357 358 359 360 -92.5833333 -75.5833333 -50.5833333 -28.5833333 1.4166667 21.4166667 361 362 363 364 365 366 63.4166667 105.4166667 133.4166667 147.4166667 -70.7500000 -59.7500000 367 368 369 370 371 372 -46.7500000 -32.7500000 -13.7500000 1.2500000 27.2500000 34.2500000 373 374 375 376 377 378 41.2500000 36.2500000 46.2500000 37.2500000 -127.8333333 -119.8333333 379 380 381 382 383 384 -105.8333333 -83.8333333 -61.8333333 -34.8333333 -4.8333333 17.1666667 385 386 387 388 389 390 66.1666667 125.1666667 158.1666667 172.1666667 -152.1666667 -140.1666667 391 392 393 394 395 396 -129.1666667 -106.1666667 -70.1666667 -35.1666667 7.8333333 44.8333333 397 398 399 400 401 402 93.8333333 138.8333333 167.8333333 179.8333333 -95.9166667 -86.9166667 403 404 405 406 407 408 -73.9166667 -58.9166667 -36.9166667 -18.9166667 10.0833333 31.0833333 409 410 411 412 413 414 63.0833333 92.0833333 90.0833333 85.0833333 -61.5000000 -54.5000000 415 416 417 418 419 420 -46.5000000 -34.5000000 -22.5000000 -19.5000000 0.5000000 9.5000000 421 422 423 424 425 426 32.5000000 54.5000000 66.5000000 75.5000000 -101.3333333 -93.3333333 427 428 429 430 431 432 -81.3333333 -68.3333333 -44.3333333 -33.3333333 -14.3333333 11.6666667 433 434 435 436 437 438 49.6666667 89.6666667 137.6666667 147.6666667 -92.2500000 -84.2500000 439 440 441 442 443 444 -73.2500000 -56.2500000 -45.2500000 -25.2500000 -4.2500000 11.7500000 445 446 447 448 449 450 35.7500000 79.7500000 115.7500000 137.7500000 -116.5833333 -102.5833333 451 452 453 454 455 456 -91.5833333 -78.5833333 -56.5833333 -37.5833333 -3.5833333 24.4166667 457 458 459 460 461 462 57.4166667 104.4166667 137.4166667 163.4166667 -86.4166667 -77.4166667 463 464 465 466 467 468 -62.4166667 -43.4166667 -25.4166667 -4.4166667 26.5833333 24.5833333 469 470 471 472 473 474 46.5833333 55.5833333 70.5833333 75.5833333 -107.0833333 -100.0833333 475 476 477 478 479 480 -86.0833333 -65.0833333 -46.0833333 -23.0833333 10.9166667 24.9166667 481 482 483 484 485 486 54.9166667 84.9166667 119.9166667 131.9166667 -101.0000000 -88.0000000 487 488 489 490 491 492 -74.0000000 -47.0000000 -12.0000000 14.0000000 41.0000000 45.0000000 493 494 495 496 497 498 54.0000000 55.0000000 56.0000000 57.0000000 -60.1000000 -51.1000000 499 500 501 502 503 504 -37.1000000 -16.1000000 0.9000000 15.9000000 24.9000000 35.9000000 505 506 507 508 509 510 42.9000000 43.9000000 -78.5833333 -69.5833333 -58.5833333 -41.5833333 511 512 513 514 515 516 -21.5833333 -2.5833333 15.4166667 21.4166667 27.4166667 54.4166667 517 518 519 520 521 522 77.4166667 76.4166667 -94.0833333 -82.0833333 -72.0833333 -52.0833333 523 524 525 526 527 528 -33.0833333 -14.0833333 9.9166667 21.9166667 38.9166667 75.9166667 529 530 531 532 533 534 96.9166667 103.9166667 -86.9166667 -74.9166667 -61.9166667 -48.9166667 535 536 537 538 539 540 -27.9166667 -4.9166667 20.0833333 29.0833333 40.0833333 57.0833333 541 542 543 544 545 546 82.0833333 77.0833333 -118.6666667 -107.6666667 -95.6666667 -77.6666667 547 548 549 550 551 552 -53.6666667 -32.6666667 -3.6666667 12.3333333 64.3333333 103.3333333 553 554 555 556 557 558 145.3333333 164.3333333 -97.7500000 -84.7500000 -73.7500000 -52.7500000 559 560 561 562 563 564 -29.7500000 -9.7500000 14.2500000 28.2500000 46.2500000 65.2500000 565 566 567 568 569 570 95.2500000 99.2500000 -106.5000000 -93.5000000 -80.5000000 -63.5000000 571 572 573 574 575 576 -42.5000000 -25.5000000 7.5000000 27.5000000 57.5000000 86.5000000 577 578 116.5000000 116.5000000 > tmp_nm <- c("Type","Treatment") # b/c Roxygen2 doesn't like a c() within a [] > center_by(x = as.data.frame(CO2)[["uptake"]], grp = as.data.frame(CO2)[tmp_nm], + scale = TRUE) # multiple grouping vectors [1] -2.014650502 -0.514083232 -0.055576566 0.194517979 -0.003473535 [6] 0.402930100 0.455033131 -2.264745047 -0.837122019 0.184097373 [11] 0.673865858 0.548818585 0.632183433 0.934381009 -1.993809290 [16] -0.305671111 0.517556767 0.705127676 0.788492524 0.892698584 [21] 1.059428281 -1.819875820 -0.793418460 -0.150586578 0.295248437 [26] 0.077515058 0.378194487 0.720346940 -2.327920372 -0.461634263 [31] 0.336721462 0.730715196 0.709978684 0.595927866 1.103972418 [36] -1.726561514 -1.114834401 0.658137403 0.233038900 0.741083452 [41] 0.813661246 1.000289857 -2.074047372 -0.912220587 0.033452377 [46] 0.546817700 0.668404224 0.871048431 1.289846458 -1.884912779 [51] -0.533951402 0.627875383 0.789990748 0.871048431 0.695423452 [56] 0.749461907 -1.979480075 -0.885201360 -0.020586078 0.263115811 [61] 0.344173494 0.290135039 0.249606197 -1.309267605 -0.225250341 [66] 0.563125852 0.760219900 0.908040436 1.573232848 1.499322580 [71] -1.999096774 -1.087536801 -0.865805997 -0.693348705 -0.816532485 [76] -0.520891413 -0.348434121 -1.284630849 0.538489096 0.513852340 [81] 0.513852340 0.513852340 0.760219900 1.006587460 > > > > cleanEx() > nameEx("centers") > ### * centers > > flush(stderr()); flush(stdout()) > > ### Name: centers > ### Title: Centering and/or Standardizing Numeric Data > ### Aliases: centers > > ### ** Examples > > centers(data = mtcars, vrb.nm = c("disp","hp","drat","wt","qsec")) disp_c hp_c drat_c wt_c qsec_c Mazda RX4 -70.721875 -36.6875 0.3034375 -0.59725 -1.38875 Mazda RX4 Wag -70.721875 -36.6875 0.3034375 -0.34225 -0.82875 Datsun 710 -122.721875 -53.6875 0.2534375 -0.89725 0.76125 Hornet 4 Drive 27.278125 -36.6875 -0.5165625 -0.00225 1.59125 Hornet Sportabout 129.278125 28.3125 -0.4465625 0.22275 -0.82875 Valiant -5.721875 -41.6875 -0.8365625 0.24275 2.37125 Duster 360 129.278125 98.3125 -0.3865625 0.35275 -2.00875 Merc 240D -84.021875 -84.6875 0.0934375 -0.02725 2.15125 Merc 230 -89.921875 -51.6875 0.3234375 -0.06725 5.05125 Merc 280 -63.121875 -23.6875 0.3234375 0.22275 0.45125 Merc 280C -63.121875 -23.6875 0.3234375 0.22275 1.05125 Merc 450SE 45.078125 33.3125 -0.5265625 0.85275 -0.44875 Merc 450SL 45.078125 33.3125 -0.5265625 0.51275 -0.24875 Merc 450SLC 45.078125 33.3125 -0.5265625 0.56275 0.15125 Cadillac Fleetwood 241.278125 58.3125 -0.6665625 2.03275 0.13125 Lincoln Continental 229.278125 68.3125 -0.5965625 2.20675 -0.02875 Chrysler Imperial 209.278125 83.3125 -0.3665625 2.12775 -0.42875 Fiat 128 -152.021875 -80.6875 0.4834375 -1.01725 1.62125 Honda Civic -155.021875 -94.6875 1.3334375 -1.60225 0.67125 Toyota Corolla -159.621875 -81.6875 0.6234375 -1.38225 2.05125 Toyota Corona -110.621875 -49.6875 0.1034375 -0.75225 2.16125 Dodge Challenger 87.278125 3.3125 -0.8365625 0.30275 -0.97875 AMC Javelin 73.278125 3.3125 -0.4465625 0.21775 -0.54875 Camaro Z28 119.278125 98.3125 0.1334375 0.62275 -2.43875 Pontiac Firebird 169.278125 28.3125 -0.5165625 0.62775 -0.79875 Fiat X1-9 -151.721875 -80.6875 0.4834375 -1.28225 1.05125 Porsche 914-2 -110.421875 -55.6875 0.8334375 -1.07725 -1.14875 Lotus Europa -135.621875 -33.6875 0.1734375 -1.70425 -0.94875 Ford Pantera L 120.278125 117.3125 0.6234375 -0.04725 -3.34875 Ferrari Dino -85.721875 28.3125 0.0234375 -0.44725 -2.34875 Maserati Bora 70.278125 188.3125 -0.0565625 0.35275 -3.24875 Volvo 142E -109.721875 -37.6875 0.5134375 -0.43725 0.75125 > centers(data = mtcars, vrb.nm = c("disp","hp","drat","wt","qsec"), + scale = TRUE) disp_z hp_z drat_z wt_z Mazda RX4 -0.57061982 -0.53509284 0.56751369 -0.610399567 Mazda RX4 Wag -0.57061982 -0.53509284 0.56751369 -0.349785269 Datsun 710 -0.99018209 -0.78304046 0.47399959 -0.917004624 Hornet 4 Drive 0.22009369 -0.53509284 -0.96611753 -0.002299538 Hornet Sportabout 1.04308123 0.41294217 -0.83519779 0.227654255 Valiant -0.04616698 -0.60801861 -1.56460776 0.248094592 Duster 360 1.04308123 1.43390296 -0.72298087 0.360516446 Merc 240D -0.67793094 -1.23518023 0.17475447 -0.027849959 Merc 230 -0.72553512 -0.75387015 0.60491932 -0.068730634 Merc 280 -0.50929918 -0.34548584 0.60491932 0.227654255 Merc 280C -0.50929918 -0.34548584 0.60491932 0.227654255 Merc 450SE 0.36371309 0.48586794 -0.98482035 0.871524874 Merc 450SL 0.36371309 0.48586794 -0.98482035 0.524039143 Merc 450SLC 0.36371309 0.48586794 -0.98482035 0.575139986 Cadillac Fleetwood 1.94675381 0.85049680 -1.24665983 2.077504765 Lincoln Continental 1.84993175 0.99634834 -1.11574009 2.255335698 Chrysler Imperial 1.68856165 1.21512565 -0.68557523 2.174596366 Fiat 128 -1.22658929 -1.17683962 0.90416444 -1.039646647 Honda Civic -1.25079481 -1.38103178 2.49390411 -1.637526508 Toyota Corolla -1.28790993 -1.19142477 1.16600392 -1.412682800 Toyota Corona -0.89255318 -0.72469984 0.19345729 -0.768812180 Dodge Challenger 0.70420401 0.04831332 -1.56460776 0.309415603 AMC Javelin 0.59124494 0.04831332 -0.83519779 0.222544170 Camaro Z28 0.96239618 1.43390296 0.24956575 0.636460997 Pontiac Firebird 1.36582144 0.41294217 -0.96611753 0.641571082 Fiat X1-9 -1.22416874 -1.17683962 0.90416444 -1.310481114 Porsche 914-2 -0.89093948 -0.81221077 1.55876313 -1.100967659 Lotus Europa -1.09426581 -0.49133738 0.32437703 -1.741772228 Ford Pantera L 0.97046468 1.71102089 1.16600392 -0.048290296 Ferrari Dino -0.69164740 0.41294217 0.04383473 -0.457097039 Maserati Bora 0.56703942 2.74656682 -0.10578782 0.360516446 Volvo 142E -0.88529152 -0.54967799 0.96027290 -0.446876870 qsec_z Mazda RX4 -0.77716515 Mazda RX4 Wag -0.46378082 Datsun 710 0.42600682 Hornet 4 Drive 0.89048716 Hornet Sportabout -0.46378082 Valiant 1.32698675 Duster 360 -1.12412636 Merc 240D 1.20387148 Merc 230 2.82675459 Merc 280 0.25252621 Merc 280C 0.58829513 Merc 450SE -0.25112717 Merc 450SL -0.13920420 Merc 450SLC 0.08464175 Cadillac Fleetwood 0.07344945 Lincoln Continental -0.01608893 Chrysler Imperial -0.23993487 Fiat 128 0.90727560 Honda Civic 0.37564148 Toyota Corolla 1.14790999 Toyota Corona 1.20946763 Dodge Challenger -0.54772305 AMC Javelin -0.30708866 Camaro Z28 -1.36476075 Pontiac Firebird -0.44699237 Fiat X1-9 0.58829513 Porsche 914-2 -0.64285758 Lotus Europa -0.53093460 Ford Pantera L -1.87401028 Ferrari Dino -1.31439542 Maserati Bora -1.81804880 Volvo 142E 0.42041067 > centers(data = mtcars, vrb.nm = c("disp","hp","drat","wt","qsec"), + center = FALSE, scale = TRUE) disp_s hp_s drat_s wt_s qsec_s Mazda RX4 0.6034061 0.6705299 1.0560458 0.7678706 0.9032949 Mazda RX4 Wag 0.6034061 0.6705299 1.0560458 0.8426061 0.9340267 Datsun 710 0.4072991 0.5669025 1.0425067 0.6799465 1.0212830 Hornet 4 Drive 0.9729923 0.6705299 0.8340054 0.9422535 1.0668319 Hornet Sportabout 1.3576637 1.0667521 0.8529600 1.0081965 0.9340267 Valiant 0.8485398 0.6400513 0.7473555 1.0140582 1.1096369 Duster 360 1.3576637 1.4934529 0.8692069 1.0462970 0.8692704 Merc 240D 0.5532480 0.3779350 0.9991818 0.9349264 1.0975637 Merc 230 0.5309974 0.5790940 1.0614614 0.9232032 1.2567104 Merc 280 0.6320679 0.7497743 1.0614614 1.0081965 1.0042708 Merc 280C 0.6320679 0.7497743 1.0614614 1.0081965 1.0371977 Merc 450SE 1.0401213 1.0972307 0.8312976 1.1928372 0.9548804 Merc 450SL 1.0401213 1.0972307 0.8312976 1.0931899 0.9658560 Merc 450SLC 1.0401213 1.0972307 0.8312976 1.1078439 0.9878073 Cadillac Fleetwood 1.7800480 1.2496239 0.7933882 1.5386721 0.9867097 Lincoln Continental 1.7347925 1.3105811 0.8123429 1.5896680 0.9779292 Chrysler Imperial 1.6593668 1.4020170 0.8746225 1.5665147 0.9559780 Fiat 128 0.2968004 0.4023179 1.1047863 0.6447769 1.0684782 Honda Civic 0.2854865 0.3169778 1.3349502 0.4733248 1.0163440 Toyota Corolla 0.2681386 0.3962222 1.1426957 0.5378025 1.0920759 Toyota Corona 0.4529317 0.5912854 1.0018896 0.7224432 1.0981125 Dodge Challenger 1.1992696 0.9143589 0.7473555 1.0316430 0.9257950 AMC Javelin 1.1464716 0.9143589 0.8529600 1.0067311 0.9493926 Camaro Z28 1.3199508 1.4934529 1.0100130 1.1254287 0.8456728 Pontiac Firebird 1.5085152 1.0667521 0.8340054 1.1268941 0.9356730 Fiat X1-9 0.2979318 0.4023179 1.1047863 0.5671106 1.0371977 Porsche 914-2 0.4536860 0.5547111 1.1995597 0.6271920 0.9164657 Lotus Europa 0.3586495 0.6888171 1.0208442 0.4434306 0.9274413 Ford Pantera L 1.3237221 1.6092717 1.1426957 0.9290648 0.7957337 Ferrari Dino 0.5468368 1.0667521 0.9802271 0.8118327 0.8506118 Maserati Bora 1.1351577 2.0420683 0.9585646 1.0462970 0.8012215 Volvo 142E 0.4563259 0.6644342 1.1129098 0.8147635 1.0207342 > centers(data = mtcars, vrb.nm = c("disp","hp","drat","wt","qsec"), + scale = TRUE, suffix = "_std") disp_std hp_std drat_std wt_std Mazda RX4 -0.57061982 -0.53509284 0.56751369 -0.610399567 Mazda RX4 Wag -0.57061982 -0.53509284 0.56751369 -0.349785269 Datsun 710 -0.99018209 -0.78304046 0.47399959 -0.917004624 Hornet 4 Drive 0.22009369 -0.53509284 -0.96611753 -0.002299538 Hornet Sportabout 1.04308123 0.41294217 -0.83519779 0.227654255 Valiant -0.04616698 -0.60801861 -1.56460776 0.248094592 Duster 360 1.04308123 1.43390296 -0.72298087 0.360516446 Merc 240D -0.67793094 -1.23518023 0.17475447 -0.027849959 Merc 230 -0.72553512 -0.75387015 0.60491932 -0.068730634 Merc 280 -0.50929918 -0.34548584 0.60491932 0.227654255 Merc 280C -0.50929918 -0.34548584 0.60491932 0.227654255 Merc 450SE 0.36371309 0.48586794 -0.98482035 0.871524874 Merc 450SL 0.36371309 0.48586794 -0.98482035 0.524039143 Merc 450SLC 0.36371309 0.48586794 -0.98482035 0.575139986 Cadillac Fleetwood 1.94675381 0.85049680 -1.24665983 2.077504765 Lincoln Continental 1.84993175 0.99634834 -1.11574009 2.255335698 Chrysler Imperial 1.68856165 1.21512565 -0.68557523 2.174596366 Fiat 128 -1.22658929 -1.17683962 0.90416444 -1.039646647 Honda Civic -1.25079481 -1.38103178 2.49390411 -1.637526508 Toyota Corolla -1.28790993 -1.19142477 1.16600392 -1.412682800 Toyota Corona -0.89255318 -0.72469984 0.19345729 -0.768812180 Dodge Challenger 0.70420401 0.04831332 -1.56460776 0.309415603 AMC Javelin 0.59124494 0.04831332 -0.83519779 0.222544170 Camaro Z28 0.96239618 1.43390296 0.24956575 0.636460997 Pontiac Firebird 1.36582144 0.41294217 -0.96611753 0.641571082 Fiat X1-9 -1.22416874 -1.17683962 0.90416444 -1.310481114 Porsche 914-2 -0.89093948 -0.81221077 1.55876313 -1.100967659 Lotus Europa -1.09426581 -0.49133738 0.32437703 -1.741772228 Ford Pantera L 0.97046468 1.71102089 1.16600392 -0.048290296 Ferrari Dino -0.69164740 0.41294217 0.04383473 -0.457097039 Maserati Bora 0.56703942 2.74656682 -0.10578782 0.360516446 Volvo 142E -0.88529152 -0.54967799 0.96027290 -0.446876870 qsec_std Mazda RX4 -0.77716515 Mazda RX4 Wag -0.46378082 Datsun 710 0.42600682 Hornet 4 Drive 0.89048716 Hornet Sportabout -0.46378082 Valiant 1.32698675 Duster 360 -1.12412636 Merc 240D 1.20387148 Merc 230 2.82675459 Merc 280 0.25252621 Merc 280C 0.58829513 Merc 450SE -0.25112717 Merc 450SL -0.13920420 Merc 450SLC 0.08464175 Cadillac Fleetwood 0.07344945 Lincoln Continental -0.01608893 Chrysler Imperial -0.23993487 Fiat 128 0.90727560 Honda Civic 0.37564148 Toyota Corolla 1.14790999 Toyota Corona 1.20946763 Dodge Challenger -0.54772305 AMC Javelin -0.30708866 Camaro Z28 -1.36476075 Pontiac Firebird -0.44699237 Fiat X1-9 0.58829513 Porsche 914-2 -0.64285758 Lotus Europa -0.53093460 Ford Pantera L -1.87401028 Ferrari Dino -1.31439542 Maserati Bora -1.81804880 Volvo 142E 0.42041067 > > > > cleanEx() > nameEx("centers_by") > ### * centers_by > > flush(stderr()); flush(stdout()) > > ### Name: centers_by > ### Title: Centering and/or Standardizing Numeric Data by Group > ### Aliases: centers_by > > ### ** Examples > > ChickWeight2 <- as.data.frame(ChickWeight) # because the "groupedData" class calls > # `[.groupedData`, which is different than `[.data.frame` > row.names(ChickWeight2) <- as.numeric(row.names(ChickWeight)) / 1000 > centers_by(data = ChickWeight2, vrb.nm = c("weight","Time"), grp.nm = "Chick") weight_cw Time_cw 0.001 -69.6666667 -10.9166667 0.002 -60.6666667 -8.9166667 0.003 -52.6666667 -6.9166667 0.004 -47.6666667 -4.9166667 0.005 -35.6666667 -2.9166667 0.006 -18.6666667 -0.9166667 0.007 -5.6666667 1.0833333 0.008 13.3333333 3.0833333 0.009 37.3333333 5.0833333 0.01 59.3333333 7.0833333 0.011 87.3333333 9.0833333 0.012 93.3333333 10.0833333 0.013 -79.9166667 -10.9166667 0.014 -70.9166667 -8.9166667 0.015 -61.9166667 -6.9166667 0.016 -47.9166667 -4.9166667 0.017 -35.9166667 -2.9166667 0.018 -16.9166667 -0.9166667 0.019 2.0833333 1.0833333 0.02 18.0833333 3.0833333 0.021 42.0833333 5.0833333 0.022 67.0833333 7.0833333 0.023 89.0833333 9.0833333 0.024 95.0833333 10.0833333 0.025 -72.8333333 -10.9166667 0.026 -76.8333333 -8.9166667 0.027 -60.8333333 -6.9166667 0.028 -48.8333333 -4.9166667 0.029 -31.8333333 -2.9166667 0.03 -16.8333333 -0.9166667 0.031 -0.8333333 1.0833333 0.032 22.1666667 3.0833333 0.033 47.1666667 5.0833333 0.034 71.1666667 7.0833333 0.035 82.1666667 9.0833333 0.036 86.1666667 10.0833333 0.037 -57.3333333 -10.9166667 0.038 -50.3333333 -8.9166667 0.039 -43.3333333 -6.9166667 0.04 -32.3333333 -4.9166667 0.041 -25.3333333 -2.9166667 0.042 -12.3333333 -0.9166667 0.043 2.6666667 1.0833333 0.044 8.6666667 3.0833333 0.045 36.6666667 5.0833333 0.046 54.6666667 7.0833333 0.047 60.6666667 9.0833333 0.048 57.6666667 10.0833333 0.049 -85.6666667 -10.9166667 0.05 -84.6666667 -8.9166667 0.051 -78.6666667 -6.9166667 0.052 -66.6666667 -4.9166667 0.053 -47.6666667 -2.9166667 0.054 -20.6666667 -0.9166667 0.055 14.3333333 1.0833333 0.056 37.3333333 3.0833333 0.057 70.3333333 5.0833333 0.058 72.3333333 7.0833333 0.059 93.3333333 9.0833333 0.06 96.3333333 10.0833333 0.061 -72.7500000 -10.9166667 0.062 -64.7500000 -8.9166667 0.063 -54.7500000 -6.9166667 0.064 -39.7500000 -4.9166667 0.065 -16.7500000 -2.9166667 0.066 10.2500000 -0.9166667 0.067 27.2500000 1.0833333 0.068 34.2500000 3.0833333 0.069 41.2500000 5.0833333 0.07 46.2500000 7.0833333 0.071 46.2500000 9.0833333 0.072 43.2500000 10.0833333 0.073 -109.0000000 -10.9166667 0.074 -101.0000000 -8.9166667 0.075 -93.0000000 -6.9166667 0.076 -79.0000000 -4.9166667 0.077 -61.0000000 -2.9166667 0.078 -38.0000000 -0.9166667 0.079 -4.0000000 1.0833333 0.08 24.0000000 3.0833333 0.081 68.0000000 5.0833333 0.082 100.0000000 7.0833333 0.083 138.0000000 9.0833333 0.084 155.0000000 10.0833333 0.085 -50.0000000 -10.0000000 0.086 -42.0000000 -8.0000000 0.087 -31.0000000 -6.0000000 0.088 -21.0000000 -4.0000000 0.089 -8.0000000 -2.0000000 0.09 1.0000000 0.0000000 0.091 18.0000000 2.0000000 0.092 24.0000000 4.0000000 0.093 34.0000000 6.0000000 0.094 42.0000000 8.0000000 0.095 33.0000000 10.0000000 0.096 -39.1666667 -10.9166667 0.097 -30.1666667 -8.9166667 0.098 -22.1666667 -6.9166667 0.099 -13.1666667 -4.9166667 0.1 3.8333333 -2.9166667 0.101 14.8333333 -0.9166667 0.102 8.8333333 1.0833333 0.103 10.8333333 3.0833333 0.104 11.8333333 5.0833333 0.105 18.8333333 7.0833333 0.106 18.8333333 9.0833333 0.107 16.8333333 10.0833333 0.108 -42.0833333 -10.9166667 0.109 -39.0833333 -8.9166667 0.11 -31.0833333 -6.9166667 0.111 -20.0833333 -4.9166667 0.112 -9.0833333 -2.9166667 0.113 -2.0833333 -0.9166667 0.114 5.9166667 1.0833333 0.115 12.9166667 3.0833333 0.116 17.9166667 5.0833333 0.117 28.9166667 7.0833333 0.118 36.9166667 9.0833333 0.119 40.9166667 10.0833333 0.12 -86.9166667 -10.9166667 0.121 -78.9166667 -8.9166667 0.122 -66.9166667 -6.9166667 0.123 -45.9166667 -4.9166667 0.124 -17.9166667 -2.9166667 0.125 9.0833333 -0.9166667 0.126 38.0833333 1.0833333 0.127 47.0833333 3.0833333 0.128 52.0833333 5.0833333 0.129 54.0833333 7.0833333 0.13 51.0833333 9.0833333 0.131 45.0833333 10.0833333 0.132 -73.0833333 -10.9166667 0.133 -65.0833333 -8.9166667 0.134 -58.0833333 -6.9166667 0.135 -52.0833333 -4.9166667 0.136 -42.0833333 -2.9166667 0.137 -26.0833333 -0.9166667 0.138 4.9166667 1.0833333 0.139 20.9166667 3.0833333 0.14 47.9166667 5.0833333 0.141 70.9166667 7.0833333 0.142 80.9166667 9.0833333 0.143 90.9166667 10.0833333 0.144 -26.8333333 -10.9166667 0.145 -19.8333333 -8.9166667 0.146 -14.8333333 -6.9166667 0.147 -7.8333333 -4.9166667 0.148 -2.8333333 -2.9166667 0.149 -0.8333333 -0.9166667 0.15 3.1666667 1.0833333 0.151 2.1666667 3.0833333 0.152 3.1666667 5.0833333 0.153 13.1666667 7.0833333 0.154 23.1666667 9.0833333 0.155 28.1666667 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0.535 -0.46889778 -0.4121357 0.536 -0.08258200 -0.1295283 0.537 0.33732647 0.1530790 0.538 0.48849351 0.4356863 0.539 0.67325324 0.7182936 0.54 0.95879099 1.0009009 0.541 1.37869946 1.2835082 0.542 1.29471776 1.4248118 0.543 -1.19807733 -1.5425649 0.544 -1.08701960 -1.2599576 0.545 -0.96586571 -0.9773503 0.546 -0.78413488 -0.6947430 0.547 -0.54182711 -0.4121357 0.548 -0.32980780 -0.1295283 0.549 -0.03701924 0.1530790 0.55 0.12451927 0.4356863 0.551 0.64951945 0.7182936 0.552 1.04326958 1.0009009 0.553 1.46730819 1.2835082 0.554 1.65913517 1.4248118 0.555 -1.41816697 -1.5425649 0.556 -1.22956165 -1.2599576 0.557 -1.06997252 -0.9773503 0.558 -0.76530238 -0.6947430 0.559 -0.43161604 -0.4121357 0.56 -0.14145399 -0.1295283 0.561 0.20674045 0.1530790 0.562 0.40985388 0.4356863 0.563 0.67099972 0.7182936 0.564 0.94665366 1.0009009 0.565 1.38189672 1.2835082 0.566 1.43992913 1.4248118 0.567 -1.31821653 -1.5425649 0.568 -1.15730747 -1.2599576 0.569 -0.99639841 -0.9773503 0.57 -0.78597887 -0.6947430 0.571 -0.52604885 -0.4121357 0.572 -0.31562931 -0.1295283 0.573 0.09283215 0.1530790 0.574 0.34038455 0.4356863 0.575 0.71171315 0.7182936 0.576 1.07066413 1.0009009 0.577 1.44199273 1.2835082 0.578 1.44199273 1.4248118 > centers_by(data = as.data.frame(CO2), vrb.nm = c("conc","uptake"), + grp.nm = c("Type","Treatment"), scale = TRUE) # multiple grouping columns conc_zw uptake_zw 1 -1.1279881 -2.014650502 2 -0.8625791 -0.514083232 3 -0.6137582 -0.055576566 4 -0.2819970 0.194517979 5 0.2156448 -0.003473535 6 0.7962269 0.402930100 7 1.8744507 0.455033131 8 -1.1279881 -2.264745047 9 -0.8625791 -0.837122019 10 -0.6137582 0.184097373 11 -0.2819970 0.673865858 12 0.2156448 0.548818585 13 0.7962269 0.632183433 14 1.8744507 0.934381009 15 -1.1279881 -1.993809290 16 -0.8625791 -0.305671111 17 -0.6137582 0.517556767 18 -0.2819970 0.705127676 19 0.2156448 0.788492524 20 0.7962269 0.892698584 21 1.8744507 1.059428281 22 -1.1279881 -1.819875820 23 -0.8625791 -0.793418460 24 -0.6137582 -0.150586578 25 -0.2819970 0.295248437 26 0.2156448 0.077515058 27 0.7962269 0.378194487 28 1.8744507 0.720346940 29 -1.1279881 -2.327920372 30 -0.8625791 -0.461634263 31 -0.6137582 0.336721462 32 -0.2819970 0.730715196 33 0.2156448 0.709978684 34 0.7962269 0.595927866 35 1.8744507 1.103972418 36 -1.1279881 -1.726561514 37 -0.8625791 -1.114834401 38 -0.6137582 0.658137403 39 -0.2819970 0.233038900 40 0.2156448 0.741083452 41 0.7962269 0.813661246 42 1.8744507 1.000289857 43 -1.1279881 -2.074047372 44 -0.8625791 -0.912220587 45 -0.6137582 0.033452377 46 -0.2819970 0.546817700 47 0.2156448 0.668404224 48 0.7962269 0.871048431 49 1.8744507 1.289846458 50 -1.1279881 -1.884912779 51 -0.8625791 -0.533951402 52 -0.6137582 0.627875383 53 -0.2819970 0.789990748 54 0.2156448 0.871048431 55 0.7962269 0.695423452 56 1.8744507 0.749461907 57 -1.1279881 -1.979480075 58 -0.8625791 -0.885201360 59 -0.6137582 -0.020586078 60 -0.2819970 0.263115811 61 0.2156448 0.344173494 62 0.7962269 0.290135039 63 1.8744507 0.249606197 64 -1.1279881 -1.309267605 65 -0.8625791 -0.225250341 66 -0.6137582 0.563125852 67 -0.2819970 0.760219900 68 0.2156448 0.908040436 69 0.7962269 1.573232848 70 1.8744507 1.499322580 71 -1.1279881 -1.999096774 72 -0.8625791 -1.087536801 73 -0.6137582 -0.865805997 74 -0.2819970 -0.693348705 75 0.2156448 -0.816532485 76 0.7962269 -0.520891413 77 1.8744507 -0.348434121 78 -1.1279881 -1.284630849 79 -0.8625791 0.538489096 80 -0.6137582 0.513852340 81 -0.2819970 0.513852340 82 0.2156448 0.513852340 83 0.7962269 0.760219900 84 1.8744507 1.006587460 > > > > cleanEx() > nameEx("change") > ### * change > > flush(stderr()); flush(stdout()) > > ### Name: change > ### Title: Change Score from a Numeric Vector > ### Aliases: change > > ### ** Examples > > change(x = attitude[[1]], n = -1L) # use L to prevent problems with floating point numbers [1] NA 20 8 -10 20 -38 15 13 1 -5 -3 3 2 -1 9 4 -7 -9 0 [20] -15 0 14 -11 -13 23 3 12 -30 37 -3 > change(x = attitude[[1]], n = -2L) # can specify any integer up to the length of `x` [1] NA NA 28 -2 10 -18 -23 28 14 -4 -8 0 5 1 8 13 -3 -16 -9 [20] -15 -15 14 3 -24 10 26 15 -18 7 34 > change(x = attitude[[1]], n = +1L) # can specify negative or positive integers [1] 20 8 -10 20 -38 15 13 1 -5 -3 3 2 -1 9 4 -7 -9 0 -15 [20] 0 14 -11 -13 23 3 12 -30 37 -3 NA > change(x = attitude[[1]], n = +2L, undefined = -999) # user-specified indefined value [1] 28 -2 10 -18 -23 28 14 -4 -8 0 5 1 8 13 -3 [16] -16 -9 -15 -15 14 3 -24 10 26 15 -18 7 34 -999 -999 > change(x = attitude[[1]], n = -2L, undefined = -999) # user-specified indefined value [1] -999 -999 28 -2 10 -18 -23 28 14 -4 -8 0 5 1 8 [16] 13 -3 -16 -9 -15 -15 14 3 -24 10 26 15 -18 7 34 > change(x = attitude[[1]], n = 0L) # returns a vector of zeros [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 > ## Not run: > ##D change(x = setNames(object = letters, nm = LETTERS), n = 3L) # character vector returns an error > ## End(Not run) > > > > cleanEx() > nameEx("change_by") > ### * change_by > > flush(stderr()); flush(stdout()) > > ### Name: change_by > ### Title: Change Scores from a Numeric Vector by Group > ### Aliases: change_by > > ### ** Examples > > change_by(x = ChickWeight[["Time"]], grp = ChickWeight[["Chick"]], n = -1L) [1] NA 2 2 2 2 2 2 2 2 2 2 1 NA 2 2 2 2 2 2 2 2 2 2 1 NA [26] 2 2 2 2 2 2 2 2 2 2 1 NA 2 2 2 2 2 2 2 2 2 2 1 NA 2 [51] 2 2 2 2 2 2 2 2 2 1 NA 2 2 2 2 2 2 2 2 2 2 1 NA 2 2 [76] 2 2 2 2 2 2 2 2 1 NA 2 2 2 2 2 2 2 2 2 2 NA 2 2 2 2 [101] 2 2 2 2 2 2 1 NA 2 2 2 2 2 2 2 2 2 2 1 NA 2 2 2 2 2 [126] 2 2 2 2 2 1 NA 2 2 2 2 2 2 2 2 2 2 1 NA 2 2 2 2 2 2 [151] 2 2 2 2 1 NA 2 2 2 2 2 2 2 2 2 2 1 NA 2 2 2 2 2 2 2 [176] NA 2 2 2 2 2 2 NA 2 2 2 2 2 2 2 2 2 2 1 NA 2 NA 2 2 2 [201] 2 2 2 2 2 2 2 1 NA 2 2 2 2 2 2 2 2 2 2 1 NA 2 2 2 2 [226] 2 2 2 2 2 2 1 NA 2 2 2 2 2 2 2 2 2 2 1 NA 2 2 2 2 2 [251] 2 2 2 2 2 1 NA 2 2 2 2 2 2 2 2 2 2 1 NA 2 2 2 2 2 2 [276] 2 2 2 2 1 NA 2 2 2 2 2 2 2 2 2 2 1 NA 2 2 2 2 2 2 2 [301] 2 2 2 1 NA 2 2 2 2 2 2 2 2 2 2 1 NA 2 2 2 2 2 2 2 2 [326] 2 2 1 NA 2 2 2 2 2 2 2 2 2 2 1 NA 2 2 2 2 2 2 2 2 2 [351] 2 1 NA 2 2 2 2 2 2 2 2 2 2 1 NA 2 2 2 2 2 2 2 2 2 2 [376] 1 NA 2 2 2 2 2 2 2 2 2 2 1 NA 2 2 2 2 2 2 2 2 2 2 1 [401] NA 2 2 2 2 2 2 2 2 2 2 1 NA 2 2 2 2 2 2 2 2 2 2 1 NA [426] 2 2 2 2 2 2 2 2 2 2 1 NA 2 2 2 2 2 2 2 2 2 2 1 NA 2 [451] 2 2 2 2 2 2 2 2 2 1 NA 2 2 2 2 2 2 2 2 2 2 1 NA 2 2 [476] 2 2 2 2 2 2 2 2 1 NA 2 2 2 2 2 2 2 2 2 2 1 NA 2 2 2 [501] 2 2 2 2 2 2 NA 2 2 2 2 2 2 2 2 2 2 1 NA 2 2 2 2 2 2 [526] 2 2 2 2 1 NA 2 2 2 2 2 2 2 2 2 2 1 NA 2 2 2 2 2 2 2 [551] 2 2 2 1 NA 2 2 2 2 2 2 2 2 2 2 1 NA 2 2 2 2 2 2 2 2 [576] 2 2 1 > tmp_nm <- c("vs","am") # multiple grouping vectors > change_by(x = mtcars[["disp"]], grp = mtcars[tmp_nm], n = +1L) [1] 0.0 -39.7 -29.3 -33.0 0.0 -78.3 -84.2 -5.9 26.8 0.0 [11] -47.5 0.0 0.0 196.2 -12.0 -20.0 -122.0 -3.0 -4.6 7.9 [21] NA -14.0 46.0 50.0 NA 16.1 230.7 25.9 -206.0 156.0 [31] NA NA > tmp_nm <- c("Type","Treatment") # multiple grouping vectors > change_by(x = as.data.frame(CO2)[["uptake"]], grp = as.data.frame(CO2)[tmp_nm], n = 2L) [1] 18.8 6.8 0.5 2.0 4.4 -25.6 -12.4 23.5 14.5 3.5 -0.4 3.7 [13] -25.2 -11.9 24.1 9.7 2.6 1.8 2.6 NA NA 16.1 10.5 2.2 [25] 0.8 6.2 -26.1 -11.4 25.7 11.5 3.6 -1.3 3.8 -22.4 -21.4 23.0 [37] 13.0 0.8 5.6 2.5 NA NA 15.6 10.8 4.7 2.4 4.6 -20.4 [49] -13.5 18.6 9.8 1.8 -0.7 -0.9 -19.8 -12.1 14.5 8.5 2.7 0.2 [61] -0.7 NA NA 7.6 4.0 1.4 3.3 2.4 -14.5 -10.5 4.6 1.6 [73] 0.2 0.7 1.9 -3.1 3.6 7.3 -0.1 0.0 1.0 2.0 NA NA > > > > cleanEx() > nameEx("changes") > ### * changes > > flush(stderr()); flush(stdout()) > > ### Name: changes > ### Title: Change Scores from Numeric Data > ### Aliases: changes > > ### ** Examples > > changes(attitude, vrb.nm = names(attitude), + n = -1L) # use L to prevent problems with floating point numbers rating_hg1 complaints_hg1 privileges_hg1 learning_hg1 raises_hg1 1 NA NA NA NA NA 2 20 13 21 15 2 3 8 6 17 15 13 4 -10 -7 -23 -22 -22 5 20 15 11 19 17 6 -38 -23 -7 -22 -17 7 15 12 -7 12 12 8 13 8 8 -1 4 9 1 7 22 12 1 10 -5 -21 -27 -20 -9 11 -3 -8 8 11 -4 12 3 7 -6 -19 1 13 2 2 10 3 -4 14 -1 21 26 3 4 15 9 -6 -29 27 20 16 4 13 -4 0 -19 17 -7 -5 14 -3 19 18 -9 -25 1 6 -24 19 0 10 -19 -18 20 20 -15 -12 22 -3 -11 21 0 -18 -35 -20 -21 22 14 21 19 28 23 23 -11 5 0 -12 -3 24 -13 -29 -10 8 -13 25 23 17 0 -10 16 26 3 23 24 15 22 27 12 -2 -8 11 -8 28 -30 -18 -14 -29 -29 29 37 28 27 26 26 30 -3 -3 -32 -12 -13 critical_hg1 advance_hg1 1 NA NA 2 -19 2 3 13 1 4 -2 -13 5 -1 12 6 -34 -13 7 19 1 8 -2 6 9 17 -10 10 -3 10 11 -13 -7 12 7 7 13 -11 -16 14 14 10 15 0 11 16 -23 -10 17 25 27 18 1 -3 19 5 -14 20 -7 6 21 -14 -19 22 16 8 23 0 -4 24 -23 12 25 18 -16 26 1 39 27 2 -23 28 5 -11 29 -9 17 30 4 -16 > changes(attitude, vrb.nm = names(attitude), + n = -2L) # can specify any integer up to the length of `x` rating_hg2 complaints_hg2 privileges_hg2 learning_hg2 raises_hg2 1 NA NA NA NA NA 2 NA NA NA NA NA 3 28 19 38 30 15 4 -2 -1 -6 -7 -9 5 10 8 -12 -3 -5 6 -18 -8 4 -3 0 7 -23 -11 -14 -10 -5 8 28 20 1 11 16 9 14 15 30 11 5 10 -4 -14 -5 -8 -8 11 -8 -29 -19 -9 -13 12 0 -1 2 -8 -3 13 5 9 4 -16 -3 14 1 23 36 6 0 15 8 15 -3 30 24 16 13 7 -33 27 1 17 -3 8 10 -3 0 18 -16 -30 15 3 -5 19 -9 -15 -18 -12 -4 20 -15 -2 3 -21 9 21 -15 -30 -13 -23 -32 22 14 3 -16 8 2 23 3 26 19 16 20 24 -24 -24 -10 -4 -16 25 10 -12 -10 -2 3 26 26 40 24 5 38 27 15 21 16 26 14 28 -18 -20 -22 -18 -37 29 7 10 13 -3 -3 30 34 25 -5 14 13 critical_hg2 advance_hg2 1 NA NA 2 NA NA 3 -6 3 4 11 -12 5 -3 -1 6 -35 -1 7 -15 -12 8 17 7 9 15 -4 10 14 0 11 -16 3 12 -6 0 13 -4 -9 14 3 -6 15 14 21 16 -23 1 17 2 17 18 26 24 19 6 -17 20 -2 -8 21 -21 -13 22 2 -11 23 16 4 24 -23 8 25 -5 -4 26 19 23 27 3 16 28 7 -34 29 -4 6 30 -5 1 > changes(attitude, vrb.nm = names(attitude), + n = +1L) # can specify negative or positive integers rating_hd1 complaints_hd1 privileges_hd1 learning_hd1 raises_hd1 1 20 13 21 15 2 2 8 6 17 15 13 3 -10 -7 -23 -22 -22 4 20 15 11 19 17 5 -38 -23 -7 -22 -17 6 15 12 -7 12 12 7 13 8 8 -1 4 8 1 7 22 12 1 9 -5 -21 -27 -20 -9 10 -3 -8 8 11 -4 11 3 7 -6 -19 1 12 2 2 10 3 -4 13 -1 21 26 3 4 14 9 -6 -29 27 20 15 4 13 -4 0 -19 16 -7 -5 14 -3 19 17 -9 -25 1 6 -24 18 0 10 -19 -18 20 19 -15 -12 22 -3 -11 20 0 -18 -35 -20 -21 21 14 21 19 28 23 22 -11 5 0 -12 -3 23 -13 -29 -10 8 -13 24 23 17 0 -10 16 25 3 23 24 15 22 26 12 -2 -8 11 -8 27 -30 -18 -14 -29 -29 28 37 28 27 26 26 29 -3 -3 -32 -12 -13 30 NA NA NA NA NA critical_hd1 advance_hd1 1 -19 2 2 13 1 3 -2 -13 4 -1 12 5 -34 -13 6 19 1 7 -2 6 8 17 -10 9 -3 10 10 -13 -7 11 7 7 12 -11 -16 13 14 10 14 0 11 15 -23 -10 16 25 27 17 1 -3 18 5 -14 19 -7 6 20 -14 -19 21 16 8 22 0 -4 23 -23 12 24 18 -16 25 1 39 26 2 -23 27 5 -11 28 -9 17 29 4 -16 30 NA NA > changes(attitude, vrb.nm = names(attitude), + n = +2L, undefined = -999) # user-specified indefined value rating_hd2 complaints_hd2 privileges_hd2 learning_hd2 raises_hd2 1 28 19 38 30 15 2 -2 -1 -6 -7 -9 3 10 8 -12 -3 -5 4 -18 -8 4 -3 0 5 -23 -11 -14 -10 -5 6 28 20 1 11 16 7 14 15 30 11 5 8 -4 -14 -5 -8 -8 9 -8 -29 -19 -9 -13 10 0 -1 2 -8 -3 11 5 9 4 -16 -3 12 1 23 36 6 0 13 8 15 -3 30 24 14 13 7 -33 27 1 15 -3 8 10 -3 0 16 -16 -30 15 3 -5 17 -9 -15 -18 -12 -4 18 -15 -2 3 -21 9 19 -15 -30 -13 -23 -32 20 14 3 -16 8 2 21 3 26 19 16 20 22 -24 -24 -10 -4 -16 23 10 -12 -10 -2 3 24 26 40 24 5 38 25 15 21 16 26 14 26 -18 -20 -22 -18 -37 27 7 10 13 -3 -3 28 34 25 -5 14 13 29 -999 -999 -999 -999 -999 30 -999 -999 -999 -999 -999 critical_hd2 advance_hd2 1 -6 3 2 11 -12 3 -3 -1 4 -35 -1 5 -15 -12 6 17 7 7 15 -4 8 14 0 9 -16 3 10 -6 0 11 -4 -9 12 3 -6 13 14 21 14 -23 1 15 2 17 16 26 24 17 6 -17 18 -2 -8 19 -21 -13 20 2 -11 21 16 4 22 -23 8 23 -5 -4 24 19 23 25 3 16 26 7 -34 27 -4 6 28 -5 1 29 -999 -999 30 -999 -999 > changes(attitude, vrb.nm = names(attitude), + n = -2L, undefined = -999) # user-specified indefined value rating_hg2 complaints_hg2 privileges_hg2 learning_hg2 raises_hg2 1 -999 -999 -999 -999 -999 2 -999 -999 -999 -999 -999 3 28 19 38 30 15 4 -2 -1 -6 -7 -9 5 10 8 -12 -3 -5 6 -18 -8 4 -3 0 7 -23 -11 -14 -10 -5 8 28 20 1 11 16 9 14 15 30 11 5 10 -4 -14 -5 -8 -8 11 -8 -29 -19 -9 -13 12 0 -1 2 -8 -3 13 5 9 4 -16 -3 14 1 23 36 6 0 15 8 15 -3 30 24 16 13 7 -33 27 1 17 -3 8 10 -3 0 18 -16 -30 15 3 -5 19 -9 -15 -18 -12 -4 20 -15 -2 3 -21 9 21 -15 -30 -13 -23 -32 22 14 3 -16 8 2 23 3 26 19 16 20 24 -24 -24 -10 -4 -16 25 10 -12 -10 -2 3 26 26 40 24 5 38 27 15 21 16 26 14 28 -18 -20 -22 -18 -37 29 7 10 13 -3 -3 30 34 25 -5 14 13 critical_hg2 advance_hg2 1 -999 -999 2 -999 -999 3 -6 3 4 11 -12 5 -3 -1 6 -35 -1 7 -15 -12 8 17 7 9 15 -4 10 14 0 11 -16 3 12 -6 0 13 -4 -9 14 3 -6 15 14 21 16 -23 1 17 2 17 18 26 24 19 6 -17 20 -2 -8 21 -21 -13 22 2 -11 23 16 4 24 -23 8 25 -5 -4 26 19 23 27 3 16 28 7 -34 29 -4 6 30 -5 1 > ## Not run: > ##D changes(str2str::d2d(InsectSprays), names(InsectSprays), > ##D n = 3L) # character vector returns an error > ## End(Not run) > > > > cleanEx() > nameEx("changes_by") > ### * changes_by > > flush(stderr()); flush(stdout()) > > ### Name: changes_by > ### Title: Change Scores from Numeric Data by Group > ### Aliases: changes_by > > ### ** Examples > > changes_by(data = ChickWeight, vrb.nm = c("weight","Time"), grp.nm = "Chick", n = -1L) weight_hgw1 Time_hgw1 1 NA NA 2 9 2 3 8 2 4 5 2 5 12 2 6 17 2 7 13 2 8 19 2 9 24 2 10 22 2 11 28 2 12 6 1 13 NA NA 14 9 2 15 9 2 16 14 2 17 12 2 18 19 2 19 19 2 20 16 2 21 24 2 22 25 2 23 22 2 24 6 1 25 NA NA 26 -4 2 27 16 2 28 12 2 29 17 2 30 15 2 31 16 2 32 23 2 33 25 2 34 24 2 35 11 2 36 4 1 37 NA NA 38 7 2 39 7 2 40 11 2 41 7 2 42 13 2 43 15 2 44 6 2 45 28 2 46 18 2 47 6 2 48 -3 1 49 NA NA 50 1 2 51 6 2 52 12 2 53 19 2 54 27 2 55 35 2 56 23 2 57 33 2 58 2 2 59 21 2 60 3 1 61 NA NA 62 8 2 63 10 2 64 15 2 65 23 2 66 27 2 67 17 2 68 7 2 69 7 2 70 5 2 71 0 2 72 -3 1 73 NA NA 74 8 2 75 8 2 76 14 2 77 18 2 78 23 2 79 34 2 80 28 2 81 44 2 82 32 2 83 38 2 84 17 1 85 NA NA 86 8 2 87 11 2 88 10 2 89 13 2 90 9 2 91 17 2 92 6 2 93 10 2 94 8 2 95 -9 2 96 NA NA 97 9 2 98 8 2 99 9 2 100 17 2 101 11 2 102 -6 2 103 2 2 104 1 2 105 7 2 106 0 2 107 -2 1 108 NA NA 109 3 2 110 8 2 111 11 2 112 11 2 113 7 2 114 8 2 115 7 2 116 5 2 117 11 2 118 8 2 119 4 1 120 NA NA 121 8 2 122 12 2 123 21 2 124 28 2 125 27 2 126 29 2 127 9 2 128 5 2 129 2 2 130 -3 2 131 -6 1 132 NA NA 133 8 2 134 7 2 135 6 2 136 10 2 137 16 2 138 31 2 139 16 2 140 27 2 141 23 2 142 10 2 143 10 1 144 NA NA 145 7 2 146 5 2 147 7 2 148 5 2 149 2 2 150 4 2 151 -1 2 152 1 2 153 10 2 154 10 2 155 5 1 156 NA NA 157 8 2 158 13 2 159 17 2 160 22 2 161 27 2 162 36 2 163 28 2 164 35 2 165 21 2 166 11 2 167 7 1 168 NA NA 169 8 2 170 7 2 171 8 2 172 4 2 173 0 2 174 -1 2 175 1 2 176 NA NA 177 4 2 178 4 2 179 2 2 180 6 2 181 -6 2 182 3 2 183 NA NA 184 9 2 185 10 2 186 11 2 187 11 2 188 6 2 189 9 2 190 5 2 191 10 2 192 10 2 193 10 2 194 9 1 195 NA NA 196 -4 2 197 NA NA 198 5 2 199 7 2 200 7 2 201 3 2 202 6 2 203 11 2 204 6 2 205 18 2 206 14 2 207 24 2 208 13 1 209 NA NA 210 6 2 211 7 2 212 4 2 213 7 2 214 8 2 215 4 2 216 12 2 217 9 2 218 9 2 219 8 2 220 2 1 221 NA NA 222 10 2 223 12 2 224 24 2 225 39 2 226 38 2 227 54 2 228 23 2 229 35 2 230 32 2 231 11 2 232 13 1 233 NA NA 234 14 2 235 9 2 236 13 2 237 13 2 238 5 2 239 13 2 240 3 2 241 20 2 242 17 2 243 16 2 244 3 1 245 NA NA 246 9 2 247 9 2 248 12 2 249 17 2 250 13 2 251 24 2 252 8 2 253 10 2 254 18 2 255 7 2 256 5 1 257 NA NA 258 10 2 259 6 2 260 16 2 261 -8 2 262 2 2 263 2 2 264 1 2 265 1 2 266 0 2 267 4 2 268 -2 1 269 NA NA 270 9 2 271 13 2 272 16 2 273 24 2 274 22 2 275 22 2 276 18 2 277 33 2 278 34 2 279 28 2 280 6 1 281 NA NA 282 6 2 283 9 2 284 17 2 285 19 2 286 21 2 287 22 2 288 11 2 289 22 2 290 36 2 291 31 2 292 15 1 293 NA NA 294 7 2 295 12 2 296 15 2 297 14 2 298 13 2 299 15 2 300 8 2 301 21 2 302 19 2 303 22 2 304 7 1 305 NA NA 306 7 2 307 12 2 308 15 2 309 19 2 310 22 2 311 31 2 312 11 2 313 28 2 314 23 2 315 5 2 316 21 1 317 NA NA 318 9 2 319 11 2 320 15 2 321 13 2 322 19 2 323 28 2 324 16 2 325 37 2 326 43 2 327 49 2 328 30 1 329 NA NA 330 6 2 331 11 2 332 13 2 333 13 2 334 13 2 335 17 2 336 7 2 337 21 2 338 8 2 339 6 2 340 -7 1 341 NA NA 342 11 2 343 9 2 344 11 2 345 12 2 346 17 2 347 21 2 348 15 2 349 32 2 350 34 2 351 31 2 352 21 1 353 NA NA 354 8 2 355 16 2 356 17 2 357 25 2 358 22 2 359 30 2 360 20 2 361 42 2 362 42 2 363 28 2 364 14 1 365 NA NA 366 11 2 367 13 2 368 14 2 369 19 2 370 15 2 371 26 2 372 7 2 373 7 2 374 -5 2 375 10 2 376 -9 1 377 NA NA 378 8 2 379 14 2 380 22 2 381 22 2 382 27 2 383 30 2 384 22 2 385 49 2 386 59 2 387 33 2 388 14 1 389 NA NA 390 12 2 391 11 2 392 23 2 393 36 2 394 35 2 395 43 2 396 37 2 397 49 2 398 45 2 399 29 2 400 12 1 401 NA NA 402 9 2 403 13 2 404 15 2 405 22 2 406 18 2 407 29 2 408 21 2 409 32 2 410 29 2 411 -2 2 412 -5 1 413 NA NA 414 7 2 415 8 2 416 12 2 417 12 2 418 3 2 419 20 2 420 9 2 421 23 2 422 22 2 423 12 2 424 9 1 425 NA NA 426 8 2 427 12 2 428 13 2 429 24 2 430 11 2 431 19 2 432 26 2 433 38 2 434 40 2 435 48 2 436 10 1 437 NA NA 438 8 2 439 11 2 440 17 2 441 11 2 442 20 2 443 21 2 444 16 2 445 24 2 446 44 2 447 36 2 448 22 1 449 NA NA 450 14 2 451 11 2 452 13 2 453 22 2 454 19 2 455 34 2 456 28 2 457 33 2 458 47 2 459 33 2 460 26 1 461 NA NA 462 9 2 463 15 2 464 19 2 465 18 2 466 21 2 467 31 2 468 -2 2 469 22 2 470 9 2 471 15 2 472 5 1 473 NA NA 474 7 2 475 14 2 476 21 2 477 19 2 478 23 2 479 34 2 480 14 2 481 30 2 482 30 2 483 35 2 484 12 1 485 NA NA 486 13 2 487 14 2 488 27 2 489 35 2 490 26 2 491 27 2 492 4 2 493 9 2 494 1 2 495 1 2 496 1 1 497 NA NA 498 9 2 499 14 2 500 21 2 501 17 2 502 15 2 503 9 2 504 11 2 505 7 2 506 1 2 507 NA NA 508 9 2 509 11 2 510 17 2 511 20 2 512 19 2 513 18 2 514 6 2 515 6 2 516 27 2 517 23 2 518 -1 1 519 NA NA 520 12 2 521 10 2 522 20 2 523 19 2 524 19 2 525 24 2 526 12 2 527 17 2 528 37 2 529 21 2 530 7 1 531 NA NA 532 12 2 533 13 2 534 13 2 535 21 2 536 23 2 537 25 2 538 9 2 539 11 2 540 17 2 541 25 2 542 -5 1 543 NA NA 544 11 2 545 12 2 546 18 2 547 24 2 548 21 2 549 29 2 550 16 2 551 52 2 552 39 2 553 42 2 554 19 1 555 NA NA 556 13 2 557 11 2 558 21 2 559 23 2 560 20 2 561 24 2 562 14 2 563 18 2 564 19 2 565 30 2 566 4 1 567 NA NA 568 13 2 569 13 2 570 17 2 571 21 2 572 17 2 573 33 2 574 20 2 575 30 2 576 29 2 577 30 2 578 0 1 > changes_by(data = mtcars, vrb.nm = c("disp","mpg"), grp.nm = c("vs","am"), n = 1L) disp_hdw1 mpg_hdw1 Mazda RX4 0.0 0.0 Mazda RX4 Wag -39.7 5.0 Datsun 710 -29.3 9.6 Hornet 4 Drive -33.0 -3.3 Hornet Sportabout 0.0 -4.4 Valiant -78.3 6.3 Duster 360 -84.2 2.1 Merc 240D -5.9 -1.6 Merc 230 26.8 -3.6 Merc 280 0.0 -1.4 Merc 280C -47.5 3.7 Merc 450SE 0.0 0.9 Merc 450SL 0.0 -2.1 Merc 450SLC 196.2 -4.8 Cadillac Fleetwood -12.0 0.0 Lincoln Continental -20.0 4.3 Chrysler Imperial -122.0 0.8 Fiat 128 -3.0 -2.0 Honda Civic -4.6 3.5 Toyota Corolla 7.9 -6.6 Toyota Corona NA NA Dodge Challenger -14.0 -0.3 AMC Javelin 46.0 -1.9 Camaro Z28 50.0 5.9 Pontiac Firebird NA NA Fiat X1-9 16.1 3.1 Porsche 914-2 230.7 -10.2 Lotus Europa 25.9 -9.0 Ford Pantera L -206.0 3.9 Ferrari Dino 156.0 -4.7 Maserati Bora NA NA Volvo 142E NA NA > changes_by(data = as.data.frame(CO2), vrb.nm = c("conc","uptake"), + grp.nm = c("Type","Treatment"), n = 2L) # multiple grouping columns conc_hdw2 uptake_hdw2 1 155 18.8 2 175 6.8 3 250 0.5 4 325 2.0 5 500 4.4 6 -580 -25.6 7 -825 -12.4 8 155 23.5 9 175 14.5 10 250 3.5 11 325 -0.4 12 500 3.7 13 -580 -25.2 14 -825 -11.9 15 155 24.1 16 175 9.7 17 250 2.6 18 325 1.8 19 500 2.6 20 NA NA 21 NA NA 22 155 16.1 23 175 10.5 24 250 2.2 25 325 0.8 26 500 6.2 27 -580 -26.1 28 -825 -11.4 29 155 25.7 30 175 11.5 31 250 3.6 32 325 -1.3 33 500 3.8 34 -580 -22.4 35 -825 -21.4 36 155 23.0 37 175 13.0 38 250 0.8 39 325 5.6 40 500 2.5 41 NA NA 42 NA NA 43 155 15.6 44 175 10.8 45 250 4.7 46 325 2.4 47 500 4.6 48 -580 -20.4 49 -825 -13.5 50 155 18.6 51 175 9.8 52 250 1.8 53 325 -0.7 54 500 -0.9 55 -580 -19.8 56 -825 -12.1 57 155 14.5 58 175 8.5 59 250 2.7 60 325 0.2 61 500 -0.7 62 NA NA 63 NA NA 64 155 7.6 65 175 4.0 66 250 1.4 67 325 3.3 68 500 2.4 69 -580 -14.5 70 -825 -10.5 71 155 4.6 72 175 1.6 73 250 0.2 74 325 0.7 75 500 1.9 76 -580 -3.1 77 -825 3.6 78 155 7.3 79 175 -0.1 80 250 0.0 81 325 1.0 82 500 2.0 83 NA NA 84 NA NA > > > > cleanEx() > nameEx("colMeans_if") > ### * colMeans_if > > flush(stderr()); flush(stdout()) > > ### Name: colMeans_if > ### Title: Column Means Conditional on Frequency of Observed Values > ### Aliases: colMeans_if > > ### ** Examples > > colMeans_if(airquality) Ozone Solar.R Wind Temp Month Day 42.129310 185.931507 9.957516 77.882353 6.993464 15.803922 > colMeans_if(x = airquality, ov.min = 150, prop = FALSE) Ozone Solar.R Wind Temp Month Day NA NA 9.957516 77.882353 6.993464 15.803922 > > > > cleanEx() > nameEx("colNA") > ### * colNA > > flush(stderr()); flush(stdout()) > > ### Name: colNA > ### Title: Frequency of Missing Values by Column > ### Aliases: colNA > > ### ** Examples > > colNA(as.matrix(airquality)) # count of missing values Ozone Solar.R Wind Temp Month Day 37 7 0 0 0 0 > colNA(as.matrix(airquality), prop = TRUE) # proportion of missing values Ozone Solar.R Wind Temp Month Day 0.24183007 0.04575163 0.00000000 0.00000000 0.00000000 0.00000000 > colNA(as.matrix(airquality), ov = TRUE) # count of observed values Ozone Solar.R Wind Temp Month Day 116 146 153 153 153 153 > colNA(as.data.frame(airquality), prop = TRUE, ov = TRUE) # proportion of observed values Ozone Solar.R Wind Temp Month Day 0.7581699 0.9542484 1.0000000 1.0000000 1.0000000 1.0000000 > > > > cleanEx() > nameEx("colSums_if") > ### * colSums_if > > flush(stderr()); flush(stdout()) > > ### Name: colSums_if > ### Title: Column Sums Conditional on Frequency of Observed Values > ### Aliases: colSums_if > > ### ** Examples > > colSums_if(airquality) Ozone Solar.R Wind Temp Month Day 6445.784 28447.521 1523.500 11916.000 1070.000 2418.000 > colSums_if(x = airquality, ov.min = 150, prop = FALSE) Ozone Solar.R Wind Temp Month Day NA NA 1523.5 11916.0 1070.0 2418.0 > x <- data.frame("x" = c(1, 2, NA), "y" = c(1, NA, NA), "z" = c(NA, NA, NA)) > colSums_if(x) x y z NA NA NA > colSums_if(x, ov.min = 0) x y z 4.5 3.0 NA > colSums_if(x, ov.min = 0, allNA = 0) x y z 4.5 3.0 0.0 > identical(x = colSums(x, na.rm = TRUE), + y = colSums_if(x, impute = FALSE, ov.min = 0, allNA = 0)) # identical to [1] TRUE > # colSums(x, na.rm = TRUE) > > > > cleanEx() > nameEx("decompose") > ### * decompose > > flush(stderr()); flush(stdout()) > > ### Name: decompose > ### Title: Decompose a Numeric Vector by Group > ### Aliases: decompose > > ### ** Examples > > > # single grouping variable > chick_data <- as.data.frame(ChickWeight) # because the "groupedData" class > # calls `[.groupedData`, which is different than `[.data.frame` > decompose(x = ChickWeight[["weight"]], grp = ChickWeight[["Chick"]]) wth btw btw_c 1 -69.6666667 111.66667 -10.151672 2 -60.6666667 111.66667 -10.151672 3 -52.6666667 111.66667 -10.151672 4 -47.6666667 111.66667 -10.151672 5 -35.6666667 111.66667 -10.151672 6 -18.6666667 111.66667 -10.151672 7 -5.6666667 111.66667 -10.151672 8 13.3333333 111.66667 -10.151672 9 37.3333333 111.66667 -10.151672 10 59.3333333 111.66667 -10.151672 11 87.3333333 111.66667 -10.151672 12 93.3333333 111.66667 -10.151672 13 -79.9166667 119.91667 -1.901672 14 -70.9166667 119.91667 -1.901672 15 -61.9166667 119.91667 -1.901672 16 -47.9166667 119.91667 -1.901672 17 -35.9166667 119.91667 -1.901672 18 -16.9166667 119.91667 -1.901672 19 2.0833333 119.91667 -1.901672 20 18.0833333 119.91667 -1.901672 21 42.0833333 119.91667 -1.901672 22 67.0833333 119.91667 -1.901672 23 89.0833333 119.91667 -1.901672 24 95.0833333 119.91667 -1.901672 25 -72.8333333 115.83333 -5.985006 26 -76.8333333 115.83333 -5.985006 27 -60.8333333 115.83333 -5.985006 28 -48.8333333 115.83333 -5.985006 29 -31.8333333 115.83333 -5.985006 30 -16.8333333 115.83333 -5.985006 31 -0.8333333 115.83333 -5.985006 32 22.1666667 115.83333 -5.985006 33 47.1666667 115.83333 -5.985006 34 71.1666667 115.83333 -5.985006 35 82.1666667 115.83333 -5.985006 36 86.1666667 115.83333 -5.985006 37 -57.3333333 99.33333 -22.485006 38 -50.3333333 99.33333 -22.485006 39 -43.3333333 99.33333 -22.485006 40 -32.3333333 99.33333 -22.485006 41 -25.3333333 99.33333 -22.485006 42 -12.3333333 99.33333 -22.485006 43 2.6666667 99.33333 -22.485006 44 8.6666667 99.33333 -22.485006 45 36.6666667 99.33333 -22.485006 46 54.6666667 99.33333 -22.485006 47 60.6666667 99.33333 -22.485006 48 57.6666667 99.33333 -22.485006 49 -85.6666667 126.66667 4.848328 50 -84.6666667 126.66667 4.848328 51 -78.6666667 126.66667 4.848328 52 -66.6666667 126.66667 4.848328 53 -47.6666667 126.66667 4.848328 54 -20.6666667 126.66667 4.848328 55 14.3333333 126.66667 4.848328 56 37.3333333 126.66667 4.848328 57 70.3333333 126.66667 4.848328 58 72.3333333 126.66667 4.848328 59 93.3333333 126.66667 4.848328 60 96.3333333 126.66667 4.848328 61 -72.7500000 113.75000 -8.068339 62 -64.7500000 113.75000 -8.068339 63 -54.7500000 113.75000 -8.068339 64 -39.7500000 113.75000 -8.068339 65 -16.7500000 113.75000 -8.068339 66 10.2500000 113.75000 -8.068339 67 27.2500000 113.75000 -8.068339 68 34.2500000 113.75000 -8.068339 69 41.2500000 113.75000 -8.068339 70 46.2500000 113.75000 -8.068339 71 46.2500000 113.75000 -8.068339 72 43.2500000 113.75000 -8.068339 73 -109.0000000 150.00000 28.181661 74 -101.0000000 150.00000 28.181661 75 -93.0000000 150.00000 28.181661 76 -79.0000000 150.00000 28.181661 77 -61.0000000 150.00000 28.181661 78 -38.0000000 150.00000 28.181661 79 -4.0000000 150.00000 28.181661 80 24.0000000 150.00000 28.181661 81 68.0000000 150.00000 28.181661 82 100.0000000 150.00000 28.181661 83 138.0000000 150.00000 28.181661 84 155.0000000 150.00000 28.181661 85 -50.0000000 92.00000 -29.818339 86 -42.0000000 92.00000 -29.818339 87 -31.0000000 92.00000 -29.818339 88 -21.0000000 92.00000 -29.818339 89 -8.0000000 92.00000 -29.818339 90 1.0000000 92.00000 -29.818339 91 18.0000000 92.00000 -29.818339 92 24.0000000 92.00000 -29.818339 93 34.0000000 92.00000 -29.818339 94 42.0000000 92.00000 -29.818339 95 33.0000000 92.00000 -29.818339 96 -39.1666667 81.16667 -40.651672 97 -30.1666667 81.16667 -40.651672 98 -22.1666667 81.16667 -40.651672 99 -13.1666667 81.16667 -40.651672 100 3.8333333 81.16667 -40.651672 101 14.8333333 81.16667 -40.651672 102 8.8333333 81.16667 -40.651672 103 10.8333333 81.16667 -40.651672 104 11.8333333 81.16667 -40.651672 105 18.8333333 81.16667 -40.651672 106 18.8333333 81.16667 -40.651672 107 16.8333333 81.16667 -40.651672 108 -42.0833333 83.08333 -38.735006 109 -39.0833333 83.08333 -38.735006 110 -31.0833333 83.08333 -38.735006 111 -20.0833333 83.08333 -38.735006 112 -9.0833333 83.08333 -38.735006 113 -2.0833333 83.08333 -38.735006 114 5.9166667 83.08333 -38.735006 115 12.9166667 83.08333 -38.735006 116 17.9166667 83.08333 -38.735006 117 28.9166667 83.08333 -38.735006 118 36.9166667 83.08333 -38.735006 119 40.9166667 83.08333 -38.735006 120 -86.9166667 129.91667 8.098328 121 -78.9166667 129.91667 8.098328 122 -66.9166667 129.91667 8.098328 123 -45.9166667 129.91667 8.098328 124 -17.9166667 129.91667 8.098328 125 9.0833333 129.91667 8.098328 126 38.0833333 129.91667 8.098328 127 47.0833333 129.91667 8.098328 128 52.0833333 129.91667 8.098328 129 54.0833333 129.91667 8.098328 130 51.0833333 129.91667 8.098328 131 45.0833333 129.91667 8.098328 132 -73.0833333 114.08333 -7.735006 133 -65.0833333 114.08333 -7.735006 134 -58.0833333 114.08333 -7.735006 135 -52.0833333 114.08333 -7.735006 136 -42.0833333 114.08333 -7.735006 137 -26.0833333 114.08333 -7.735006 138 4.9166667 114.08333 -7.735006 139 20.9166667 114.08333 -7.735006 140 47.9166667 114.08333 -7.735006 141 70.9166667 114.08333 -7.735006 142 80.9166667 114.08333 -7.735006 143 90.9166667 114.08333 -7.735006 144 -26.8333333 67.83333 -53.985006 145 -19.8333333 67.83333 -53.985006 146 -14.8333333 67.83333 -53.985006 147 -7.8333333 67.83333 -53.985006 148 -2.8333333 67.83333 -53.985006 149 -0.8333333 67.83333 -53.985006 150 3.1666667 67.83333 -53.985006 151 2.1666667 67.83333 -53.985006 152 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516 54.4166667 119.58333 -2.235006 517 77.4166667 119.58333 -2.235006 518 76.4166667 119.58333 -2.235006 519 -94.0833333 134.08333 12.264994 520 -82.0833333 134.08333 12.264994 521 -72.0833333 134.08333 12.264994 522 -52.0833333 134.08333 12.264994 523 -33.0833333 134.08333 12.264994 524 -14.0833333 134.08333 12.264994 525 9.9166667 134.08333 12.264994 526 21.9166667 134.08333 12.264994 527 38.9166667 134.08333 12.264994 528 75.9166667 134.08333 12.264994 529 96.9166667 134.08333 12.264994 530 103.9166667 134.08333 12.264994 531 -86.9166667 127.91667 6.098328 532 -74.9166667 127.91667 6.098328 533 -61.9166667 127.91667 6.098328 534 -48.9166667 127.91667 6.098328 535 -27.9166667 127.91667 6.098328 536 -4.9166667 127.91667 6.098328 537 20.0833333 127.91667 6.098328 538 29.0833333 127.91667 6.098328 539 40.0833333 127.91667 6.098328 540 57.0833333 127.91667 6.098328 541 82.0833333 127.91667 6.098328 542 77.0833333 127.91667 6.098328 543 -118.6666667 157.66667 35.848328 544 -107.6666667 157.66667 35.848328 545 -95.6666667 157.66667 35.848328 546 -77.6666667 157.66667 35.848328 547 -53.6666667 157.66667 35.848328 548 -32.6666667 157.66667 35.848328 549 -3.6666667 157.66667 35.848328 550 12.3333333 157.66667 35.848328 551 64.3333333 157.66667 35.848328 552 103.3333333 157.66667 35.848328 553 145.3333333 157.66667 35.848328 554 164.3333333 157.66667 35.848328 555 -97.7500000 137.75000 15.931661 556 -84.7500000 137.75000 15.931661 557 -73.7500000 137.75000 15.931661 558 -52.7500000 137.75000 15.931661 559 -29.7500000 137.75000 15.931661 560 -9.7500000 137.75000 15.931661 561 14.2500000 137.75000 15.931661 562 28.2500000 137.75000 15.931661 563 46.2500000 137.75000 15.931661 564 65.2500000 137.75000 15.931661 565 95.2500000 137.75000 15.931661 566 99.2500000 137.75000 15.931661 567 -106.5000000 147.50000 25.681661 568 -93.5000000 147.50000 25.681661 569 -80.5000000 147.50000 25.681661 570 -63.5000000 147.50000 25.681661 571 -42.5000000 147.50000 25.681661 572 -25.5000000 147.50000 25.681661 573 7.5000000 147.50000 25.681661 574 27.5000000 147.50000 25.681661 575 57.5000000 147.50000 25.681661 576 86.5000000 147.50000 25.681661 577 116.5000000 147.50000 25.681661 578 116.5000000 147.50000 25.681661 > decompose(x = ChickWeight[["weight"]], grp = ChickWeight[["Chick"]], + grand = FALSE) # no grand-mean centering wth btw 1 -69.6666667 111.66667 2 -60.6666667 111.66667 3 -52.6666667 111.66667 4 -47.6666667 111.66667 5 -35.6666667 111.66667 6 -18.6666667 111.66667 7 -5.6666667 111.66667 8 13.3333333 111.66667 9 37.3333333 111.66667 10 59.3333333 111.66667 11 87.3333333 111.66667 12 93.3333333 111.66667 13 -79.9166667 119.91667 14 -70.9166667 119.91667 15 -61.9166667 119.91667 16 -47.9166667 119.91667 17 -35.9166667 119.91667 18 -16.9166667 119.91667 19 2.0833333 119.91667 20 18.0833333 119.91667 21 42.0833333 119.91667 22 67.0833333 119.91667 23 89.0833333 119.91667 24 95.0833333 119.91667 25 -72.8333333 115.83333 26 -76.8333333 115.83333 27 -60.8333333 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69 41.2500000 113.75000 70 46.2500000 113.75000 71 46.2500000 113.75000 72 43.2500000 113.75000 73 -109.0000000 150.00000 74 -101.0000000 150.00000 75 -93.0000000 150.00000 76 -79.0000000 150.00000 77 -61.0000000 150.00000 78 -38.0000000 150.00000 79 -4.0000000 150.00000 80 24.0000000 150.00000 81 68.0000000 150.00000 82 100.0000000 150.00000 83 138.0000000 150.00000 84 155.0000000 150.00000 85 -50.0000000 92.00000 86 -42.0000000 92.00000 87 -31.0000000 92.00000 88 -21.0000000 92.00000 89 -8.0000000 92.00000 90 1.0000000 92.00000 91 18.0000000 92.00000 92 24.0000000 92.00000 93 34.0000000 92.00000 94 42.0000000 92.00000 95 33.0000000 92.00000 96 -39.1666667 81.16667 97 -30.1666667 81.16667 98 -22.1666667 81.16667 99 -13.1666667 81.16667 100 3.8333333 81.16667 101 14.8333333 81.16667 102 8.8333333 81.16667 103 10.8333333 81.16667 104 11.8333333 81.16667 105 18.8333333 81.16667 106 18.8333333 81.16667 107 16.8333333 81.16667 108 -42.0833333 83.08333 109 -39.0833333 83.08333 110 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143.08333 272 -65.0833333 143.08333 273 -41.0833333 143.08333 274 -19.0833333 143.08333 275 2.9166667 143.08333 276 20.9166667 143.08333 277 53.9166667 143.08333 278 87.9166667 143.08333 279 115.9166667 143.08333 280 121.9166667 143.08333 281 -89.0000000 131.00000 282 -83.0000000 131.00000 283 -74.0000000 131.00000 284 -57.0000000 131.00000 285 -38.0000000 131.00000 286 -17.0000000 131.00000 287 5.0000000 131.00000 288 16.0000000 131.00000 289 38.0000000 131.00000 290 74.0000000 131.00000 291 105.0000000 131.00000 292 120.0000000 131.00000 293 -71.4166667 110.41667 294 -64.4166667 110.41667 295 -52.4166667 110.41667 296 -37.4166667 110.41667 297 -23.4166667 110.41667 298 -10.4166667 110.41667 299 4.5833333 110.41667 300 12.5833333 110.41667 301 33.5833333 110.41667 302 52.5833333 110.41667 303 74.5833333 110.41667 304 81.5833333 110.41667 305 -90.9166667 129.91667 306 -83.9166667 129.91667 307 -71.9166667 129.91667 308 -56.9166667 129.91667 309 -37.9166667 129.91667 310 -15.9166667 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-11.401672 305_row -90.9166667 129.91667 8.098328 306_row -83.9166667 129.91667 8.098328 307_row -71.9166667 129.91667 8.098328 308_row -56.9166667 129.91667 8.098328 309_row -37.9166667 129.91667 8.098328 310_row -15.9166667 129.91667 8.098328 311_row 15.0833333 129.91667 8.098328 312_row 26.0833333 129.91667 8.098328 313_row 54.0833333 129.91667 8.098328 314_row 77.0833333 129.91667 8.098328 315_row 82.0833333 129.91667 8.098328 316_row 103.0833333 129.91667 8.098328 317_row -102.8333333 141.83333 20.014994 318_row -93.8333333 141.83333 20.014994 319_row -82.8333333 141.83333 20.014994 320_row -67.8333333 141.83333 20.014994 321_row -54.8333333 141.83333 20.014994 322_row -35.8333333 141.83333 20.014994 323_row -7.8333333 141.83333 20.014994 324_row 8.1666667 141.83333 20.014994 325_row 45.1666667 141.83333 20.014994 326_row 88.1666667 141.83333 20.014994 327_row 137.1666667 141.83333 20.014994 328_row 167.1666667 141.83333 20.014994 329_row -61.5000000 103.50000 -18.318339 330_row 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157.58333 35.764994 356_row -75.5833333 157.58333 35.764994 357_row -50.5833333 157.58333 35.764994 358_row -28.5833333 157.58333 35.764994 359_row 1.4166667 157.58333 35.764994 360_row 21.4166667 157.58333 35.764994 361_row 63.4166667 157.58333 35.764994 362_row 105.4166667 157.58333 35.764994 363_row 133.4166667 157.58333 35.764994 364_row 147.4166667 157.58333 35.764994 365_row -70.7500000 109.75000 -12.068339 366_row -59.7500000 109.75000 -12.068339 367_row -46.7500000 109.75000 -12.068339 368_row -32.7500000 109.75000 -12.068339 369_row -13.7500000 109.75000 -12.068339 370_row 1.2500000 109.75000 -12.068339 371_row 27.2500000 109.75000 -12.068339 372_row 34.2500000 109.75000 -12.068339 373_row 41.2500000 109.75000 -12.068339 374_row 36.2500000 109.75000 -12.068339 375_row 46.2500000 109.75000 -12.068339 376_row 37.2500000 109.75000 -12.068339 377_row -127.8333333 168.83333 47.014994 378_row -119.8333333 168.83333 47.014994 379_row -105.8333333 168.83333 47.014994 380_row 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-3.5833333 157.58333 35.764994 456_row 24.4166667 157.58333 35.764994 457_row 57.4166667 157.58333 35.764994 458_row 104.4166667 157.58333 35.764994 459_row 137.4166667 157.58333 35.764994 460_row 163.4166667 157.58333 35.764994 461_row -86.4166667 128.41667 6.598328 462_row -77.4166667 128.41667 6.598328 463_row -62.4166667 128.41667 6.598328 464_row -43.4166667 128.41667 6.598328 465_row -25.4166667 128.41667 6.598328 466_row -4.4166667 128.41667 6.598328 467_row 26.5833333 128.41667 6.598328 468_row 24.5833333 128.41667 6.598328 469_row 46.5833333 128.41667 6.598328 470_row 55.5833333 128.41667 6.598328 471_row 70.5833333 128.41667 6.598328 472_row 75.5833333 128.41667 6.598328 473_row -107.0833333 149.08333 27.264994 474_row -100.0833333 149.08333 27.264994 475_row -86.0833333 149.08333 27.264994 476_row -65.0833333 149.08333 27.264994 477_row -46.0833333 149.08333 27.264994 478_row -23.0833333 149.08333 27.264994 479_row 10.9166667 149.08333 27.264994 480_row 24.9166667 149.08333 27.264994 481_row 54.9166667 149.08333 27.264994 482_row 84.9166667 149.08333 27.264994 483_row 119.9166667 149.08333 27.264994 484_row 131.9166667 149.08333 27.264994 485_row -101.0000000 143.00000 21.181661 486_row -88.0000000 143.00000 21.181661 487_row -74.0000000 143.00000 21.181661 488_row -47.0000000 143.00000 21.181661 489_row -12.0000000 143.00000 21.181661 490_row 14.0000000 143.00000 21.181661 491_row 41.0000000 143.00000 21.181661 492_row 45.0000000 143.00000 21.181661 493_row 54.0000000 143.00000 21.181661 494_row 55.0000000 143.00000 21.181661 495_row 56.0000000 143.00000 21.181661 496_row 57.0000000 143.00000 21.181661 497_row -60.1000000 102.10000 -19.718339 498_row -51.1000000 102.10000 -19.718339 499_row -37.1000000 102.10000 -19.718339 500_row -16.1000000 102.10000 -19.718339 501_row 0.9000000 102.10000 -19.718339 502_row 15.9000000 102.10000 -19.718339 503_row 24.9000000 102.10000 -19.718339 504_row 35.9000000 102.10000 -19.718339 505_row 42.9000000 102.10000 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531_row -86.9166667 127.91667 6.098328 532_row -74.9166667 127.91667 6.098328 533_row -61.9166667 127.91667 6.098328 534_row -48.9166667 127.91667 6.098328 535_row -27.9166667 127.91667 6.098328 536_row -4.9166667 127.91667 6.098328 537_row 20.0833333 127.91667 6.098328 538_row 29.0833333 127.91667 6.098328 539_row 40.0833333 127.91667 6.098328 540_row 57.0833333 127.91667 6.098328 541_row 82.0833333 127.91667 6.098328 542_row 77.0833333 127.91667 6.098328 543_row -118.6666667 157.66667 35.848328 544_row -107.6666667 157.66667 35.848328 545_row -95.6666667 157.66667 35.848328 546_row -77.6666667 157.66667 35.848328 547_row -53.6666667 157.66667 35.848328 548_row -32.6666667 157.66667 35.848328 549_row -3.6666667 157.66667 35.848328 550_row 12.3333333 157.66667 35.848328 551_row 64.3333333 157.66667 35.848328 552_row 103.3333333 157.66667 35.848328 553_row 145.3333333 157.66667 35.848328 554_row 164.3333333 157.66667 35.848328 555_row -97.7500000 137.75000 15.931661 556_row -84.7500000 137.75000 15.931661 557_row -73.7500000 137.75000 15.931661 558_row -52.7500000 137.75000 15.931661 559_row -29.7500000 137.75000 15.931661 560_row -9.7500000 137.75000 15.931661 561_row 14.2500000 137.75000 15.931661 562_row 28.2500000 137.75000 15.931661 563_row 46.2500000 137.75000 15.931661 564_row 65.2500000 137.75000 15.931661 565_row 95.2500000 137.75000 15.931661 566_row 99.2500000 137.75000 15.931661 567_row -106.5000000 147.50000 25.681661 568_row -93.5000000 147.50000 25.681661 569_row -80.5000000 147.50000 25.681661 570_row -63.5000000 147.50000 25.681661 571_row -42.5000000 147.50000 25.681661 572_row -25.5000000 147.50000 25.681661 573_row 7.5000000 147.50000 25.681661 574_row 27.5000000 147.50000 25.681661 575_row 57.5000000 147.50000 25.681661 576_row 86.5000000 147.50000 25.681661 577_row 116.5000000 147.50000 25.681661 578_row 116.5000000 147.50000 25.681661 > > # multiple grouping variables > tmp_nm <- c("Type","Treatment") # b/c Roxygen2 doesn't like c() in a [] > decompose(x = as.data.frame(CO2)[["uptake"]], grp = as.data.frame(CO2)[tmp_nm]) wth btw btw_c 1 -19.33333333 35.33333 8.120238 2 -4.93333333 35.33333 8.120238 3 -0.53333333 35.33333 8.120238 4 1.86666667 35.33333 8.120238 5 -0.03333333 35.33333 8.120238 6 3.86666667 35.33333 8.120238 7 4.36666667 35.33333 8.120238 8 -21.73333333 35.33333 8.120238 9 -8.03333333 35.33333 8.120238 10 1.76666667 35.33333 8.120238 11 6.46666667 35.33333 8.120238 12 5.26666667 35.33333 8.120238 13 6.06666667 35.33333 8.120238 14 8.96666667 35.33333 8.120238 15 -19.13333333 35.33333 8.120238 16 -2.93333333 35.33333 8.120238 17 4.96666667 35.33333 8.120238 18 6.76666667 35.33333 8.120238 19 7.56666667 35.33333 8.120238 20 8.56666667 35.33333 8.120238 21 10.16666667 35.33333 8.120238 22 -17.55238095 31.75238 4.539286 23 -7.65238095 31.75238 4.539286 24 -1.45238095 31.75238 4.539286 25 2.84761905 31.75238 4.539286 26 0.74761905 31.75238 4.539286 27 3.64761905 31.75238 4.539286 28 6.94761905 31.75238 4.539286 29 -22.45238095 31.75238 4.539286 30 -4.45238095 31.75238 4.539286 31 3.24761905 31.75238 4.539286 32 7.04761905 31.75238 4.539286 33 6.84761905 31.75238 4.539286 34 5.74761905 31.75238 4.539286 35 10.64761905 31.75238 4.539286 36 -16.65238095 31.75238 4.539286 37 -10.75238095 31.75238 4.539286 38 6.34761905 31.75238 4.539286 39 2.24761905 31.75238 4.539286 40 7.14761905 31.75238 4.539286 41 7.84761905 31.75238 4.539286 42 9.64761905 31.75238 4.539286 43 -15.35238095 25.95238 -1.260714 44 -6.75238095 25.95238 -1.260714 45 0.24761905 25.95238 -1.260714 46 4.04761905 25.95238 -1.260714 47 4.94761905 25.95238 -1.260714 48 6.44761905 25.95238 -1.260714 49 9.54761905 25.95238 -1.260714 50 -13.95238095 25.95238 -1.260714 51 -3.95238095 25.95238 -1.260714 52 4.64761905 25.95238 -1.260714 53 5.84761905 25.95238 -1.260714 54 6.44761905 25.95238 -1.260714 55 5.14761905 25.95238 -1.260714 56 5.54761905 25.95238 -1.260714 57 -14.65238095 25.95238 -1.260714 58 -6.55238095 25.95238 -1.260714 59 -0.15238095 25.95238 -1.260714 60 1.94761905 25.95238 -1.260714 61 2.54761905 25.95238 -1.260714 62 2.14761905 25.95238 -1.260714 63 1.84761905 25.95238 -1.260714 64 -5.31428571 15.81429 -11.398810 65 -0.91428571 15.81429 -11.398810 66 2.28571429 15.81429 -11.398810 67 3.08571429 15.81429 -11.398810 68 3.68571429 15.81429 -11.398810 69 6.38571429 15.81429 -11.398810 70 6.08571429 15.81429 -11.398810 71 -8.11428571 15.81429 -11.398810 72 -4.41428571 15.81429 -11.398810 73 -3.51428571 15.81429 -11.398810 74 -2.81428571 15.81429 -11.398810 75 -3.31428571 15.81429 -11.398810 76 -2.11428571 15.81429 -11.398810 77 -1.41428571 15.81429 -11.398810 78 -5.21428571 15.81429 -11.398810 79 2.18571429 15.81429 -11.398810 80 2.08571429 15.81429 -11.398810 81 2.08571429 15.81429 -11.398810 82 2.08571429 15.81429 -11.398810 83 3.08571429 15.81429 -11.398810 84 4.08571429 15.81429 -11.398810 > decompose(x = as.data.frame(CO2)[["uptake"]], grp = as.data.frame(CO2)[tmp_nm], + n.shift = 1) wth btw btw_c tot_dw1 wth_dw1 1 -19.33333333 35.33333 8.120238 30.4 -4.93333333 2 -4.93333333 35.33333 8.120238 34.8 -0.53333333 3 -0.53333333 35.33333 8.120238 37.2 1.86666667 4 1.86666667 35.33333 8.120238 35.3 -0.03333333 5 -0.03333333 35.33333 8.120238 39.2 3.86666667 6 3.86666667 35.33333 8.120238 39.7 4.36666667 7 4.36666667 35.33333 8.120238 13.6 -21.73333333 8 -21.73333333 35.33333 8.120238 27.3 -8.03333333 9 -8.03333333 35.33333 8.120238 37.1 1.76666667 10 1.76666667 35.33333 8.120238 41.8 6.46666667 11 6.46666667 35.33333 8.120238 40.6 5.26666667 12 5.26666667 35.33333 8.120238 41.4 6.06666667 13 6.06666667 35.33333 8.120238 44.3 8.96666667 14 8.96666667 35.33333 8.120238 16.2 -19.13333333 15 -19.13333333 35.33333 8.120238 32.4 -2.93333333 16 -2.93333333 35.33333 8.120238 40.3 4.96666667 17 4.96666667 35.33333 8.120238 42.1 6.76666667 18 6.76666667 35.33333 8.120238 42.9 7.56666667 19 7.56666667 35.33333 8.120238 43.9 8.56666667 20 8.56666667 35.33333 8.120238 45.5 10.16666667 21 10.16666667 35.33333 8.120238 NA NA 22 -17.55238095 31.75238 4.539286 24.1 -7.65238095 23 -7.65238095 31.75238 4.539286 30.3 -1.45238095 24 -1.45238095 31.75238 4.539286 34.6 2.84761905 25 2.84761905 31.75238 4.539286 32.5 0.74761905 26 0.74761905 31.75238 4.539286 35.4 3.64761905 27 3.64761905 31.75238 4.539286 38.7 6.94761905 28 6.94761905 31.75238 4.539286 9.3 -22.45238095 29 -22.45238095 31.75238 4.539286 27.3 -4.45238095 30 -4.45238095 31.75238 4.539286 35.0 3.24761905 31 3.24761905 31.75238 4.539286 38.8 7.04761905 32 7.04761905 31.75238 4.539286 38.6 6.84761905 33 6.84761905 31.75238 4.539286 37.5 5.74761905 34 5.74761905 31.75238 4.539286 42.4 10.64761905 35 10.64761905 31.75238 4.539286 15.1 -16.65238095 36 -16.65238095 31.75238 4.539286 21.0 -10.75238095 37 -10.75238095 31.75238 4.539286 38.1 6.34761905 38 6.34761905 31.75238 4.539286 34.0 2.24761905 39 2.24761905 31.75238 4.539286 38.9 7.14761905 40 7.14761905 31.75238 4.539286 39.6 7.84761905 41 7.84761905 31.75238 4.539286 41.4 9.64761905 42 9.64761905 31.75238 4.539286 NA NA 43 -15.35238095 25.95238 -1.260714 19.2 -6.75238095 44 -6.75238095 25.95238 -1.260714 26.2 0.24761905 45 0.24761905 25.95238 -1.260714 30.0 4.04761905 46 4.04761905 25.95238 -1.260714 30.9 4.94761905 47 4.94761905 25.95238 -1.260714 32.4 6.44761905 48 6.44761905 25.95238 -1.260714 35.5 9.54761905 49 9.54761905 25.95238 -1.260714 12.0 -13.95238095 50 -13.95238095 25.95238 -1.260714 22.0 -3.95238095 51 -3.95238095 25.95238 -1.260714 30.6 4.64761905 52 4.64761905 25.95238 -1.260714 31.8 5.84761905 53 5.84761905 25.95238 -1.260714 32.4 6.44761905 54 6.44761905 25.95238 -1.260714 31.1 5.14761905 55 5.14761905 25.95238 -1.260714 31.5 5.54761905 56 5.54761905 25.95238 -1.260714 11.3 -14.65238095 57 -14.65238095 25.95238 -1.260714 19.4 -6.55238095 58 -6.55238095 25.95238 -1.260714 25.8 -0.15238095 59 -0.15238095 25.95238 -1.260714 27.9 1.94761905 60 1.94761905 25.95238 -1.260714 28.5 2.54761905 61 2.54761905 25.95238 -1.260714 28.1 2.14761905 62 2.14761905 25.95238 -1.260714 27.8 1.84761905 63 1.84761905 25.95238 -1.260714 NA NA 64 -5.31428571 15.81429 -11.398810 14.9 -0.91428571 65 -0.91428571 15.81429 -11.398810 18.1 2.28571429 66 2.28571429 15.81429 -11.398810 18.9 3.08571429 67 3.08571429 15.81429 -11.398810 19.5 3.68571429 68 3.68571429 15.81429 -11.398810 22.2 6.38571429 69 6.38571429 15.81429 -11.398810 21.9 6.08571429 70 6.08571429 15.81429 -11.398810 7.7 -8.11428571 71 -8.11428571 15.81429 -11.398810 11.4 -4.41428571 72 -4.41428571 15.81429 -11.398810 12.3 -3.51428571 73 -3.51428571 15.81429 -11.398810 13.0 -2.81428571 74 -2.81428571 15.81429 -11.398810 12.5 -3.31428571 75 -3.31428571 15.81429 -11.398810 13.7 -2.11428571 76 -2.11428571 15.81429 -11.398810 14.4 -1.41428571 77 -1.41428571 15.81429 -11.398810 10.6 -5.21428571 78 -5.21428571 15.81429 -11.398810 18.0 2.18571429 79 2.18571429 15.81429 -11.398810 17.9 2.08571429 80 2.08571429 15.81429 -11.398810 17.9 2.08571429 81 2.08571429 15.81429 -11.398810 17.9 2.08571429 82 2.08571429 15.81429 -11.398810 18.9 3.08571429 83 3.08571429 15.81429 -11.398810 19.9 4.08571429 84 4.08571429 15.81429 -11.398810 NA NA > decompose(x = as.data.frame(CO2)[["uptake"]], grp = as.data.frame(CO2)[tmp_nm], + n.shift = c(+2, +1, -1, -2)) wth btw btw_c tot_dw2 tot_dw1 tot_gw1 tot_gw2 1 -19.33333333 35.33333 8.120238 34.8 30.4 NA NA 2 -4.93333333 35.33333 8.120238 37.2 34.8 16.0 NA 3 -0.53333333 35.33333 8.120238 35.3 37.2 30.4 16.0 4 1.86666667 35.33333 8.120238 39.2 35.3 34.8 30.4 5 -0.03333333 35.33333 8.120238 39.7 39.2 37.2 34.8 6 3.86666667 35.33333 8.120238 13.6 39.7 35.3 37.2 7 4.36666667 35.33333 8.120238 27.3 13.6 39.2 35.3 8 -21.73333333 35.33333 8.120238 37.1 27.3 39.7 39.2 9 -8.03333333 35.33333 8.120238 41.8 37.1 13.6 39.7 10 1.76666667 35.33333 8.120238 40.6 41.8 27.3 13.6 11 6.46666667 35.33333 8.120238 41.4 40.6 37.1 27.3 12 5.26666667 35.33333 8.120238 44.3 41.4 41.8 37.1 13 6.06666667 35.33333 8.120238 16.2 44.3 40.6 41.8 14 8.96666667 35.33333 8.120238 32.4 16.2 41.4 40.6 15 -19.13333333 35.33333 8.120238 40.3 32.4 44.3 41.4 16 -2.93333333 35.33333 8.120238 42.1 40.3 16.2 44.3 17 4.96666667 35.33333 8.120238 42.9 42.1 32.4 16.2 18 6.76666667 35.33333 8.120238 43.9 42.9 40.3 32.4 19 7.56666667 35.33333 8.120238 45.5 43.9 42.1 40.3 20 8.56666667 35.33333 8.120238 NA 45.5 42.9 42.1 21 10.16666667 35.33333 8.120238 NA NA 43.9 42.9 22 -17.55238095 31.75238 4.539286 30.3 24.1 NA NA 23 -7.65238095 31.75238 4.539286 34.6 30.3 14.2 NA 24 -1.45238095 31.75238 4.539286 32.5 34.6 24.1 14.2 25 2.84761905 31.75238 4.539286 35.4 32.5 30.3 24.1 26 0.74761905 31.75238 4.539286 38.7 35.4 34.6 30.3 27 3.64761905 31.75238 4.539286 9.3 38.7 32.5 34.6 28 6.94761905 31.75238 4.539286 27.3 9.3 35.4 32.5 29 -22.45238095 31.75238 4.539286 35.0 27.3 38.7 35.4 30 -4.45238095 31.75238 4.539286 38.8 35.0 9.3 38.7 31 3.24761905 31.75238 4.539286 38.6 38.8 27.3 9.3 32 7.04761905 31.75238 4.539286 37.5 38.6 35.0 27.3 33 6.84761905 31.75238 4.539286 42.4 37.5 38.8 35.0 34 5.74761905 31.75238 4.539286 15.1 42.4 38.6 38.8 35 10.64761905 31.75238 4.539286 21.0 15.1 37.5 38.6 36 -16.65238095 31.75238 4.539286 38.1 21.0 42.4 37.5 37 -10.75238095 31.75238 4.539286 34.0 38.1 15.1 42.4 38 6.34761905 31.75238 4.539286 38.9 34.0 21.0 15.1 39 2.24761905 31.75238 4.539286 39.6 38.9 38.1 21.0 40 7.14761905 31.75238 4.539286 41.4 39.6 34.0 38.1 41 7.84761905 31.75238 4.539286 NA 41.4 38.9 34.0 42 9.64761905 31.75238 4.539286 NA NA 39.6 38.9 43 -15.35238095 25.95238 -1.260714 26.2 19.2 NA NA 44 -6.75238095 25.95238 -1.260714 30.0 26.2 10.6 NA 45 0.24761905 25.95238 -1.260714 30.9 30.0 19.2 10.6 46 4.04761905 25.95238 -1.260714 32.4 30.9 26.2 19.2 47 4.94761905 25.95238 -1.260714 35.5 32.4 30.0 26.2 48 6.44761905 25.95238 -1.260714 12.0 35.5 30.9 30.0 49 9.54761905 25.95238 -1.260714 22.0 12.0 32.4 30.9 50 -13.95238095 25.95238 -1.260714 30.6 22.0 35.5 32.4 51 -3.95238095 25.95238 -1.260714 31.8 30.6 12.0 35.5 52 4.64761905 25.95238 -1.260714 32.4 31.8 22.0 12.0 53 5.84761905 25.95238 -1.260714 31.1 32.4 30.6 22.0 54 6.44761905 25.95238 -1.260714 31.5 31.1 31.8 30.6 55 5.14761905 25.95238 -1.260714 11.3 31.5 32.4 31.8 56 5.54761905 25.95238 -1.260714 19.4 11.3 31.1 32.4 57 -14.65238095 25.95238 -1.260714 25.8 19.4 31.5 31.1 58 -6.55238095 25.95238 -1.260714 27.9 25.8 11.3 31.5 59 -0.15238095 25.95238 -1.260714 28.5 27.9 19.4 11.3 60 1.94761905 25.95238 -1.260714 28.1 28.5 25.8 19.4 61 2.54761905 25.95238 -1.260714 27.8 28.1 27.9 25.8 62 2.14761905 25.95238 -1.260714 NA 27.8 28.5 27.9 63 1.84761905 25.95238 -1.260714 NA NA 28.1 28.5 64 -5.31428571 15.81429 -11.398810 18.1 14.9 NA NA 65 -0.91428571 15.81429 -11.398810 18.9 18.1 10.5 NA 66 2.28571429 15.81429 -11.398810 19.5 18.9 14.9 10.5 67 3.08571429 15.81429 -11.398810 22.2 19.5 18.1 14.9 68 3.68571429 15.81429 -11.398810 21.9 22.2 18.9 18.1 69 6.38571429 15.81429 -11.398810 7.7 21.9 19.5 18.9 70 6.08571429 15.81429 -11.398810 11.4 7.7 22.2 19.5 71 -8.11428571 15.81429 -11.398810 12.3 11.4 21.9 22.2 72 -4.41428571 15.81429 -11.398810 13.0 12.3 7.7 21.9 73 -3.51428571 15.81429 -11.398810 12.5 13.0 11.4 7.7 74 -2.81428571 15.81429 -11.398810 13.7 12.5 12.3 11.4 75 -3.31428571 15.81429 -11.398810 14.4 13.7 13.0 12.3 76 -2.11428571 15.81429 -11.398810 10.6 14.4 12.5 13.0 77 -1.41428571 15.81429 -11.398810 18.0 10.6 13.7 12.5 78 -5.21428571 15.81429 -11.398810 17.9 18.0 14.4 13.7 79 2.18571429 15.81429 -11.398810 17.9 17.9 10.6 14.4 80 2.08571429 15.81429 -11.398810 17.9 17.9 18.0 10.6 81 2.08571429 15.81429 -11.398810 18.9 17.9 17.9 18.0 82 2.08571429 15.81429 -11.398810 19.9 18.9 17.9 17.9 83 3.08571429 15.81429 -11.398810 NA 19.9 17.9 17.9 84 4.08571429 15.81429 -11.398810 NA NA 18.9 17.9 wth_dw2 wth_dw1 wth_gw1 wth_gw2 1 -0.53333333 -4.93333333 NA NA 2 1.86666667 -0.53333333 -19.33333333 NA 3 -0.03333333 1.86666667 -4.93333333 -19.33333333 4 3.86666667 -0.03333333 -0.53333333 -4.93333333 5 4.36666667 3.86666667 1.86666667 -0.53333333 6 -21.73333333 4.36666667 -0.03333333 1.86666667 7 -8.03333333 -21.73333333 3.86666667 -0.03333333 8 1.76666667 -8.03333333 4.36666667 3.86666667 9 6.46666667 1.76666667 -21.73333333 4.36666667 10 5.26666667 6.46666667 -8.03333333 -21.73333333 11 6.06666667 5.26666667 1.76666667 -8.03333333 12 8.96666667 6.06666667 6.46666667 1.76666667 13 -19.13333333 8.96666667 5.26666667 6.46666667 14 -2.93333333 -19.13333333 6.06666667 5.26666667 15 4.96666667 -2.93333333 8.96666667 6.06666667 16 6.76666667 4.96666667 -19.13333333 8.96666667 17 7.56666667 6.76666667 -2.93333333 -19.13333333 18 8.56666667 7.56666667 4.96666667 -2.93333333 19 10.16666667 8.56666667 6.76666667 4.96666667 20 NA 10.16666667 7.56666667 6.76666667 21 NA NA 8.56666667 7.56666667 22 -1.45238095 -7.65238095 NA NA 23 2.84761905 -1.45238095 -17.55238095 NA 24 0.74761905 2.84761905 -7.65238095 -17.55238095 25 3.64761905 0.74761905 -1.45238095 -7.65238095 26 6.94761905 3.64761905 2.84761905 -1.45238095 27 -22.45238095 6.94761905 0.74761905 2.84761905 28 -4.45238095 -22.45238095 3.64761905 0.74761905 29 3.24761905 -4.45238095 6.94761905 3.64761905 30 7.04761905 3.24761905 -22.45238095 6.94761905 31 6.84761905 7.04761905 -4.45238095 -22.45238095 32 5.74761905 6.84761905 3.24761905 -4.45238095 33 10.64761905 5.74761905 7.04761905 3.24761905 34 -16.65238095 10.64761905 6.84761905 7.04761905 35 -10.75238095 -16.65238095 5.74761905 6.84761905 36 6.34761905 -10.75238095 10.64761905 5.74761905 37 2.24761905 6.34761905 -16.65238095 10.64761905 38 7.14761905 2.24761905 -10.75238095 -16.65238095 39 7.84761905 7.14761905 6.34761905 -10.75238095 40 9.64761905 7.84761905 2.24761905 6.34761905 41 NA 9.64761905 7.14761905 2.24761905 42 NA NA 7.84761905 7.14761905 43 0.24761905 -6.75238095 NA NA 44 4.04761905 0.24761905 -15.35238095 NA 45 4.94761905 4.04761905 -6.75238095 -15.35238095 46 6.44761905 4.94761905 0.24761905 -6.75238095 47 9.54761905 6.44761905 4.04761905 0.24761905 48 -13.95238095 9.54761905 4.94761905 4.04761905 49 -3.95238095 -13.95238095 6.44761905 4.94761905 50 4.64761905 -3.95238095 9.54761905 6.44761905 51 5.84761905 4.64761905 -13.95238095 9.54761905 52 6.44761905 5.84761905 -3.95238095 -13.95238095 53 5.14761905 6.44761905 4.64761905 -3.95238095 54 5.54761905 5.14761905 5.84761905 4.64761905 55 -14.65238095 5.54761905 6.44761905 5.84761905 56 -6.55238095 -14.65238095 5.14761905 6.44761905 57 -0.15238095 -6.55238095 5.54761905 5.14761905 58 1.94761905 -0.15238095 -14.65238095 5.54761905 59 2.54761905 1.94761905 -6.55238095 -14.65238095 60 2.14761905 2.54761905 -0.15238095 -6.55238095 61 1.84761905 2.14761905 1.94761905 -0.15238095 62 NA 1.84761905 2.54761905 1.94761905 63 NA NA 2.14761905 2.54761905 64 2.28571429 -0.91428571 NA NA 65 3.08571429 2.28571429 -5.31428571 NA 66 3.68571429 3.08571429 -0.91428571 -5.31428571 67 6.38571429 3.68571429 2.28571429 -0.91428571 68 6.08571429 6.38571429 3.08571429 2.28571429 69 -8.11428571 6.08571429 3.68571429 3.08571429 70 -4.41428571 -8.11428571 6.38571429 3.68571429 71 -3.51428571 -4.41428571 6.08571429 6.38571429 72 -2.81428571 -3.51428571 -8.11428571 6.08571429 73 -3.31428571 -2.81428571 -4.41428571 -8.11428571 74 -2.11428571 -3.31428571 -3.51428571 -4.41428571 75 -1.41428571 -2.11428571 -2.81428571 -3.51428571 76 -5.21428571 -1.41428571 -3.31428571 -2.81428571 77 2.18571429 -5.21428571 -2.11428571 -3.31428571 78 2.08571429 2.18571429 -1.41428571 -2.11428571 79 2.08571429 2.08571429 -5.21428571 -1.41428571 80 2.08571429 2.08571429 2.18571429 -5.21428571 81 3.08571429 2.08571429 2.08571429 2.18571429 82 4.08571429 3.08571429 2.08571429 2.08571429 83 NA 4.08571429 2.08571429 2.08571429 84 NA NA 3.08571429 2.08571429 > > > > cleanEx() > nameEx("decomposes") > ### * decomposes > > flush(stderr()); flush(stdout()) > > ### Name: decomposes > ### Title: Decompose Numeric Data by Group > ### Aliases: decomposes > > ### ** Examples > > ChickWeight2 <- as.data.frame(ChickWeight) > row.names(ChickWeight2) <- as.numeric(row.names(ChickWeight)) / 1000 > decomposes(data = ChickWeight2, vrb.nm = c("weight","Time"), grp.nm = "Chick") weight_w Time_w weight_b Time_b weight_bc Time_bc 0.001 -69.6666667 -10.9166667 111.66667 10.91667 -10.151672 0.1986736 0.002 -60.6666667 -8.9166667 111.66667 10.91667 -10.151672 0.1986736 0.003 -52.6666667 -6.9166667 111.66667 10.91667 -10.151672 0.1986736 0.004 -47.6666667 -4.9166667 111.66667 10.91667 -10.151672 0.1986736 0.005 -35.6666667 -2.9166667 111.66667 10.91667 -10.151672 0.1986736 0.006 -18.6666667 -0.9166667 111.66667 10.91667 -10.151672 0.1986736 0.007 -5.6666667 1.0833333 111.66667 10.91667 -10.151672 0.1986736 0.008 13.3333333 3.0833333 111.66667 10.91667 -10.151672 0.1986736 0.009 37.3333333 5.0833333 111.66667 10.91667 -10.151672 0.1986736 0.01 59.3333333 7.0833333 111.66667 10.91667 -10.151672 0.1986736 0.011 87.3333333 9.0833333 111.66667 10.91667 -10.151672 0.1986736 0.012 93.3333333 10.0833333 111.66667 10.91667 -10.151672 0.1986736 0.013 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0.291 0.1986736 0.292 0.1986736 0.293 0.1986736 0.294 0.1986736 0.295 0.1986736 0.296 0.1986736 0.297 0.1986736 0.298 0.1986736 0.299 0.1986736 0.3 0.1986736 0.301 0.1986736 0.302 0.1986736 0.303 0.1986736 0.304 0.1986736 0.305 0.1986736 0.306 0.1986736 0.307 0.1986736 0.308 0.1986736 0.309 0.1986736 0.31 0.1986736 0.311 0.1986736 0.312 0.1986736 0.313 0.1986736 0.314 0.1986736 0.315 0.1986736 0.316 0.1986736 0.317 0.1986736 0.318 0.1986736 0.319 0.1986736 0.32 0.1986736 0.321 0.1986736 0.322 0.1986736 0.323 0.1986736 0.324 0.1986736 0.325 0.1986736 0.326 0.1986736 0.327 0.1986736 0.328 0.1986736 0.329 0.1986736 0.33 0.1986736 0.331 0.1986736 0.332 0.1986736 0.333 0.1986736 0.334 0.1986736 0.335 0.1986736 0.336 0.1986736 0.337 0.1986736 0.338 0.1986736 0.339 0.1986736 0.34 0.1986736 0.341 0.1986736 0.342 0.1986736 0.343 0.1986736 0.344 0.1986736 0.345 0.1986736 0.346 0.1986736 0.347 0.1986736 0.348 0.1986736 0.349 0.1986736 0.35 0.1986736 0.351 0.1986736 0.352 0.1986736 0.353 0.1986736 0.354 0.1986736 0.355 0.1986736 0.356 0.1986736 0.357 0.1986736 0.358 0.1986736 0.359 0.1986736 0.36 0.1986736 0.361 0.1986736 0.362 0.1986736 0.363 0.1986736 0.364 0.1986736 0.365 0.1986736 0.366 0.1986736 0.367 0.1986736 0.368 0.1986736 0.369 0.1986736 0.37 0.1986736 0.371 0.1986736 0.372 0.1986736 0.373 0.1986736 0.374 0.1986736 0.375 0.1986736 0.376 0.1986736 0.377 0.1986736 0.378 0.1986736 0.379 0.1986736 0.38 0.1986736 0.381 0.1986736 0.382 0.1986736 0.383 0.1986736 0.384 0.1986736 0.385 0.1986736 0.386 0.1986736 0.387 0.1986736 0.388 0.1986736 0.389 0.1986736 0.39 0.1986736 0.391 0.1986736 0.392 0.1986736 0.393 0.1986736 0.394 0.1986736 0.395 0.1986736 0.396 0.1986736 0.397 0.1986736 0.398 0.1986736 0.399 0.1986736 0.4 0.1986736 0.401 0.1986736 0.402 0.1986736 0.403 0.1986736 0.404 0.1986736 0.405 0.1986736 0.406 0.1986736 0.407 0.1986736 0.408 0.1986736 0.409 0.1986736 0.41 0.1986736 0.411 0.1986736 0.412 0.1986736 0.413 0.1986736 0.414 0.1986736 0.415 0.1986736 0.416 0.1986736 0.417 0.1986736 0.418 0.1986736 0.419 0.1986736 0.42 0.1986736 0.421 0.1986736 0.422 0.1986736 0.423 0.1986736 0.424 0.1986736 0.425 0.1986736 0.426 0.1986736 0.427 0.1986736 0.428 0.1986736 0.429 0.1986736 0.43 0.1986736 0.431 0.1986736 0.432 0.1986736 0.433 0.1986736 0.434 0.1986736 0.435 0.1986736 0.436 0.1986736 0.437 0.1986736 0.438 0.1986736 0.439 0.1986736 0.44 0.1986736 0.441 0.1986736 0.442 0.1986736 0.443 0.1986736 0.444 0.1986736 0.445 0.1986736 0.446 0.1986736 0.447 0.1986736 0.448 0.1986736 0.449 0.1986736 0.45 0.1986736 0.451 0.1986736 0.452 0.1986736 0.453 0.1986736 0.454 0.1986736 0.455 0.1986736 0.456 0.1986736 0.457 0.1986736 0.458 0.1986736 0.459 0.1986736 0.46 0.1986736 0.461 0.1986736 0.462 0.1986736 0.463 0.1986736 0.464 0.1986736 0.465 0.1986736 0.466 0.1986736 0.467 0.1986736 0.468 0.1986736 0.469 0.1986736 0.47 0.1986736 0.471 0.1986736 0.472 0.1986736 0.473 0.1986736 0.474 0.1986736 0.475 0.1986736 0.476 0.1986736 0.477 0.1986736 0.478 0.1986736 0.479 0.1986736 0.48 0.1986736 0.481 0.1986736 0.482 0.1986736 0.483 0.1986736 0.484 0.1986736 0.485 0.1986736 0.486 0.1986736 0.487 0.1986736 0.488 0.1986736 0.489 0.1986736 0.49 0.1986736 0.491 0.1986736 0.492 0.1986736 0.493 0.1986736 0.494 0.1986736 0.495 0.1986736 0.496 0.1986736 0.497 -1.7179931 0.498 -1.7179931 0.499 -1.7179931 0.5 -1.7179931 0.501 -1.7179931 0.502 -1.7179931 0.503 -1.7179931 0.504 -1.7179931 0.505 -1.7179931 0.506 -1.7179931 0.507 0.1986736 0.508 0.1986736 0.509 0.1986736 0.51 0.1986736 0.511 0.1986736 0.512 0.1986736 0.513 0.1986736 0.514 0.1986736 0.515 0.1986736 0.516 0.1986736 0.517 0.1986736 0.518 0.1986736 0.519 0.1986736 0.52 0.1986736 0.521 0.1986736 0.522 0.1986736 0.523 0.1986736 0.524 0.1986736 0.525 0.1986736 0.526 0.1986736 0.527 0.1986736 0.528 0.1986736 0.529 0.1986736 0.53 0.1986736 0.531 0.1986736 0.532 0.1986736 0.533 0.1986736 0.534 0.1986736 0.535 0.1986736 0.536 0.1986736 0.537 0.1986736 0.538 0.1986736 0.539 0.1986736 0.54 0.1986736 0.541 0.1986736 0.542 0.1986736 0.543 0.1986736 0.544 0.1986736 0.545 0.1986736 0.546 0.1986736 0.547 0.1986736 0.548 0.1986736 0.549 0.1986736 0.55 0.1986736 0.551 0.1986736 0.552 0.1986736 0.553 0.1986736 0.554 0.1986736 0.555 0.1986736 0.556 0.1986736 0.557 0.1986736 0.558 0.1986736 0.559 0.1986736 0.56 0.1986736 0.561 0.1986736 0.562 0.1986736 0.563 0.1986736 0.564 0.1986736 0.565 0.1986736 0.566 0.1986736 0.567 0.1986736 0.568 0.1986736 0.569 0.1986736 0.57 0.1986736 0.571 0.1986736 0.572 0.1986736 0.573 0.1986736 0.574 0.1986736 0.575 0.1986736 0.576 0.1986736 0.577 0.1986736 0.578 0.1986736 > decomposes(data = as.data.frame(CO2), vrb.nm = c("conc","uptake"), + grp.nm = c("Type","Treatment")) # multiple grouping columns conc_w uptake_w conc_b uptake_b conc_bc uptake_bc 1 -340 -19.33333333 435 35.33333 0 8.120238 2 -260 -4.93333333 435 35.33333 0 8.120238 3 -185 -0.53333333 435 35.33333 0 8.120238 4 -85 1.86666667 435 35.33333 0 8.120238 5 65 -0.03333333 435 35.33333 0 8.120238 6 240 3.86666667 435 35.33333 0 8.120238 7 565 4.36666667 435 35.33333 0 8.120238 8 -340 -21.73333333 435 35.33333 0 8.120238 9 -260 -8.03333333 435 35.33333 0 8.120238 10 -185 1.76666667 435 35.33333 0 8.120238 11 -85 6.46666667 435 35.33333 0 8.120238 12 65 5.26666667 435 35.33333 0 8.120238 13 240 6.06666667 435 35.33333 0 8.120238 14 565 8.96666667 435 35.33333 0 8.120238 15 -340 -19.13333333 435 35.33333 0 8.120238 16 -260 -2.93333333 435 35.33333 0 8.120238 17 -185 4.96666667 435 35.33333 0 8.120238 18 -85 6.76666667 435 35.33333 0 8.120238 19 65 7.56666667 435 35.33333 0 8.120238 20 240 8.56666667 435 35.33333 0 8.120238 21 565 10.16666667 435 35.33333 0 8.120238 22 -340 -17.55238095 435 31.75238 0 4.539286 23 -260 -7.65238095 435 31.75238 0 4.539286 24 -185 -1.45238095 435 31.75238 0 4.539286 25 -85 2.84761905 435 31.75238 0 4.539286 26 65 0.74761905 435 31.75238 0 4.539286 27 240 3.64761905 435 31.75238 0 4.539286 28 565 6.94761905 435 31.75238 0 4.539286 29 -340 -22.45238095 435 31.75238 0 4.539286 30 -260 -4.45238095 435 31.75238 0 4.539286 31 -185 3.24761905 435 31.75238 0 4.539286 32 -85 7.04761905 435 31.75238 0 4.539286 33 65 6.84761905 435 31.75238 0 4.539286 34 240 5.74761905 435 31.75238 0 4.539286 35 565 10.64761905 435 31.75238 0 4.539286 36 -340 -16.65238095 435 31.75238 0 4.539286 37 -260 -10.75238095 435 31.75238 0 4.539286 38 -185 6.34761905 435 31.75238 0 4.539286 39 -85 2.24761905 435 31.75238 0 4.539286 40 65 7.14761905 435 31.75238 0 4.539286 41 240 7.84761905 435 31.75238 0 4.539286 42 565 9.64761905 435 31.75238 0 4.539286 43 -340 -15.35238095 435 25.95238 0 -1.260714 44 -260 -6.75238095 435 25.95238 0 -1.260714 45 -185 0.24761905 435 25.95238 0 -1.260714 46 -85 4.04761905 435 25.95238 0 -1.260714 47 65 4.94761905 435 25.95238 0 -1.260714 48 240 6.44761905 435 25.95238 0 -1.260714 49 565 9.54761905 435 25.95238 0 -1.260714 50 -340 -13.95238095 435 25.95238 0 -1.260714 51 -260 -3.95238095 435 25.95238 0 -1.260714 52 -185 4.64761905 435 25.95238 0 -1.260714 53 -85 5.84761905 435 25.95238 0 -1.260714 54 65 6.44761905 435 25.95238 0 -1.260714 55 240 5.14761905 435 25.95238 0 -1.260714 56 565 5.54761905 435 25.95238 0 -1.260714 57 -340 -14.65238095 435 25.95238 0 -1.260714 58 -260 -6.55238095 435 25.95238 0 -1.260714 59 -185 -0.15238095 435 25.95238 0 -1.260714 60 -85 1.94761905 435 25.95238 0 -1.260714 61 65 2.54761905 435 25.95238 0 -1.260714 62 240 2.14761905 435 25.95238 0 -1.260714 63 565 1.84761905 435 25.95238 0 -1.260714 64 -340 -5.31428571 435 15.81429 0 -11.398810 65 -260 -0.91428571 435 15.81429 0 -11.398810 66 -185 2.28571429 435 15.81429 0 -11.398810 67 -85 3.08571429 435 15.81429 0 -11.398810 68 65 3.68571429 435 15.81429 0 -11.398810 69 240 6.38571429 435 15.81429 0 -11.398810 70 565 6.08571429 435 15.81429 0 -11.398810 71 -340 -8.11428571 435 15.81429 0 -11.398810 72 -260 -4.41428571 435 15.81429 0 -11.398810 73 -185 -3.51428571 435 15.81429 0 -11.398810 74 -85 -2.81428571 435 15.81429 0 -11.398810 75 65 -3.31428571 435 15.81429 0 -11.398810 76 240 -2.11428571 435 15.81429 0 -11.398810 77 565 -1.41428571 435 15.81429 0 -11.398810 78 -340 -5.21428571 435 15.81429 0 -11.398810 79 -260 2.18571429 435 15.81429 0 -11.398810 80 -185 2.08571429 435 15.81429 0 -11.398810 81 -85 2.08571429 435 15.81429 0 -11.398810 82 65 2.08571429 435 15.81429 0 -11.398810 83 240 3.08571429 435 15.81429 0 -11.398810 84 565 4.08571429 435 15.81429 0 -11.398810 > decomposes(data = as.data.frame(CO2), vrb.nm = c("conc","uptake"), + grp.nm = c("Type","Treatment"), n.shift = 1) # with lead conc_w uptake_w conc_b uptake_b conc_bc uptake_bc conc_dw1 uptake_dw1 1 -340 -19.33333333 435 35.33333 0 8.120238 175 30.4 2 -260 -4.93333333 435 35.33333 0 8.120238 250 34.8 3 -185 -0.53333333 435 35.33333 0 8.120238 350 37.2 4 -85 1.86666667 435 35.33333 0 8.120238 500 35.3 5 65 -0.03333333 435 35.33333 0 8.120238 675 39.2 6 240 3.86666667 435 35.33333 0 8.120238 1000 39.7 7 565 4.36666667 435 35.33333 0 8.120238 95 13.6 8 -340 -21.73333333 435 35.33333 0 8.120238 175 27.3 9 -260 -8.03333333 435 35.33333 0 8.120238 250 37.1 10 -185 1.76666667 435 35.33333 0 8.120238 350 41.8 11 -85 6.46666667 435 35.33333 0 8.120238 500 40.6 12 65 5.26666667 435 35.33333 0 8.120238 675 41.4 13 240 6.06666667 435 35.33333 0 8.120238 1000 44.3 14 565 8.96666667 435 35.33333 0 8.120238 95 16.2 15 -340 -19.13333333 435 35.33333 0 8.120238 175 32.4 16 -260 -2.93333333 435 35.33333 0 8.120238 250 40.3 17 -185 4.96666667 435 35.33333 0 8.120238 350 42.1 18 -85 6.76666667 435 35.33333 0 8.120238 500 42.9 19 65 7.56666667 435 35.33333 0 8.120238 675 43.9 20 240 8.56666667 435 35.33333 0 8.120238 1000 45.5 21 565 10.16666667 435 35.33333 0 8.120238 NA NA 22 -340 -17.55238095 435 31.75238 0 4.539286 175 24.1 23 -260 -7.65238095 435 31.75238 0 4.539286 250 30.3 24 -185 -1.45238095 435 31.75238 0 4.539286 350 34.6 25 -85 2.84761905 435 31.75238 0 4.539286 500 32.5 26 65 0.74761905 435 31.75238 0 4.539286 675 35.4 27 240 3.64761905 435 31.75238 0 4.539286 1000 38.7 28 565 6.94761905 435 31.75238 0 4.539286 95 9.3 29 -340 -22.45238095 435 31.75238 0 4.539286 175 27.3 30 -260 -4.45238095 435 31.75238 0 4.539286 250 35.0 31 -185 3.24761905 435 31.75238 0 4.539286 350 38.8 32 -85 7.04761905 435 31.75238 0 4.539286 500 38.6 33 65 6.84761905 435 31.75238 0 4.539286 675 37.5 34 240 5.74761905 435 31.75238 0 4.539286 1000 42.4 35 565 10.64761905 435 31.75238 0 4.539286 95 15.1 36 -340 -16.65238095 435 31.75238 0 4.539286 175 21.0 37 -260 -10.75238095 435 31.75238 0 4.539286 250 38.1 38 -185 6.34761905 435 31.75238 0 4.539286 350 34.0 39 -85 2.24761905 435 31.75238 0 4.539286 500 38.9 40 65 7.14761905 435 31.75238 0 4.539286 675 39.6 41 240 7.84761905 435 31.75238 0 4.539286 1000 41.4 42 565 9.64761905 435 31.75238 0 4.539286 NA NA 43 -340 -15.35238095 435 25.95238 0 -1.260714 175 19.2 44 -260 -6.75238095 435 25.95238 0 -1.260714 250 26.2 45 -185 0.24761905 435 25.95238 0 -1.260714 350 30.0 46 -85 4.04761905 435 25.95238 0 -1.260714 500 30.9 47 65 4.94761905 435 25.95238 0 -1.260714 675 32.4 48 240 6.44761905 435 25.95238 0 -1.260714 1000 35.5 49 565 9.54761905 435 25.95238 0 -1.260714 95 12.0 50 -340 -13.95238095 435 25.95238 0 -1.260714 175 22.0 51 -260 -3.95238095 435 25.95238 0 -1.260714 250 30.6 52 -185 4.64761905 435 25.95238 0 -1.260714 350 31.8 53 -85 5.84761905 435 25.95238 0 -1.260714 500 32.4 54 65 6.44761905 435 25.95238 0 -1.260714 675 31.1 55 240 5.14761905 435 25.95238 0 -1.260714 1000 31.5 56 565 5.54761905 435 25.95238 0 -1.260714 95 11.3 57 -340 -14.65238095 435 25.95238 0 -1.260714 175 19.4 58 -260 -6.55238095 435 25.95238 0 -1.260714 250 25.8 59 -185 -0.15238095 435 25.95238 0 -1.260714 350 27.9 60 -85 1.94761905 435 25.95238 0 -1.260714 500 28.5 61 65 2.54761905 435 25.95238 0 -1.260714 675 28.1 62 240 2.14761905 435 25.95238 0 -1.260714 1000 27.8 63 565 1.84761905 435 25.95238 0 -1.260714 NA NA 64 -340 -5.31428571 435 15.81429 0 -11.398810 175 14.9 65 -260 -0.91428571 435 15.81429 0 -11.398810 250 18.1 66 -185 2.28571429 435 15.81429 0 -11.398810 350 18.9 67 -85 3.08571429 435 15.81429 0 -11.398810 500 19.5 68 65 3.68571429 435 15.81429 0 -11.398810 675 22.2 69 240 6.38571429 435 15.81429 0 -11.398810 1000 21.9 70 565 6.08571429 435 15.81429 0 -11.398810 95 7.7 71 -340 -8.11428571 435 15.81429 0 -11.398810 175 11.4 72 -260 -4.41428571 435 15.81429 0 -11.398810 250 12.3 73 -185 -3.51428571 435 15.81429 0 -11.398810 350 13.0 74 -85 -2.81428571 435 15.81429 0 -11.398810 500 12.5 75 65 -3.31428571 435 15.81429 0 -11.398810 675 13.7 76 240 -2.11428571 435 15.81429 0 -11.398810 1000 14.4 77 565 -1.41428571 435 15.81429 0 -11.398810 95 10.6 78 -340 -5.21428571 435 15.81429 0 -11.398810 175 18.0 79 -260 2.18571429 435 15.81429 0 -11.398810 250 17.9 80 -185 2.08571429 435 15.81429 0 -11.398810 350 17.9 81 -85 2.08571429 435 15.81429 0 -11.398810 500 17.9 82 65 2.08571429 435 15.81429 0 -11.398810 675 18.9 83 240 3.08571429 435 15.81429 0 -11.398810 1000 19.9 84 565 4.08571429 435 15.81429 0 -11.398810 NA NA conc_w_dw1 uptake_w_dw1 1 -260 -4.93333333 2 -185 -0.53333333 3 -85 1.86666667 4 65 -0.03333333 5 240 3.86666667 6 565 4.36666667 7 -340 -21.73333333 8 -260 -8.03333333 9 -185 1.76666667 10 -85 6.46666667 11 65 5.26666667 12 240 6.06666667 13 565 8.96666667 14 -340 -19.13333333 15 -260 -2.93333333 16 -185 4.96666667 17 -85 6.76666667 18 65 7.56666667 19 240 8.56666667 20 565 10.16666667 21 NA NA 22 -260 -7.65238095 23 -185 -1.45238095 24 -85 2.84761905 25 65 0.74761905 26 240 3.64761905 27 565 6.94761905 28 -340 -22.45238095 29 -260 -4.45238095 30 -185 3.24761905 31 -85 7.04761905 32 65 6.84761905 33 240 5.74761905 34 565 10.64761905 35 -340 -16.65238095 36 -260 -10.75238095 37 -185 6.34761905 38 -85 2.24761905 39 65 7.14761905 40 240 7.84761905 41 565 9.64761905 42 NA NA 43 -260 -6.75238095 44 -185 0.24761905 45 -85 4.04761905 46 65 4.94761905 47 240 6.44761905 48 565 9.54761905 49 -340 -13.95238095 50 -260 -3.95238095 51 -185 4.64761905 52 -85 5.84761905 53 65 6.44761905 54 240 5.14761905 55 565 5.54761905 56 -340 -14.65238095 57 -260 -6.55238095 58 -185 -0.15238095 59 -85 1.94761905 60 65 2.54761905 61 240 2.14761905 62 565 1.84761905 63 NA NA 64 -260 -0.91428571 65 -185 2.28571429 66 -85 3.08571429 67 65 3.68571429 68 240 6.38571429 69 565 6.08571429 70 -340 -8.11428571 71 -260 -4.41428571 72 -185 -3.51428571 73 -85 -2.81428571 74 65 -3.31428571 75 240 -2.11428571 76 565 -1.41428571 77 -340 -5.21428571 78 -260 2.18571429 79 -185 2.08571429 80 -85 2.08571429 81 65 2.08571429 82 240 3.08571429 83 565 4.08571429 84 NA NA > decomposes(data = as.data.frame(CO2), vrb.nm = c("conc","uptake"), grp.nm = c("Type","Treatment"), + n.shift = c(+2, +1, -1, -2)) # with multiple lead/lags conc_w uptake_w conc_b uptake_b conc_bc uptake_bc conc_dw2 uptake_dw2 1 -340 -19.33333333 435 35.33333 0 8.120238 250 34.8 2 -260 -4.93333333 435 35.33333 0 8.120238 350 37.2 3 -185 -0.53333333 435 35.33333 0 8.120238 500 35.3 4 -85 1.86666667 435 35.33333 0 8.120238 675 39.2 5 65 -0.03333333 435 35.33333 0 8.120238 1000 39.7 6 240 3.86666667 435 35.33333 0 8.120238 95 13.6 7 565 4.36666667 435 35.33333 0 8.120238 175 27.3 8 -340 -21.73333333 435 35.33333 0 8.120238 250 37.1 9 -260 -8.03333333 435 35.33333 0 8.120238 350 41.8 10 -185 1.76666667 435 35.33333 0 8.120238 500 40.6 11 -85 6.46666667 435 35.33333 0 8.120238 675 41.4 12 65 5.26666667 435 35.33333 0 8.120238 1000 44.3 13 240 6.06666667 435 35.33333 0 8.120238 95 16.2 14 565 8.96666667 435 35.33333 0 8.120238 175 32.4 15 -340 -19.13333333 435 35.33333 0 8.120238 250 40.3 16 -260 -2.93333333 435 35.33333 0 8.120238 350 42.1 17 -185 4.96666667 435 35.33333 0 8.120238 500 42.9 18 -85 6.76666667 435 35.33333 0 8.120238 675 43.9 19 65 7.56666667 435 35.33333 0 8.120238 1000 45.5 20 240 8.56666667 435 35.33333 0 8.120238 NA NA 21 565 10.16666667 435 35.33333 0 8.120238 NA NA 22 -340 -17.55238095 435 31.75238 0 4.539286 250 30.3 23 -260 -7.65238095 435 31.75238 0 4.539286 350 34.6 24 -185 -1.45238095 435 31.75238 0 4.539286 500 32.5 25 -85 2.84761905 435 31.75238 0 4.539286 675 35.4 26 65 0.74761905 435 31.75238 0 4.539286 1000 38.7 27 240 3.64761905 435 31.75238 0 4.539286 95 9.3 28 565 6.94761905 435 31.75238 0 4.539286 175 27.3 29 -340 -22.45238095 435 31.75238 0 4.539286 250 35.0 30 -260 -4.45238095 435 31.75238 0 4.539286 350 38.8 31 -185 3.24761905 435 31.75238 0 4.539286 500 38.6 32 -85 7.04761905 435 31.75238 0 4.539286 675 37.5 33 65 6.84761905 435 31.75238 0 4.539286 1000 42.4 34 240 5.74761905 435 31.75238 0 4.539286 95 15.1 35 565 10.64761905 435 31.75238 0 4.539286 175 21.0 36 -340 -16.65238095 435 31.75238 0 4.539286 250 38.1 37 -260 -10.75238095 435 31.75238 0 4.539286 350 34.0 38 -185 6.34761905 435 31.75238 0 4.539286 500 38.9 39 -85 2.24761905 435 31.75238 0 4.539286 675 39.6 40 65 7.14761905 435 31.75238 0 4.539286 1000 41.4 41 240 7.84761905 435 31.75238 0 4.539286 NA NA 42 565 9.64761905 435 31.75238 0 4.539286 NA NA 43 -340 -15.35238095 435 25.95238 0 -1.260714 250 26.2 44 -260 -6.75238095 435 25.95238 0 -1.260714 350 30.0 45 -185 0.24761905 435 25.95238 0 -1.260714 500 30.9 46 -85 4.04761905 435 25.95238 0 -1.260714 675 32.4 47 65 4.94761905 435 25.95238 0 -1.260714 1000 35.5 48 240 6.44761905 435 25.95238 0 -1.260714 95 12.0 49 565 9.54761905 435 25.95238 0 -1.260714 175 22.0 50 -340 -13.95238095 435 25.95238 0 -1.260714 250 30.6 51 -260 -3.95238095 435 25.95238 0 -1.260714 350 31.8 52 -185 4.64761905 435 25.95238 0 -1.260714 500 32.4 53 -85 5.84761905 435 25.95238 0 -1.260714 675 31.1 54 65 6.44761905 435 25.95238 0 -1.260714 1000 31.5 55 240 5.14761905 435 25.95238 0 -1.260714 95 11.3 56 565 5.54761905 435 25.95238 0 -1.260714 175 19.4 57 -340 -14.65238095 435 25.95238 0 -1.260714 250 25.8 58 -260 -6.55238095 435 25.95238 0 -1.260714 350 27.9 59 -185 -0.15238095 435 25.95238 0 -1.260714 500 28.5 60 -85 1.94761905 435 25.95238 0 -1.260714 675 28.1 61 65 2.54761905 435 25.95238 0 -1.260714 1000 27.8 62 240 2.14761905 435 25.95238 0 -1.260714 NA NA 63 565 1.84761905 435 25.95238 0 -1.260714 NA NA 64 -340 -5.31428571 435 15.81429 0 -11.398810 250 18.1 65 -260 -0.91428571 435 15.81429 0 -11.398810 350 18.9 66 -185 2.28571429 435 15.81429 0 -11.398810 500 19.5 67 -85 3.08571429 435 15.81429 0 -11.398810 675 22.2 68 65 3.68571429 435 15.81429 0 -11.398810 1000 21.9 69 240 6.38571429 435 15.81429 0 -11.398810 95 7.7 70 565 6.08571429 435 15.81429 0 -11.398810 175 11.4 71 -340 -8.11428571 435 15.81429 0 -11.398810 250 12.3 72 -260 -4.41428571 435 15.81429 0 -11.398810 350 13.0 73 -185 -3.51428571 435 15.81429 0 -11.398810 500 12.5 74 -85 -2.81428571 435 15.81429 0 -11.398810 675 13.7 75 65 -3.31428571 435 15.81429 0 -11.398810 1000 14.4 76 240 -2.11428571 435 15.81429 0 -11.398810 95 10.6 77 565 -1.41428571 435 15.81429 0 -11.398810 175 18.0 78 -340 -5.21428571 435 15.81429 0 -11.398810 250 17.9 79 -260 2.18571429 435 15.81429 0 -11.398810 350 17.9 80 -185 2.08571429 435 15.81429 0 -11.398810 500 17.9 81 -85 2.08571429 435 15.81429 0 -11.398810 675 18.9 82 65 2.08571429 435 15.81429 0 -11.398810 1000 19.9 83 240 3.08571429 435 15.81429 0 -11.398810 NA NA 84 565 4.08571429 435 15.81429 0 -11.398810 NA NA conc_dw1 uptake_dw1 conc_gw1 uptake_gw1 conc_gw2 uptake_gw2 conc_w_dw2 1 175 30.4 NA NA NA NA -185 2 250 34.8 95 16.0 NA NA -85 3 350 37.2 175 30.4 95 16.0 65 4 500 35.3 250 34.8 175 30.4 240 5 675 39.2 350 37.2 250 34.8 565 6 1000 39.7 500 35.3 350 37.2 -340 7 95 13.6 675 39.2 500 35.3 -260 8 175 27.3 1000 39.7 675 39.2 -185 9 250 37.1 95 13.6 1000 39.7 -85 10 350 41.8 175 27.3 95 13.6 65 11 500 40.6 250 37.1 175 27.3 240 12 675 41.4 350 41.8 250 37.1 565 13 1000 44.3 500 40.6 350 41.8 -340 14 95 16.2 675 41.4 500 40.6 -260 15 175 32.4 1000 44.3 675 41.4 -185 16 250 40.3 95 16.2 1000 44.3 -85 17 350 42.1 175 32.4 95 16.2 65 18 500 42.9 250 40.3 175 32.4 240 19 675 43.9 350 42.1 250 40.3 565 20 1000 45.5 500 42.9 350 42.1 NA 21 NA NA 675 43.9 500 42.9 NA 22 175 24.1 NA NA NA NA -185 23 250 30.3 95 14.2 NA NA -85 24 350 34.6 175 24.1 95 14.2 65 25 500 32.5 250 30.3 175 24.1 240 26 675 35.4 350 34.6 250 30.3 565 27 1000 38.7 500 32.5 350 34.6 -340 28 95 9.3 675 35.4 500 32.5 -260 29 175 27.3 1000 38.7 675 35.4 -185 30 250 35.0 95 9.3 1000 38.7 -85 31 350 38.8 175 27.3 95 9.3 65 32 500 38.6 250 35.0 175 27.3 240 33 675 37.5 350 38.8 250 35.0 565 34 1000 42.4 500 38.6 350 38.8 -340 35 95 15.1 675 37.5 500 38.6 -260 36 175 21.0 1000 42.4 675 37.5 -185 37 250 38.1 95 15.1 1000 42.4 -85 38 350 34.0 175 21.0 95 15.1 65 39 500 38.9 250 38.1 175 21.0 240 40 675 39.6 350 34.0 250 38.1 565 41 1000 41.4 500 38.9 350 34.0 NA 42 NA NA 675 39.6 500 38.9 NA 43 175 19.2 NA NA NA NA -185 44 250 26.2 95 10.6 NA NA -85 45 350 30.0 175 19.2 95 10.6 65 46 500 30.9 250 26.2 175 19.2 240 47 675 32.4 350 30.0 250 26.2 565 48 1000 35.5 500 30.9 350 30.0 -340 49 95 12.0 675 32.4 500 30.9 -260 50 175 22.0 1000 35.5 675 32.4 -185 51 250 30.6 95 12.0 1000 35.5 -85 52 350 31.8 175 22.0 95 12.0 65 53 500 32.4 250 30.6 175 22.0 240 54 675 31.1 350 31.8 250 30.6 565 55 1000 31.5 500 32.4 350 31.8 -340 56 95 11.3 675 31.1 500 32.4 -260 57 175 19.4 1000 31.5 675 31.1 -185 58 250 25.8 95 11.3 1000 31.5 -85 59 350 27.9 175 19.4 95 11.3 65 60 500 28.5 250 25.8 175 19.4 240 61 675 28.1 350 27.9 250 25.8 565 62 1000 27.8 500 28.5 350 27.9 NA 63 NA NA 675 28.1 500 28.5 NA 64 175 14.9 NA NA NA NA -185 65 250 18.1 95 10.5 NA NA -85 66 350 18.9 175 14.9 95 10.5 65 67 500 19.5 250 18.1 175 14.9 240 68 675 22.2 350 18.9 250 18.1 565 69 1000 21.9 500 19.5 350 18.9 -340 70 95 7.7 675 22.2 500 19.5 -260 71 175 11.4 1000 21.9 675 22.2 -185 72 250 12.3 95 7.7 1000 21.9 -85 73 350 13.0 175 11.4 95 7.7 65 74 500 12.5 250 12.3 175 11.4 240 75 675 13.7 350 13.0 250 12.3 565 76 1000 14.4 500 12.5 350 13.0 -340 77 95 10.6 675 13.7 500 12.5 -260 78 175 18.0 1000 14.4 675 13.7 -185 79 250 17.9 95 10.6 1000 14.4 -85 80 350 17.9 175 18.0 95 10.6 65 81 500 17.9 250 17.9 175 18.0 240 82 675 18.9 350 17.9 250 17.9 565 83 1000 19.9 500 17.9 350 17.9 NA 84 NA NA 675 18.9 500 17.9 NA uptake_w_dw2 conc_w_dw1 uptake_w_dw1 conc_w_gw1 uptake_w_gw1 conc_w_gw2 1 -0.53333333 -260 -4.93333333 NA NA NA 2 1.86666667 -185 -0.53333333 -340 -19.33333333 NA 3 -0.03333333 -85 1.86666667 -260 -4.93333333 -340 4 3.86666667 65 -0.03333333 -185 -0.53333333 -260 5 4.36666667 240 3.86666667 -85 1.86666667 -185 6 -21.73333333 565 4.36666667 65 -0.03333333 -85 7 -8.03333333 -340 -21.73333333 240 3.86666667 65 8 1.76666667 -260 -8.03333333 565 4.36666667 240 9 6.46666667 -185 1.76666667 -340 -21.73333333 565 10 5.26666667 -85 6.46666667 -260 -8.03333333 -340 11 6.06666667 65 5.26666667 -185 1.76666667 -260 12 8.96666667 240 6.06666667 -85 6.46666667 -185 13 -19.13333333 565 8.96666667 65 5.26666667 -85 14 -2.93333333 -340 -19.13333333 240 6.06666667 65 15 4.96666667 -260 -2.93333333 565 8.96666667 240 16 6.76666667 -185 4.96666667 -340 -19.13333333 565 17 7.56666667 -85 6.76666667 -260 -2.93333333 -340 18 8.56666667 65 7.56666667 -185 4.96666667 -260 19 10.16666667 240 8.56666667 -85 6.76666667 -185 20 NA 565 10.16666667 65 7.56666667 -85 21 NA NA NA 240 8.56666667 65 22 -1.45238095 -260 -7.65238095 NA NA NA 23 2.84761905 -185 -1.45238095 -340 -17.55238095 NA 24 0.74761905 -85 2.84761905 -260 -7.65238095 -340 25 3.64761905 65 0.74761905 -185 -1.45238095 -260 26 6.94761905 240 3.64761905 -85 2.84761905 -185 27 -22.45238095 565 6.94761905 65 0.74761905 -85 28 -4.45238095 -340 -22.45238095 240 3.64761905 65 29 3.24761905 -260 -4.45238095 565 6.94761905 240 30 7.04761905 -185 3.24761905 -340 -22.45238095 565 31 6.84761905 -85 7.04761905 -260 -4.45238095 -340 32 5.74761905 65 6.84761905 -185 3.24761905 -260 33 10.64761905 240 5.74761905 -85 7.04761905 -185 34 -16.65238095 565 10.64761905 65 6.84761905 -85 35 -10.75238095 -340 -16.65238095 240 5.74761905 65 36 6.34761905 -260 -10.75238095 565 10.64761905 240 37 2.24761905 -185 6.34761905 -340 -16.65238095 565 38 7.14761905 -85 2.24761905 -260 -10.75238095 -340 39 7.84761905 65 7.14761905 -185 6.34761905 -260 40 9.64761905 240 7.84761905 -85 2.24761905 -185 41 NA 565 9.64761905 65 7.14761905 -85 42 NA NA NA 240 7.84761905 65 43 0.24761905 -260 -6.75238095 NA NA NA 44 4.04761905 -185 0.24761905 -340 -15.35238095 NA 45 4.94761905 -85 4.04761905 -260 -6.75238095 -340 46 6.44761905 65 4.94761905 -185 0.24761905 -260 47 9.54761905 240 6.44761905 -85 4.04761905 -185 48 -13.95238095 565 9.54761905 65 4.94761905 -85 49 -3.95238095 -340 -13.95238095 240 6.44761905 65 50 4.64761905 -260 -3.95238095 565 9.54761905 240 51 5.84761905 -185 4.64761905 -340 -13.95238095 565 52 6.44761905 -85 5.84761905 -260 -3.95238095 -340 53 5.14761905 65 6.44761905 -185 4.64761905 -260 54 5.54761905 240 5.14761905 -85 5.84761905 -185 55 -14.65238095 565 5.54761905 65 6.44761905 -85 56 -6.55238095 -340 -14.65238095 240 5.14761905 65 57 -0.15238095 -260 -6.55238095 565 5.54761905 240 58 1.94761905 -185 -0.15238095 -340 -14.65238095 565 59 2.54761905 -85 1.94761905 -260 -6.55238095 -340 60 2.14761905 65 2.54761905 -185 -0.15238095 -260 61 1.84761905 240 2.14761905 -85 1.94761905 -185 62 NA 565 1.84761905 65 2.54761905 -85 63 NA NA NA 240 2.14761905 65 64 2.28571429 -260 -0.91428571 NA NA NA 65 3.08571429 -185 2.28571429 -340 -5.31428571 NA 66 3.68571429 -85 3.08571429 -260 -0.91428571 -340 67 6.38571429 65 3.68571429 -185 2.28571429 -260 68 6.08571429 240 6.38571429 -85 3.08571429 -185 69 -8.11428571 565 6.08571429 65 3.68571429 -85 70 -4.41428571 -340 -8.11428571 240 6.38571429 65 71 -3.51428571 -260 -4.41428571 565 6.08571429 240 72 -2.81428571 -185 -3.51428571 -340 -8.11428571 565 73 -3.31428571 -85 -2.81428571 -260 -4.41428571 -340 74 -2.11428571 65 -3.31428571 -185 -3.51428571 -260 75 -1.41428571 240 -2.11428571 -85 -2.81428571 -185 76 -5.21428571 565 -1.41428571 65 -3.31428571 -85 77 2.18571429 -340 -5.21428571 240 -2.11428571 65 78 2.08571429 -260 2.18571429 565 -1.41428571 240 79 2.08571429 -185 2.08571429 -340 -5.21428571 565 80 2.08571429 -85 2.08571429 -260 2.18571429 -340 81 3.08571429 65 2.08571429 -185 2.08571429 -260 82 4.08571429 240 3.08571429 -85 2.08571429 -185 83 NA 565 4.08571429 65 2.08571429 -85 84 NA NA NA 240 3.08571429 65 uptake_w_gw2 1 NA 2 NA 3 -19.33333333 4 -4.93333333 5 -0.53333333 6 1.86666667 7 -0.03333333 8 3.86666667 9 4.36666667 10 -21.73333333 11 -8.03333333 12 1.76666667 13 6.46666667 14 5.26666667 15 6.06666667 16 8.96666667 17 -19.13333333 18 -2.93333333 19 4.96666667 20 6.76666667 21 7.56666667 22 NA 23 NA 24 -17.55238095 25 -7.65238095 26 -1.45238095 27 2.84761905 28 0.74761905 29 3.64761905 30 6.94761905 31 -22.45238095 32 -4.45238095 33 3.24761905 34 7.04761905 35 6.84761905 36 5.74761905 37 10.64761905 38 -16.65238095 39 -10.75238095 40 6.34761905 41 2.24761905 42 7.14761905 43 NA 44 NA 45 -15.35238095 46 -6.75238095 47 0.24761905 48 4.04761905 49 4.94761905 50 6.44761905 51 9.54761905 52 -13.95238095 53 -3.95238095 54 4.64761905 55 5.84761905 56 6.44761905 57 5.14761905 58 5.54761905 59 -14.65238095 60 -6.55238095 61 -0.15238095 62 1.94761905 63 2.54761905 64 NA 65 NA 66 -5.31428571 67 -0.91428571 68 2.28571429 69 3.08571429 70 3.68571429 71 6.38571429 72 6.08571429 73 -8.11428571 74 -4.41428571 75 -3.51428571 76 -2.81428571 77 -3.31428571 78 -2.11428571 79 -1.41428571 80 -5.21428571 81 2.18571429 82 2.08571429 83 2.08571429 84 2.08571429 > > > > cleanEx() > nameEx("dum2nom") > ### * dum2nom > > flush(stderr()); flush(stdout()) > > ### Name: dum2nom > ### Title: Dummy Variables to a Nominal Variable > ### Aliases: dum2nom > > ### ** Examples > > dum <- data.frame( + "Quebec_nonchilled" = ifelse(CO2$"Type" == "Quebec" & CO2$"Treatment" == "nonchilled", + yes = 1L, no = 0L), + "Quebec_chilled" = ifelse(CO2$"Type" == "Quebec" & CO2$"Treatment" == "chilled", + yes = 1L, no = 0L), + "Mississippi_nonchilled" = ifelse(CO2$"Type" == "Mississippi" & CO2$"Treatment" == "nonchilled", + yes = 1L, no = 0L), + "Mississippi_chilled" = ifelse(CO2$"Type" == "Mississippi" & CO2$"Treatment" == "chilled", + yes = 1L, no = 0L) + ) > dum2nom(data = dum, dum.nm = names(dum)) # default [1] "Quebec_nonchilled" "Quebec_nonchilled" "Quebec_nonchilled" [4] "Quebec_nonchilled" "Quebec_nonchilled" "Quebec_nonchilled" [7] "Quebec_nonchilled" "Quebec_nonchilled" "Quebec_nonchilled" [10] "Quebec_nonchilled" "Quebec_nonchilled" "Quebec_nonchilled" [13] "Quebec_nonchilled" "Quebec_nonchilled" "Quebec_nonchilled" [16] "Quebec_nonchilled" "Quebec_nonchilled" "Quebec_nonchilled" [19] "Quebec_nonchilled" "Quebec_nonchilled" "Quebec_nonchilled" [22] "Quebec_chilled" "Quebec_chilled" "Quebec_chilled" [25] "Quebec_chilled" "Quebec_chilled" "Quebec_chilled" [28] "Quebec_chilled" "Quebec_chilled" "Quebec_chilled" [31] "Quebec_chilled" "Quebec_chilled" "Quebec_chilled" [34] "Quebec_chilled" "Quebec_chilled" "Quebec_chilled" [37] "Quebec_chilled" "Quebec_chilled" "Quebec_chilled" [40] "Quebec_chilled" "Quebec_chilled" "Quebec_chilled" [43] "Mississippi_nonchilled" "Mississippi_nonchilled" "Mississippi_nonchilled" [46] "Mississippi_nonchilled" "Mississippi_nonchilled" "Mississippi_nonchilled" [49] "Mississippi_nonchilled" "Mississippi_nonchilled" "Mississippi_nonchilled" [52] "Mississippi_nonchilled" "Mississippi_nonchilled" "Mississippi_nonchilled" [55] "Mississippi_nonchilled" "Mississippi_nonchilled" "Mississippi_nonchilled" [58] "Mississippi_nonchilled" "Mississippi_nonchilled" "Mississippi_nonchilled" [61] "Mississippi_nonchilled" "Mississippi_nonchilled" "Mississippi_nonchilled" [64] "Mississippi_chilled" "Mississippi_chilled" "Mississippi_chilled" [67] "Mississippi_chilled" "Mississippi_chilled" "Mississippi_chilled" [70] "Mississippi_chilled" "Mississippi_chilled" "Mississippi_chilled" [73] "Mississippi_chilled" "Mississippi_chilled" "Mississippi_chilled" [76] "Mississippi_chilled" "Mississippi_chilled" "Mississippi_chilled" [79] "Mississippi_chilled" "Mississippi_chilled" "Mississippi_chilled" [82] "Mississippi_chilled" "Mississippi_chilled" "Mississippi_chilled" > dum2nom(data = dum, dum.nm = names(dum), rtn.fct = TRUE) # return as a factor [1] Quebec_nonchilled Quebec_nonchilled Quebec_nonchilled [4] Quebec_nonchilled Quebec_nonchilled Quebec_nonchilled [7] Quebec_nonchilled Quebec_nonchilled Quebec_nonchilled [10] Quebec_nonchilled Quebec_nonchilled Quebec_nonchilled [13] Quebec_nonchilled Quebec_nonchilled Quebec_nonchilled [16] Quebec_nonchilled Quebec_nonchilled Quebec_nonchilled [19] Quebec_nonchilled Quebec_nonchilled Quebec_nonchilled [22] Quebec_chilled Quebec_chilled Quebec_chilled [25] Quebec_chilled Quebec_chilled Quebec_chilled [28] Quebec_chilled Quebec_chilled Quebec_chilled [31] Quebec_chilled Quebec_chilled Quebec_chilled [34] Quebec_chilled Quebec_chilled Quebec_chilled [37] Quebec_chilled Quebec_chilled Quebec_chilled [40] Quebec_chilled Quebec_chilled Quebec_chilled [43] Mississippi_nonchilled Mississippi_nonchilled Mississippi_nonchilled [46] Mississippi_nonchilled Mississippi_nonchilled Mississippi_nonchilled [49] Mississippi_nonchilled Mississippi_nonchilled Mississippi_nonchilled [52] Mississippi_nonchilled Mississippi_nonchilled Mississippi_nonchilled [55] Mississippi_nonchilled Mississippi_nonchilled Mississippi_nonchilled [58] Mississippi_nonchilled Mississippi_nonchilled Mississippi_nonchilled [61] Mississippi_nonchilled Mississippi_nonchilled Mississippi_nonchilled [64] Mississippi_chilled Mississippi_chilled Mississippi_chilled [67] Mississippi_chilled Mississippi_chilled Mississippi_chilled [70] Mississippi_chilled Mississippi_chilled Mississippi_chilled [73] Mississippi_chilled Mississippi_chilled Mississippi_chilled [76] Mississippi_chilled Mississippi_chilled Mississippi_chilled [79] Mississippi_chilled Mississippi_chilled Mississippi_chilled [82] Mississippi_chilled Mississippi_chilled Mississippi_chilled 4 Levels: Mississippi_chilled Mississippi_nonchilled ... Quebec_nonchilled > ## Not run: > ##D dum2nom(data = npk, dum.nm = c("N","P","K")) # error due to overlapping dummy columns > ##D dum2nom(data = mtcars, dum.nm = c("vs","am"))# error due to overlapping dummy columns > ## End(Not run) > > > > cleanEx() > nameEx("freq") > ### * freq > > flush(stderr()); flush(stdout()) > > ### Name: freq > ### Title: Univariate Frequency Table > ### Aliases: freq > > ### ** Examples > > freq(c(mtcars$"carb", NA, NA, mtcars$"gear"), prop = FALSE, + sort = "frequency", decreasing = TRUE, na.last = TRUE) 4 3 2 1 5 8 6 (NA) 22 18 10 7 5 1 1 2 > freq(c(mtcars$"carb", NA, NA, mtcars$"gear"), prop = FALSE, + sort = "frequency", decreasing = TRUE, na.last = FALSE) 4 3 2 1 5 (NA) 8 6 22 18 10 7 5 2 1 1 > freq(c(mtcars$"carb", NA, NA, mtcars$"gear"), prop = TRUE, + sort = "frequency", decreasing = FALSE, na.last = TRUE) 6 8 5 1 2 3 4 0.01515152 0.01515152 0.07575758 0.10606061 0.15151515 0.27272727 0.33333333 (NA) 0.03030303 > freq(c(mtcars$"carb", NA, NA, mtcars$"gear"), prop = TRUE, + sort = "frequency", decreasing = FALSE, na.last = FALSE) 6 8 (NA) 5 1 2 3 0.01515152 0.01515152 0.03030303 0.07575758 0.10606061 0.15151515 0.27272727 4 0.33333333 > freq(c(mtcars$"carb", NA, NA, mtcars$"gear"), prop = FALSE, + sort = "position", decreasing = TRUE, na.last = TRUE) 5 8 6 3 2 1 4 (NA) 5 1 1 18 10 7 22 2 > freq(c(mtcars$"carb", NA, NA, mtcars$"gear"), prop = FALSE, + sort = "position", decreasing = TRUE, na.last = FALSE) 5 (NA) 8 6 3 2 1 4 5 2 1 1 18 10 7 22 > freq(c(mtcars$"carb", NA, NA, mtcars$"gear"), prop = TRUE, + sort = "position", decreasing = FALSE, na.last = TRUE) 4 1 2 3 6 8 5 0.33333333 0.10606061 0.15151515 0.27272727 0.01515152 0.01515152 0.07575758 (NA) 0.03030303 > freq(c(mtcars$"carb", NA, NA, mtcars$"gear"), prop = TRUE, + sort = "position", decreasing = FALSE, na.last = FALSE) 4 1 2 3 6 8 (NA) 0.33333333 0.10606061 0.15151515 0.27272727 0.01515152 0.01515152 0.03030303 5 0.07575758 > freq(c(mtcars$"carb", NA, NA, mtcars$"gear"), prop = FALSE, + sort = "alphanum", decreasing = TRUE, na.last = TRUE) 8 6 5 4 3 2 1 (NA) 1 1 5 22 18 10 7 2 > freq(c(mtcars$"carb", NA, NA, mtcars$"gear"), prop = FALSE, + sort = "alphanum", decreasing = TRUE, na.last = FALSE) (NA) 8 6 5 4 3 2 1 2 1 1 5 22 18 10 7 > freq(c(mtcars$"carb", NA, NA, mtcars$"gear"), prop = TRUE, + sort = "alphanum", decreasing = FALSE, na.last = TRUE) 1 2 3 4 5 6 8 0.10606061 0.15151515 0.27272727 0.33333333 0.07575758 0.01515152 0.01515152 (NA) 0.03030303 > freq(c(mtcars$"carb", NA, NA, mtcars$"gear"), prop = TRUE, + sort = "alphanum", decreasing = FALSE, na.last = FALSE) 1 2 3 4 5 6 8 0.10606061 0.15151515 0.27272727 0.33333333 0.07575758 0.01515152 0.01515152 (NA) 0.03030303 > > > > cleanEx() > nameEx("freq_by") > ### * freq_by > > flush(stderr()); flush(stdout()) > > ### Name: freq_by > ### Title: Univariate Frequency Table By Group > ### Aliases: freq_by > > ### ** Examples > > x <- freq_by(mtcars$"gear", grp = mtcars$"vs") > str(x) List of 2 $ 0: Named int [1:4] 12 4 2 0 ..- attr(*, "names")= chr [1:4] "3" "5" "4" "(NA)" $ 1: Named int [1:4] 10 3 1 0 ..- attr(*, "names")= chr [1:4] "4" "3" "5" "(NA)" > y <- freq_by(mtcars$"am", grp = mtcars$"vs", useNA = "no") > str(y) List of 2 $ 0: Named int [1:2] 12 6 ..- attr(*, "names")= chr [1:2] "0" "1" $ 1: Named int [1:2] 7 7 ..- attr(*, "names")= chr [1:2] "1" "0" > str2str::lv2m(lapply(X = y, FUN = rev), along = 1) # ready to pass to prop.test() 1 0 0 6 12 1 7 7 > > > > cleanEx() > nameEx("freqs") > ### * freqs > > flush(stderr()); flush(stdout()) > > ### Name: freqs > ### Title: Multiple Univariate Frequency Tables > ### Aliases: freqs > > ### ** Examples > > vrb_nm <- str2str::inbtw(names(psych::bfi), "A1","O5") > freqs(data = psych::bfi, vrb.nm = vrb_nm) # default 1 2 3 4 5 6 (NA) A1 922 818 402 337 223 82 16 A2 47 126 151 553 1023 873 27 A3 90 172 207 564 986 755 26 A4 129 215 185 451 654 1147 19 A5 59 186 254 617 973 695 16 C1 73 161 275 655 1018 597 21 C2 89 236 296 643 962 550 24 C3 84 248 293 741 942 472 20 C4 769 794 472 448 228 63 26 C5 504 567 348 614 466 285 16 E1 663 652 404 450 367 241 23 E2 533 670 343 599 385 254 16 E3 149 293 412 826 743 352 25 E4 140 262 271 450 942 726 9 E5 95 221 288 619 940 616 21 N1 654 654 427 515 334 194 22 N2 325 535 411 709 510 289 21 N3 499 638 365 591 439 257 11 N4 472 655 401 608 380 248 36 N5 654 660 382 507 327 241 29 O1 22 103 210 606 925 912 22 O2 805 717 388 435 276 179 0 O3 76 145 292 775 943 541 28 O4 55 125 154 481 887 1084 14 O5 746 883 526 364 191 70 20 > freqs(data = psych::bfi, vrb.nm = vrb_nm, prop = TRUE) # proportions by row 1 2 3 4 5 6 A1 0.329285714 0.29214286 0.14357143 0.1203571 0.07964286 0.02928571 A2 0.016785714 0.04500000 0.05392857 0.1975000 0.36535714 0.31178571 A3 0.032142857 0.06142857 0.07392857 0.2014286 0.35214286 0.26964286 A4 0.046071429 0.07678571 0.06607143 0.1610714 0.23357143 0.40964286 A5 0.021071429 0.06642857 0.09071429 0.2203571 0.34750000 0.24821429 C1 0.026071429 0.05750000 0.09821429 0.2339286 0.36357143 0.21321429 C2 0.031785714 0.08428571 0.10571429 0.2296429 0.34357143 0.19642857 C3 0.030000000 0.08857143 0.10464286 0.2646429 0.33642857 0.16857143 C4 0.274642857 0.28357143 0.16857143 0.1600000 0.08142857 0.02250000 C5 0.180000000 0.20250000 0.12428571 0.2192857 0.16642857 0.10178571 E1 0.236785714 0.23285714 0.14428571 0.1607143 0.13107143 0.08607143 E2 0.190357143 0.23928571 0.12250000 0.2139286 0.13750000 0.09071429 E3 0.053214286 0.10464286 0.14714286 0.2950000 0.26535714 0.12571429 E4 0.050000000 0.09357143 0.09678571 0.1607143 0.33642857 0.25928571 E5 0.033928571 0.07892857 0.10285714 0.2210714 0.33571429 0.22000000 N1 0.233571429 0.23357143 0.15250000 0.1839286 0.11928571 0.06928571 N2 0.116071429 0.19107143 0.14678571 0.2532143 0.18214286 0.10321429 N3 0.178214286 0.22785714 0.13035714 0.2110714 0.15678571 0.09178571 N4 0.168571429 0.23392857 0.14321429 0.2171429 0.13571429 0.08857143 N5 0.233571429 0.23571429 0.13642857 0.1810714 0.11678571 0.08607143 O1 0.007857143 0.03678571 0.07500000 0.2164286 0.33035714 0.32571429 O2 0.287500000 0.25607143 0.13857143 0.1553571 0.09857143 0.06392857 O3 0.027142857 0.05178571 0.10428571 0.2767857 0.33678571 0.19321429 O4 0.019642857 0.04464286 0.05500000 0.1717857 0.31678571 0.38714286 O5 0.266428571 0.31535714 0.18785714 0.1300000 0.06821429 0.02500000 (NA) A1 0.005714286 A2 0.009642857 A3 0.009285714 A4 0.006785714 A5 0.005714286 C1 0.007500000 C2 0.008571429 C3 0.007142857 C4 0.009285714 C5 0.005714286 E1 0.008214286 E2 0.005714286 E3 0.008928571 E4 0.003214286 E5 0.007500000 N1 0.007857143 N2 0.007500000 N3 0.003928571 N4 0.012857143 N5 0.010357143 O1 0.007857143 O2 0.000000000 O3 0.010000000 O4 0.005000000 O5 0.007142857 > freqs(data = psych::bfi, vrb.nm = vrb_nm, useNA = "no") # without NA counts 1 2 3 4 5 6 A1 922 818 402 337 223 82 A2 47 126 151 553 1023 873 A3 90 172 207 564 986 755 A4 129 215 185 451 654 1147 A5 59 186 254 617 973 695 C1 73 161 275 655 1018 597 C2 89 236 296 643 962 550 C3 84 248 293 741 942 472 C4 769 794 472 448 228 63 C5 504 567 348 614 466 285 E1 663 652 404 450 367 241 E2 533 670 343 599 385 254 E3 149 293 412 826 743 352 E4 140 262 271 450 942 726 E5 95 221 288 619 940 616 N1 654 654 427 515 334 194 N2 325 535 411 709 510 289 N3 499 638 365 591 439 257 N4 472 655 401 608 380 248 N5 654 660 382 507 327 241 O1 22 103 210 606 925 912 O2 805 717 388 435 276 179 O3 76 145 292 775 943 541 O4 55 125 154 481 887 1084 O5 746 883 526 364 191 70 > freqs(data = psych::bfi, vrb.nm = vrb_nm, total = "yes") # include total counts 1 2 3 4 5 6 (NA) _total_ 8654 10736 8157 14158 16064 11723 508 A1 922 818 402 337 223 82 16 A2 47 126 151 553 1023 873 27 A3 90 172 207 564 986 755 26 A4 129 215 185 451 654 1147 19 A5 59 186 254 617 973 695 16 C1 73 161 275 655 1018 597 21 C2 89 236 296 643 962 550 24 C3 84 248 293 741 942 472 20 C4 769 794 472 448 228 63 26 C5 504 567 348 614 466 285 16 E1 663 652 404 450 367 241 23 E2 533 670 343 599 385 254 16 E3 149 293 412 826 743 352 25 E4 140 262 271 450 942 726 9 E5 95 221 288 619 940 616 21 N1 654 654 427 515 334 194 22 N2 325 535 411 709 510 289 21 N3 499 638 365 591 439 257 11 N4 472 655 401 608 380 248 36 N5 654 660 382 507 327 241 29 O1 22 103 210 606 925 912 22 O2 805 717 388 435 276 179 0 O3 76 145 292 775 943 541 28 O4 55 125 154 481 887 1084 14 O5 746 883 526 364 191 70 20 > > > > cleanEx() > nameEx("freqs_by") > ### * freqs_by > > flush(stderr()); flush(stdout()) > > ### Name: freqs_by > ### Title: Multiple Univariate Frequency Tables > ### Aliases: freqs_by > > ### ** Examples > > vrb_nm <- str2str::inbtw(names(psych::bfi), "A1","O5") > freqs_by(data = psych::bfi, vrb.nm = vrb_nm, grp.nm = "gender") # default $`1` 1 2 3 4 5 6 (NA) A1 202 284 160 139 99 34 1 A2 26 63 67 228 326 198 11 A3 33 79 91 222 313 174 7 A4 47 83 86 184 242 274 3 A5 27 79 102 206 314 187 4 C1 23 53 103 211 333 190 6 C2 31 95 112 217 300 158 6 C3 31 97 105 234 308 135 9 C4 229 229 158 188 81 26 8 C5 147 160 97 207 192 111 5 E1 169 197 128 164 149 106 6 E2 156 205 116 205 139 92 6 E3 60 109 136 260 231 112 11 E4 56 107 85 160 304 203 4 E5 35 88 104 223 284 181 4 N1 240 213 130 161 117 49 9 N2 137 188 146 225 147 72 4 N3 205 227 128 182 125 50 2 N4 169 198 126 205 134 78 9 N5 310 244 112 130 76 37 10 O1 9 20 59 155 317 353 6 O2 280 234 132 132 80 61 0 O3 29 38 86 251 316 195 4 O4 19 41 54 157 270 372 6 O5 270 287 157 110 62 29 4 $`2` 1 2 3 4 5 6 (NA) A1 720 534 242 198 124 48 15 A2 21 63 84 325 697 675 16 A3 57 93 116 342 673 581 19 A4 82 132 99 267 412 873 16 A5 32 107 152 411 659 508 12 C1 50 108 172 444 685 407 15 C2 58 141 184 426 662 392 18 C3 53 151 188 507 634 337 11 C4 540 565 314 260 147 37 18 C5 357 407 251 407 274 174 11 E1 494 455 276 286 218 135 17 E2 377 465 227 394 246 162 10 E3 89 184 276 566 512 240 14 E4 84 155 186 290 638 523 5 E5 60 133 184 396 656 435 17 N1 414 441 297 354 217 145 13 N2 188 347 265 484 363 217 17 N3 294 411 237 409 314 207 9 N4 303 457 275 403 246 170 27 N5 344 416 270 377 251 204 19 O1 13 83 151 451 608 559 16 O2 525 483 256 303 196 118 0 O3 47 107 206 524 627 346 24 O4 36 84 100 324 617 712 8 O5 476 596 369 254 129 41 16 > freqs_by(data = psych::bfi, vrb.nm = vrb_nm, grp.nm = "gender", + prop = TRUE) # proportions by row $`1` 1 2 3 4 5 6 A1 0.219804135 0.30903156 0.17410229 0.1512514 0.10772579 0.03699674 A2 0.028291621 0.06855277 0.07290533 0.2480958 0.35473341 0.21545158 A3 0.035908596 0.08596300 0.09902067 0.2415669 0.34058760 0.18933624 A4 0.051142546 0.09031556 0.09357998 0.2002176 0.26332971 0.29815016 A5 0.029379761 0.08596300 0.11099021 0.2241567 0.34167573 0.20348205 C1 0.025027203 0.05767138 0.11207835 0.2295974 0.36235038 0.20674646 C2 0.033732318 0.10337323 0.12187160 0.2361262 0.32644178 0.17192601 C3 0.033732318 0.10554951 0.11425462 0.2546246 0.33514690 0.14689880 C4 0.249183896 0.24918390 0.17192601 0.2045702 0.08813928 0.02829162 C5 0.159956474 0.17410229 0.10554951 0.2252448 0.20892274 0.12078346 E1 0.183895539 0.21436344 0.13928183 0.1784548 0.16213275 0.11534276 E2 0.169749728 0.22306855 0.12622416 0.2230686 0.15125136 0.10010881 E3 0.065288357 0.11860718 0.14798694 0.2829162 0.25136017 0.12187160 E4 0.060935800 0.11643090 0.09249184 0.1741023 0.33079434 0.22089227 E5 0.038084875 0.09575626 0.11316649 0.2426551 0.30903156 0.19695321 N1 0.261153428 0.23177367 0.14145811 0.1751904 0.12731230 0.05331882 N2 0.149075082 0.20457018 0.15886834 0.2448313 0.15995647 0.07834603 N3 0.223068553 0.24700762 0.13928183 0.1980413 0.13601741 0.05440696 N4 0.183895539 0.21545158 0.13710555 0.2230686 0.14581066 0.08487486 N5 0.337323177 0.26550598 0.12187160 0.1414581 0.08269859 0.04026115 O1 0.009793254 0.02176279 0.06420022 0.1686616 0.34494015 0.38411317 O2 0.304678999 0.25462459 0.14363439 0.1436344 0.08705114 0.06637650 O3 0.031556039 0.04134929 0.09357998 0.2731230 0.34385201 0.21218716 O4 0.020674646 0.04461371 0.05875952 0.1708379 0.29379761 0.40478781 O5 0.293797606 0.31229597 0.17083787 0.1196953 0.06746464 0.03155604 (NA) A1 0.001088139 A2 0.011969532 A3 0.007616975 A4 0.003264418 A5 0.004352557 C1 0.006528836 C2 0.006528836 C3 0.009793254 C4 0.008705114 C5 0.005440696 E1 0.006528836 E2 0.006528836 E3 0.011969532 E4 0.004352557 E5 0.004352557 N1 0.009793254 N2 0.004352557 N3 0.002176279 N4 0.009793254 N5 0.010881393 O1 0.006528836 O2 0.000000000 O3 0.004352557 O4 0.006528836 O5 0.004352557 $`2` 1 2 3 4 5 6 A1 0.382775120 0.28389155 0.12865497 0.1052632 0.06592238 0.02551834 A2 0.011164274 0.03349282 0.04465710 0.1727804 0.37054758 0.35885167 A3 0.030303030 0.04944179 0.06166932 0.1818182 0.35778841 0.30887826 A4 0.043593833 0.07017544 0.05263158 0.1419458 0.21903243 0.46411483 A5 0.017012228 0.05688464 0.08080808 0.2185008 0.35034556 0.27006911 C1 0.026581606 0.05741627 0.09144072 0.2360447 0.36416800 0.21637427 C2 0.030834662 0.07496013 0.09782031 0.2264753 0.35194046 0.20839979 C3 0.028176502 0.08027645 0.09994684 0.2695375 0.33705476 0.17916002 C4 0.287081340 0.30037214 0.16693248 0.1382243 0.07814992 0.01967039 C5 0.189792663 0.21637427 0.13343966 0.2163743 0.14566720 0.09250399 E1 0.262626263 0.24189261 0.14673046 0.1520468 0.11589580 0.07177033 E2 0.200425306 0.24720893 0.12068049 0.2094631 0.13078150 0.08612440 E3 0.047315258 0.09782031 0.14673046 0.3009038 0.27219564 0.12759171 E4 0.044657097 0.08240298 0.09888357 0.1541733 0.33918129 0.27804359 E5 0.031897927 0.07070707 0.09782031 0.2105263 0.34875066 0.23125997 N1 0.220095694 0.23444976 0.15789474 0.1881978 0.11536417 0.07708666 N2 0.099946837 0.18447634 0.14088251 0.2573099 0.19298246 0.11536417 N3 0.156299841 0.21850080 0.12599681 0.2174375 0.16693248 0.11004785 N4 0.161084530 0.24295587 0.14619883 0.2142477 0.13078150 0.09037746 N5 0.182881446 0.22115896 0.14354067 0.2004253 0.13343966 0.10845295 O1 0.006911217 0.04412547 0.08027645 0.2397661 0.32323232 0.29718235 O2 0.279106858 0.25677831 0.13609782 0.1610845 0.10419989 0.06273259 O3 0.024986709 0.05688464 0.10951621 0.2785752 0.33333333 0.18394471 O4 0.019138756 0.04465710 0.05316321 0.1722488 0.32801701 0.37852206 O5 0.253056885 0.31685274 0.19617225 0.1350346 0.06858054 0.02179692 (NA) A1 0.007974482 A2 0.008506114 A3 0.010101010 A4 0.008506114 A5 0.006379585 C1 0.007974482 C2 0.009569378 C3 0.005847953 C4 0.009569378 C5 0.005847953 E1 0.009037746 E2 0.005316321 E3 0.007442850 E4 0.002658161 E5 0.009037746 N1 0.006911217 N2 0.009037746 N3 0.004784689 N4 0.014354067 N5 0.010101010 O1 0.008506114 O2 0.000000000 O3 0.012759171 O4 0.004253057 O5 0.008506114 > freqs_by(data = psych::bfi, vrb.nm = vrb_nm, grp.nm = "gender", + useNA = "no") # without NA counts $`1` 1 2 3 4 5 6 A1 202 284 160 139 99 34 A2 26 63 67 228 326 198 A3 33 79 91 222 313 174 A4 47 83 86 184 242 274 A5 27 79 102 206 314 187 C1 23 53 103 211 333 190 C2 31 95 112 217 300 158 C3 31 97 105 234 308 135 C4 229 229 158 188 81 26 C5 147 160 97 207 192 111 E1 169 197 128 164 149 106 E2 156 205 116 205 139 92 E3 60 109 136 260 231 112 E4 56 107 85 160 304 203 E5 35 88 104 223 284 181 N1 240 213 130 161 117 49 N2 137 188 146 225 147 72 N3 205 227 128 182 125 50 N4 169 198 126 205 134 78 N5 310 244 112 130 76 37 O1 9 20 59 155 317 353 O2 280 234 132 132 80 61 O3 29 38 86 251 316 195 O4 19 41 54 157 270 372 O5 270 287 157 110 62 29 $`2` 1 2 3 4 5 6 A1 720 534 242 198 124 48 A2 21 63 84 325 697 675 A3 57 93 116 342 673 581 A4 82 132 99 267 412 873 A5 32 107 152 411 659 508 C1 50 108 172 444 685 407 C2 58 141 184 426 662 392 C3 53 151 188 507 634 337 C4 540 565 314 260 147 37 C5 357 407 251 407 274 174 E1 494 455 276 286 218 135 E2 377 465 227 394 246 162 E3 89 184 276 566 512 240 E4 84 155 186 290 638 523 E5 60 133 184 396 656 435 N1 414 441 297 354 217 145 N2 188 347 265 484 363 217 N3 294 411 237 409 314 207 N4 303 457 275 403 246 170 N5 344 416 270 377 251 204 O1 13 83 151 451 608 559 O2 525 483 256 303 196 118 O3 47 107 206 524 627 346 O4 36 84 100 324 617 712 O5 476 596 369 254 129 41 > freqs_by(data = psych::bfi, vrb.nm = vrb_nm, grp.nm = "gender", + total = "yes") # include total counts $`1` 1 2 3 4 5 6 (NA) _total_ 2940 3618 2780 4756 5259 3477 145 A1 202 284 160 139 99 34 1 A2 26 63 67 228 326 198 11 A3 33 79 91 222 313 174 7 A4 47 83 86 184 242 274 3 A5 27 79 102 206 314 187 4 C1 23 53 103 211 333 190 6 C2 31 95 112 217 300 158 6 C3 31 97 105 234 308 135 9 C4 229 229 158 188 81 26 8 C5 147 160 97 207 192 111 5 E1 169 197 128 164 149 106 6 E2 156 205 116 205 139 92 6 E3 60 109 136 260 231 112 11 E4 56 107 85 160 304 203 4 E5 35 88 104 223 284 181 4 N1 240 213 130 161 117 49 9 N2 137 188 146 225 147 72 4 N3 205 227 128 182 125 50 2 N4 169 198 126 205 134 78 9 N5 310 244 112 130 76 37 10 O1 9 20 59 155 317 353 6 O2 280 234 132 132 80 61 0 O3 29 38 86 251 316 195 4 O4 19 41 54 157 270 372 6 O5 270 287 157 110 62 29 4 $`2` 1 2 3 4 5 6 (NA) _total_ 5714 7118 5377 9402 10805 8246 363 A1 720 534 242 198 124 48 15 A2 21 63 84 325 697 675 16 A3 57 93 116 342 673 581 19 A4 82 132 99 267 412 873 16 A5 32 107 152 411 659 508 12 C1 50 108 172 444 685 407 15 C2 58 141 184 426 662 392 18 C3 53 151 188 507 634 337 11 C4 540 565 314 260 147 37 18 C5 357 407 251 407 274 174 11 E1 494 455 276 286 218 135 17 E2 377 465 227 394 246 162 10 E3 89 184 276 566 512 240 14 E4 84 155 186 290 638 523 5 E5 60 133 184 396 656 435 17 N1 414 441 297 354 217 145 13 N2 188 347 265 484 363 217 17 N3 294 411 237 409 314 207 9 N4 303 457 275 403 246 170 27 N5 344 416 270 377 251 204 19 O1 13 83 151 451 608 559 16 O2 525 483 256 303 196 118 0 O3 47 107 206 524 627 346 24 O4 36 84 100 324 617 712 8 O5 476 596 369 254 129 41 16 > freqs_by(data = psych::bfi, vrb.nm = vrb_nm, + grp.nm = c("gender","education")) # multiple grouping variables $`1.1` 1 2 3 4 5 6 (NA) A1 17 24 21 15 9 7 0 A2 1 3 11 18 37 22 1 A3 3 8 10 23 23 25 1 A4 6 8 8 16 28 27 0 A5 3 6 11 18 39 16 0 C1 1 6 7 17 46 16 0 C2 2 5 13 18 27 25 3 C3 2 9 8 26 33 14 1 C4 24 17 16 21 12 2 1 C5 20 10 12 23 14 13 1 E1 16 24 9 19 12 13 0 E2 11 28 9 22 12 11 0 E3 6 11 15 25 24 10 2 E4 6 15 12 13 28 19 0 E5 4 7 6 34 23 18 1 N1 25 27 15 12 12 2 0 N2 16 20 17 13 22 5 0 N3 16 19 20 19 13 6 0 N4 12 19 16 23 15 8 0 N5 26 32 10 11 11 3 0 O1 1 2 4 14 37 33 2 O2 24 28 14 15 6 6 0 O3 7 3 8 18 41 16 0 O4 2 2 4 15 18 51 1 O5 29 30 11 14 5 4 0 $`2.1` 1 2 3 4 5 6 (NA) A1 42 42 18 17 7 4 1 A2 3 4 10 26 44 43 1 A3 7 11 5 30 42 35 1 A4 12 10 8 18 34 48 1 A5 0 9 12 35 42 33 0 C1 7 6 21 32 46 18 1 C2 9 13 12 27 53 16 1 C3 7 17 20 37 32 18 0 C4 26 36 31 21 14 2 1 C5 21 25 23 31 12 19 0 E1 32 30 22 17 16 13 1 E2 22 25 15 34 21 11 3 E3 4 17 27 33 32 18 0 E4 8 12 21 15 43 32 0 E5 6 11 22 36 42 14 0 N1 30 19 25 28 17 11 1 N2 11 21 9 34 34 21 1 N3 18 31 14 21 27 20 0 N4 24 26 17 32 20 11 1 N5 29 18 16 22 18 26 2 O1 1 5 17 25 40 41 2 O2 33 31 22 22 15 8 0 O3 7 10 18 37 33 24 2 O4 4 11 7 17 43 48 1 O5 31 42 31 15 8 4 0 $`1.2` 1 2 3 4 5 6 (NA) A1 22 33 10 23 11 3 1 A2 1 10 9 21 27 33 2 A3 4 9 7 21 38 22 2 A4 7 9 7 19 28 33 0 A5 6 13 12 21 26 25 0 C1 2 6 12 24 32 26 1 C2 4 14 14 24 25 21 1 C3 4 11 17 28 29 14 0 C4 26 18 14 29 14 2 0 C5 15 14 7 27 25 15 0 E1 20 16 20 22 11 13 1 E2 20 23 9 21 15 15 0 E3 7 9 11 33 24 18 1 E4 14 10 9 18 30 22 0 E5 6 11 13 24 24 25 0 N1 27 15 16 18 21 6 0 N2 15 13 17 27 20 10 1 N3 26 18 14 15 19 11 0 N4 18 14 10 26 19 15 1 N5 37 29 7 14 8 7 1 O1 1 2 6 13 28 53 0 O2 32 23 14 16 12 6 0 O3 2 4 6 33 28 30 0 O4 1 5 7 17 21 50 2 O5 39 31 14 12 4 3 0 $`2.2` 1 2 3 4 5 6 (NA) A1 50 49 41 18 15 15 1 A2 1 15 10 33 62 66 2 A3 3 5 9 49 63 56 4 A4 5 14 18 24 37 90 1 A5 3 10 16 36 60 61 3 C1 2 14 12 45 75 38 3 C2 5 17 15 50 57 43 2 C3 6 13 16 59 52 42 1 C4 63 50 21 24 22 8 1 C5 46 39 20 36 25 21 2 E1 50 52 33 29 15 9 1 E2 40 44 29 34 20 22 0 E3 9 13 27 56 55 28 1 E4 6 15 13 30 54 70 1 E5 2 17 19 43 57 48 3 N1 48 37 28 34 21 21 0 N2 26 35 27 39 35 26 1 N3 25 45 24 39 31 23 2 N4 31 54 26 25 28 23 2 N5 40 40 27 32 24 25 1 O1 1 5 12 55 61 53 2 O2 53 44 29 31 17 15 0 O3 5 6 28 51 61 37 1 O4 7 7 10 38 59 66 2 O5 46 65 30 27 16 4 1 $`1.3` 1 2 3 4 5 6 (NA) A1 72 101 79 50 38 16 0 A2 13 22 20 97 125 73 6 A3 14 27 29 93 122 69 2 A4 17 30 25 73 87 124 0 A5 11 26 33 81 121 81 3 C1 6 17 38 90 130 71 4 C2 16 34 42 80 120 64 0 C3 17 28 38 81 131 59 2 C4 102 97 63 56 24 8 6 C5 62 76 46 77 58 34 3 E1 65 71 57 58 60 44 1 E2 66 79 55 80 42 31 3 E3 28 40 44 98 93 50 3 E4 16 29 34 58 130 87 2 E5 10 35 40 77 125 68 1 N1 98 84 46 60 42 22 4 N2 55 74 61 82 51 31 2 N3 88 99 44 66 43 15 1 N4 84 78 48 70 48 26 2 N5 126 84 45 57 27 13 4 O1 4 8 23 73 118 127 3 O2 88 83 54 58 44 29 0 O3 12 20 35 110 105 73 1 O4 6 18 16 71 113 131 1 O5 83 109 77 51 25 10 1 $`2.3` 1 2 3 4 5 6 (NA) A1 345 246 104 105 67 20 6 A2 9 22 26 129 339 358 10 A3 17 36 53 151 330 297 9 A4 27 36 29 108 195 489 9 A5 15 46 65 195 310 255 7 C1 20 43 74 225 315 211 5 C2 14 56 83 198 319 213 10 C3 23 64 93 240 296 170 7 C4 291 257 149 125 52 9 10 C5 191 213 120 186 112 65 6 E1 245 205 137 142 98 58 8 E2 188 233 109 191 100 67 5 E3 41 72 125 304 233 111 7 E4 29 56 72 136 328 270 2 E5 27 57 78 190 315 217 9 N1 196 206 145 165 100 71 10 N2 89 173 125 231 162 105 8 N3 143 198 108 198 144 99 3 N4 157 230 144 182 100 67 13 N5 145 200 142 180 122 95 9 O1 8 43 66 216 302 252 6 O2 227 226 120 157 97 66 0 O3 21 45 100 266 304 144 13 O4 17 50 43 157 308 316 2 O5 210 274 181 140 58 22 8 $`1.4` 1 2 3 4 5 (NA) 6 A1 29 54 19 18 14 0 0 A2 1 16 12 31 49 0 25 A3 3 13 18 40 40 0 20 A4 3 14 15 30 36 0 36 A5 1 8 25 25 50 0 25 C1 1 8 17 26 51 0 31 C2 2 14 18 29 56 0 15 C3 3 19 13 38 41 3 17 C4 28 41 22 26 13 0 4 C5 16 31 8 27 36 1 15 E1 21 28 16 25 27 1 16 E2 18 29 18 23 27 0 19 E3 7 24 28 42 23 1 9 E4 12 21 12 23 40 0 26 E5 9 11 22 31 37 1 23 N1 34 37 17 18 19 2 7 N2 20 33 22 33 17 0 9 N3 30 32 17 28 20 0 7 N4 24 26 20 33 16 2 13 N5 47 40 19 12 9 1 6 O1 0 3 9 19 58 0 45 O2 48 39 21 13 6 0 7 O3 2 3 12 39 54 1 23 O4 2 7 6 24 44 1 50 O5 50 36 19 14 8 1 6 $`2.4` 1 2 3 4 5 6 (NA) A1 115 80 29 18 12 3 3 A2 4 6 9 53 107 81 0 A3 12 14 20 42 103 68 1 A4 19 24 18 42 66 90 1 A5 5 12 14 64 99 64 2 C1 4 18 23 58 92 61 4 C2 10 26 35 50 100 39 0 C3 6 22 23 64 103 41 1 C4 70 87 45 31 19 7 1 C5 37 57 39 50 55 19 3 E1 58 79 35 31 38 18 1 E2 54 60 34 56 35 21 0 E3 15 22 36 70 83 33 1 E4 15 24 26 38 93 64 0 E5 10 18 16 53 101 60 2 N1 64 65 48 49 20 14 0 N2 32 50 36 78 42 21 1 N3 50 53 29 54 51 20 3 N4 45 57 33 61 30 28 6 N5 48 74 23 59 30 23 3 O1 1 16 19 66 83 74 1 O2 79 70 36 36 28 11 0 O3 7 13 25 74 85 56 0 O4 2 3 16 42 86 109 2 O5 74 81 54 31 15 3 2 $`1.5` 1 2 3 4 5 6 (NA) A1 47 49 16 22 15 3 0 A2 5 9 8 40 59 30 1 A3 6 14 19 27 59 26 1 A4 6 12 20 28 46 39 1 A5 2 17 12 39 51 30 1 C1 8 9 14 31 51 38 1 C2 5 15 12 41 58 21 0 C3 2 19 11 38 56 25 1 C4 34 44 23 34 12 5 0 C5 26 16 17 30 41 22 0 E1 23 39 17 28 30 14 1 E2 25 30 12 39 29 14 3 E3 4 22 17 43 44 20 2 E4 6 27 11 27 55 25 1 E5 4 16 12 32 58 30 0 N1 40 32 23 36 16 3 2 N2 23 32 15 49 23 9 1 N3 29 42 20 33 21 6 1 N4 15 37 25 39 23 13 0 N5 47 46 17 23 11 6 2 O1 0 4 9 20 53 66 0 O2 59 41 19 19 10 4 0 O3 1 4 12 25 68 41 1 O4 1 5 11 17 52 65 1 O5 51 57 16 11 14 3 0 $`2.5` 1 2 3 4 5 6 (NA) A1 134 80 26 16 6 2 2 A2 0 5 9 54 102 94 2 A3 6 14 16 43 93 91 3 A4 7 18 17 51 60 112 1 A5 5 13 23 46 102 77 0 C1 8 15 21 49 108 63 2 C2 11 18 19 60 96 59 3 C3 5 21 18 72 100 49 1 C4 78 88 38 39 17 4 2 C5 51 50 28 72 42 23 0 E1 74 62 35 45 27 18 5 E2 53 78 30 50 38 15 2 E3 7 37 37 64 84 35 2 E4 13 28 37 47 82 58 1 E5 6 13 27 45 104 69 2 N1 60 79 37 50 29 11 0 N2 24 54 45 70 51 18 4 N3 45 65 42 62 29 23 0 N4 32 65 38 73 36 18 4 N5 60 64 35 52 37 16 2 O1 1 7 23 51 80 100 4 O2 86 79 32 41 20 8 0 O3 3 17 14 59 102 68 3 O4 2 10 9 42 81 122 0 O5 89 94 33 23 18 6 3 > > > > cleanEx() > nameEx("long2wide") > ### * long2wide > > flush(stderr()); flush(stdout()) > > ### Name: long2wide > ### Title: Reshape Multiple Scores From Long to Wide > ### Aliases: long2wide > > ### ** Examples > > > # SINGLE GROUPING VARIABLE > dat_long <- as.data.frame(ChickWeight) # b/c groupedData class does weird things... > w1 <- long2wide(data = dat_long, vrb.nm = "weight", grp.nm = "Chick", + obs.nm = "Time") # NAs inserted for missing observations in some groups > w2 <- long2wide(data = dat_long, vrb.nm = "weight", grp.nm = "Chick", + obs.nm = "Time", sep = "_") > head(w1); head(w2) Chick weight.0 weight.2 weight.4 weight.6 weight.8 weight.10 weight.12 1 1 42 51 59 64 76 93 106 13 2 40 49 58 72 84 103 122 25 3 43 39 55 67 84 99 115 37 4 42 49 56 67 74 87 102 49 5 41 42 48 60 79 106 141 61 6 41 49 59 74 97 124 141 weight.14 weight.16 weight.18 weight.20 weight.21 Diet 1 125 149 171 199 205 1 13 138 162 187 209 215 1 25 138 163 187 198 202 1 37 108 136 154 160 157 1 49 164 197 199 220 223 1 61 148 155 160 160 157 1 Chick weight_0 weight_2 weight_4 weight_6 weight_8 weight_10 weight_12 1 1 42 51 59 64 76 93 106 13 2 40 49 58 72 84 103 122 25 3 43 39 55 67 84 99 115 37 4 42 49 56 67 74 87 102 49 5 41 42 48 60 79 106 141 61 6 41 49 59 74 97 124 141 weight_14 weight_16 weight_18 weight_20 weight_21 Diet 1 125 149 171 199 205 1 13 138 162 187 209 215 1 25 138 163 187 198 202 1 37 108 136 154 160 157 1 49 164 197 199 220 223 1 61 148 155 160 160 157 1 > w3 <- long2wide(data = dat_long, vrb.nm = "weight", grp.nm = "Chick", + obs.nm = "Time", sep = "_T", keep.attr = TRUE) > attributes(w3) $names [1] "Chick" "weight_T0" "weight_T2" "weight_T4" "weight_T6" [6] "weight_T8" "weight_T10" "weight_T12" "weight_T14" "weight_T16" [11] "weight_T18" "weight_T20" "weight_T21" "Diet" $row.names [1] 1 13 25 37 49 61 73 85 96 108 120 132 144 156 168 176 183 195 197 [20] 209 221 233 245 257 269 281 293 305 317 329 341 353 365 377 389 401 413 425 [39] 437 449 461 473 485 497 507 519 531 543 555 567 $class [1] "data.frame" $reshapeWide $reshapeWide$v.names [1] "weight" $reshapeWide$timevar [1] "Time" $reshapeWide$idvar [1] "Chick" $reshapeWide$times [1] 0 2 4 6 8 10 12 14 16 18 20 21 $reshapeWide$varying [,1] [,2] [,3] [,4] [,5] [,6] [1,] "weight_T0" "weight_T2" "weight_T4" "weight_T6" "weight_T8" "weight_T10" [,7] [,8] [,9] [,10] [,11] [1,] "weight_T12" "weight_T14" "weight_T16" "weight_T18" "weight_T20" [,12] [1,] "weight_T21" > > # MULTIPLE GROUPING VARIABLE > tmp <- psychTools::sai > grps <- interaction(tmp[1:3], drop = TRUE) > dups <- duplicated(grps) > dat_long <- tmp[!(dups), ] # for some reason there are duplicate groups in the data > vrb_nm <- str2str::pick(names(dat_long), val = c("study","time","id"), not = TRUE) > w4 <- long2wide(data = dat_long, vrb.nm = vrb_nm, grp.nm = c("study","id"), + obs.nm = "time") > w5 <- long2wide(data = dat_long, vrb.nm = vrb_nm, grp.nm = c("study","id"), + obs.nm = "time", colnames.by.obs = FALSE) # colnames sorted by `vrb.nm` instead > head(w4); head(w5) study id calm.1 secure.1 tense.1 regretful.1 at.ease.1 upset.1 worrying.1 1 AGES 1 3 3 2 1 2 1 1 2 AGES 2 3 3 2 2 3 2 1 3 AGES 3 3 3 2 1 3 1 2 4 AGES 4 3 3 1 1 3 1 1 5 AGES 5 4 4 1 1 3 1 1 6 AGES 6 4 4 1 1 4 1 1 rested.1 anxious.1 comfortable.1 confident.1 nervous.1 jittery.1 1 3 2 2 3 2 2 2 1 2 3 2 2 1 3 1 1 3 3 2 2 4 2 1 3 4 1 2 5 3 1 2 3 1 2 6 4 2 4 4 1 1 high.strung.1 relaxed.1 content.1 worried.1 rattled.1 joyful.1 pleasant.1 1 2 2 3 1 1 3 3 2 1 2 3 2 1 1 2 3 1 2 3 1 1 3 3 4 1 3 4 1 1 2 3 5 1 3 4 1 1 3 3 6 3 3 4 1 1 4 4 calm.2 secure.2 tense.2 regretful.2 at.ease.2 upset.2 worrying.2 rested.2 1 3 3 2 1 2 1 1 3 2 3 3 2 2 2 3 2 1 3 2 3 2 1 3 1 2 2 4 2 4 2 1 2 1 1 3 5 3 3 1 1 2 1 1 3 6 4 4 1 1 4 1 1 4 anxious.2 comfortable.2 confident.2 nervous.2 jittery.2 high.strung.2 1 2 3 3 2 2 2 2 2 2 2 2 1 1 3 1 3 3 2 2 1 4 2 4 3 2 3 2 5 1 3 3 1 1 1 6 4 4 4 1 1 2 relaxed.2 content.2 worried.2 rattled.2 joyful.2 pleasant.2 calm.3 secure.3 1 3 3 1 1 3 3 NA NA 2 3 2 3 1 2 3 NA NA 3 3 3 2 1 2 3 NA NA 4 2 4 1 3 3 3 NA NA 5 2 3 1 1 1 3 NA NA 6 4 4 1 1 4 4 NA NA tense.3 regretful.3 at.ease.3 upset.3 worrying.3 rested.3 anxious.3 1 NA NA NA NA NA NA NA 2 NA NA NA NA NA NA NA 3 NA NA NA NA NA NA NA 4 NA NA NA NA NA NA NA 5 NA NA NA NA NA NA NA 6 NA NA NA NA NA NA NA comfortable.3 confident.3 nervous.3 jittery.3 high.strung.3 relaxed.3 1 NA NA NA NA NA NA 2 NA NA NA NA NA NA 3 NA NA NA NA NA NA 4 NA NA NA NA NA NA 5 NA NA NA NA NA NA 6 NA NA NA NA NA NA content.3 worried.3 rattled.3 joyful.3 pleasant.3 calm.4 secure.4 tense.4 1 NA NA NA NA NA NA NA NA 2 NA NA NA NA NA NA NA NA 3 NA NA NA NA NA NA NA NA 4 NA NA NA NA NA NA NA NA 5 NA NA NA NA NA NA NA NA 6 NA NA NA NA NA NA NA NA regretful.4 at.ease.4 upset.4 worrying.4 rested.4 anxious.4 comfortable.4 1 NA NA NA NA NA NA NA 2 NA NA NA NA NA NA NA 3 NA NA NA NA NA NA NA 4 NA NA NA NA NA NA NA 5 NA NA NA NA NA NA NA 6 NA NA NA NA NA NA NA confident.4 nervous.4 jittery.4 high.strung.4 relaxed.4 content.4 worried.4 1 NA NA NA NA NA NA NA 2 NA NA NA NA NA NA NA 3 NA NA NA NA NA NA NA 4 NA NA NA NA NA NA NA 5 NA NA NA NA NA NA NA 6 NA NA NA NA NA NA NA rattled.4 joyful.4 pleasant.4 1 NA NA NA 2 NA NA NA 3 NA NA NA 4 NA NA NA 5 NA NA NA 6 NA NA NA study id calm.1 calm.2 calm.3 calm.4 secure.1 secure.2 secure.3 secure.4 1 AGES 1 3 3 NA NA 3 3 NA NA 2 AGES 2 3 3 NA NA 3 3 NA NA 3 AGES 3 3 2 NA NA 3 3 NA NA 4 AGES 4 3 2 NA NA 3 4 NA NA 5 AGES 5 4 3 NA NA 4 3 NA NA 6 AGES 6 4 4 NA NA 4 4 NA NA tense.1 tense.2 tense.3 tense.4 regretful.1 regretful.2 regretful.3 1 2 2 NA NA 1 1 NA 2 2 2 NA NA 2 2 NA 3 2 2 NA NA 1 1 NA 4 1 2 NA NA 1 1 NA 5 1 1 NA NA 1 1 NA 6 1 1 NA NA 1 1 NA regretful.4 at.ease.1 at.ease.2 at.ease.3 at.ease.4 upset.1 upset.2 upset.3 1 NA 2 2 NA NA 1 1 NA 2 NA 3 2 NA NA 2 3 NA 3 NA 3 3 NA NA 1 1 NA 4 NA 3 2 NA NA 1 1 NA 5 NA 3 2 NA NA 1 1 NA 6 NA 4 4 NA NA 1 1 NA upset.4 worrying.1 worrying.2 worrying.3 worrying.4 rested.1 rested.2 1 NA 1 1 NA NA 3 3 2 NA 1 2 NA NA 1 1 3 NA 2 2 NA NA 1 2 4 NA 1 1 NA NA 2 3 5 NA 1 1 NA NA 3 3 6 NA 1 1 NA NA 4 4 rested.3 rested.4 anxious.1 anxious.2 anxious.3 anxious.4 comfortable.1 1 NA NA 2 2 NA NA 2 2 NA NA 2 2 NA NA 3 3 NA NA 1 1 NA NA 3 4 NA NA 1 2 NA NA 3 5 NA NA 1 1 NA NA 2 6 NA NA 2 4 NA NA 4 comfortable.2 comfortable.3 comfortable.4 confident.1 confident.2 confident.3 1 3 NA NA 3 3 NA 2 2 NA NA 2 2 NA 3 3 NA NA 3 3 NA 4 4 NA NA 4 3 NA 5 3 NA NA 3 3 NA 6 4 NA NA 4 4 NA confident.4 nervous.1 nervous.2 nervous.3 nervous.4 jittery.1 jittery.2 1 NA 2 2 NA NA 2 2 2 NA 2 2 NA NA 1 1 3 NA 2 2 NA NA 2 2 4 NA 1 2 NA NA 2 3 5 NA 1 1 NA NA 2 1 6 NA 1 1 NA NA 1 1 jittery.3 jittery.4 high.strung.1 high.strung.2 high.strung.3 high.strung.4 1 NA NA 2 2 NA NA 2 NA NA 1 1 NA NA 3 NA NA 1 1 NA NA 4 NA NA 1 2 NA NA 5 NA NA 1 1 NA NA 6 NA NA 3 2 NA NA relaxed.1 relaxed.2 relaxed.3 relaxed.4 content.1 content.2 content.3 1 2 3 NA NA 3 3 NA 2 2 3 NA NA 3 2 NA 3 2 3 NA NA 3 3 NA 4 3 2 NA NA 4 4 NA 5 3 2 NA NA 4 3 NA 6 3 4 NA NA 4 4 NA content.4 worried.1 worried.2 worried.3 worried.4 rattled.1 rattled.2 1 NA 1 1 NA NA 1 1 2 NA 2 3 NA NA 1 1 3 NA 1 2 NA NA 1 1 4 NA 1 1 NA NA 1 3 5 NA 1 1 NA NA 1 1 6 NA 1 1 NA NA 1 1 rattled.3 rattled.4 joyful.1 joyful.2 joyful.3 joyful.4 pleasant.1 pleasant.2 1 NA NA 3 3 NA NA 3 3 2 NA NA 1 2 NA NA 2 3 3 NA NA 3 2 NA NA 3 3 4 NA NA 2 3 NA NA 3 3 5 NA NA 3 1 NA NA 3 3 6 NA NA 4 4 NA NA 4 4 pleasant.3 pleasant.4 1 NA NA 2 NA NA 3 NA NA 4 NA NA 5 NA NA 6 NA NA > > > > > cleanEx() > nameEx("make.dumNA") > ### * make.dumNA > > flush(stderr()); flush(stdout()) > > ### Name: make.dumNA > ### Title: Make Dummy Columns For Missing Data. > ### Aliases: make.dumNA > > ### ** Examples > > make.dumNA(data = airquality, vrb.nm = c("Ozone","Solar.R")) Ozone_m Solar.R_m 1 0 0 2 0 0 3 0 0 4 0 0 5 1 1 6 0 1 7 0 0 8 0 0 9 0 0 10 1 0 11 0 1 12 0 0 13 0 0 14 0 0 15 0 0 16 0 0 17 0 0 18 0 0 19 0 0 20 0 0 21 0 0 22 0 0 23 0 0 24 0 0 25 1 0 26 1 0 27 1 1 28 0 0 29 0 0 30 0 0 31 0 0 32 1 0 33 1 0 34 1 0 35 1 0 36 1 0 37 1 0 38 0 0 39 1 0 40 0 0 41 0 0 42 1 0 43 1 0 44 0 0 45 1 0 46 1 0 47 0 0 48 0 0 49 0 0 50 0 0 51 0 0 52 1 0 53 1 0 54 1 0 55 1 0 56 1 0 57 1 0 58 1 0 59 1 0 60 1 0 61 1 0 62 0 0 63 0 0 64 0 0 65 1 0 66 0 0 67 0 0 68 0 0 69 0 0 70 0 0 71 0 0 72 1 0 73 0 0 74 0 0 75 1 0 76 0 0 77 0 0 78 0 0 79 0 0 80 0 0 81 0 0 82 0 0 83 1 0 84 1 0 85 0 0 86 0 0 87 0 0 88 0 0 89 0 0 90 0 0 91 0 0 92 0 0 93 0 0 94 0 0 95 0 0 96 0 1 97 0 1 98 0 1 99 0 0 100 0 0 101 0 0 102 1 0 103 1 0 104 0 0 105 0 0 106 0 0 107 1 0 108 0 0 109 0 0 110 0 0 111 0 0 112 0 0 113 0 0 114 0 0 115 1 0 116 0 0 117 0 0 118 0 0 119 1 0 120 0 0 121 0 0 122 0 0 123 0 0 124 0 0 125 0 0 126 0 0 127 0 0 128 0 0 129 0 0 130 0 0 131 0 0 132 0 0 133 0 0 134 0 0 135 0 0 136 0 0 137 0 0 138 0 0 139 0 0 140 0 0 141 0 0 142 0 0 143 0 0 144 0 0 145 0 0 146 0 0 147 0 0 148 0 0 149 0 0 150 1 0 151 0 0 152 0 0 153 0 0 > make.dumNA(data = airquality, vrb.nm = c("Ozone","Solar.R"), + rtn.lgl = TRUE) # logical vectors returned Ozone_m Solar.R_m 1 FALSE FALSE 2 FALSE FALSE 3 FALSE FALSE 4 FALSE FALSE 5 TRUE TRUE 6 FALSE TRUE 7 FALSE FALSE 8 FALSE FALSE 9 FALSE FALSE 10 TRUE FALSE 11 FALSE TRUE 12 FALSE FALSE 13 FALSE FALSE 14 FALSE FALSE 15 FALSE FALSE 16 FALSE FALSE 17 FALSE FALSE 18 FALSE FALSE 19 FALSE FALSE 20 FALSE FALSE 21 FALSE FALSE 22 FALSE FALSE 23 FALSE FALSE 24 FALSE FALSE 25 TRUE FALSE 26 TRUE FALSE 27 TRUE TRUE 28 FALSE FALSE 29 FALSE FALSE 30 FALSE FALSE 31 FALSE FALSE 32 TRUE FALSE 33 TRUE FALSE 34 TRUE FALSE 35 TRUE FALSE 36 TRUE FALSE 37 TRUE FALSE 38 FALSE FALSE 39 TRUE FALSE 40 FALSE FALSE 41 FALSE FALSE 42 TRUE FALSE 43 TRUE FALSE 44 FALSE FALSE 45 TRUE FALSE 46 TRUE FALSE 47 FALSE FALSE 48 FALSE FALSE 49 FALSE FALSE 50 FALSE FALSE 51 FALSE FALSE 52 TRUE FALSE 53 TRUE FALSE 54 TRUE FALSE 55 TRUE FALSE 56 TRUE FALSE 57 TRUE FALSE 58 TRUE FALSE 59 TRUE FALSE 60 TRUE FALSE 61 TRUE FALSE 62 FALSE FALSE 63 FALSE FALSE 64 FALSE FALSE 65 TRUE FALSE 66 FALSE FALSE 67 FALSE FALSE 68 FALSE FALSE 69 FALSE FALSE 70 FALSE FALSE 71 FALSE FALSE 72 TRUE FALSE 73 FALSE FALSE 74 FALSE FALSE 75 TRUE FALSE 76 FALSE FALSE 77 FALSE FALSE 78 FALSE FALSE 79 FALSE FALSE 80 FALSE FALSE 81 FALSE FALSE 82 FALSE FALSE 83 TRUE FALSE 84 TRUE FALSE 85 FALSE FALSE 86 FALSE FALSE 87 FALSE FALSE 88 FALSE FALSE 89 FALSE FALSE 90 FALSE FALSE 91 FALSE FALSE 92 FALSE FALSE 93 FALSE FALSE 94 FALSE FALSE 95 FALSE FALSE 96 FALSE TRUE 97 FALSE TRUE 98 FALSE TRUE 99 FALSE FALSE 100 FALSE FALSE 101 FALSE FALSE 102 TRUE FALSE 103 TRUE FALSE 104 FALSE FALSE 105 FALSE FALSE 106 FALSE FALSE 107 TRUE FALSE 108 FALSE FALSE 109 FALSE FALSE 110 FALSE FALSE 111 FALSE FALSE 112 FALSE FALSE 113 FALSE FALSE 114 FALSE FALSE 115 TRUE FALSE 116 FALSE FALSE 117 FALSE FALSE 118 FALSE FALSE 119 TRUE FALSE 120 FALSE FALSE 121 FALSE FALSE 122 FALSE FALSE 123 FALSE FALSE 124 FALSE FALSE 125 FALSE FALSE 126 FALSE FALSE 127 FALSE FALSE 128 FALSE FALSE 129 FALSE FALSE 130 FALSE FALSE 131 FALSE FALSE 132 FALSE FALSE 133 FALSE FALSE 134 FALSE FALSE 135 FALSE FALSE 136 FALSE FALSE 137 FALSE FALSE 138 FALSE FALSE 139 FALSE FALSE 140 FALSE FALSE 141 FALSE FALSE 142 FALSE FALSE 143 FALSE FALSE 144 FALSE FALSE 145 FALSE FALSE 146 FALSE FALSE 147 FALSE FALSE 148 FALSE FALSE 149 FALSE FALSE 150 TRUE FALSE 151 FALSE FALSE 152 FALSE FALSE 153 FALSE FALSE > make.dumNA(data = airquality, vrb.nm = c("Ozone","Solar.R"), + ov = TRUE, suffix = "_o") # 1 = observed value Ozone_o Solar.R_o 1 1 1 2 1 1 3 1 1 4 1 1 5 0 0 6 1 0 7 1 1 8 1 1 9 1 1 10 0 1 11 1 0 12 1 1 13 1 1 14 1 1 15 1 1 16 1 1 17 1 1 18 1 1 19 1 1 20 1 1 21 1 1 22 1 1 23 1 1 24 1 1 25 0 1 26 0 1 27 0 0 28 1 1 29 1 1 30 1 1 31 1 1 32 0 1 33 0 1 34 0 1 35 0 1 36 0 1 37 0 1 38 1 1 39 0 1 40 1 1 41 1 1 42 0 1 43 0 1 44 1 1 45 0 1 46 0 1 47 1 1 48 1 1 49 1 1 50 1 1 51 1 1 52 0 1 53 0 1 54 0 1 55 0 1 56 0 1 57 0 1 58 0 1 59 0 1 60 0 1 61 0 1 62 1 1 63 1 1 64 1 1 65 0 1 66 1 1 67 1 1 68 1 1 69 1 1 70 1 1 71 1 1 72 0 1 73 1 1 74 1 1 75 0 1 76 1 1 77 1 1 78 1 1 79 1 1 80 1 1 81 1 1 82 1 1 83 0 1 84 0 1 85 1 1 86 1 1 87 1 1 88 1 1 89 1 1 90 1 1 91 1 1 92 1 1 93 1 1 94 1 1 95 1 1 96 1 0 97 1 0 98 1 0 99 1 1 100 1 1 101 1 1 102 0 1 103 0 1 104 1 1 105 1 1 106 1 1 107 0 1 108 1 1 109 1 1 110 1 1 111 1 1 112 1 1 113 1 1 114 1 1 115 0 1 116 1 1 117 1 1 118 1 1 119 0 1 120 1 1 121 1 1 122 1 1 123 1 1 124 1 1 125 1 1 126 1 1 127 1 1 128 1 1 129 1 1 130 1 1 131 1 1 132 1 1 133 1 1 134 1 1 135 1 1 136 1 1 137 1 1 138 1 1 139 1 1 140 1 1 141 1 1 142 1 1 143 1 1 144 1 1 145 1 1 146 1 1 147 1 1 148 1 1 149 1 1 150 0 1 151 1 1 152 1 1 153 1 1 > > > > cleanEx() > nameEx("make.dummy") > ### * make.dummy > > flush(stderr()); flush(stdout()) > > ### Name: make.dummy > ### Title: Make Dummy Columns > ### Aliases: make.dummy > > ### ** Examples > > make.dummy(attitude$"rating" > 50) # ugly colnames c.0..1..1..1..1..0..1..1..1..1..1..1..1..1..1..1..1..1..1..0.. 1 0 2 1 3 1 4 1 5 1 6 0 7 1 8 1 9 1 10 1 11 1 12 1 13 1 14 1 15 1 16 1 17 1 18 1 19 1 20 0 21 0 22 1 23 1 24 0 25 1 26 1 27 1 28 0 29 1 30 1 > make.dummy("rating_50plus" = attitude$"rating" > 50, + "advance_50minus" = attitude$"advance" < 50) rating_50plus advance_50minus 1 0 1 2 1 1 3 1 1 4 1 1 5 1 1 6 0 1 7 1 1 8 1 1 9 1 1 10 1 1 11 1 1 12 1 1 13 1 1 14 1 1 15 1 1 16 1 1 17 1 0 18 1 0 19 1 1 20 0 0 21 0 1 22 1 1 23 1 1 24 0 1 25 1 1 26 1 0 27 1 1 28 0 1 29 1 0 30 1 1 > make.dummy("rating_50plus" = attitude$"rating" > 50, + "advance_50minus" = attitude$"advance" < 50, rtn.lgl = TRUE) rating_50plus advance_50minus 1 FALSE TRUE 2 TRUE TRUE 3 TRUE TRUE 4 TRUE TRUE 5 TRUE TRUE 6 FALSE TRUE 7 TRUE TRUE 8 TRUE TRUE 9 TRUE TRUE 10 TRUE TRUE 11 TRUE TRUE 12 TRUE TRUE 13 TRUE TRUE 14 TRUE TRUE 15 TRUE TRUE 16 TRUE TRUE 17 TRUE FALSE 18 TRUE FALSE 19 TRUE TRUE 20 FALSE FALSE 21 FALSE TRUE 22 TRUE TRUE 23 TRUE TRUE 24 FALSE TRUE 25 TRUE TRUE 26 TRUE FALSE 27 TRUE TRUE 28 FALSE TRUE 29 TRUE FALSE 30 TRUE TRUE > ## Not run: > ##D make.dummy("rating_50plus" = attitude$"rating" > 50, > ##D "mpg_20plus" = mtcars$"mpg" > 20) > ## End(Not run) > > > > cleanEx() > nameEx("make.fun_if") > ### * make.fun_if > > flush(stderr()); flush(stdout()) > > ### Name: make.fun_if > ### Title: Make a Function Conditional on Frequency of Observed Values > ### Aliases: make.fun_if > > ### ** Examples > > > # SD > sd_if <- make.fun_if(fun = sd, na.rm = TRUE) # always have na.rm = TRUE > sd_if(x = airquality[[1]], ov.min = .75) # proportion of observed values [1] 32.98788 > sd_if(x = airquality[[1]], ov.min = 116, + prop = FALSE) # count of observed values [1] 32.98788 > sd_if(x = airquality[[1]], ov.min = 116, prop = FALSE, + inclusive = FALSE) # not include ov.min values itself [1] NA > > # skewness > skew_if <- make.fun_if(fun = psych::skew, type = 1) # always have type = 1 > skew_if(x = airquality[[1]], ov.min = .75) # proportion of observed values [1] 1.225681 > skew_if(x = airquality[[1]], ov.min = 116, + prop = FALSE) # count of observed values [1] 1.225681 > skew_if(x = airquality[[1]], ov.min = 116, prop = FALSE, + inclusive = FALSE) # not include ov.min values itself [1] NA > > # mode > popular <- function(x) names(sort(table(x), decreasing = TRUE))[1] > popular_if <- make.fun_if(fun = popular) # works with character vectors too > popular_if(x = c(unlist(dimnames(HairEyeColor)), rep.int(x = NA, times = 10)), + ov.min = .50) [1] "Brown" > popular_if(x = c(unlist(dimnames(HairEyeColor)), rep.int(x = NA, times = 10)), + ov.min = .60) [1] NA > > > > cleanEx() > nameEx("make.product") > ### * make.product > > flush(stderr()); flush(stdout()) > > ### Name: make.product > ### Title: Make Product Terms (e.g., interactions) > ### Aliases: make.product > > ### ** Examples > > make.product(data = attitude, x.nm = c("complaints","privileges"), + m.nm = "learning", center.x = TRUE, center.m = TRUE, + suffix.x = "_c", suffix.m = "_c") # with grand-mean centering complaints_c:learning_c privileges_c:learning_c 1 270.9200000 401.7488889 2 6.1533333 5.0488889 3 42.9533333 187.8155556 4 33.7200000 76.1822222 5 109.8200000 27.6155556 6 143.4533333 51.1155556 7 -0.1466667 4.0822222 8 -11.4800000 4.2822222 9 163.7533333 200.6155556 10 52.4533333 76.1822222 11 -22.2133333 -0.2177778 12 114.6200000 106.5155556 13 66.0866667 -55.5511111 14 -186.4133333 -339.4844444 15 162.5866667 13.5488889 16 365.8200000 -48.9844444 17 232.4533333 137.2822222 18 -122.9800000 221.1155556 19 2.1533333 -4.5177778 20 20.3533333 -35.1844444 21 594.9533333 450.3155556 22 -31.5466667 -6.3844444 23 3.8200000 7.2155556 24 -48.3466667 -18.1844444 25 105.4200000 93.1488889 26 68.9866667 85.3488889 27 148.1200000 85.8155556 28 109.1200000 103.8155556 29 269.2533333 261.4488889 30 40.5533333 -37.2177778 > make.product(data = attitude, x.nm = c("complaints","privileges"), + m.nm = c("learning","raises"), combo = TRUE) # all possible combinations complaints:learning privileges:learning complaints:raises privileges:raises 1 1989 1170 3111 1830 2 3456 2754 4032 3213 3 4830 4692 5320 5168 4 2961 2115 3402 2430 5 5148 3696 5538 3976 6 2420 2156 2970 2646 7 3752 2352 4422 2772 8 4125 2750 5250 3500 9 5494 4824 5822 5112 10 2867 2115 3782 2790 11 3074 3074 3074 3074 12 2340 1833 3540 2773 13 2604 2394 3410 3135 14 3735 3735 4897 4897 15 5544 3888 6083 4266 16 6480 3600 5400 3000 17 5865 4416 6715 5056 18 4500 4875 3300 3575 19 3990 2622 5250 3450 20 3132 3672 3712 4352 21 1360 1122 1720 1419 22 3782 3224 4026 3432 23 3300 2600 4158 3276 24 2146 2436 1850 2100 25 2592 2016 3564 2772 26 4851 4158 6776 5808 27 5550 4292 6000 4640 28 2565 1980 2907 2244 29 6035 5041 6545 5467 30 4838 2301 5248 2496 > make.product(data = attitude, x.nm = c("complaints","privileges"), + m.nm = c("learning","raises"), combo = FALSE) # only combinations "in parallel" complaints:learning privileges:raises 1 1989 1830 2 3456 3213 3 4830 5168 4 2961 2430 5 5148 3976 6 2420 2646 7 3752 2772 8 4125 3500 9 5494 5112 10 2867 2790 11 3074 3074 12 2340 2773 13 2604 3135 14 3735 4897 15 5544 4266 16 6480 3000 17 5865 5056 18 4500 3575 19 3990 3450 20 3132 4352 21 1360 1419 22 3782 3432 23 3300 3276 24 2146 2100 25 2592 2772 26 4851 5808 27 5550 4640 28 2565 2244 29 6035 5467 30 4838 2496 > > > > cleanEx() > nameEx("mean_if") > ### * mean_if > > flush(stderr()); flush(stdout()) > > ### Name: mean_if > ### Title: Mean Conditional on Minimum Frequency of Observed Values > ### Aliases: mean_if > > ### ** Examples > > mean_if(x = airquality[[1]], ov.min = .75) # proportion of observed values [1] 42.12931 > mean_if(x = airquality[[1]], ov.min = 116, + prop = FALSE) # count of observe values [1] 42.12931 > mean_if(x = airquality[[1]], ov.min = 116, prop = FALSE, + inclusive = FALSE) # not include ov.min value itself [1] NA > mean_if(x = c(TRUE, NA, FALSE, NA), + ov.min = .50) # works with logical vectors as well as numeric [1] 0.5 > > > > cleanEx() > nameEx("mode2") > ### * mode2 > > flush(stderr()); flush(stdout()) > > ### Name: mode2 > ### Title: Statistical Mode of a Numeric Vector > ### Aliases: mode2 > > ### ** Examples > > > # ONE MODE > vec <- c(7,8,9,7,8,9,9) > mode2(vec) [1] 9 > mode2(vec, multiple = TRUE) [1] 9 > > # TWO MODES > vec <- c(7,8,9,7,8,9,8,9) > mode2(vec) [1] 8 > mode2(vec, multiple = TRUE) [1] 8 9 > > # WITH NA > vec <- c(7,8,9,7,8,9,NA,9) > mode2(vec) [1] 9 > mode2(vec, na.rm = TRUE) [1] 9 > vec <- c(7,8,9,7,8,9,NA,9,NA,NA) > mode2(vec) [1] 9 > mode2(vec, multiple = TRUE) [1] 9 NA > > > > cleanEx() > nameEx("ncases") > ### * ncases > > flush(stderr()); flush(stdout()) > > ### Name: ncases > ### Title: Number of Cases in Data > ### Aliases: ncases > > ### ** Examples > > vrb_nm <- c("Ozone","Solar.R","Wind") > nrow(airquality[vrb_nm]) # number of cases regardless of missing data [1] 153 > sum(complete.cases(airquality[vrb_nm])) # number of complete cases [1] 111 > ncases(data = airquality, vrb.nm = c("Ozone","Solar.R","Wind"), + ov.min = 2/3) # number of rows with at least 2 of the 3 variables observed [1] 151 > > > > cleanEx() > nameEx("nom2dum") > ### * nom2dum > > flush(stderr()); flush(stdout()) > > ### Name: nom2dum > ### Title: Nominal Variable to Dummy Variables > ### Aliases: nom2dum > > ### ** Examples > > nom2dum(infert$"education") # default 0-5yrs 6-11yrs 12+ yrs 1 1 0 0 2 1 0 0 3 1 0 0 4 1 0 0 5 0 1 0 6 0 1 0 7 0 1 0 8 0 1 0 9 0 1 0 10 0 1 0 11 0 1 0 12 0 1 0 13 0 1 0 14 0 1 0 15 0 1 0 16 0 1 0 17 0 1 0 18 0 1 0 19 0 1 0 20 0 1 0 21 0 1 0 22 0 1 0 23 0 1 0 24 0 1 0 25 0 1 0 26 0 1 0 27 0 1 0 28 0 1 0 29 0 1 0 30 0 1 0 31 0 1 0 32 0 1 0 33 0 1 0 34 0 1 0 35 0 1 0 36 0 1 0 37 0 1 0 38 0 1 0 39 0 1 0 40 0 1 0 41 0 1 0 42 0 1 0 43 0 1 0 44 0 1 0 45 0 0 1 46 0 0 1 47 0 0 1 48 0 0 1 49 0 0 1 50 0 0 1 51 0 0 1 52 0 0 1 53 0 0 1 54 0 0 1 55 0 0 1 56 0 0 1 57 0 0 1 58 0 0 1 59 0 0 1 60 0 0 1 61 0 0 1 62 0 0 1 63 0 0 1 64 0 0 1 65 0 0 1 66 0 0 1 67 0 0 1 68 0 0 1 69 0 0 1 70 0 0 1 71 0 0 1 72 0 0 1 73 0 0 1 74 0 0 1 75 0 0 1 76 0 0 1 77 0 0 1 78 0 0 1 79 0 0 1 80 0 0 1 81 0 0 1 82 0 0 1 83 0 0 1 84 1 0 0 85 1 0 0 86 1 0 0 87 1 0 0 88 0 1 0 89 0 1 0 90 0 1 0 91 0 1 0 92 0 1 0 93 0 1 0 94 0 1 0 95 0 1 0 96 0 1 0 97 0 1 0 98 0 1 0 99 0 1 0 100 0 1 0 101 0 1 0 102 0 1 0 103 0 1 0 104 0 1 0 105 0 1 0 106 0 1 0 107 0 1 0 108 0 1 0 109 0 1 0 110 0 1 0 111 0 1 0 112 0 1 0 113 0 1 0 114 0 1 0 115 0 1 0 116 0 1 0 117 0 1 0 118 0 1 0 119 0 1 0 120 0 1 0 121 0 1 0 122 0 1 0 123 0 1 0 124 0 1 0 125 0 1 0 126 0 1 0 127 0 1 0 128 0 0 1 129 0 0 1 130 0 0 1 131 0 0 1 132 0 0 1 133 0 0 1 134 0 0 1 135 0 0 1 136 0 0 1 137 0 0 1 138 0 0 1 139 0 0 1 140 0 0 1 141 0 0 1 142 0 0 1 143 0 0 1 144 0 0 1 145 0 0 1 146 0 0 1 147 0 0 1 148 0 0 1 149 0 0 1 150 0 0 1 151 0 0 1 152 0 0 1 153 0 0 1 154 0 0 1 155 0 0 1 156 0 0 1 157 0 0 1 158 0 0 1 159 0 0 1 160 0 0 1 161 0 0 1 162 0 0 1 163 0 0 1 164 0 0 1 165 0 0 1 166 1 0 0 167 1 0 0 168 1 0 0 169 1 0 0 170 0 1 0 171 0 1 0 172 0 1 0 173 0 1 0 174 0 1 0 175 0 1 0 176 0 1 0 177 0 1 0 178 0 1 0 179 0 1 0 180 0 1 0 181 0 1 0 182 0 1 0 183 0 1 0 184 0 1 0 185 0 1 0 186 0 1 0 187 0 1 0 188 0 1 0 189 0 1 0 190 0 1 0 191 0 1 0 192 0 1 0 193 0 1 0 194 0 1 0 195 0 1 0 196 0 1 0 197 0 1 0 198 0 1 0 199 0 1 0 200 0 1 0 201 0 1 0 202 0 1 0 203 0 1 0 204 0 1 0 205 0 1 0 206 0 1 0 207 0 1 0 208 0 1 0 209 0 1 0 210 0 0 1 211 0 0 1 212 0 0 1 213 0 0 1 214 0 0 1 215 0 0 1 216 0 0 1 217 0 0 1 218 0 0 1 219 0 0 1 220 0 0 1 221 0 0 1 222 0 0 1 223 0 0 1 224 0 0 1 225 0 0 1 226 0 0 1 227 0 0 1 228 0 0 1 229 0 0 1 230 0 0 1 231 0 0 1 232 0 0 1 233 0 0 1 234 0 0 1 235 0 0 1 236 0 0 1 237 0 0 1 238 0 0 1 239 0 0 1 240 0 0 1 241 0 0 1 242 0 0 1 243 0 0 1 244 0 0 1 245 0 0 1 246 0 0 1 247 0 0 1 248 0 0 1 > nom2dum(infert$"education", prefix = "edu_") # use of the `prefix` argument edu_0-5yrs edu_6-11yrs edu_12+ yrs 1 1 0 0 2 1 0 0 3 1 0 0 4 1 0 0 5 0 1 0 6 0 1 0 7 0 1 0 8 0 1 0 9 0 1 0 10 0 1 0 11 0 1 0 12 0 1 0 13 0 1 0 14 0 1 0 15 0 1 0 16 0 1 0 17 0 1 0 18 0 1 0 19 0 1 0 20 0 1 0 21 0 1 0 22 0 1 0 23 0 1 0 24 0 1 0 25 0 1 0 26 0 1 0 27 0 1 0 28 0 1 0 29 0 1 0 30 0 1 0 31 0 1 0 32 0 1 0 33 0 1 0 34 0 1 0 35 0 1 0 36 0 1 0 37 0 1 0 38 0 1 0 39 0 1 0 40 0 1 0 41 0 1 0 42 0 1 0 43 0 1 0 44 0 1 0 45 0 0 1 46 0 0 1 47 0 0 1 48 0 0 1 49 0 0 1 50 0 0 1 51 0 0 1 52 0 0 1 53 0 0 1 54 0 0 1 55 0 0 1 56 0 0 1 57 0 0 1 58 0 0 1 59 0 0 1 60 0 0 1 61 0 0 1 62 0 0 1 63 0 0 1 64 0 0 1 65 0 0 1 66 0 0 1 67 0 0 1 68 0 0 1 69 0 0 1 70 0 0 1 71 0 0 1 72 0 0 1 73 0 0 1 74 0 0 1 75 0 0 1 76 0 0 1 77 0 0 1 78 0 0 1 79 0 0 1 80 0 0 1 81 0 0 1 82 0 0 1 83 0 0 1 84 1 0 0 85 1 0 0 86 1 0 0 87 1 0 0 88 0 1 0 89 0 1 0 90 0 1 0 91 0 1 0 92 0 1 0 93 0 1 0 94 0 1 0 95 0 1 0 96 0 1 0 97 0 1 0 98 0 1 0 99 0 1 0 100 0 1 0 101 0 1 0 102 0 1 0 103 0 1 0 104 0 1 0 105 0 1 0 106 0 1 0 107 0 1 0 108 0 1 0 109 0 1 0 110 0 1 0 111 0 1 0 112 0 1 0 113 0 1 0 114 0 1 0 115 0 1 0 116 0 1 0 117 0 1 0 118 0 1 0 119 0 1 0 120 0 1 0 121 0 1 0 122 0 1 0 123 0 1 0 124 0 1 0 125 0 1 0 126 0 1 0 127 0 1 0 128 0 0 1 129 0 0 1 130 0 0 1 131 0 0 1 132 0 0 1 133 0 0 1 134 0 0 1 135 0 0 1 136 0 0 1 137 0 0 1 138 0 0 1 139 0 0 1 140 0 0 1 141 0 0 1 142 0 0 1 143 0 0 1 144 0 0 1 145 0 0 1 146 0 0 1 147 0 0 1 148 0 0 1 149 0 0 1 150 0 0 1 151 0 0 1 152 0 0 1 153 0 0 1 154 0 0 1 155 0 0 1 156 0 0 1 157 0 0 1 158 0 0 1 159 0 0 1 160 0 0 1 161 0 0 1 162 0 0 1 163 0 0 1 164 0 0 1 165 0 0 1 166 1 0 0 167 1 0 0 168 1 0 0 169 1 0 0 170 0 1 0 171 0 1 0 172 0 1 0 173 0 1 0 174 0 1 0 175 0 1 0 176 0 1 0 177 0 1 0 178 0 1 0 179 0 1 0 180 0 1 0 181 0 1 0 182 0 1 0 183 0 1 0 184 0 1 0 185 0 1 0 186 0 1 0 187 0 1 0 188 0 1 0 189 0 1 0 190 0 1 0 191 0 1 0 192 0 1 0 193 0 1 0 194 0 1 0 195 0 1 0 196 0 1 0 197 0 1 0 198 0 1 0 199 0 1 0 200 0 1 0 201 0 1 0 202 0 1 0 203 0 1 0 204 0 1 0 205 0 1 0 206 0 1 0 207 0 1 0 208 0 1 0 209 0 1 0 210 0 0 1 211 0 0 1 212 0 0 1 213 0 0 1 214 0 0 1 215 0 0 1 216 0 0 1 217 0 0 1 218 0 0 1 219 0 0 1 220 0 0 1 221 0 0 1 222 0 0 1 223 0 0 1 224 0 0 1 225 0 0 1 226 0 0 1 227 0 0 1 228 0 0 1 229 0 0 1 230 0 0 1 231 0 0 1 232 0 0 1 233 0 0 1 234 0 0 1 235 0 0 1 236 0 0 1 237 0 0 1 238 0 0 1 239 0 0 1 240 0 0 1 241 0 0 1 242 0 0 1 243 0 0 1 244 0 0 1 245 0 0 1 246 0 0 1 247 0 0 1 248 0 0 1 > nom2dum(nom = infert$"education", yes = "one", no = "zero", + rtn.fct = TRUE) # returns factor columns 0-5yrs 6-11yrs 12+ yrs 1 one zero zero 2 one zero zero 3 one zero zero 4 one zero zero 5 zero one zero 6 zero one zero 7 zero one zero 8 zero one zero 9 zero one zero 10 zero one zero 11 zero one zero 12 zero one zero 13 zero one zero 14 zero one zero 15 zero one zero 16 zero one zero 17 zero one zero 18 zero one zero 19 zero one zero 20 zero one zero 21 zero one zero 22 zero one zero 23 zero one zero 24 zero one zero 25 zero one zero 26 zero one zero 27 zero one zero 28 zero one zero 29 zero one zero 30 zero one zero 31 zero one zero 32 zero one zero 33 zero one zero 34 zero one zero 35 zero one zero 36 zero one zero 37 zero one zero 38 zero one zero 39 zero one zero 40 zero one zero 41 zero one zero 42 zero one zero 43 zero one zero 44 zero one zero 45 zero zero one 46 zero zero one 47 zero zero one 48 zero zero one 49 zero zero one 50 zero zero one 51 zero zero one 52 zero zero one 53 zero zero one 54 zero zero one 55 zero zero one 56 zero zero one 57 zero zero one 58 zero zero one 59 zero zero one 60 zero zero one 61 zero zero one 62 zero zero one 63 zero zero one 64 zero zero one 65 zero zero one 66 zero zero one 67 zero zero one 68 zero zero one 69 zero zero one 70 zero zero one 71 zero zero one 72 zero zero one 73 zero zero one 74 zero zero one 75 zero zero one 76 zero zero one 77 zero zero one 78 zero zero one 79 zero zero one 80 zero zero one 81 zero zero one 82 zero zero one 83 zero zero one 84 one zero zero 85 one zero zero 86 one zero zero 87 one zero zero 88 zero one zero 89 zero one zero 90 zero one zero 91 zero one zero 92 zero one zero 93 zero one zero 94 zero one zero 95 zero one zero 96 zero one zero 97 zero one zero 98 zero one zero 99 zero one zero 100 zero one zero 101 zero one zero 102 zero one zero 103 zero one zero 104 zero one zero 105 zero one zero 106 zero one zero 107 zero one zero 108 zero one zero 109 zero one zero 110 zero one zero 111 zero one zero 112 zero one zero 113 zero one zero 114 zero one zero 115 zero one zero 116 zero one zero 117 zero one zero 118 zero one zero 119 zero one zero 120 zero one zero 121 zero one zero 122 zero one zero 123 zero one zero 124 zero one zero 125 zero one zero 126 zero one zero 127 zero one zero 128 zero zero one 129 zero zero one 130 zero zero one 131 zero zero one 132 zero zero one 133 zero zero one 134 zero zero one 135 zero zero one 136 zero zero one 137 zero zero one 138 zero zero one 139 zero zero one 140 zero zero one 141 zero zero one 142 zero zero one 143 zero zero one 144 zero zero one 145 zero zero one 146 zero zero one 147 zero zero one 148 zero zero one 149 zero zero one 150 zero zero one 151 zero zero one 152 zero zero one 153 zero zero one 154 zero zero one 155 zero zero one 156 zero zero one 157 zero zero one 158 zero zero one 159 zero zero one 160 zero zero one 161 zero zero one 162 zero zero one 163 zero zero one 164 zero zero one 165 zero zero one 166 one zero zero 167 one zero zero 168 one zero zero 169 one zero zero 170 zero one zero 171 zero one zero 172 zero one zero 173 zero one zero 174 zero one zero 175 zero one zero 176 zero one zero 177 zero one zero 178 zero one zero 179 zero one zero 180 zero one zero 181 zero one zero 182 zero one zero 183 zero one zero 184 zero one zero 185 zero one zero 186 zero one zero 187 zero one zero 188 zero one zero 189 zero one zero 190 zero one zero 191 zero one zero 192 zero one zero 193 zero one zero 194 zero one zero 195 zero one zero 196 zero one zero 197 zero one zero 198 zero one zero 199 zero one zero 200 zero one zero 201 zero one zero 202 zero one zero 203 zero one zero 204 zero one zero 205 zero one zero 206 zero one zero 207 zero one zero 208 zero one zero 209 zero one zero 210 zero zero one 211 zero zero one 212 zero zero one 213 zero zero one 214 zero zero one 215 zero zero one 216 zero zero one 217 zero zero one 218 zero zero one 219 zero zero one 220 zero zero one 221 zero zero one 222 zero zero one 223 zero zero one 224 zero zero one 225 zero zero one 226 zero zero one 227 zero zero one 228 zero zero one 229 zero zero one 230 zero zero one 231 zero zero one 232 zero zero one 233 zero zero one 234 zero zero one 235 zero zero one 236 zero zero one 237 zero zero one 238 zero zero one 239 zero zero one 240 zero zero one 241 zero zero one 242 zero zero one 243 zero zero one 244 zero zero one 245 zero zero one 246 zero zero one 247 zero zero one 248 zero zero one > > > > cleanEx() > nameEx("partial.cases") > ### * partial.cases > > flush(stderr()); flush(stdout()) > > ### Name: partial.cases > ### Title: Find Partial Cases > ### Aliases: partial.cases > > ### ** Examples > > cases2keep <- partial.cases(data = airquality, + vrb.nm = c("Ozone","Solar.R","Wind"), ov.min = .66) > airquality2 <- airquality[cases2keep, ] # all cases with 2/3 variables observed > cases2keep <- partial.cases(data = airquality, + vrb.nm = c("Ozone","Solar.R","Wind"), ov.min = 1, prop = TRUE, inclusive = TRUE) > complete_cases <- complete.cases(airquality) > identical(x = unname(cases2keep), + y = complete_cases) # partial.cases(ov.min = 1, prop = TRUE, [1] TRUE > # inclusive = TRUE) = complete.cases() > > > > cleanEx() > nameEx("pomp") > ### * pomp > > flush(stderr()); flush(stdout()) > > ### Name: pomp > ### Title: Recode a Numeric Vector to Percentage of Maximum Possible (POMP) > ### Units > ### Aliases: pomp > > ### ** Examples > > vec <- psych::bfi[[1]] > pomp(x = vec, mini = 1, maxi = 6) # absolute POMP units [1] 20 20 80 60 20 100 20 60 60 20 60 20 80 80 60 60 60 80 [19] 60 60 80 0 0 20 60 0 20 20 20 60 0 20 0 20 60 80 [37] 0 0 0 0 0 80 0 80 20 0 80 20 0 0 0 0 40 60 [55] 0 0 0 40 0 40 20 20 0 60 20 20 0 60 20 0 60 20 [73] 60 0 40 20 20 80 0 20 0 0 0 0 0 60 0 0 0 40 [91] 0 0 20 0 20 20 40 0 60 20 20 40 20 0 0 0 0 100 [109] 20 40 0 NA 0 0 0 60 0 80 20 0 60 20 100 20 20 20 [127] 20 20 20 60 20 40 60 20 0 0 0 0 0 40 20 20 20 0 [145] 80 20 0 20 20 0 0 0 20 60 20 40 0 20 20 20 0 0 [163] 20 0 0 20 20 20 0 0 40 60 0 20 20 0 0 100 60 40 [181] 60 0 40 60 40 0 0 20 20 0 20 0 20 80 0 0 20 20 [199] 20 0 20 0 40 20 0 20 20 0 0 20 20 0 80 40 0 20 [217] 0 20 80 80 20 0 0 0 0 0 40 20 0 20 80 20 80 40 [235] 40 20 0 0 40 40 0 20 60 0 60 40 80 40 60 40 40 60 [253] 100 40 80 40 80 0 0 60 80 40 100 20 60 40 20 0 0 60 [271] 40 60 0 0 20 40 0 0 60 20 40 20 20 20 40 0 20 20 [289] 0 0 0 20 40 40 0 80 0 40 60 40 0 0 20 40 60 20 [307] 80 0 40 0 40 0 20 0 40 20 40 40 20 20 20 20 80 60 [325] 40 0 20 60 0 20 0 20 60 0 20 40 40 0 20 0 40 0 [343] 20 0 20 0 20 80 20 20 60 20 40 20 20 0 60 0 20 60 [361] 40 0 0 0 60 0 NA 60 0 100 0 40 0 20 0 0 0 60 [379] 20 20 60 60 20 20 80 0 20 60 0 60 20 20 0 0 20 40 [397] 60 0 20 0 40 0 40 20 60 60 0 0 40 40 0 40 0 0 [415] 40 60 0 40 40 0 0 40 0 80 80 20 NA 0 20 20 20 20 [433] 20 20 20 20 20 20 60 20 80 20 0 60 0 20 20 80 100 0 [451] 60 60 40 80 0 20 0 20 20 80 20 20 0 40 0 0 20 20 [469] 0 0 40 20 0 20 20 60 100 20 20 0 0 0 0 40 20 0 [487] 20 0 20 0 0 60 0 20 0 60 60 20 60 0 40 0 80 80 [505] 20 20 0 0 20 0 40 100 20 40 20 20 0 0 0 40 60 0 [523] 20 0 20 100 0 0 20 60 0 40 0 20 0 0 40 80 80 40 [541] 40 60 0 0 20 20 80 20 0 60 0 60 60 0 40 0 20 0 [559] 0 0 20 80 40 0 0 0 0 0 80 20 0 0 40 100 0 40 [577] 0 40 0 20 60 0 40 0 20 20 60 20 100 0 0 0 20 60 [595] 0 0 80 NA 20 40 0 20 40 60 20 0 40 0 0 0 0 0 [613] 0 20 20 0 20 40 0 20 0 20 20 60 20 20 20 40 40 60 [631] 20 60 20 40 0 0 0 60 0 20 40 20 0 60 40 0 0 0 [649] 20 20 0 20 60 0 40 80 0 0 0 40 100 80 0 100 20 20 [667] 80 60 20 40 20 80 0 20 0 0 60 NA 20 80 0 60 60 20 [685] 20 0 0 40 20 20 40 0 40 20 20 80 0 40 0 0 40 20 [703] 0 20 80 80 40 0 0 0 40 0 80 60 20 20 20 20 80 20 [721] 20 20 40 20 20 20 0 0 20 20 0 0 20 0 0 0 20 40 [739] 0 20 0 0 80 80 40 0 40 20 40 40 20 40 60 20 40 0 [757] 20 0 40 40 20 20 20 0 0 20 0 40 20 0 0 20 0 20 [775] 0 60 40 0 40 0 NA 0 0 40 80 0 60 20 80 40 0 60 [793] 0 20 0 0 40 60 0 20 60 40 0 60 0 20 80 20 20 20 [811] 20 0 0 100 60 40 20 20 20 60 20 20 0 20 80 40 80 20 [829] 0 20 0 20 0 80 20 40 20 0 60 40 40 40 100 100 40 40 [847] 60 60 0 20 20 40 0 0 20 0 20 0 80 60 0 0 0 0 [865] 20 0 100 40 60 100 0 40 0 0 20 20 80 0 100 60 60 20 [883] 20 0 60 40 0 0 20 100 80 40 20 80 0 40 0 40 20 20 [901] 60 0 20 20 0 20 60 0 0 40 80 0 0 20 20 20 0 20 [919] 0 20 0 20 40 20 20 0 20 0 20 60 20 20 0 0 20 0 [937] 20 20 20 20 60 0 20 40 0 80 20 40 0 0 0 20 20 20 [955] 60 20 60 0 60 40 40 20 0 20 20 20 20 80 0 0 20 0 [973] 80 0 100 0 20 0 0 20 20 0 40 60 0 0 20 60 40 40 [991] 0 60 0 60 40 20 20 20 0 100 20 60 0 20 20 0 0 40 [1009] 0 20 40 20 0 0 40 0 20 20 60 60 80 0 100 0 0 0 [1027] 20 40 80 0 20 20 40 0 60 0 60 0 0 60 0 40 0 80 [1045] 80 80 0 60 20 80 0 40 60 40 0 20 20 80 40 60 20 0 [1063] 0 80 60 60 20 80 40 40 20 80 40 100 0 60 0 0 40 0 [1081] 40 40 0 0 20 0 60 20 60 20 0 0 0 20 40 80 20 0 [1099] 0 20 20 0 20 20 20 60 0 80 0 20 40 20 80 40 20 60 [1117] 20 20 100 0 40 40 20 20 20 40 40 20 20 0 20 60 0 60 [1135] 40 0 0 0 0 0 60 80 80 100 60 0 20 40 20 0 40 0 [1153] 20 0 60 20 60 0 20 60 0 80 20 0 0 20 60 0 0 20 [1171] 40 0 60 40 40 20 60 20 0 0 0 20 20 40 100 40 0 40 [1189] 40 40 20 20 20 20 20 40 20 40 40 0 0 20 80 0 20 0 [1207] 0 20 NA 20 60 20 100 20 20 60 60 20 20 20 60 20 0 20 [1225] 0 20 20 20 20 60 20 20 20 20 0 0 80 0 40 20 80 20 [1243] 20 20 80 40 40 0 20 20 20 20 60 20 20 20 80 20 20 0 [1261] 60 40 80 60 80 0 0 40 40 0 20 0 20 20 20 0 80 20 [1279] 0 20 60 20 20 40 20 100 0 20 40 20 40 60 60 60 40 20 [1297] 40 80 20 20 20 20 0 0 40 0 20 60 20 0 20 0 80 0 [1315] 80 40 60 40 40 0 20 60 20 40 40 20 20 20 40 60 60 40 [1333] 20 20 0 20 0 80 20 0 40 0 20 0 40 40 80 60 20 0 [1351] 20 0 40 NA 0 40 20 40 20 20 80 0 40 60 0 0 20 80 [1369] 40 0 20 80 0 0 40 80 60 40 0 20 0 0 80 100 0 20 [1387] 0 20 20 0 20 80 0 0 0 0 20 0 60 60 40 60 100 0 [1405] 40 0 100 0 20 60 0 20 80 0 60 0 0 20 40 40 0 40 [1423] 0 20 20 40 80 40 0 0 0 0 60 20 20 20 60 0 40 20 [1441] 20 20 20 0 60 0 20 40 0 80 20 60 60 0 0 80 0 20 [1459] 60 40 60 0 100 20 20 0 40 0 40 60 20 40 20 0 60 80 [1477] 20 20 0 60 20 0 0 0 20 80 20 0 40 20 20 0 20 60 [1495] 60 20 80 80 60 20 20 60 60 20 20 0 0 100 40 20 60 0 [1513] 20 60 0 20 0 0 60 20 0 20 20 20 60 20 40 40 60 60 [1531] 60 0 80 40 0 20 40 0 0 0 40 40 60 80 0 60 0 60 [1549] 60 100 100 0 40 0 80 0 80 0 0 0 40 0 100 100 100 100 [1567] 100 100 100 100 100 100 40 0 20 20 20 60 0 40 60 0 80 60 [1585] 20 80 60 60 40 40 20 20 40 80 0 0 20 20 40 80 20 60 [1603] 20 0 0 40 20 40 0 60 0 0 20 20 20 20 20 40 0 20 [1621] 20 60 40 0 20 0 0 0 20 80 20 80 100 20 20 40 0 0 [1639] 0 20 20 0 0 60 40 40 20 40 0 40 40 60 0 0 40 0 [1657] 0 40 0 60 60 20 20 20 0 0 40 20 60 40 0 20 20 0 [1675] 40 40 60 20 80 60 60 20 40 0 20 0 20 40 0 80 40 80 [1693] 40 20 0 60 0 0 20 0 0 40 0 20 20 20 40 20 60 NA [1711] 60 0 0 20 20 20 0 0 80 60 80 0 0 20 40 20 20 20 [1729] 20 20 0 0 20 20 20 20 40 0 40 80 0 0 40 0 60 0 [1747] 20 0 0 0 80 80 80 0 20 40 0 0 60 20 20 0 0 60 [1765] 0 0 40 40 100 20 0 0 60 0 100 0 20 0 20 80 60 20 [1783] 80 100 40 20 0 0 20 0 60 40 0 40 0 0 20 0 20 80 [1801] 20 0 0 0 60 60 0 0 0 80 0 0 0 40 80 40 0 0 [1819] 0 80 20 20 0 20 0 20 80 0 0 20 20 60 20 20 0 60 [1837] 0 40 20 20 20 20 0 0 0 0 20 80 20 0 20 0 0 40 [1855] 40 0 0 0 20 0 40 40 0 20 0 0 20 80 20 20 0 0 [1873] 40 0 60 20 60 0 0 40 20 0 40 0 80 20 NA 40 20 20 [1891] 40 60 NA 80 40 80 0 40 20 20 0 0 20 0 80 80 60 0 [1909] 0 80 80 100 80 20 0 40 100 60 20 80 40 40 0 40 80 0 [1927] 80 20 80 80 20 NA 60 0 0 20 60 0 40 0 80 80 20 0 [1945] 0 40 0 20 20 60 20 0 100 20 0 0 0 0 0 80 0 20 [1963] 0 100 40 40 60 80 80 80 100 0 40 0 0 20 40 80 0 20 [1981] 20 0 0 80 40 60 0 60 0 80 20 20 0 0 40 0 40 80 [1999] 0 0 20 40 0 40 0 20 100 40 0 60 60 40 20 20 20 0 [2017] 20 NA 20 60 80 80 0 40 40 0 80 20 20 20 60 0 20 80 [2035] 40 0 0 0 20 0 0 0 0 0 0 20 40 80 20 0 20 40 [2053] 20 20 20 0 80 0 80 0 20 0 20 60 40 20 100 20 60 20 [2071] 20 40 20 20 60 0 20 0 20 80 40 0 0 20 20 0 80 0 [2089] 40 0 60 60 80 20 80 0 0 20 0 0 0 20 20 20 0 0 [2107] 60 0 20 0 20 60 0 0 40 20 60 20 20 0 20 20 20 100 [2125] 60 100 0 40 20 0 20 60 60 20 0 20 0 0 60 20 0 20 [2143] 0 0 20 0 40 0 60 20 80 20 20 0 0 0 20 0 60 60 [2161] 0 80 60 0 40 80 0 40 20 20 20 60 0 40 0 20 20 80 [2179] 0 0 20 0 40 20 0 20 0 40 0 60 80 0 0 20 0 0 [2197] 20 40 0 60 0 80 40 40 20 40 20 0 20 20 60 0 20 0 [2215] 0 60 0 20 20 0 20 20 20 20 20 60 100 20 40 0 0 20 [2233] 40 100 0 0 0 20 20 40 20 20 80 60 0 0 40 0 20 0 [2251] 0 60 0 20 60 40 80 60 0 40 0 0 0 60 80 0 0 0 [2269] 0 60 0 20 100 20 20 20 0 20 40 0 20 20 0 0 40 20 [2287] 60 0 0 20 20 20 20 60 40 60 0 20 0 40 40 20 20 40 [2305] 0 20 100 20 0 20 0 60 100 40 0 60 0 40 80 20 60 80 [2323] 60 100 0 0 0 20 0 40 20 0 20 80 0 60 60 20 40 80 [2341] 20 20 60 40 0 0 40 40 20 80 40 60 40 20 0 0 0 60 [2359] 0 40 20 60 20 40 20 20 20 0 0 20 0 0 60 0 40 80 [2377] 0 0 0 80 20 40 60 60 0 0 20 60 0 20 20 60 60 20 [2395] 40 NA 0 0 0 60 0 40 0 20 0 20 20 20 0 0 40 20 [2413] 0 20 80 0 20 20 20 40 80 60 20 80 0 0 0 0 80 0 [2431] 40 20 0 0 0 0 40 0 20 0 40 0 0 80 20 40 20 0 [2449] 80 20 0 0 20 60 40 40 0 0 0 60 0 0 0 40 0 20 [2467] 0 80 0 40 20 20 0 0 0 60 0 20 40 60 20 80 40 40 [2485] 60 60 40 60 0 20 20 20 60 0 0 0 0 20 60 20 0 0 [2503] 60 20 0 0 0 0 20 20 60 40 60 0 40 20 80 0 0 60 [2521] 0 20 0 0 40 80 0 20 0 0 60 0 20 80 0 40 20 80 [2539] 20 0 60 40 20 20 0 20 40 0 60 20 0 0 20 20 20 0 [2557] 0 20 40 100 20 0 0 0 60 0 40 80 60 100 0 0 60 20 [2575] 80 20 40 40 60 20 0 0 0 0 60 60 0 80 20 20 60 0 [2593] 60 40 40 60 0 80 0 60 60 60 40 60 40 80 NA 20 100 80 [2611] 80 0 40 60 0 40 80 80 60 60 40 20 0 0 20 40 80 0 [2629] 80 100 60 20 80 60 20 60 20 60 100 20 80 20 20 NA 0 0 [2647] 40 0 20 40 60 60 40 0 0 20 0 0 100 20 100 0 60 20 [2665] 0 20 80 80 0 40 60 0 40 60 20 60 0 20 20 20 40 20 [2683] 20 0 20 0 20 20 80 20 0 40 0 80 60 20 20 0 0 40 [2701] 0 80 60 60 20 0 20 40 20 0 40 20 0 100 60 60 20 60 [2719] 0 0 40 0 40 0 40 0 20 0 60 0 20 40 60 20 100 80 [2737] 20 20 40 20 0 0 100 0 40 0 0 40 20 100 100 0 20 20 [2755] 100 20 20 20 20 20 40 20 20 40 20 60 20 0 40 20 100 40 [2773] 0 20 40 20 0 0 80 0 0 40 0 0 20 20 0 20 20 40 [2791] 20 60 80 40 80 100 20 20 80 20 > pomp(x = vec, relative = TRUE) # relative POMP units [1] 20 20 80 60 20 100 20 60 60 20 60 20 80 80 60 60 60 80 [19] 60 60 80 0 0 20 60 0 20 20 20 60 0 20 0 20 60 80 [37] 0 0 0 0 0 80 0 80 20 0 80 20 0 0 0 0 40 60 [55] 0 0 0 40 0 40 20 20 0 60 20 20 0 60 20 0 60 20 [73] 60 0 40 20 20 80 0 20 0 0 0 0 0 60 0 0 0 40 [91] 0 0 20 0 20 20 40 0 60 20 20 40 20 0 0 0 0 100 [109] 20 40 0 NA 0 0 0 60 0 80 20 0 60 20 100 20 20 20 [127] 20 20 20 60 20 40 60 20 0 0 0 0 0 40 20 20 20 0 [145] 80 20 0 20 20 0 0 0 20 60 20 40 0 20 20 20 0 0 [163] 20 0 0 20 20 20 0 0 40 60 0 20 20 0 0 100 60 40 [181] 60 0 40 60 40 0 0 20 20 0 20 0 20 80 0 0 20 20 [199] 20 0 20 0 40 20 0 20 20 0 0 20 20 0 80 40 0 20 [217] 0 20 80 80 20 0 0 0 0 0 40 20 0 20 80 20 80 40 [235] 40 20 0 0 40 40 0 20 60 0 60 40 80 40 60 40 40 60 [253] 100 40 80 40 80 0 0 60 80 40 100 20 60 40 20 0 0 60 [271] 40 60 0 0 20 40 0 0 60 20 40 20 20 20 40 0 20 20 [289] 0 0 0 20 40 40 0 80 0 40 60 40 0 0 20 40 60 20 [307] 80 0 40 0 40 0 20 0 40 20 40 40 20 20 20 20 80 60 [325] 40 0 20 60 0 20 0 20 60 0 20 40 40 0 20 0 40 0 [343] 20 0 20 0 20 80 20 20 60 20 40 20 20 0 60 0 20 60 [361] 40 0 0 0 60 0 NA 60 0 100 0 40 0 20 0 0 0 60 [379] 20 20 60 60 20 20 80 0 20 60 0 60 20 20 0 0 20 40 [397] 60 0 20 0 40 0 40 20 60 60 0 0 40 40 0 40 0 0 [415] 40 60 0 40 40 0 0 40 0 80 80 20 NA 0 20 20 20 20 [433] 20 20 20 20 20 20 60 20 80 20 0 60 0 20 20 80 100 0 [451] 60 60 40 80 0 20 0 20 20 80 20 20 0 40 0 0 20 20 [469] 0 0 40 20 0 20 20 60 100 20 20 0 0 0 0 40 20 0 [487] 20 0 20 0 0 60 0 20 0 60 60 20 60 0 40 0 80 80 [505] 20 20 0 0 20 0 40 100 20 40 20 20 0 0 0 40 60 0 [523] 20 0 20 100 0 0 20 60 0 40 0 20 0 0 40 80 80 40 [541] 40 60 0 0 20 20 80 20 0 60 0 60 60 0 40 0 20 0 [559] 0 0 20 80 40 0 0 0 0 0 80 20 0 0 40 100 0 40 [577] 0 40 0 20 60 0 40 0 20 20 60 20 100 0 0 0 20 60 [595] 0 0 80 NA 20 40 0 20 40 60 20 0 40 0 0 0 0 0 [613] 0 20 20 0 20 40 0 20 0 20 20 60 20 20 20 40 40 60 [631] 20 60 20 40 0 0 0 60 0 20 40 20 0 60 40 0 0 0 [649] 20 20 0 20 60 0 40 80 0 0 0 40 100 80 0 100 20 20 [667] 80 60 20 40 20 80 0 20 0 0 60 NA 20 80 0 60 60 20 [685] 20 0 0 40 20 20 40 0 40 20 20 80 0 40 0 0 40 20 [703] 0 20 80 80 40 0 0 0 40 0 80 60 20 20 20 20 80 20 [721] 20 20 40 20 20 20 0 0 20 20 0 0 20 0 0 0 20 40 [739] 0 20 0 0 80 80 40 0 40 20 40 40 20 40 60 20 40 0 [757] 20 0 40 40 20 20 20 0 0 20 0 40 20 0 0 20 0 20 [775] 0 60 40 0 40 0 NA 0 0 40 80 0 60 20 80 40 0 60 [793] 0 20 0 0 40 60 0 20 60 40 0 60 0 20 80 20 20 20 [811] 20 0 0 100 60 40 20 20 20 60 20 20 0 20 80 40 80 20 [829] 0 20 0 20 0 80 20 40 20 0 60 40 40 40 100 100 40 40 [847] 60 60 0 20 20 40 0 0 20 0 20 0 80 60 0 0 0 0 [865] 20 0 100 40 60 100 0 40 0 0 20 20 80 0 100 60 60 20 [883] 20 0 60 40 0 0 20 100 80 40 20 80 0 40 0 40 20 20 [901] 60 0 20 20 0 20 60 0 0 40 80 0 0 20 20 20 0 20 [919] 0 20 0 20 40 20 20 0 20 0 20 60 20 20 0 0 20 0 [937] 20 20 20 20 60 0 20 40 0 80 20 40 0 0 0 20 20 20 [955] 60 20 60 0 60 40 40 20 0 20 20 20 20 80 0 0 20 0 [973] 80 0 100 0 20 0 0 20 20 0 40 60 0 0 20 60 40 40 [991] 0 60 0 60 40 20 20 20 0 100 20 60 0 20 20 0 0 40 [1009] 0 20 40 20 0 0 40 0 20 20 60 60 80 0 100 0 0 0 [1027] 20 40 80 0 20 20 40 0 60 0 60 0 0 60 0 40 0 80 [1045] 80 80 0 60 20 80 0 40 60 40 0 20 20 80 40 60 20 0 [1063] 0 80 60 60 20 80 40 40 20 80 40 100 0 60 0 0 40 0 [1081] 40 40 0 0 20 0 60 20 60 20 0 0 0 20 40 80 20 0 [1099] 0 20 20 0 20 20 20 60 0 80 0 20 40 20 80 40 20 60 [1117] 20 20 100 0 40 40 20 20 20 40 40 20 20 0 20 60 0 60 [1135] 40 0 0 0 0 0 60 80 80 100 60 0 20 40 20 0 40 0 [1153] 20 0 60 20 60 0 20 60 0 80 20 0 0 20 60 0 0 20 [1171] 40 0 60 40 40 20 60 20 0 0 0 20 20 40 100 40 0 40 [1189] 40 40 20 20 20 20 20 40 20 40 40 0 0 20 80 0 20 0 [1207] 0 20 NA 20 60 20 100 20 20 60 60 20 20 20 60 20 0 20 [1225] 0 20 20 20 20 60 20 20 20 20 0 0 80 0 40 20 80 20 [1243] 20 20 80 40 40 0 20 20 20 20 60 20 20 20 80 20 20 0 [1261] 60 40 80 60 80 0 0 40 40 0 20 0 20 20 20 0 80 20 [1279] 0 20 60 20 20 40 20 100 0 20 40 20 40 60 60 60 40 20 [1297] 40 80 20 20 20 20 0 0 40 0 20 60 20 0 20 0 80 0 [1315] 80 40 60 40 40 0 20 60 20 40 40 20 20 20 40 60 60 40 [1333] 20 20 0 20 0 80 20 0 40 0 20 0 40 40 80 60 20 0 [1351] 20 0 40 NA 0 40 20 40 20 20 80 0 40 60 0 0 20 80 [1369] 40 0 20 80 0 0 40 80 60 40 0 20 0 0 80 100 0 20 [1387] 0 20 20 0 20 80 0 0 0 0 20 0 60 60 40 60 100 0 [1405] 40 0 100 0 20 60 0 20 80 0 60 0 0 20 40 40 0 40 [1423] 0 20 20 40 80 40 0 0 0 0 60 20 20 20 60 0 40 20 [1441] 20 20 20 0 60 0 20 40 0 80 20 60 60 0 0 80 0 20 [1459] 60 40 60 0 100 20 20 0 40 0 40 60 20 40 20 0 60 80 [1477] 20 20 0 60 20 0 0 0 20 80 20 0 40 20 20 0 20 60 [1495] 60 20 80 80 60 20 20 60 60 20 20 0 0 100 40 20 60 0 [1513] 20 60 0 20 0 0 60 20 0 20 20 20 60 20 40 40 60 60 [1531] 60 0 80 40 0 20 40 0 0 0 40 40 60 80 0 60 0 60 [1549] 60 100 100 0 40 0 80 0 80 0 0 0 40 0 100 100 100 100 [1567] 100 100 100 100 100 100 40 0 20 20 20 60 0 40 60 0 80 60 [1585] 20 80 60 60 40 40 20 20 40 80 0 0 20 20 40 80 20 60 [1603] 20 0 0 40 20 40 0 60 0 0 20 20 20 20 20 40 0 20 [1621] 20 60 40 0 20 0 0 0 20 80 20 80 100 20 20 40 0 0 [1639] 0 20 20 0 0 60 40 40 20 40 0 40 40 60 0 0 40 0 [1657] 0 40 0 60 60 20 20 20 0 0 40 20 60 40 0 20 20 0 [1675] 40 40 60 20 80 60 60 20 40 0 20 0 20 40 0 80 40 80 [1693] 40 20 0 60 0 0 20 0 0 40 0 20 20 20 40 20 60 NA [1711] 60 0 0 20 20 20 0 0 80 60 80 0 0 20 40 20 20 20 [1729] 20 20 0 0 20 20 20 20 40 0 40 80 0 0 40 0 60 0 [1747] 20 0 0 0 80 80 80 0 20 40 0 0 60 20 20 0 0 60 [1765] 0 0 40 40 100 20 0 0 60 0 100 0 20 0 20 80 60 20 [1783] 80 100 40 20 0 0 20 0 60 40 0 40 0 0 20 0 20 80 [1801] 20 0 0 0 60 60 0 0 0 80 0 0 0 40 80 40 0 0 [1819] 0 80 20 20 0 20 0 20 80 0 0 20 20 60 20 20 0 60 [1837] 0 40 20 20 20 20 0 0 0 0 20 80 20 0 20 0 0 40 [1855] 40 0 0 0 20 0 40 40 0 20 0 0 20 80 20 20 0 0 [1873] 40 0 60 20 60 0 0 40 20 0 40 0 80 20 NA 40 20 20 [1891] 40 60 NA 80 40 80 0 40 20 20 0 0 20 0 80 80 60 0 [1909] 0 80 80 100 80 20 0 40 100 60 20 80 40 40 0 40 80 0 [1927] 80 20 80 80 20 NA 60 0 0 20 60 0 40 0 80 80 20 0 [1945] 0 40 0 20 20 60 20 0 100 20 0 0 0 0 0 80 0 20 [1963] 0 100 40 40 60 80 80 80 100 0 40 0 0 20 40 80 0 20 [1981] 20 0 0 80 40 60 0 60 0 80 20 20 0 0 40 0 40 80 [1999] 0 0 20 40 0 40 0 20 100 40 0 60 60 40 20 20 20 0 [2017] 20 NA 20 60 80 80 0 40 40 0 80 20 20 20 60 0 20 80 [2035] 40 0 0 0 20 0 0 0 0 0 0 20 40 80 20 0 20 40 [2053] 20 20 20 0 80 0 80 0 20 0 20 60 40 20 100 20 60 20 [2071] 20 40 20 20 60 0 20 0 20 80 40 0 0 20 20 0 80 0 [2089] 40 0 60 60 80 20 80 0 0 20 0 0 0 20 20 20 0 0 [2107] 60 0 20 0 20 60 0 0 40 20 60 20 20 0 20 20 20 100 [2125] 60 100 0 40 20 0 20 60 60 20 0 20 0 0 60 20 0 20 [2143] 0 0 20 0 40 0 60 20 80 20 20 0 0 0 20 0 60 60 [2161] 0 80 60 0 40 80 0 40 20 20 20 60 0 40 0 20 20 80 [2179] 0 0 20 0 40 20 0 20 0 40 0 60 80 0 0 20 0 0 [2197] 20 40 0 60 0 80 40 40 20 40 20 0 20 20 60 0 20 0 [2215] 0 60 0 20 20 0 20 20 20 20 20 60 100 20 40 0 0 20 [2233] 40 100 0 0 0 20 20 40 20 20 80 60 0 0 40 0 20 0 [2251] 0 60 0 20 60 40 80 60 0 40 0 0 0 60 80 0 0 0 [2269] 0 60 0 20 100 20 20 20 0 20 40 0 20 20 0 0 40 20 [2287] 60 0 0 20 20 20 20 60 40 60 0 20 0 40 40 20 20 40 [2305] 0 20 100 20 0 20 0 60 100 40 0 60 0 40 80 20 60 80 [2323] 60 100 0 0 0 20 0 40 20 0 20 80 0 60 60 20 40 80 [2341] 20 20 60 40 0 0 40 40 20 80 40 60 40 20 0 0 0 60 [2359] 0 40 20 60 20 40 20 20 20 0 0 20 0 0 60 0 40 80 [2377] 0 0 0 80 20 40 60 60 0 0 20 60 0 20 20 60 60 20 [2395] 40 NA 0 0 0 60 0 40 0 20 0 20 20 20 0 0 40 20 [2413] 0 20 80 0 20 20 20 40 80 60 20 80 0 0 0 0 80 0 [2431] 40 20 0 0 0 0 40 0 20 0 40 0 0 80 20 40 20 0 [2449] 80 20 0 0 20 60 40 40 0 0 0 60 0 0 0 40 0 20 [2467] 0 80 0 40 20 20 0 0 0 60 0 20 40 60 20 80 40 40 [2485] 60 60 40 60 0 20 20 20 60 0 0 0 0 20 60 20 0 0 [2503] 60 20 0 0 0 0 20 20 60 40 60 0 40 20 80 0 0 60 [2521] 0 20 0 0 40 80 0 20 0 0 60 0 20 80 0 40 20 80 [2539] 20 0 60 40 20 20 0 20 40 0 60 20 0 0 20 20 20 0 [2557] 0 20 40 100 20 0 0 0 60 0 40 80 60 100 0 0 60 20 [2575] 80 20 40 40 60 20 0 0 0 0 60 60 0 80 20 20 60 0 [2593] 60 40 40 60 0 80 0 60 60 60 40 60 40 80 NA 20 100 80 [2611] 80 0 40 60 0 40 80 80 60 60 40 20 0 0 20 40 80 0 [2629] 80 100 60 20 80 60 20 60 20 60 100 20 80 20 20 NA 0 0 [2647] 40 0 20 40 60 60 40 0 0 20 0 0 100 20 100 0 60 20 [2665] 0 20 80 80 0 40 60 0 40 60 20 60 0 20 20 20 40 20 [2683] 20 0 20 0 20 20 80 20 0 40 0 80 60 20 20 0 0 40 [2701] 0 80 60 60 20 0 20 40 20 0 40 20 0 100 60 60 20 60 [2719] 0 0 40 0 40 0 40 0 20 0 60 0 20 40 60 20 100 80 [2737] 20 20 40 20 0 0 100 0 40 0 0 40 20 100 100 0 20 20 [2755] 100 20 20 20 20 20 40 20 20 40 20 60 20 0 40 20 100 40 [2773] 0 20 40 20 0 0 80 0 0 40 0 0 20 20 0 20 20 40 [2791] 20 60 80 40 80 100 20 20 80 20 > pomp(x = vec, mini = 1, maxi = 6, unit = 100) # unit = 100 [1] 0.2 0.2 0.8 0.6 0.2 1.0 0.2 0.6 0.6 0.2 0.6 0.2 0.8 0.8 0.6 0.6 0.6 0.8 [19] 0.6 0.6 0.8 0.0 0.0 0.2 0.6 0.0 0.2 0.2 0.2 0.6 0.0 0.2 0.0 0.2 0.6 0.8 [37] 0.0 0.0 0.0 0.0 0.0 0.8 0.0 0.8 0.2 0.0 0.8 0.2 0.0 0.0 0.0 0.0 0.4 0.6 [55] 0.0 0.0 0.0 0.4 0.0 0.4 0.2 0.2 0.0 0.6 0.2 0.2 0.0 0.6 0.2 0.0 0.6 0.2 [73] 0.6 0.0 0.4 0.2 0.2 0.8 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.6 0.0 0.0 0.0 0.4 [91] 0.0 0.0 0.2 0.0 0.2 0.2 0.4 0.0 0.6 0.2 0.2 0.4 0.2 0.0 0.0 0.0 0.0 1.0 [109] 0.2 0.4 0.0 NA 0.0 0.0 0.0 0.6 0.0 0.8 0.2 0.0 0.6 0.2 1.0 0.2 0.2 0.2 [127] 0.2 0.2 0.2 0.6 0.2 0.4 0.6 0.2 0.0 0.0 0.0 0.0 0.0 0.4 0.2 0.2 0.2 0.0 [145] 0.8 0.2 0.0 0.2 0.2 0.0 0.0 0.0 0.2 0.6 0.2 0.4 0.0 0.2 0.2 0.2 0.0 0.0 [163] 0.2 0.0 0.0 0.2 0.2 0.2 0.0 0.0 0.4 0.6 0.0 0.2 0.2 0.0 0.0 1.0 0.6 0.4 [181] 0.6 0.0 0.4 0.6 0.4 0.0 0.0 0.2 0.2 0.0 0.2 0.0 0.2 0.8 0.0 0.0 0.2 0.2 [199] 0.2 0.0 0.2 0.0 0.4 0.2 0.0 0.2 0.2 0.0 0.0 0.2 0.2 0.0 0.8 0.4 0.0 0.2 [217] 0.0 0.2 0.8 0.8 0.2 0.0 0.0 0.0 0.0 0.0 0.4 0.2 0.0 0.2 0.8 0.2 0.8 0.4 [235] 0.4 0.2 0.0 0.0 0.4 0.4 0.0 0.2 0.6 0.0 0.6 0.4 0.8 0.4 0.6 0.4 0.4 0.6 [253] 1.0 0.4 0.8 0.4 0.8 0.0 0.0 0.6 0.8 0.4 1.0 0.2 0.6 0.4 0.2 0.0 0.0 0.6 [271] 0.4 0.6 0.0 0.0 0.2 0.4 0.0 0.0 0.6 0.2 0.4 0.2 0.2 0.2 0.4 0.0 0.2 0.2 [289] 0.0 0.0 0.0 0.2 0.4 0.4 0.0 0.8 0.0 0.4 0.6 0.4 0.0 0.0 0.2 0.4 0.6 0.2 [307] 0.8 0.0 0.4 0.0 0.4 0.0 0.2 0.0 0.4 0.2 0.4 0.4 0.2 0.2 0.2 0.2 0.8 0.6 [325] 0.4 0.0 0.2 0.6 0.0 0.2 0.0 0.2 0.6 0.0 0.2 0.4 0.4 0.0 0.2 0.0 0.4 0.0 [343] 0.2 0.0 0.2 0.0 0.2 0.8 0.2 0.2 0.6 0.2 0.4 0.2 0.2 0.0 0.6 0.0 0.2 0.6 [361] 0.4 0.0 0.0 0.0 0.6 0.0 NA 0.6 0.0 1.0 0.0 0.4 0.0 0.2 0.0 0.0 0.0 0.6 [379] 0.2 0.2 0.6 0.6 0.2 0.2 0.8 0.0 0.2 0.6 0.0 0.6 0.2 0.2 0.0 0.0 0.2 0.4 [397] 0.6 0.0 0.2 0.0 0.4 0.0 0.4 0.2 0.6 0.6 0.0 0.0 0.4 0.4 0.0 0.4 0.0 0.0 [415] 0.4 0.6 0.0 0.4 0.4 0.0 0.0 0.4 0.0 0.8 0.8 0.2 NA 0.0 0.2 0.2 0.2 0.2 [433] 0.2 0.2 0.2 0.2 0.2 0.2 0.6 0.2 0.8 0.2 0.0 0.6 0.0 0.2 0.2 0.8 1.0 0.0 [451] 0.6 0.6 0.4 0.8 0.0 0.2 0.0 0.2 0.2 0.8 0.2 0.2 0.0 0.4 0.0 0.0 0.2 0.2 [469] 0.0 0.0 0.4 0.2 0.0 0.2 0.2 0.6 1.0 0.2 0.2 0.0 0.0 0.0 0.0 0.4 0.2 0.0 [487] 0.2 0.0 0.2 0.0 0.0 0.6 0.0 0.2 0.0 0.6 0.6 0.2 0.6 0.0 0.4 0.0 0.8 0.8 [505] 0.2 0.2 0.0 0.0 0.2 0.0 0.4 1.0 0.2 0.4 0.2 0.2 0.0 0.0 0.0 0.4 0.6 0.0 [523] 0.2 0.0 0.2 1.0 0.0 0.0 0.2 0.6 0.0 0.4 0.0 0.2 0.0 0.0 0.4 0.8 0.8 0.4 [541] 0.4 0.6 0.0 0.0 0.2 0.2 0.8 0.2 0.0 0.6 0.0 0.6 0.6 0.0 0.4 0.0 0.2 0.0 [559] 0.0 0.0 0.2 0.8 0.4 0.0 0.0 0.0 0.0 0.0 0.8 0.2 0.0 0.0 0.4 1.0 0.0 0.4 [577] 0.0 0.4 0.0 0.2 0.6 0.0 0.4 0.0 0.2 0.2 0.6 0.2 1.0 0.0 0.0 0.0 0.2 0.6 [595] 0.0 0.0 0.8 NA 0.2 0.4 0.0 0.2 0.4 0.6 0.2 0.0 0.4 0.0 0.0 0.0 0.0 0.0 [613] 0.0 0.2 0.2 0.0 0.2 0.4 0.0 0.2 0.0 0.2 0.2 0.6 0.2 0.2 0.2 0.4 0.4 0.6 [631] 0.2 0.6 0.2 0.4 0.0 0.0 0.0 0.6 0.0 0.2 0.4 0.2 0.0 0.6 0.4 0.0 0.0 0.0 [649] 0.2 0.2 0.0 0.2 0.6 0.0 0.4 0.8 0.0 0.0 0.0 0.4 1.0 0.8 0.0 1.0 0.2 0.2 [667] 0.8 0.6 0.2 0.4 0.2 0.8 0.0 0.2 0.0 0.0 0.6 NA 0.2 0.8 0.0 0.6 0.6 0.2 [685] 0.2 0.0 0.0 0.4 0.2 0.2 0.4 0.0 0.4 0.2 0.2 0.8 0.0 0.4 0.0 0.0 0.4 0.2 [703] 0.0 0.2 0.8 0.8 0.4 0.0 0.0 0.0 0.4 0.0 0.8 0.6 0.2 0.2 0.2 0.2 0.8 0.2 [721] 0.2 0.2 0.4 0.2 0.2 0.2 0.0 0.0 0.2 0.2 0.0 0.0 0.2 0.0 0.0 0.0 0.2 0.4 [739] 0.0 0.2 0.0 0.0 0.8 0.8 0.4 0.0 0.4 0.2 0.4 0.4 0.2 0.4 0.6 0.2 0.4 0.0 [757] 0.2 0.0 0.4 0.4 0.2 0.2 0.2 0.0 0.0 0.2 0.0 0.4 0.2 0.0 0.0 0.2 0.0 0.2 [775] 0.0 0.6 0.4 0.0 0.4 0.0 NA 0.0 0.0 0.4 0.8 0.0 0.6 0.2 0.8 0.4 0.0 0.6 [793] 0.0 0.2 0.0 0.0 0.4 0.6 0.0 0.2 0.6 0.4 0.0 0.6 0.0 0.2 0.8 0.2 0.2 0.2 [811] 0.2 0.0 0.0 1.0 0.6 0.4 0.2 0.2 0.2 0.6 0.2 0.2 0.0 0.2 0.8 0.4 0.8 0.2 [829] 0.0 0.2 0.0 0.2 0.0 0.8 0.2 0.4 0.2 0.0 0.6 0.4 0.4 0.4 1.0 1.0 0.4 0.4 [847] 0.6 0.6 0.0 0.2 0.2 0.4 0.0 0.0 0.2 0.0 0.2 0.0 0.8 0.6 0.0 0.0 0.0 0.0 [865] 0.2 0.0 1.0 0.4 0.6 1.0 0.0 0.4 0.0 0.0 0.2 0.2 0.8 0.0 1.0 0.6 0.6 0.2 [883] 0.2 0.0 0.6 0.4 0.0 0.0 0.2 1.0 0.8 0.4 0.2 0.8 0.0 0.4 0.0 0.4 0.2 0.2 [901] 0.6 0.0 0.2 0.2 0.0 0.2 0.6 0.0 0.0 0.4 0.8 0.0 0.0 0.2 0.2 0.2 0.0 0.2 [919] 0.0 0.2 0.0 0.2 0.4 0.2 0.2 0.0 0.2 0.0 0.2 0.6 0.2 0.2 0.0 0.0 0.2 0.0 [937] 0.2 0.2 0.2 0.2 0.6 0.0 0.2 0.4 0.0 0.8 0.2 0.4 0.0 0.0 0.0 0.2 0.2 0.2 [955] 0.6 0.2 0.6 0.0 0.6 0.4 0.4 0.2 0.0 0.2 0.2 0.2 0.2 0.8 0.0 0.0 0.2 0.0 [973] 0.8 0.0 1.0 0.0 0.2 0.0 0.0 0.2 0.2 0.0 0.4 0.6 0.0 0.0 0.2 0.6 0.4 0.4 [991] 0.0 0.6 0.0 0.6 0.4 0.2 0.2 0.2 0.0 1.0 0.2 0.6 0.0 0.2 0.2 0.0 0.0 0.4 [1009] 0.0 0.2 0.4 0.2 0.0 0.0 0.4 0.0 0.2 0.2 0.6 0.6 0.8 0.0 1.0 0.0 0.0 0.0 [1027] 0.2 0.4 0.8 0.0 0.2 0.2 0.4 0.0 0.6 0.0 0.6 0.0 0.0 0.6 0.0 0.4 0.0 0.8 [1045] 0.8 0.8 0.0 0.6 0.2 0.8 0.0 0.4 0.6 0.4 0.0 0.2 0.2 0.8 0.4 0.6 0.2 0.0 [1063] 0.0 0.8 0.6 0.6 0.2 0.8 0.4 0.4 0.2 0.8 0.4 1.0 0.0 0.6 0.0 0.0 0.4 0.0 [1081] 0.4 0.4 0.0 0.0 0.2 0.0 0.6 0.2 0.6 0.2 0.0 0.0 0.0 0.2 0.4 0.8 0.2 0.0 [1099] 0.0 0.2 0.2 0.0 0.2 0.2 0.2 0.6 0.0 0.8 0.0 0.2 0.4 0.2 0.8 0.4 0.2 0.6 [1117] 0.2 0.2 1.0 0.0 0.4 0.4 0.2 0.2 0.2 0.4 0.4 0.2 0.2 0.0 0.2 0.6 0.0 0.6 [1135] 0.4 0.0 0.0 0.0 0.0 0.0 0.6 0.8 0.8 1.0 0.6 0.0 0.2 0.4 0.2 0.0 0.4 0.0 [1153] 0.2 0.0 0.6 0.2 0.6 0.0 0.2 0.6 0.0 0.8 0.2 0.0 0.0 0.2 0.6 0.0 0.0 0.2 [1171] 0.4 0.0 0.6 0.4 0.4 0.2 0.6 0.2 0.0 0.0 0.0 0.2 0.2 0.4 1.0 0.4 0.0 0.4 [1189] 0.4 0.4 0.2 0.2 0.2 0.2 0.2 0.4 0.2 0.4 0.4 0.0 0.0 0.2 0.8 0.0 0.2 0.0 [1207] 0.0 0.2 NA 0.2 0.6 0.2 1.0 0.2 0.2 0.6 0.6 0.2 0.2 0.2 0.6 0.2 0.0 0.2 [1225] 0.0 0.2 0.2 0.2 0.2 0.6 0.2 0.2 0.2 0.2 0.0 0.0 0.8 0.0 0.4 0.2 0.8 0.2 [1243] 0.2 0.2 0.8 0.4 0.4 0.0 0.2 0.2 0.2 0.2 0.6 0.2 0.2 0.2 0.8 0.2 0.2 0.0 [1261] 0.6 0.4 0.8 0.6 0.8 0.0 0.0 0.4 0.4 0.0 0.2 0.0 0.2 0.2 0.2 0.0 0.8 0.2 [1279] 0.0 0.2 0.6 0.2 0.2 0.4 0.2 1.0 0.0 0.2 0.4 0.2 0.4 0.6 0.6 0.6 0.4 0.2 [1297] 0.4 0.8 0.2 0.2 0.2 0.2 0.0 0.0 0.4 0.0 0.2 0.6 0.2 0.0 0.2 0.0 0.8 0.0 [1315] 0.8 0.4 0.6 0.4 0.4 0.0 0.2 0.6 0.2 0.4 0.4 0.2 0.2 0.2 0.4 0.6 0.6 0.4 [1333] 0.2 0.2 0.0 0.2 0.0 0.8 0.2 0.0 0.4 0.0 0.2 0.0 0.4 0.4 0.8 0.6 0.2 0.0 [1351] 0.2 0.0 0.4 NA 0.0 0.4 0.2 0.4 0.2 0.2 0.8 0.0 0.4 0.6 0.0 0.0 0.2 0.8 [1369] 0.4 0.0 0.2 0.8 0.0 0.0 0.4 0.8 0.6 0.4 0.0 0.2 0.0 0.0 0.8 1.0 0.0 0.2 [1387] 0.0 0.2 0.2 0.0 0.2 0.8 0.0 0.0 0.0 0.0 0.2 0.0 0.6 0.6 0.4 0.6 1.0 0.0 [1405] 0.4 0.0 1.0 0.0 0.2 0.6 0.0 0.2 0.8 0.0 0.6 0.0 0.0 0.2 0.4 0.4 0.0 0.4 [1423] 0.0 0.2 0.2 0.4 0.8 0.4 0.0 0.0 0.0 0.0 0.6 0.2 0.2 0.2 0.6 0.0 0.4 0.2 [1441] 0.2 0.2 0.2 0.0 0.6 0.0 0.2 0.4 0.0 0.8 0.2 0.6 0.6 0.0 0.0 0.8 0.0 0.2 [1459] 0.6 0.4 0.6 0.0 1.0 0.2 0.2 0.0 0.4 0.0 0.4 0.6 0.2 0.4 0.2 0.0 0.6 0.8 [1477] 0.2 0.2 0.0 0.6 0.2 0.0 0.0 0.0 0.2 0.8 0.2 0.0 0.4 0.2 0.2 0.0 0.2 0.6 [1495] 0.6 0.2 0.8 0.8 0.6 0.2 0.2 0.6 0.6 0.2 0.2 0.0 0.0 1.0 0.4 0.2 0.6 0.0 [1513] 0.2 0.6 0.0 0.2 0.0 0.0 0.6 0.2 0.0 0.2 0.2 0.2 0.6 0.2 0.4 0.4 0.6 0.6 [1531] 0.6 0.0 0.8 0.4 0.0 0.2 0.4 0.0 0.0 0.0 0.4 0.4 0.6 0.8 0.0 0.6 0.0 0.6 [1549] 0.6 1.0 1.0 0.0 0.4 0.0 0.8 0.0 0.8 0.0 0.0 0.0 0.4 0.0 1.0 1.0 1.0 1.0 [1567] 1.0 1.0 1.0 1.0 1.0 1.0 0.4 0.0 0.2 0.2 0.2 0.6 0.0 0.4 0.6 0.0 0.8 0.6 [1585] 0.2 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.4 0.8 0.0 0.0 0.2 0.2 0.4 0.8 0.2 0.6 [1603] 0.2 0.0 0.0 0.4 0.2 0.4 0.0 0.6 0.0 0.0 0.2 0.2 0.2 0.2 0.2 0.4 0.0 0.2 [1621] 0.2 0.6 0.4 0.0 0.2 0.0 0.0 0.0 0.2 0.8 0.2 0.8 1.0 0.2 0.2 0.4 0.0 0.0 [1639] 0.0 0.2 0.2 0.0 0.0 0.6 0.4 0.4 0.2 0.4 0.0 0.4 0.4 0.6 0.0 0.0 0.4 0.0 [1657] 0.0 0.4 0.0 0.6 0.6 0.2 0.2 0.2 0.0 0.0 0.4 0.2 0.6 0.4 0.0 0.2 0.2 0.0 [1675] 0.4 0.4 0.6 0.2 0.8 0.6 0.6 0.2 0.4 0.0 0.2 0.0 0.2 0.4 0.0 0.8 0.4 0.8 [1693] 0.4 0.2 0.0 0.6 0.0 0.0 0.2 0.0 0.0 0.4 0.0 0.2 0.2 0.2 0.4 0.2 0.6 NA [1711] 0.6 0.0 0.0 0.2 0.2 0.2 0.0 0.0 0.8 0.6 0.8 0.0 0.0 0.2 0.4 0.2 0.2 0.2 [1729] 0.2 0.2 0.0 0.0 0.2 0.2 0.2 0.2 0.4 0.0 0.4 0.8 0.0 0.0 0.4 0.0 0.6 0.0 [1747] 0.2 0.0 0.0 0.0 0.8 0.8 0.8 0.0 0.2 0.4 0.0 0.0 0.6 0.2 0.2 0.0 0.0 0.6 [1765] 0.0 0.0 0.4 0.4 1.0 0.2 0.0 0.0 0.6 0.0 1.0 0.0 0.2 0.0 0.2 0.8 0.6 0.2 [1783] 0.8 1.0 0.4 0.2 0.0 0.0 0.2 0.0 0.6 0.4 0.0 0.4 0.0 0.0 0.2 0.0 0.2 0.8 [1801] 0.2 0.0 0.0 0.0 0.6 0.6 0.0 0.0 0.0 0.8 0.0 0.0 0.0 0.4 0.8 0.4 0.0 0.0 [1819] 0.0 0.8 0.2 0.2 0.0 0.2 0.0 0.2 0.8 0.0 0.0 0.2 0.2 0.6 0.2 0.2 0.0 0.6 [1837] 0.0 0.4 0.2 0.2 0.2 0.2 0.0 0.0 0.0 0.0 0.2 0.8 0.2 0.0 0.2 0.0 0.0 0.4 [1855] 0.4 0.0 0.0 0.0 0.2 0.0 0.4 0.4 0.0 0.2 0.0 0.0 0.2 0.8 0.2 0.2 0.0 0.0 [1873] 0.4 0.0 0.6 0.2 0.6 0.0 0.0 0.4 0.2 0.0 0.4 0.0 0.8 0.2 NA 0.4 0.2 0.2 [1891] 0.4 0.6 NA 0.8 0.4 0.8 0.0 0.4 0.2 0.2 0.0 0.0 0.2 0.0 0.8 0.8 0.6 0.0 [1909] 0.0 0.8 0.8 1.0 0.8 0.2 0.0 0.4 1.0 0.6 0.2 0.8 0.4 0.4 0.0 0.4 0.8 0.0 [1927] 0.8 0.2 0.8 0.8 0.2 NA 0.6 0.0 0.0 0.2 0.6 0.0 0.4 0.0 0.8 0.8 0.2 0.0 [1945] 0.0 0.4 0.0 0.2 0.2 0.6 0.2 0.0 1.0 0.2 0.0 0.0 0.0 0.0 0.0 0.8 0.0 0.2 [1963] 0.0 1.0 0.4 0.4 0.6 0.8 0.8 0.8 1.0 0.0 0.4 0.0 0.0 0.2 0.4 0.8 0.0 0.2 [1981] 0.2 0.0 0.0 0.8 0.4 0.6 0.0 0.6 0.0 0.8 0.2 0.2 0.0 0.0 0.4 0.0 0.4 0.8 [1999] 0.0 0.0 0.2 0.4 0.0 0.4 0.0 0.2 1.0 0.4 0.0 0.6 0.6 0.4 0.2 0.2 0.2 0.0 [2017] 0.2 NA 0.2 0.6 0.8 0.8 0.0 0.4 0.4 0.0 0.8 0.2 0.2 0.2 0.6 0.0 0.2 0.8 [2035] 0.4 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.4 0.8 0.2 0.0 0.2 0.4 [2053] 0.2 0.2 0.2 0.0 0.8 0.0 0.8 0.0 0.2 0.0 0.2 0.6 0.4 0.2 1.0 0.2 0.6 0.2 [2071] 0.2 0.4 0.2 0.2 0.6 0.0 0.2 0.0 0.2 0.8 0.4 0.0 0.0 0.2 0.2 0.0 0.8 0.0 [2089] 0.4 0.0 0.6 0.6 0.8 0.2 0.8 0.0 0.0 0.2 0.0 0.0 0.0 0.2 0.2 0.2 0.0 0.0 [2107] 0.6 0.0 0.2 0.0 0.2 0.6 0.0 0.0 0.4 0.2 0.6 0.2 0.2 0.0 0.2 0.2 0.2 1.0 [2125] 0.6 1.0 0.0 0.4 0.2 0.0 0.2 0.6 0.6 0.2 0.0 0.2 0.0 0.0 0.6 0.2 0.0 0.2 [2143] 0.0 0.0 0.2 0.0 0.4 0.0 0.6 0.2 0.8 0.2 0.2 0.0 0.0 0.0 0.2 0.0 0.6 0.6 [2161] 0.0 0.8 0.6 0.0 0.4 0.8 0.0 0.4 0.2 0.2 0.2 0.6 0.0 0.4 0.0 0.2 0.2 0.8 [2179] 0.0 0.0 0.2 0.0 0.4 0.2 0.0 0.2 0.0 0.4 0.0 0.6 0.8 0.0 0.0 0.2 0.0 0.0 [2197] 0.2 0.4 0.0 0.6 0.0 0.8 0.4 0.4 0.2 0.4 0.2 0.0 0.2 0.2 0.6 0.0 0.2 0.0 [2215] 0.0 0.6 0.0 0.2 0.2 0.0 0.2 0.2 0.2 0.2 0.2 0.6 1.0 0.2 0.4 0.0 0.0 0.2 [2233] 0.4 1.0 0.0 0.0 0.0 0.2 0.2 0.4 0.2 0.2 0.8 0.6 0.0 0.0 0.4 0.0 0.2 0.0 [2251] 0.0 0.6 0.0 0.2 0.6 0.4 0.8 0.6 0.0 0.4 0.0 0.0 0.0 0.6 0.8 0.0 0.0 0.0 [2269] 0.0 0.6 0.0 0.2 1.0 0.2 0.2 0.2 0.0 0.2 0.4 0.0 0.2 0.2 0.0 0.0 0.4 0.2 [2287] 0.6 0.0 0.0 0.2 0.2 0.2 0.2 0.6 0.4 0.6 0.0 0.2 0.0 0.4 0.4 0.2 0.2 0.4 [2305] 0.0 0.2 1.0 0.2 0.0 0.2 0.0 0.6 1.0 0.4 0.0 0.6 0.0 0.4 0.8 0.2 0.6 0.8 [2323] 0.6 1.0 0.0 0.0 0.0 0.2 0.0 0.4 0.2 0.0 0.2 0.8 0.0 0.6 0.6 0.2 0.4 0.8 [2341] 0.2 0.2 0.6 0.4 0.0 0.0 0.4 0.4 0.2 0.8 0.4 0.6 0.4 0.2 0.0 0.0 0.0 0.6 [2359] 0.0 0.4 0.2 0.6 0.2 0.4 0.2 0.2 0.2 0.0 0.0 0.2 0.0 0.0 0.6 0.0 0.4 0.8 [2377] 0.0 0.0 0.0 0.8 0.2 0.4 0.6 0.6 0.0 0.0 0.2 0.6 0.0 0.2 0.2 0.6 0.6 0.2 [2395] 0.4 NA 0.0 0.0 0.0 0.6 0.0 0.4 0.0 0.2 0.0 0.2 0.2 0.2 0.0 0.0 0.4 0.2 [2413] 0.0 0.2 0.8 0.0 0.2 0.2 0.2 0.4 0.8 0.6 0.2 0.8 0.0 0.0 0.0 0.0 0.8 0.0 [2431] 0.4 0.2 0.0 0.0 0.0 0.0 0.4 0.0 0.2 0.0 0.4 0.0 0.0 0.8 0.2 0.4 0.2 0.0 [2449] 0.8 0.2 0.0 0.0 0.2 0.6 0.4 0.4 0.0 0.0 0.0 0.6 0.0 0.0 0.0 0.4 0.0 0.2 [2467] 0.0 0.8 0.0 0.4 0.2 0.2 0.0 0.0 0.0 0.6 0.0 0.2 0.4 0.6 0.2 0.8 0.4 0.4 [2485] 0.6 0.6 0.4 0.6 0.0 0.2 0.2 0.2 0.6 0.0 0.0 0.0 0.0 0.2 0.6 0.2 0.0 0.0 [2503] 0.6 0.2 0.0 0.0 0.0 0.0 0.2 0.2 0.6 0.4 0.6 0.0 0.4 0.2 0.8 0.0 0.0 0.6 [2521] 0.0 0.2 0.0 0.0 0.4 0.8 0.0 0.2 0.0 0.0 0.6 0.0 0.2 0.8 0.0 0.4 0.2 0.8 [2539] 0.2 0.0 0.6 0.4 0.2 0.2 0.0 0.2 0.4 0.0 0.6 0.2 0.0 0.0 0.2 0.2 0.2 0.0 [2557] 0.0 0.2 0.4 1.0 0.2 0.0 0.0 0.0 0.6 0.0 0.4 0.8 0.6 1.0 0.0 0.0 0.6 0.2 [2575] 0.8 0.2 0.4 0.4 0.6 0.2 0.0 0.0 0.0 0.0 0.6 0.6 0.0 0.8 0.2 0.2 0.6 0.0 [2593] 0.6 0.4 0.4 0.6 0.0 0.8 0.0 0.6 0.6 0.6 0.4 0.6 0.4 0.8 NA 0.2 1.0 0.8 [2611] 0.8 0.0 0.4 0.6 0.0 0.4 0.8 0.8 0.6 0.6 0.4 0.2 0.0 0.0 0.2 0.4 0.8 0.0 [2629] 0.8 1.0 0.6 0.2 0.8 0.6 0.2 0.6 0.2 0.6 1.0 0.2 0.8 0.2 0.2 NA 0.0 0.0 [2647] 0.4 0.0 0.2 0.4 0.6 0.6 0.4 0.0 0.0 0.2 0.0 0.0 1.0 0.2 1.0 0.0 0.6 0.2 [2665] 0.0 0.2 0.8 0.8 0.0 0.4 0.6 0.0 0.4 0.6 0.2 0.6 0.0 0.2 0.2 0.2 0.4 0.2 [2683] 0.2 0.0 0.2 0.0 0.2 0.2 0.8 0.2 0.0 0.4 0.0 0.8 0.6 0.2 0.2 0.0 0.0 0.4 [2701] 0.0 0.8 0.6 0.6 0.2 0.0 0.2 0.4 0.2 0.0 0.4 0.2 0.0 1.0 0.6 0.6 0.2 0.6 [2719] 0.0 0.0 0.4 0.0 0.4 0.0 0.4 0.0 0.2 0.0 0.6 0.0 0.2 0.4 0.6 0.2 1.0 0.8 [2737] 0.2 0.2 0.4 0.2 0.0 0.0 1.0 0.0 0.4 0.0 0.0 0.4 0.2 1.0 1.0 0.0 0.2 0.2 [2755] 1.0 0.2 0.2 0.2 0.2 0.2 0.4 0.2 0.2 0.4 0.2 0.6 0.2 0.0 0.4 0.2 1.0 0.4 [2773] 0.0 0.2 0.4 0.2 0.0 0.0 0.8 0.0 0.0 0.4 0.0 0.0 0.2 0.2 0.0 0.2 0.2 0.4 [2791] 0.2 0.6 0.8 0.4 0.8 1.0 0.2 0.2 0.8 0.2 > pomp(x = vec, mini = 1, maxi = 6, unit = 50) # unit = 50 [1] 0.4 0.4 1.6 1.2 0.4 2.0 0.4 1.2 1.2 0.4 1.2 0.4 1.6 1.6 1.2 1.2 1.2 1.6 [19] 1.2 1.2 1.6 0.0 0.0 0.4 1.2 0.0 0.4 0.4 0.4 1.2 0.0 0.4 0.0 0.4 1.2 1.6 [37] 0.0 0.0 0.0 0.0 0.0 1.6 0.0 1.6 0.4 0.0 1.6 0.4 0.0 0.0 0.0 0.0 0.8 1.2 [55] 0.0 0.0 0.0 0.8 0.0 0.8 0.4 0.4 0.0 1.2 0.4 0.4 0.0 1.2 0.4 0.0 1.2 0.4 [73] 1.2 0.0 0.8 0.4 0.4 1.6 0.0 0.4 0.0 0.0 0.0 0.0 0.0 1.2 0.0 0.0 0.0 0.8 [91] 0.0 0.0 0.4 0.0 0.4 0.4 0.8 0.0 1.2 0.4 0.4 0.8 0.4 0.0 0.0 0.0 0.0 2.0 [109] 0.4 0.8 0.0 NA 0.0 0.0 0.0 1.2 0.0 1.6 0.4 0.0 1.2 0.4 2.0 0.4 0.4 0.4 [127] 0.4 0.4 0.4 1.2 0.4 0.8 1.2 0.4 0.0 0.0 0.0 0.0 0.0 0.8 0.4 0.4 0.4 0.0 [145] 1.6 0.4 0.0 0.4 0.4 0.0 0.0 0.0 0.4 1.2 0.4 0.8 0.0 0.4 0.4 0.4 0.0 0.0 [163] 0.4 0.0 0.0 0.4 0.4 0.4 0.0 0.0 0.8 1.2 0.0 0.4 0.4 0.0 0.0 2.0 1.2 0.8 [181] 1.2 0.0 0.8 1.2 0.8 0.0 0.0 0.4 0.4 0.0 0.4 0.0 0.4 1.6 0.0 0.0 0.4 0.4 [199] 0.4 0.0 0.4 0.0 0.8 0.4 0.0 0.4 0.4 0.0 0.0 0.4 0.4 0.0 1.6 0.8 0.0 0.4 [217] 0.0 0.4 1.6 1.6 0.4 0.0 0.0 0.0 0.0 0.0 0.8 0.4 0.0 0.4 1.6 0.4 1.6 0.8 [235] 0.8 0.4 0.0 0.0 0.8 0.8 0.0 0.4 1.2 0.0 1.2 0.8 1.6 0.8 1.2 0.8 0.8 1.2 [253] 2.0 0.8 1.6 0.8 1.6 0.0 0.0 1.2 1.6 0.8 2.0 0.4 1.2 0.8 0.4 0.0 0.0 1.2 [271] 0.8 1.2 0.0 0.0 0.4 0.8 0.0 0.0 1.2 0.4 0.8 0.4 0.4 0.4 0.8 0.0 0.4 0.4 [289] 0.0 0.0 0.0 0.4 0.8 0.8 0.0 1.6 0.0 0.8 1.2 0.8 0.0 0.0 0.4 0.8 1.2 0.4 [307] 1.6 0.0 0.8 0.0 0.8 0.0 0.4 0.0 0.8 0.4 0.8 0.8 0.4 0.4 0.4 0.4 1.6 1.2 [325] 0.8 0.0 0.4 1.2 0.0 0.4 0.0 0.4 1.2 0.0 0.4 0.8 0.8 0.0 0.4 0.0 0.8 0.0 [343] 0.4 0.0 0.4 0.0 0.4 1.6 0.4 0.4 1.2 0.4 0.8 0.4 0.4 0.0 1.2 0.0 0.4 1.2 [361] 0.8 0.0 0.0 0.0 1.2 0.0 NA 1.2 0.0 2.0 0.0 0.8 0.0 0.4 0.0 0.0 0.0 1.2 [379] 0.4 0.4 1.2 1.2 0.4 0.4 1.6 0.0 0.4 1.2 0.0 1.2 0.4 0.4 0.0 0.0 0.4 0.8 [397] 1.2 0.0 0.4 0.0 0.8 0.0 0.8 0.4 1.2 1.2 0.0 0.0 0.8 0.8 0.0 0.8 0.0 0.0 [415] 0.8 1.2 0.0 0.8 0.8 0.0 0.0 0.8 0.0 1.6 1.6 0.4 NA 0.0 0.4 0.4 0.4 0.4 [433] 0.4 0.4 0.4 0.4 0.4 0.4 1.2 0.4 1.6 0.4 0.0 1.2 0.0 0.4 0.4 1.6 2.0 0.0 [451] 1.2 1.2 0.8 1.6 0.0 0.4 0.0 0.4 0.4 1.6 0.4 0.4 0.0 0.8 0.0 0.0 0.4 0.4 [469] 0.0 0.0 0.8 0.4 0.0 0.4 0.4 1.2 2.0 0.4 0.4 0.0 0.0 0.0 0.0 0.8 0.4 0.0 [487] 0.4 0.0 0.4 0.0 0.0 1.2 0.0 0.4 0.0 1.2 1.2 0.4 1.2 0.0 0.8 0.0 1.6 1.6 [505] 0.4 0.4 0.0 0.0 0.4 0.0 0.8 2.0 0.4 0.8 0.4 0.4 0.0 0.0 0.0 0.8 1.2 0.0 [523] 0.4 0.0 0.4 2.0 0.0 0.0 0.4 1.2 0.0 0.8 0.0 0.4 0.0 0.0 0.8 1.6 1.6 0.8 [541] 0.8 1.2 0.0 0.0 0.4 0.4 1.6 0.4 0.0 1.2 0.0 1.2 1.2 0.0 0.8 0.0 0.4 0.0 [559] 0.0 0.0 0.4 1.6 0.8 0.0 0.0 0.0 0.0 0.0 1.6 0.4 0.0 0.0 0.8 2.0 0.0 0.8 [577] 0.0 0.8 0.0 0.4 1.2 0.0 0.8 0.0 0.4 0.4 1.2 0.4 2.0 0.0 0.0 0.0 0.4 1.2 [595] 0.0 0.0 1.6 NA 0.4 0.8 0.0 0.4 0.8 1.2 0.4 0.0 0.8 0.0 0.0 0.0 0.0 0.0 [613] 0.0 0.4 0.4 0.0 0.4 0.8 0.0 0.4 0.0 0.4 0.4 1.2 0.4 0.4 0.4 0.8 0.8 1.2 [631] 0.4 1.2 0.4 0.8 0.0 0.0 0.0 1.2 0.0 0.4 0.8 0.4 0.0 1.2 0.8 0.0 0.0 0.0 [649] 0.4 0.4 0.0 0.4 1.2 0.0 0.8 1.6 0.0 0.0 0.0 0.8 2.0 1.6 0.0 2.0 0.4 0.4 [667] 1.6 1.2 0.4 0.8 0.4 1.6 0.0 0.4 0.0 0.0 1.2 NA 0.4 1.6 0.0 1.2 1.2 0.4 [685] 0.4 0.0 0.0 0.8 0.4 0.4 0.8 0.0 0.8 0.4 0.4 1.6 0.0 0.8 0.0 0.0 0.8 0.4 [703] 0.0 0.4 1.6 1.6 0.8 0.0 0.0 0.0 0.8 0.0 1.6 1.2 0.4 0.4 0.4 0.4 1.6 0.4 [721] 0.4 0.4 0.8 0.4 0.4 0.4 0.0 0.0 0.4 0.4 0.0 0.0 0.4 0.0 0.0 0.0 0.4 0.8 [739] 0.0 0.4 0.0 0.0 1.6 1.6 0.8 0.0 0.8 0.4 0.8 0.8 0.4 0.8 1.2 0.4 0.8 0.0 [757] 0.4 0.0 0.8 0.8 0.4 0.4 0.4 0.0 0.0 0.4 0.0 0.8 0.4 0.0 0.0 0.4 0.0 0.4 [775] 0.0 1.2 0.8 0.0 0.8 0.0 NA 0.0 0.0 0.8 1.6 0.0 1.2 0.4 1.6 0.8 0.0 1.2 [793] 0.0 0.4 0.0 0.0 0.8 1.2 0.0 0.4 1.2 0.8 0.0 1.2 0.0 0.4 1.6 0.4 0.4 0.4 [811] 0.4 0.0 0.0 2.0 1.2 0.8 0.4 0.4 0.4 1.2 0.4 0.4 0.0 0.4 1.6 0.8 1.6 0.4 [829] 0.0 0.4 0.0 0.4 0.0 1.6 0.4 0.8 0.4 0.0 1.2 0.8 0.8 0.8 2.0 2.0 0.8 0.8 [847] 1.2 1.2 0.0 0.4 0.4 0.8 0.0 0.0 0.4 0.0 0.4 0.0 1.6 1.2 0.0 0.0 0.0 0.0 [865] 0.4 0.0 2.0 0.8 1.2 2.0 0.0 0.8 0.0 0.0 0.4 0.4 1.6 0.0 2.0 1.2 1.2 0.4 [883] 0.4 0.0 1.2 0.8 0.0 0.0 0.4 2.0 1.6 0.8 0.4 1.6 0.0 0.8 0.0 0.8 0.4 0.4 [901] 1.2 0.0 0.4 0.4 0.0 0.4 1.2 0.0 0.0 0.8 1.6 0.0 0.0 0.4 0.4 0.4 0.0 0.4 [919] 0.0 0.4 0.0 0.4 0.8 0.4 0.4 0.0 0.4 0.0 0.4 1.2 0.4 0.4 0.0 0.0 0.4 0.0 [937] 0.4 0.4 0.4 0.4 1.2 0.0 0.4 0.8 0.0 1.6 0.4 0.8 0.0 0.0 0.0 0.4 0.4 0.4 [955] 1.2 0.4 1.2 0.0 1.2 0.8 0.8 0.4 0.0 0.4 0.4 0.4 0.4 1.6 0.0 0.0 0.4 0.0 [973] 1.6 0.0 2.0 0.0 0.4 0.0 0.0 0.4 0.4 0.0 0.8 1.2 0.0 0.0 0.4 1.2 0.8 0.8 [991] 0.0 1.2 0.0 1.2 0.8 0.4 0.4 0.4 0.0 2.0 0.4 1.2 0.0 0.4 0.4 0.0 0.0 0.8 [1009] 0.0 0.4 0.8 0.4 0.0 0.0 0.8 0.0 0.4 0.4 1.2 1.2 1.6 0.0 2.0 0.0 0.0 0.0 [1027] 0.4 0.8 1.6 0.0 0.4 0.4 0.8 0.0 1.2 0.0 1.2 0.0 0.0 1.2 0.0 0.8 0.0 1.6 [1045] 1.6 1.6 0.0 1.2 0.4 1.6 0.0 0.8 1.2 0.8 0.0 0.4 0.4 1.6 0.8 1.2 0.4 0.0 [1063] 0.0 1.6 1.2 1.2 0.4 1.6 0.8 0.8 0.4 1.6 0.8 2.0 0.0 1.2 0.0 0.0 0.8 0.0 [1081] 0.8 0.8 0.0 0.0 0.4 0.0 1.2 0.4 1.2 0.4 0.0 0.0 0.0 0.4 0.8 1.6 0.4 0.0 [1099] 0.0 0.4 0.4 0.0 0.4 0.4 0.4 1.2 0.0 1.6 0.0 0.4 0.8 0.4 1.6 0.8 0.4 1.2 [1117] 0.4 0.4 2.0 0.0 0.8 0.8 0.4 0.4 0.4 0.8 0.8 0.4 0.4 0.0 0.4 1.2 0.0 1.2 [1135] 0.8 0.0 0.0 0.0 0.0 0.0 1.2 1.6 1.6 2.0 1.2 0.0 0.4 0.8 0.4 0.0 0.8 0.0 [1153] 0.4 0.0 1.2 0.4 1.2 0.0 0.4 1.2 0.0 1.6 0.4 0.0 0.0 0.4 1.2 0.0 0.0 0.4 [1171] 0.8 0.0 1.2 0.8 0.8 0.4 1.2 0.4 0.0 0.0 0.0 0.4 0.4 0.8 2.0 0.8 0.0 0.8 [1189] 0.8 0.8 0.4 0.4 0.4 0.4 0.4 0.8 0.4 0.8 0.8 0.0 0.0 0.4 1.6 0.0 0.4 0.0 [1207] 0.0 0.4 NA 0.4 1.2 0.4 2.0 0.4 0.4 1.2 1.2 0.4 0.4 0.4 1.2 0.4 0.0 0.4 [1225] 0.0 0.4 0.4 0.4 0.4 1.2 0.4 0.4 0.4 0.4 0.0 0.0 1.6 0.0 0.8 0.4 1.6 0.4 [1243] 0.4 0.4 1.6 0.8 0.8 0.0 0.4 0.4 0.4 0.4 1.2 0.4 0.4 0.4 1.6 0.4 0.4 0.0 [1261] 1.2 0.8 1.6 1.2 1.6 0.0 0.0 0.8 0.8 0.0 0.4 0.0 0.4 0.4 0.4 0.0 1.6 0.4 [1279] 0.0 0.4 1.2 0.4 0.4 0.8 0.4 2.0 0.0 0.4 0.8 0.4 0.8 1.2 1.2 1.2 0.8 0.4 [1297] 0.8 1.6 0.4 0.4 0.4 0.4 0.0 0.0 0.8 0.0 0.4 1.2 0.4 0.0 0.4 0.0 1.6 0.0 [1315] 1.6 0.8 1.2 0.8 0.8 0.0 0.4 1.2 0.4 0.8 0.8 0.4 0.4 0.4 0.8 1.2 1.2 0.8 [1333] 0.4 0.4 0.0 0.4 0.0 1.6 0.4 0.0 0.8 0.0 0.4 0.0 0.8 0.8 1.6 1.2 0.4 0.0 [1351] 0.4 0.0 0.8 NA 0.0 0.8 0.4 0.8 0.4 0.4 1.6 0.0 0.8 1.2 0.0 0.0 0.4 1.6 [1369] 0.8 0.0 0.4 1.6 0.0 0.0 0.8 1.6 1.2 0.8 0.0 0.4 0.0 0.0 1.6 2.0 0.0 0.4 [1387] 0.0 0.4 0.4 0.0 0.4 1.6 0.0 0.0 0.0 0.0 0.4 0.0 1.2 1.2 0.8 1.2 2.0 0.0 [1405] 0.8 0.0 2.0 0.0 0.4 1.2 0.0 0.4 1.6 0.0 1.2 0.0 0.0 0.4 0.8 0.8 0.0 0.8 [1423] 0.0 0.4 0.4 0.8 1.6 0.8 0.0 0.0 0.0 0.0 1.2 0.4 0.4 0.4 1.2 0.0 0.8 0.4 [1441] 0.4 0.4 0.4 0.0 1.2 0.0 0.4 0.8 0.0 1.6 0.4 1.2 1.2 0.0 0.0 1.6 0.0 0.4 [1459] 1.2 0.8 1.2 0.0 2.0 0.4 0.4 0.0 0.8 0.0 0.8 1.2 0.4 0.8 0.4 0.0 1.2 1.6 [1477] 0.4 0.4 0.0 1.2 0.4 0.0 0.0 0.0 0.4 1.6 0.4 0.0 0.8 0.4 0.4 0.0 0.4 1.2 [1495] 1.2 0.4 1.6 1.6 1.2 0.4 0.4 1.2 1.2 0.4 0.4 0.0 0.0 2.0 0.8 0.4 1.2 0.0 [1513] 0.4 1.2 0.0 0.4 0.0 0.0 1.2 0.4 0.0 0.4 0.4 0.4 1.2 0.4 0.8 0.8 1.2 1.2 [1531] 1.2 0.0 1.6 0.8 0.0 0.4 0.8 0.0 0.0 0.0 0.8 0.8 1.2 1.6 0.0 1.2 0.0 1.2 [1549] 1.2 2.0 2.0 0.0 0.8 0.0 1.6 0.0 1.6 0.0 0.0 0.0 0.8 0.0 2.0 2.0 2.0 2.0 [1567] 2.0 2.0 2.0 2.0 2.0 2.0 0.8 0.0 0.4 0.4 0.4 1.2 0.0 0.8 1.2 0.0 1.6 1.2 [1585] 0.4 1.6 1.2 1.2 0.8 0.8 0.4 0.4 0.8 1.6 0.0 0.0 0.4 0.4 0.8 1.6 0.4 1.2 [1603] 0.4 0.0 0.0 0.8 0.4 0.8 0.0 1.2 0.0 0.0 0.4 0.4 0.4 0.4 0.4 0.8 0.0 0.4 [1621] 0.4 1.2 0.8 0.0 0.4 0.0 0.0 0.0 0.4 1.6 0.4 1.6 2.0 0.4 0.4 0.8 0.0 0.0 [1639] 0.0 0.4 0.4 0.0 0.0 1.2 0.8 0.8 0.4 0.8 0.0 0.8 0.8 1.2 0.0 0.0 0.8 0.0 [1657] 0.0 0.8 0.0 1.2 1.2 0.4 0.4 0.4 0.0 0.0 0.8 0.4 1.2 0.8 0.0 0.4 0.4 0.0 [1675] 0.8 0.8 1.2 0.4 1.6 1.2 1.2 0.4 0.8 0.0 0.4 0.0 0.4 0.8 0.0 1.6 0.8 1.6 [1693] 0.8 0.4 0.0 1.2 0.0 0.0 0.4 0.0 0.0 0.8 0.0 0.4 0.4 0.4 0.8 0.4 1.2 NA [1711] 1.2 0.0 0.0 0.4 0.4 0.4 0.0 0.0 1.6 1.2 1.6 0.0 0.0 0.4 0.8 0.4 0.4 0.4 [1729] 0.4 0.4 0.0 0.0 0.4 0.4 0.4 0.4 0.8 0.0 0.8 1.6 0.0 0.0 0.8 0.0 1.2 0.0 [1747] 0.4 0.0 0.0 0.0 1.6 1.6 1.6 0.0 0.4 0.8 0.0 0.0 1.2 0.4 0.4 0.0 0.0 1.2 [1765] 0.0 0.0 0.8 0.8 2.0 0.4 0.0 0.0 1.2 0.0 2.0 0.0 0.4 0.0 0.4 1.6 1.2 0.4 [1783] 1.6 2.0 0.8 0.4 0.0 0.0 0.4 0.0 1.2 0.8 0.0 0.8 0.0 0.0 0.4 0.0 0.4 1.6 [1801] 0.4 0.0 0.0 0.0 1.2 1.2 0.0 0.0 0.0 1.6 0.0 0.0 0.0 0.8 1.6 0.8 0.0 0.0 [1819] 0.0 1.6 0.4 0.4 0.0 0.4 0.0 0.4 1.6 0.0 0.0 0.4 0.4 1.2 0.4 0.4 0.0 1.2 [1837] 0.0 0.8 0.4 0.4 0.4 0.4 0.0 0.0 0.0 0.0 0.4 1.6 0.4 0.0 0.4 0.0 0.0 0.8 [1855] 0.8 0.0 0.0 0.0 0.4 0.0 0.8 0.8 0.0 0.4 0.0 0.0 0.4 1.6 0.4 0.4 0.0 0.0 [1873] 0.8 0.0 1.2 0.4 1.2 0.0 0.0 0.8 0.4 0.0 0.8 0.0 1.6 0.4 NA 0.8 0.4 0.4 [1891] 0.8 1.2 NA 1.6 0.8 1.6 0.0 0.8 0.4 0.4 0.0 0.0 0.4 0.0 1.6 1.6 1.2 0.0 [1909] 0.0 1.6 1.6 2.0 1.6 0.4 0.0 0.8 2.0 1.2 0.4 1.6 0.8 0.8 0.0 0.8 1.6 0.0 [1927] 1.6 0.4 1.6 1.6 0.4 NA 1.2 0.0 0.0 0.4 1.2 0.0 0.8 0.0 1.6 1.6 0.4 0.0 [1945] 0.0 0.8 0.0 0.4 0.4 1.2 0.4 0.0 2.0 0.4 0.0 0.0 0.0 0.0 0.0 1.6 0.0 0.4 [1963] 0.0 2.0 0.8 0.8 1.2 1.6 1.6 1.6 2.0 0.0 0.8 0.0 0.0 0.4 0.8 1.6 0.0 0.4 [1981] 0.4 0.0 0.0 1.6 0.8 1.2 0.0 1.2 0.0 1.6 0.4 0.4 0.0 0.0 0.8 0.0 0.8 1.6 [1999] 0.0 0.0 0.4 0.8 0.0 0.8 0.0 0.4 2.0 0.8 0.0 1.2 1.2 0.8 0.4 0.4 0.4 0.0 [2017] 0.4 NA 0.4 1.2 1.6 1.6 0.0 0.8 0.8 0.0 1.6 0.4 0.4 0.4 1.2 0.0 0.4 1.6 [2035] 0.8 0.0 0.0 0.0 0.4 0.0 0.0 0.0 0.0 0.0 0.0 0.4 0.8 1.6 0.4 0.0 0.4 0.8 [2053] 0.4 0.4 0.4 0.0 1.6 0.0 1.6 0.0 0.4 0.0 0.4 1.2 0.8 0.4 2.0 0.4 1.2 0.4 [2071] 0.4 0.8 0.4 0.4 1.2 0.0 0.4 0.0 0.4 1.6 0.8 0.0 0.0 0.4 0.4 0.0 1.6 0.0 [2089] 0.8 0.0 1.2 1.2 1.6 0.4 1.6 0.0 0.0 0.4 0.0 0.0 0.0 0.4 0.4 0.4 0.0 0.0 [2107] 1.2 0.0 0.4 0.0 0.4 1.2 0.0 0.0 0.8 0.4 1.2 0.4 0.4 0.0 0.4 0.4 0.4 2.0 [2125] 1.2 2.0 0.0 0.8 0.4 0.0 0.4 1.2 1.2 0.4 0.0 0.4 0.0 0.0 1.2 0.4 0.0 0.4 [2143] 0.0 0.0 0.4 0.0 0.8 0.0 1.2 0.4 1.6 0.4 0.4 0.0 0.0 0.0 0.4 0.0 1.2 1.2 [2161] 0.0 1.6 1.2 0.0 0.8 1.6 0.0 0.8 0.4 0.4 0.4 1.2 0.0 0.8 0.0 0.4 0.4 1.6 [2179] 0.0 0.0 0.4 0.0 0.8 0.4 0.0 0.4 0.0 0.8 0.0 1.2 1.6 0.0 0.0 0.4 0.0 0.0 [2197] 0.4 0.8 0.0 1.2 0.0 1.6 0.8 0.8 0.4 0.8 0.4 0.0 0.4 0.4 1.2 0.0 0.4 0.0 [2215] 0.0 1.2 0.0 0.4 0.4 0.0 0.4 0.4 0.4 0.4 0.4 1.2 2.0 0.4 0.8 0.0 0.0 0.4 [2233] 0.8 2.0 0.0 0.0 0.0 0.4 0.4 0.8 0.4 0.4 1.6 1.2 0.0 0.0 0.8 0.0 0.4 0.0 [2251] 0.0 1.2 0.0 0.4 1.2 0.8 1.6 1.2 0.0 0.8 0.0 0.0 0.0 1.2 1.6 0.0 0.0 0.0 [2269] 0.0 1.2 0.0 0.4 2.0 0.4 0.4 0.4 0.0 0.4 0.8 0.0 0.4 0.4 0.0 0.0 0.8 0.4 [2287] 1.2 0.0 0.0 0.4 0.4 0.4 0.4 1.2 0.8 1.2 0.0 0.4 0.0 0.8 0.8 0.4 0.4 0.8 [2305] 0.0 0.4 2.0 0.4 0.0 0.4 0.0 1.2 2.0 0.8 0.0 1.2 0.0 0.8 1.6 0.4 1.2 1.6 [2323] 1.2 2.0 0.0 0.0 0.0 0.4 0.0 0.8 0.4 0.0 0.4 1.6 0.0 1.2 1.2 0.4 0.8 1.6 [2341] 0.4 0.4 1.2 0.8 0.0 0.0 0.8 0.8 0.4 1.6 0.8 1.2 0.8 0.4 0.0 0.0 0.0 1.2 [2359] 0.0 0.8 0.4 1.2 0.4 0.8 0.4 0.4 0.4 0.0 0.0 0.4 0.0 0.0 1.2 0.0 0.8 1.6 [2377] 0.0 0.0 0.0 1.6 0.4 0.8 1.2 1.2 0.0 0.0 0.4 1.2 0.0 0.4 0.4 1.2 1.2 0.4 [2395] 0.8 NA 0.0 0.0 0.0 1.2 0.0 0.8 0.0 0.4 0.0 0.4 0.4 0.4 0.0 0.0 0.8 0.4 [2413] 0.0 0.4 1.6 0.0 0.4 0.4 0.4 0.8 1.6 1.2 0.4 1.6 0.0 0.0 0.0 0.0 1.6 0.0 [2431] 0.8 0.4 0.0 0.0 0.0 0.0 0.8 0.0 0.4 0.0 0.8 0.0 0.0 1.6 0.4 0.8 0.4 0.0 [2449] 1.6 0.4 0.0 0.0 0.4 1.2 0.8 0.8 0.0 0.0 0.0 1.2 0.0 0.0 0.0 0.8 0.0 0.4 [2467] 0.0 1.6 0.0 0.8 0.4 0.4 0.0 0.0 0.0 1.2 0.0 0.4 0.8 1.2 0.4 1.6 0.8 0.8 [2485] 1.2 1.2 0.8 1.2 0.0 0.4 0.4 0.4 1.2 0.0 0.0 0.0 0.0 0.4 1.2 0.4 0.0 0.0 [2503] 1.2 0.4 0.0 0.0 0.0 0.0 0.4 0.4 1.2 0.8 1.2 0.0 0.8 0.4 1.6 0.0 0.0 1.2 [2521] 0.0 0.4 0.0 0.0 0.8 1.6 0.0 0.4 0.0 0.0 1.2 0.0 0.4 1.6 0.0 0.8 0.4 1.6 [2539] 0.4 0.0 1.2 0.8 0.4 0.4 0.0 0.4 0.8 0.0 1.2 0.4 0.0 0.0 0.4 0.4 0.4 0.0 [2557] 0.0 0.4 0.8 2.0 0.4 0.0 0.0 0.0 1.2 0.0 0.8 1.6 1.2 2.0 0.0 0.0 1.2 0.4 [2575] 1.6 0.4 0.8 0.8 1.2 0.4 0.0 0.0 0.0 0.0 1.2 1.2 0.0 1.6 0.4 0.4 1.2 0.0 [2593] 1.2 0.8 0.8 1.2 0.0 1.6 0.0 1.2 1.2 1.2 0.8 1.2 0.8 1.6 NA 0.4 2.0 1.6 [2611] 1.6 0.0 0.8 1.2 0.0 0.8 1.6 1.6 1.2 1.2 0.8 0.4 0.0 0.0 0.4 0.8 1.6 0.0 [2629] 1.6 2.0 1.2 0.4 1.6 1.2 0.4 1.2 0.4 1.2 2.0 0.4 1.6 0.4 0.4 NA 0.0 0.0 [2647] 0.8 0.0 0.4 0.8 1.2 1.2 0.8 0.0 0.0 0.4 0.0 0.0 2.0 0.4 2.0 0.0 1.2 0.4 [2665] 0.0 0.4 1.6 1.6 0.0 0.8 1.2 0.0 0.8 1.2 0.4 1.2 0.0 0.4 0.4 0.4 0.8 0.4 [2683] 0.4 0.0 0.4 0.0 0.4 0.4 1.6 0.4 0.0 0.8 0.0 1.6 1.2 0.4 0.4 0.0 0.0 0.8 [2701] 0.0 1.6 1.2 1.2 0.4 0.0 0.4 0.8 0.4 0.0 0.8 0.4 0.0 2.0 1.2 1.2 0.4 1.2 [2719] 0.0 0.0 0.8 0.0 0.8 0.0 0.8 0.0 0.4 0.0 1.2 0.0 0.4 0.8 1.2 0.4 2.0 1.6 [2737] 0.4 0.4 0.8 0.4 0.0 0.0 2.0 0.0 0.8 0.0 0.0 0.8 0.4 2.0 2.0 0.0 0.4 0.4 [2755] 2.0 0.4 0.4 0.4 0.4 0.4 0.8 0.4 0.4 0.8 0.4 1.2 0.4 0.0 0.8 0.4 2.0 0.8 [2773] 0.0 0.4 0.8 0.4 0.0 0.0 1.6 0.0 0.0 0.8 0.0 0.0 0.4 0.4 0.0 0.4 0.4 0.8 [2791] 0.4 1.2 1.6 0.8 1.6 2.0 0.4 0.4 1.6 0.4 > > > > cleanEx() > nameEx("pomps") > ### * pomps > > flush(stderr()); flush(stdout()) > > ### Name: pomps > ### Title: Recode Numeric Data to Percentage of Maximum Possible (POMP) > ### Units > ### Aliases: pomps > > ### ** Examples > > vrb_nm <- names(psych::bfi)[grepl(pattern = "A", x = names(psych::bfi))] > pomps(data = psych::bfi, vrb.nm = vrb_nm, min = 1, max = 6) # absolute POMP units A1_p1 A2_p1 A3_p1 A4_p1 A5_p1 61617 20 60 40 60 60 61618 20 60 80 20 80 61620 80 60 80 60 60 61621 60 60 100 80 80 61622 20 40 40 60 80 61623 100 100 80 100 80 61624 20 80 80 40 80 61629 60 40 0 80 0 61630 60 40 100 40 40 61633 20 80 100 100 80 61634 60 60 80 100 80 61636 20 80 80 80 80 61637 80 80 80 100 60 61639 80 80 80 100 100 61640 60 80 20 20 0 61643 60 40 100 100 40 61650 60 100 100 20 80 61651 80 80 80 60 80 61653 60 60 80 60 40 61654 60 60 100 80 80 61656 80 60 20 0 20 61659 0 100 100 0 80 61661 0 80 100 80 100 61664 20 100 80 100 80 61667 60 80 80 100 80 61668 0 100 100 0 100 61669 20 60 60 60 40 61670 20 80 100 100 100 61672 20 80 0 40 80 61673 60 80 100 80 80 61678 0 100 80 100 40 61679 20 80 100 100 100 61682 0 80 100 80 60 61683 20 60 80 100 80 61684 60 60 60 60 60 61685 80 40 80 60 20 61686 0 100 60 100 60 61687 0 60 60 20 40 61688 0 100 100 100 100 61691 0 80 60 40 80 61692 0 80 80 100 80 61693 80 60 40 100 60 61696 0 80 60 60 80 61698 80 100 60 40 80 61700 20 100 100 100 100 61701 0 100 100 100 100 61702 80 80 40 60 40 61703 20 100 60 80 80 61713 0 80 40 20 40 61715 0 100 100 100 100 61716 0 100 100 100 60 61723 0 80 100 80 60 61724 40 100 60 60 60 61725 60 40 80 100 40 61728 0 100 100 100 100 61730 0 60 40 80 80 61731 0 60 20 20 20 61732 40 60 80 20 60 61740 0 100 80 60 60 61742 40 40 80 60 80 61748 20 40 60 60 80 61749 20 60 60 80 40 61754 0 60 100 100 100 61756 60 80 40 80 60 61757 20 100 100 100 100 61759 20 NA 60 100 60 61761 0 80 60 20 80 61762 60 40 20 20 20 61763 20 40 60 80 100 61764 0 100 100 40 100 61771 60 80 100 100 60 61772 20 60 100 20 80 61773 60 60 60 80 40 61775 0 80 80 80 60 61776 40 80 80 80 60 61777 20 80 80 100 80 61778 20 100 100 100 100 61780 80 100 100 100 80 61782 0 20 20 60 20 61783 20 80 80 80 80 61784 0 80 100 80 40 61788 0 100 40 40 0 61789 0 60 100 20 100 61793 0 100 100 80 100 61794 0 100 80 20 100 61797 60 60 60 100 80 61798 0 80 80 80 80 61801 0 100 80 60 100 61808 0 100 60 100 60 61812 40 60 80 80 60 61813 0 80 80 80 80 61816 0 80 100 20 80 61818 20 60 80 60 80 61819 0 100 100 40 100 61821 20 100 60 100 60 61822 20 40 40 60 40 61825 40 0 20 20 20 61826 0 60 80 80 100 61829 60 40 20 100 80 61831 20 100 20 40 60 61834 20 80 80 40 80 61835 40 60 60 20 60 61838 20 80 80 80 20 61839 0 60 80 20 100 61840 0 100 60 0 0 61841 0 100 80 100 100 61847 0 80 80 80 20 61848 100 0 0 0 20 61851 20 100 80 100 80 61852 40 100 80 100 60 61854 0 100 100 100 100 61856 NA 60 80 100 60 61857 0 100 100 80 60 61861 0 100 100 100 100 61862 0 100 80 60 80 61865 60 20 0 20 20 61868 0 80 60 40 60 61873 80 20 20 20 0 61874 20 80 60 40 60 61880 0 80 100 100 100 61886 60 100 80 80 100 61888 20 80 60 60 60 61889 100 80 80 20 80 61890 20 80 80 0 100 61891 20 60 80 80 80 61895 20 80 80 80 20 61896 20 80 100 80 100 61900 20 80 60 60 80 61901 20 80 80 40 20 61907 60 80 60 NA 40 61908 20 60 60 60 60 61909 40 80 60 80 60 61911 60 40 40 40 40 61913 20 60 40 40 40 61915 0 100 100 100 100 61918 0 100 100 80 100 61921 0 100 100 60 100 61922 0 100 80 100 80 61923 0 100 80 100 80 61925 40 100 100 60 100 61926 20 40 0 40 0 61928 20 60 80 80 80 61932 20 80 40 80 0 61935 0 100 80 100 100 61936 80 60 60 80 100 61939 20 80 60 40 40 61944 0 100 100 40 100 61945 20 100 60 80 60 61949 20 100 40 80 20 61952 0 80 80 100 80 61953 0 80 80 40 40 61954 0 100 100 80 100 61957 20 80 80 100 80 61958 60 60 40 0 60 61965 20 100 80 80 40 61967 40 80 100 80 80 61968 0 100 100 100 80 61969 20 80 80 80 60 61971 20 60 60 0 40 61972 20 60 80 80 60 61973 0 80 80 100 80 61974 0 100 100 20 80 61975 20 100 60 80 60 61976 0 80 20 40 60 61978 0 100 100 100 100 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60 60 40 67518 40 80 60 40 60 67521 20 80 80 80 80 67522 0 100 100 100 100 67523 0 100 100 100 100 67524 80 100 60 0 80 67525 0 100 0 100 100 67526 0 100 60 100 40 67528 40 80 100 100 100 67529 0 NA 100 NA 20 67530 0 80 100 80 100 67531 20 80 60 80 60 67533 20 100 100 100 80 67534 0 60 40 80 80 67535 20 100 80 100 100 67537 20 100 80 60 80 67539 40 60 60 40 80 67540 20 80 40 80 60 67541 60 60 80 0 20 67544 80 80 80 100 80 67547 40 60 40 0 40 67549 80 80 80 80 40 67551 100 0 40 40 40 67552 20 60 60 40 80 67556 20 40 80 20 80 67559 80 20 20 60 60 67560 20 40 0 60 20 > pomps(data = psych::bfi, vrb.nm = vrb_nm, relative = TRUE) # relative POMP units A1_p1 A2_p1 A3_p1 A4_p1 A5_p1 61617 20 60 40 60 60 61618 20 60 80 20 80 61620 80 60 80 60 60 61621 60 60 100 80 80 61622 20 40 40 60 80 61623 100 100 80 100 80 61624 20 80 80 40 80 61629 60 40 0 80 0 61630 60 40 100 40 40 61633 20 80 100 100 80 61634 60 60 80 100 80 61636 20 80 80 80 80 61637 80 80 80 100 60 61639 80 80 80 100 100 61640 60 80 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40 40 66718 20 80 40 100 80 66722 40 80 100 100 100 66723 NA 100 100 100 NA 66724 0 100 80 60 80 66727 0 100 100 60 80 66728 0 80 80 40 60 66729 60 80 100 0 100 66730 0 80 80 100 100 66731 40 80 80 40 40 66733 0 60 60 60 80 66734 20 80 80 0 80 66737 0 100 100 100 100 66739 20 100 80 100 100 66741 20 80 80 40 80 66743 20 80 80 40 100 66744 0 80 80 60 80 66745 0 100 80 100 80 66748 40 20 80 100 100 66749 20 80 40 0 60 66751 0 100 100 100 100 66755 20 80 80 80 80 66756 80 40 100 100 NA 66757 0 80 60 20 40 66760 20 20 60 100 60 66764 20 80 80 100 80 66766 20 80 100 80 100 66768 40 80 20 100 20 66769 80 100 80 100 100 66770 60 80 80 40 100 66771 20 60 60 80 60 66772 80 60 60 60 40 66775 0 100 100 80 100 66778 0 100 80 80 80 66779 0 100 80 100 100 66785 0 100 100 80 100 66788 80 100 80 100 80 66790 0 80 80 80 60 66792 40 60 80 100 80 66793 20 100 100 100 40 66797 0 80 60 100 80 66800 0 100 100 100 100 66801 0 80 80 100 80 66804 0 100 100 100 100 66806 40 80 80 80 80 66807 0 100 80 100 100 66810 20 60 40 80 80 66812 0 80 100 80 100 66813 40 80 100 80 80 66814 0 100 100 80 100 66817 0 80 80 100 80 66819 80 100 100 80 80 66820 20 80 60 80 40 66824 40 60 60 100 40 66826 20 80 80 60 100 66830 0 80 80 100 80 66831 80 80 80 80 60 66832 20 60 60 80 60 66833 0 80 60 100 60 66838 0 100 100 100 80 66839 20 80 80 60 80 66840 60 100 80 100 60 66842 40 100 80 80 80 66843 40 60 60 20 100 66845 0 60 100 20 100 66847 0 60 60 80 60 66848 0 100 100 80 100 66850 60 60 60 60 60 66853 0 100 80 80 80 66855 0 80 80 100 80 66856 0 100 80 100 100 66857 40 20 20 60 60 66858 0 80 80 20 80 66860 20 80 80 60 80 66863 0 80 80 60 80 66864 80 80 60 80 60 66867 0 60 80 60 80 66868 40 20 20 0 80 66869 20 60 80 80 80 66872 20 80 80 100 80 66873 0 60 20 60 100 66879 0 100 100 0 100 66886 0 100 100 100 100 66887 60 0 20 60 20 66890 0 100 100 80 100 66892 20 80 80 100 60 66896 40 40 0 60 40 66897 60 60 40 0 40 66898 20 80 100 60 60 66899 80 20 0 0 60 66902 40 60 20 20 40 66903 40 60 40 60 80 66905 60 60 60 80 60 66911 60 100 60 20 80 66916 40 20 20 20 20 66917 60 0 40 40 80 66918 0 100 80 80 80 66919 20 60 60 20 20 66920 20 80 80 80 60 66921 20 80 80 80 100 66924 60 60 40 100 80 66925 0 100 80 0 80 66926 0 80 100 100 100 66927 0 100 100 100 100 66929 0 80 60 100 60 66930 20 100 80 100 60 66932 60 20 0 0 20 66934 20 80 60 100 20 66935 0 80 80 0 20 66937 0 100 100 40 100 66944 60 60 60 80 80 66945 20 60 40 100 80 66946 0 80 40 100 40 66947 0 100 80 80 60 66949 0 100 60 60 20 66953 0 60 60 60 80 66960 20 80 80 80 80 66962 20 80 80 80 60 66965 60 60 20 60 100 66968 40 40 60 20 60 66970 60 20 100 100 20 66982 0 100 100 80 80 66983 40 80 60 80 80 66985 20 60 80 80 60 66986 80 60 40 60 80 66987 0 80 60 80 20 66988 0 80 80 80 100 66994 60 60 60 60 60 66995 0 100 80 60 80 66997 20 0 0 0 20 67000 0 80 80 80 60 67003 0 80 60 20 20 67007 40 20 20 20 40 67008 80 80 80 100 80 67013 0 100 80 80 100 67014 20 100 80 60 80 67017 0 80 80 20 100 67018 0 100 100 60 100 67019 60 80 60 100 80 67022 0 60 80 80 60 67023 20 60 80 20 80 67024 80 20 40 40 20 67025 0 100 100 100 100 67026 40 80 40 80 80 67027 20 80 80 60 80 67029 80 60 20 100 60 67031 20 60 40 60 60 67033 0 100 80 40 80 67034 60 60 80 60 80 67036 40 80 60 NA 100 67039 20 100 80 60 60 67040 20 20 0 20 40 67041 0 80 80 100 100 67042 20 60 60 40 60 67043 40 80 80 100 80 67045 0 80 60 80 100 67046 60 60 80 100 80 67049 20 80 100 60 80 67055 0 80 80 80 80 67056 0 100 100 100 100 67057 20 40 60 80 60 67059 20 100 60 60 60 67064 20 80 100 80 80 67065 0 100 80 20 80 67066 0 100 100 80 100 67070 20 60 60 100 40 67072 40 40 60 60 80 67073 100 100 100 100 100 67075 20 80 80 80 80 67078 0 100 100 80 40 67079 0 80 80 100 100 67080 0 80 100 80 100 67082 60 80 80 80 80 67085 0 100 100 80 60 67087 40 60 60 80 60 67091 80 0 60 40 60 67093 60 100 80 80 80 67095 100 100 80 0 100 67096 0 80 80 100 80 67098 0 100 100 100 100 67101 60 80 20 100 60 67104 20 100 80 100 80 67107 80 20 40 20 60 67109 20 100 80 100 80 67115 40 60 60 40 80 67116 40 60 60 60 60 67118 60 40 40 20 40 67119 20 80 60 100 60 67121 0 80 100 80 40 67123 0 60 40 80 40 67124 0 60 100 60 100 67126 0 80 80 100 100 67132 60 100 60 80 80 67134 60 60 100 20 100 67135 0 100 100 100 80 67136 80 80 100 20 80 67140 20 60 40 60 60 67143 20 100 100 100 80 67144 60 100 80 80 100 67145 0 100 80 100 60 67146 60 80 80 60 20 67148 40 40 60 60 40 67149 40 40 80 100 80 67152 60 80 80 80 80 67154 0 100 100 100 80 67155 80 20 20 80 60 67156 0 20 40 80 0 67157 60 80 60 100 60 67158 60 40 100 100 100 67161 60 80 0 80 60 67162 40 40 60 80 60 67168 60 80 80 100 60 67174 40 40 80 80 80 67179 80 60 0 0 0 67180 NA 20 20 80 20 67183 20 80 20 100 40 67184 100 60 60 0 100 67185 80 20 20 100 20 67186 80 80 100 100 60 67188 0 0 0 20 0 67189 40 40 20 20 100 67190 60 60 80 40 80 67191 0 60 80 20 60 67197 40 40 80 80 60 67202 80 20 0 20 20 67203 80 40 60 100 20 67206 60 100 80 100 80 67213 60 60 60 80 60 67215 40 20 40 0 40 67220 20 80 60 40 40 67222 0 80 80 80 60 67223 0 NA 100 100 100 67224 20 40 60 40 40 67225 40 80 80 100 20 67226 80 80 40 20 80 67229 0 80 80 20 80 67230 80 60 80 20 60 67232 100 100 80 100 100 67234 60 100 80 100 60 67237 20 80 80 80 60 67238 80 40 80 40 40 67239 60 20 20 60 40 67240 20 100 60 80 60 67242 60 20 60 60 60 67246 20 80 80 80 60 67247 60 100 80 80 60 67251 100 80 80 100 80 67254 20 80 60 100 20 67255 80 100 100 100 100 67257 20 80 100 100 80 67258 20 80 100 60 100 67259 NA 100 60 NA 60 67260 0 0 20 20 0 67262 0 80 80 80 60 67263 40 40 40 20 20 67269 0 80 100 100 80 67270 20 80 20 60 40 67271 40 40 60 20 80 67272 60 40 0 40 40 67274 60 80 100 100 100 67275 40 40 80 100 80 67277 0 100 100 80 100 67278 0 80 100 100 80 67279 20 80 60 60 40 67283 0 100 40 20 80 67284 0 100 60 80 80 67286 100 40 20 40 80 67287 20 0 100 100 100 67288 100 100 100 100 100 67289 0 100 80 100 100 67294 60 80 100 80 80 67295 20 100 80 80 100 67298 0 100 100 100 100 67299 20 100 80 80 80 67303 80 80 60 80 60 67307 80 20 40 20 20 67309 0 100 100 100 100 67310 40 60 80 100 60 67312 60 80 80 100 60 67313 0 100 100 80 20 67315 40 60 60 100 80 67316 60 40 60 60 40 67322 20 100 100 80 100 67324 60 60 60 40 60 67325 0 100 100 100 100 67328 20 0 40 0 40 67331 20 20 80 80 60 67332 20 100 80 40 80 67334 40 100 100 80 100 67335 20 80 60 100 80 67336 20 60 40 40 NA 67338 0 100 100 80 80 67339 20 80 80 80 80 67341 0 100 100 100 80 67342 20 100 80 80 80 67344 20 20 0 40 40 67345 80 0 20 0 20 67346 20 80 80 80 80 67347 0 80 80 NA 80 67352 40 100 100 40 20 67355 0 100 100 20 100 67356 80 60 60 0 100 67357 60 20 80 80 20 67360 20 60 40 80 60 67363 20 80 80 80 100 67364 0 60 80 80 20 67365 0 100 100 100 100 67366 40 80 20 100 80 67367 0 80 80 100 100 67368 80 100 60 NA 100 67370 60 60 100 100 100 67371 60 60 100 100 100 67374 20 60 40 40 20 67376 0 80 80 80 100 67377 20 60 80 60 60 67378 40 80 80 40 80 67379 20 80 80 100 60 67380 0 100 60 60 100 67382 40 80 80 60 60 67388 20 60 80 80 80 67390 0 80 60 80 0 67392 100 60 60 100 100 67393 60 80 80 100 80 67395 60 60 60 100 60 67396 20 80 40 40 40 67397 60 80 80 100 80 67399 0 80 80 80 80 67401 0 80 80 100 60 67403 40 20 0 20 20 67406 0 80 100 100 80 67408 40 60 60 40 60 67413 0 0 100 0 0 67415 40 60 60 80 80 67417 0 80 80 100 60 67418 20 40 20 80 60 67420 0 100 80 20 80 67421 60 80 100 60 80 67426 0 80 60 80 60 67427 20 60 40 60 80 67429 40 40 20 0 20 67431 60 60 80 80 60 67436 20 60 80 80 80 67438 100 40 40 40 40 67439 80 100 80 80 100 67440 20 40 60 20 80 67441 20 60 60 100 80 67442 40 20 80 80 40 67445 20 60 60 100 60 67446 0 80 60 20 80 67447 0 100 80 80 80 67449 100 80 20 60 80 67451 0 100 100 20 100 67453 40 100 80 100 80 67454 0 100 100 80 100 67455 0 100 100 100 100 67458 40 20 40 40 0 67460 20 100 100 100 100 67462 100 80 80 60 60 67465 100 80 100 100 100 67471 0 100 40 0 60 67473 20 80 80 80 80 67479 20 80 80 100 40 67480 100 0 20 20 20 67483 20 80 80 80 80 67485 20 20 80 100 100 67487 20 80 80 60 100 67488 20 80 40 100 40 67489 20 60 60 80 60 67490 40 80 40 80 60 67495 20 60 NA 80 80 67497 20 80 60 100 80 67502 40 60 40 60 60 67503 20 40 60 100 20 67504 60 60 80 0 60 67505 20 80 80 80 80 67506 0 100 80 100 60 67507 40 60 40 100 60 67508 20 80 60 60 80 67509 100 60 80 80 80 67510 40 60 40 80 80 67512 0 80 80 100 100 67516 20 80 60 60 40 67518 40 80 60 40 60 67521 20 80 80 80 80 67522 0 100 100 100 100 67523 0 100 100 100 100 67524 80 100 60 0 80 67525 0 100 0 100 100 67526 0 100 60 100 40 67528 40 80 100 100 100 67529 0 NA 100 NA 20 67530 0 80 100 80 100 67531 20 80 60 80 60 67533 20 100 100 100 80 67534 0 60 40 80 80 67535 20 100 80 100 100 67537 20 100 80 60 80 67539 40 60 60 40 80 67540 20 80 40 80 60 67541 60 60 80 0 20 67544 80 80 80 100 80 67547 40 60 40 0 40 67549 80 80 80 80 40 67551 100 0 40 40 40 67552 20 60 60 40 80 67556 20 40 80 20 80 67559 80 20 20 60 60 67560 20 40 0 60 20 > pomps(data = psych::bfi, vrb.nm = vrb_nm, min = 1, max = 6, unit = 100) # unit = 100 A1_p100 A2_p100 A3_p100 A4_p100 A5_p100 61617 0.2 0.6 0.4 0.6 0.6 61618 0.2 0.6 0.8 0.2 0.8 61620 0.8 0.6 0.8 0.6 0.6 61621 0.6 0.6 1.0 0.8 0.8 61622 0.2 0.4 0.4 0.6 0.8 61623 1.0 1.0 0.8 1.0 0.8 61624 0.2 0.8 0.8 0.4 0.8 61629 0.6 0.4 0.0 0.8 0.0 61630 0.6 0.4 1.0 0.4 0.4 61633 0.2 0.8 1.0 1.0 0.8 61634 0.6 0.6 0.8 1.0 0.8 61636 0.2 0.8 0.8 0.8 0.8 61637 0.8 0.8 0.8 1.0 0.6 61639 0.8 0.8 0.8 1.0 1.0 61640 0.6 0.8 0.2 0.2 0.0 61643 0.6 0.4 1.0 1.0 0.4 61650 0.6 1.0 1.0 0.2 0.8 61651 0.8 0.8 0.8 0.6 0.8 61653 0.6 0.6 0.8 0.6 0.4 61654 0.6 0.6 1.0 0.8 0.8 61656 0.8 0.6 0.2 0.0 0.2 61659 0.0 1.0 1.0 0.0 0.8 61661 0.0 0.8 1.0 0.8 1.0 61664 0.2 1.0 0.8 1.0 0.8 61667 0.6 0.8 0.8 1.0 0.8 61668 0.0 1.0 1.0 0.0 1.0 61669 0.2 0.6 0.6 0.6 0.4 61670 0.2 0.8 1.0 1.0 1.0 61672 0.2 0.8 0.0 0.4 0.8 61673 0.6 0.8 1.0 0.8 0.8 61678 0.0 1.0 0.8 1.0 0.4 61679 0.2 0.8 1.0 1.0 1.0 61682 0.0 0.8 1.0 0.8 0.6 61683 0.2 0.6 0.8 1.0 0.8 61684 0.6 0.6 0.6 0.6 0.6 61685 0.8 0.4 0.8 0.6 0.2 61686 0.0 1.0 0.6 1.0 0.6 61687 0.0 0.6 0.6 0.2 0.4 61688 0.0 1.0 1.0 1.0 1.0 61691 0.0 0.8 0.6 0.4 0.8 61692 0.0 0.8 0.8 1.0 0.8 61693 0.8 0.6 0.4 1.0 0.6 61696 0.0 0.8 0.6 0.6 0.8 61698 0.8 1.0 0.6 0.4 0.8 61700 0.2 1.0 1.0 1.0 1.0 61701 0.0 1.0 1.0 1.0 1.0 61702 0.8 0.8 0.4 0.6 0.4 61703 0.2 1.0 0.6 0.8 0.8 61713 0.0 0.8 0.4 0.2 0.4 61715 0.0 1.0 1.0 1.0 1.0 61716 0.0 1.0 1.0 1.0 0.6 61723 0.0 0.8 1.0 0.8 0.6 61724 0.4 1.0 0.6 0.6 0.6 61725 0.6 0.4 0.8 1.0 0.4 61728 0.0 1.0 1.0 1.0 1.0 61730 0.0 0.6 0.4 0.8 0.8 61731 0.0 0.6 0.2 0.2 0.2 61732 0.4 0.6 0.8 0.2 0.6 61740 0.0 1.0 0.8 0.6 0.6 61742 0.4 0.4 0.8 0.6 0.8 61748 0.2 0.4 0.6 0.6 0.8 61749 0.2 0.6 0.6 0.8 0.4 61754 0.0 0.6 1.0 1.0 1.0 61756 0.6 0.8 0.4 0.8 0.6 61757 0.2 1.0 1.0 1.0 1.0 61759 0.2 NA 0.6 1.0 0.6 61761 0.0 0.8 0.6 0.2 0.8 61762 0.6 0.4 0.2 0.2 0.2 61763 0.2 0.4 0.6 0.8 1.0 61764 0.0 1.0 1.0 0.4 1.0 61771 0.6 0.8 1.0 1.0 0.6 61772 0.2 0.6 1.0 0.2 0.8 61773 0.6 0.6 0.6 0.8 0.4 61775 0.0 0.8 0.8 0.8 0.6 61776 0.4 0.8 0.8 0.8 0.6 61777 0.2 0.8 0.8 1.0 0.8 61778 0.2 1.0 1.0 1.0 1.0 61780 0.8 1.0 1.0 1.0 0.8 61782 0.0 0.2 0.2 0.6 0.2 61783 0.2 0.8 0.8 0.8 0.8 61784 0.0 0.8 1.0 0.8 0.4 61788 0.0 1.0 0.4 0.4 0.0 61789 0.0 0.6 1.0 0.2 1.0 61793 0.0 1.0 1.0 0.8 1.0 61794 0.0 1.0 0.8 0.2 1.0 61797 0.6 0.6 0.6 1.0 0.8 61798 0.0 0.8 0.8 0.8 0.8 61801 0.0 1.0 0.8 0.6 1.0 61808 0.0 1.0 0.6 1.0 0.6 61812 0.4 0.6 0.8 0.8 0.6 61813 0.0 0.8 0.8 0.8 0.8 61816 0.0 0.8 1.0 0.2 0.8 61818 0.2 0.6 0.8 0.6 0.8 61819 0.0 1.0 1.0 0.4 1.0 61821 0.2 1.0 0.6 1.0 0.6 61822 0.2 0.4 0.4 0.6 0.4 61825 0.4 0.0 0.2 0.2 0.2 61826 0.0 0.6 0.8 0.8 1.0 61829 0.6 0.4 0.2 1.0 0.8 61831 0.2 1.0 0.2 0.4 0.6 61834 0.2 0.8 0.8 0.4 0.8 61835 0.4 0.6 0.6 0.2 0.6 61838 0.2 0.8 0.8 0.8 0.2 61839 0.0 0.6 0.8 0.2 1.0 61840 0.0 1.0 0.6 0.0 0.0 61841 0.0 1.0 0.8 1.0 1.0 61847 0.0 0.8 0.8 0.8 0.2 61848 1.0 0.0 0.0 0.0 0.2 61851 0.2 1.0 0.8 1.0 0.8 61852 0.4 1.0 0.8 1.0 0.6 61854 0.0 1.0 1.0 1.0 1.0 61856 NA 0.6 0.8 1.0 0.6 61857 0.0 1.0 1.0 0.8 0.6 61861 0.0 1.0 1.0 1.0 1.0 61862 0.0 1.0 0.8 0.6 0.8 61865 0.6 0.2 0.0 0.2 0.2 61868 0.0 0.8 0.6 0.4 0.6 61873 0.8 0.2 0.2 0.2 0.0 61874 0.2 0.8 0.6 0.4 0.6 61880 0.0 0.8 1.0 1.0 1.0 61886 0.6 1.0 0.8 0.8 1.0 61888 0.2 0.8 0.6 0.6 0.6 61889 1.0 0.8 0.8 0.2 0.8 61890 0.2 0.8 0.8 0.0 1.0 61891 0.2 0.6 0.8 0.8 0.8 61895 0.2 0.8 0.8 0.8 0.2 61896 0.2 0.8 1.0 0.8 1.0 61900 0.2 0.8 0.6 0.6 0.8 61901 0.2 0.8 0.8 0.4 0.2 61907 0.6 0.8 0.6 NA 0.4 61908 0.2 0.6 0.6 0.6 0.6 61909 0.4 0.8 0.6 0.8 0.6 61911 0.6 0.4 0.4 0.4 0.4 61913 0.2 0.6 0.4 0.4 0.4 61915 0.0 1.0 1.0 1.0 1.0 61918 0.0 1.0 1.0 0.8 1.0 61921 0.0 1.0 1.0 0.6 1.0 61922 0.0 1.0 0.8 1.0 0.8 61923 0.0 1.0 0.8 1.0 0.8 61925 0.4 1.0 1.0 0.6 1.0 61926 0.2 0.4 0.0 0.4 0.0 61928 0.2 0.6 0.8 0.8 0.8 61932 0.2 0.8 0.4 0.8 0.0 61935 0.0 1.0 0.8 1.0 1.0 61936 0.8 0.6 0.6 0.8 1.0 61939 0.2 0.8 0.6 0.4 0.4 61944 0.0 1.0 1.0 0.4 1.0 61945 0.2 1.0 0.6 0.8 0.6 61949 0.2 1.0 0.4 0.8 0.2 61952 0.0 0.8 0.8 1.0 0.8 61953 0.0 0.8 0.8 0.4 0.4 61954 0.0 1.0 1.0 0.8 1.0 61957 0.2 0.8 0.8 1.0 0.8 61958 0.6 0.6 0.4 0.0 0.6 61965 0.2 1.0 0.8 0.8 0.4 61967 0.4 0.8 1.0 0.8 0.8 61968 0.0 1.0 1.0 1.0 0.8 61969 0.2 0.8 0.8 0.8 0.6 61971 0.2 0.6 0.6 0.0 0.4 61972 0.2 0.6 0.8 0.8 0.6 61973 0.0 0.8 0.8 1.0 0.8 61974 0.0 1.0 1.0 0.2 0.8 61975 0.2 1.0 0.6 0.8 0.6 61976 0.0 0.8 0.2 0.4 0.6 61978 0.0 1.0 1.0 1.0 1.0 61979 0.2 0.6 0.8 0.8 0.8 61983 0.2 0.6 0.8 1.0 0.8 61986 0.2 1.0 0.8 0.6 0.6 61987 0.0 0.8 0.8 0.8 0.8 61989 0.0 1.0 1.0 0.0 0.4 61990 0.4 0.6 0.6 0.4 0.6 61992 0.6 0.6 0.8 1.0 0.6 61993 0.0 0.8 0.8 0.8 0.8 61994 0.2 1.0 0.8 0.8 0.6 61999 0.2 1.0 1.0 1.0 0.8 62001 0.0 0.2 0.2 0.6 0.4 62003 0.0 1.0 0.8 1.0 1.0 62004 1.0 0.8 1.0 1.0 0.8 62005 0.6 0.4 0.6 0.4 0.6 62007 0.4 0.6 0.2 0.2 0.4 62009 0.6 0.4 0.6 0.4 0.4 62011 0.0 1.0 0.8 1.0 1.0 62013 0.4 0.8 0.6 0.6 0.6 62014 0.6 0.2 0.2 0.4 0.4 62015 0.4 0.6 0.6 0.4 0.4 62022 0.0 1.0 0.8 1.0 0.4 62023 0.0 0.8 0.8 0.6 0.8 62024 0.2 0.2 0.0 1.0 0.4 62025 0.2 0.6 0.6 0.8 0.8 62026 0.0 1.0 1.0 0.2 0.8 62029 0.2 0.4 0.6 0.6 0.6 62031 0.0 1.0 0.6 0.4 1.0 62032 0.2 0.4 0.6 0.8 0.4 62033 0.8 0.6 0.4 0.8 0.6 62034 0.0 0.8 0.6 0.6 0.6 62038 0.0 1.0 1.0 0.8 0.4 62039 0.2 0.8 1.0 0.4 0.8 62041 0.2 0.6 0.8 1.0 0.8 62042 0.2 1.0 0.8 0.8 0.8 62043 0.0 0.8 1.0 0.6 0.8 62044 0.2 0.8 0.8 0.8 0.8 62047 0.0 0.8 0.2 0.2 0.2 62048 0.4 0.4 0.8 0.8 0.8 62051 0.2 0.6 0.8 1.0 0.8 62052 0.0 1.0 0.8 0.8 0.8 62054 0.2 1.0 0.6 1.0 0.2 62055 0.2 0.8 0.8 1.0 0.6 62056 0.0 0.6 NA 1.0 1.0 62059 0.0 0.8 1.0 0.8 0.8 62060 0.2 0.4 0.2 0.0 0.6 62063 0.2 0.8 0.8 0.8 0.8 62064 0.0 1.0 0.8 1.0 1.0 62067 0.8 0.6 0.6 0.6 0.8 62070 0.4 0.6 0.4 0.4 0.4 62073 0.0 1.0 1.0 0.6 1.0 62075 0.2 0.6 0.6 0.0 0.6 62077 0.0 1.0 1.0 1.0 1.0 62079 0.2 0.6 0.6 0.6 0.4 62082 0.8 0.8 0.6 0.8 0.6 62084 0.8 0.4 0.0 0.2 0.4 62090 0.2 0.8 0.8 0.8 0.6 62092 0.0 0.8 NA 0.8 1.0 62094 0.0 0.8 0.8 1.0 1.0 62099 0.0 0.8 1.0 1.0 0.6 62101 0.0 0.8 1.0 0.8 1.0 62102 0.0 1.0 1.0 1.0 0.6 62103 0.4 0.8 0.8 0.4 0.6 62105 0.2 0.8 0.6 0.4 0.2 62106 0.0 0.8 1.0 0.8 0.8 62107 0.2 1.0 1.0 0.8 0.8 62111 0.8 0.6 0.8 1.0 0.6 62115 0.2 1.0 0.6 0.4 0.8 62118 0.8 0.6 0.8 1.0 0.2 62119 0.4 0.8 0.2 0.6 0.8 62120 0.4 1.0 0.8 1.0 1.0 62121 0.2 0.8 0.6 0.6 0.6 62124 0.0 NA 0.6 1.0 0.8 62128 0.0 1.0 1.0 1.0 0.8 62130 0.4 0.6 0.6 1.0 0.4 62132 0.4 0.8 1.0 0.8 1.0 62133 0.0 0.8 NA 1.0 0.8 62136 0.2 0.6 0.6 1.0 0.4 62137 0.6 0.8 0.8 0.8 0.8 62142 0.0 1.0 0.6 1.0 1.0 62144 0.6 0.4 0.6 0.2 0.8 62147 0.4 0.8 0.8 0.8 0.8 62151 0.8 0.2 0.0 0.0 0.2 62156 0.4 0.4 0.4 0.6 0.6 62160 0.6 0.2 0.6 0.0 0.6 62161 0.4 0.8 0.6 0.8 0.8 62162 0.4 0.8 1.0 1.0 0.8 62163 0.6 0.8 0.8 1.0 0.6 62164 1.0 0.0 0.0 0.6 1.0 62165 0.4 0.8 0.8 1.0 1.0 62166 0.8 0.8 0.8 1.0 1.0 62168 0.4 0.6 0.6 0.8 0.4 62170 0.8 1.0 0.8 0.8 1.0 62171 0.0 1.0 0.8 1.0 0.8 62173 0.0 1.0 1.0 1.0 0.8 62176 0.6 0.6 1.0 0.8 0.6 62179 0.8 0.8 0.8 0.0 0.8 62180 0.4 0.6 0.6 0.4 0.2 62181 1.0 1.0 1.0 1.0 1.0 62182 0.2 1.0 0.8 1.0 0.8 62183 0.6 0.0 0.6 1.0 1.0 62189 0.4 0.8 0.6 0.8 0.8 62192 0.2 0.6 0.8 0.8 0.8 62197 0.0 0.8 0.8 0.8 0.8 62198 0.0 1.0 1.0 0.8 0.8 62199 0.6 0.8 0.6 0.4 0.6 62201 0.4 0.8 0.6 0.8 0.8 62202 0.6 0.8 1.0 0.6 1.0 62203 0.0 1.0 1.0 1.0 1.0 62204 0.0 0.8 1.0 1.0 1.0 62205 0.2 0.6 0.6 0.6 0.8 62206 0.4 0.8 1.0 1.0 1.0 62208 0.0 0.8 1.0 0.8 1.0 62209 0.0 0.6 0.6 0.2 0.8 62212 0.6 1.0 1.0 1.0 0.8 62213 0.2 1.0 0.8 1.0 0.4 62214 0.4 0.6 0.8 0.6 0.6 62215 0.2 1.0 1.0 1.0 1.0 62216 0.2 0.2 0.2 0.6 0.4 62219 0.2 0.8 0.6 1.0 0.6 62220 0.4 0.6 1.0 1.0 0.6 62224 0.0 0.8 0.8 1.0 0.8 62225 0.2 0.8 1.0 0.8 0.6 62226 0.2 0.6 0.2 0.6 0.6 62227 0.0 1.0 1.0 1.0 1.0 62228 0.0 1.0 1.0 1.0 0.8 62231 0.0 0.8 0.8 1.0 0.8 62233 0.2 0.8 0.8 0.8 0.8 62237 0.4 0.6 0.8 0.8 0.8 62239 0.4 1.0 0.8 0.8 0.8 62240 0.0 0.8 1.0 0.6 1.0 62242 0.8 1.0 1.0 1.0 1.0 62244 0.0 1.0 1.0 0.0 0.8 62245 0.4 0.6 0.6 0.6 0.4 62246 0.6 0.8 0.8 1.0 1.0 62252 0.4 0.6 0.6 0.4 0.6 62259 0.0 0.8 0.8 1.0 0.8 62260 0.0 0.8 0.8 0.8 0.6 62261 0.2 1.0 1.0 0.8 0.8 62263 0.4 0.8 1.0 0.6 0.8 62264 0.6 0.8 0.6 0.8 0.6 62265 0.2 1.0 0.6 0.2 0.8 62266 0.8 0.2 0.4 0.2 0.4 62267 0.0 1.0 0.8 1.0 0.8 62272 0.4 0.6 0.6 0.6 0.6 62276 0.0 1.0 1.0 0.6 1.0 62278 0.4 0.8 1.0 1.0 1.0 62279 0.0 1.0 0.8 0.6 0.8 62280 0.2 0.8 0.8 1.0 0.8 62281 0.0 1.0 1.0 1.0 1.0 62282 0.4 0.8 0.8 1.0 0.0 62287 0.2 0.6 0.6 1.0 0.2 62288 0.4 0.8 1.0 0.6 1.0 62289 0.4 0.8 0.6 0.2 0.2 62290 0.2 0.8 0.4 0.2 0.2 62293 0.2 1.0 1.0 1.0 1.0 62295 0.2 0.2 1.0 0.2 0.2 62296 0.2 1.0 1.0 1.0 1.0 62298 0.8 0.2 0.2 0.2 0.8 62299 0.6 0.6 0.6 0.6 0.6 62300 0.4 0.4 0.6 1.0 0.4 62301 0.0 1.0 0.8 1.0 0.8 62303 0.2 NA 0.8 0.8 0.2 62305 0.6 0.8 0.8 1.0 0.6 62307 0.0 0.8 0.6 0.8 1.0 62312 0.2 0.2 0.8 0.6 0.6 62313 0.0 1.0 1.0 1.0 0.6 62316 0.2 1.0 0.8 0.8 1.0 62317 0.6 0.6 0.4 0.2 0.4 62325 0.0 0.6 1.0 1.0 1.0 62327 0.2 1.0 1.0 1.0 0.8 62328 0.4 0.2 1.0 0.0 1.0 62330 0.4 0.8 0.8 0.8 0.8 62333 0.0 1.0 1.0 1.0 1.0 62335 0.2 0.8 0.8 1.0 0.6 62336 0.0 0.8 0.6 0.4 0.2 62339 0.4 0.8 0.4 0.6 0.6 62342 0.0 0.8 0.8 0.8 0.8 62343 0.2 0.6 0.2 1.0 0.8 62344 0.0 1.0 0.8 1.0 0.8 62345 0.2 0.6 0.6 1.0 0.8 62346 0.0 0.8 0.8 0.8 0.8 62347 0.2 0.6 0.8 1.0 0.8 62348 0.8 0.8 0.8 1.0 0.8 62349 0.2 0.8 0.8 1.0 0.8 62350 0.2 0.8 0.0 1.0 0.6 62351 0.6 1.0 1.0 0.6 0.6 62352 0.2 0.8 0.8 1.0 0.8 62353 0.4 0.8 0.0 0.6 0.6 62354 0.2 0.8 0.8 0.6 0.8 62358 0.2 0.4 1.0 0.6 0.8 62359 0.0 0.8 0.8 1.0 0.8 62360 0.6 0.2 0.8 0.8 0.8 62362 0.0 0.8 0.8 0.8 0.6 62363 0.2 0.8 0.8 1.0 1.0 62366 0.6 0.4 1.0 1.0 1.0 62367 0.4 0.6 1.0 1.0 1.0 62368 0.0 1.0 0.0 1.0 0.8 62369 0.0 0.8 0.4 1.0 0.8 62370 0.0 1.0 1.0 0.6 0.8 62371 0.6 0.8 0.2 1.0 0.8 62375 0.0 1.0 0.8 0.4 1.0 62376 NA 0.8 0.6 0.4 0.6 62377 0.6 1.0 0.8 1.0 1.0 62380 0.0 1.0 0.8 0.2 0.2 62382 1.0 1.0 1.0 1.0 1.0 62384 0.0 0.6 0.2 0.8 0.4 62387 0.4 0.8 0.6 0.6 0.4 62390 0.0 1.0 1.0 1.0 1.0 62391 0.2 0.8 0.8 0.8 0.4 62394 0.0 0.8 0.4 0.8 0.6 62397 0.0 1.0 0.8 1.0 1.0 62401 0.0 0.8 0.8 1.0 0.6 62408 0.6 0.6 0.8 0.0 0.8 62412 0.2 0.4 0.6 0.0 0.2 62416 0.2 1.0 1.0 0.6 1.0 62419 0.6 1.0 1.0 1.0 0.8 62421 0.6 0.6 0.6 0.6 0.8 62423 0.2 1.0 0.8 1.0 0.4 62426 0.2 0.6 0.6 1.0 NA 62433 0.8 1.0 1.0 0.8 1.0 62434 0.0 0.8 1.0 1.0 1.0 62435 0.2 0.8 0.8 0.6 1.0 62438 0.6 0.6 0.6 NA 0.8 62440 0.0 1.0 0.8 0.8 1.0 62443 0.6 0.2 0.6 1.0 0.8 62444 0.2 0.8 0.8 0.6 0.6 62447 0.2 1.0 1.0 1.0 1.0 62448 0.0 1.0 0.2 1.0 0.8 62450 0.0 1.0 1.0 1.0 1.0 62453 0.2 0.8 0.8 1.0 0.6 62454 0.4 0.4 1.0 0.2 1.0 62457 0.6 0.6 NA 0.4 0.6 62462 0.0 0.8 0.4 0.6 0.6 62463 0.2 1.0 0.8 0.6 0.8 62464 0.0 0.8 0.8 1.0 0.8 62467 0.4 0.6 0.4 0.8 0.8 62468 0.0 1.0 1.0 0.8 0.2 62469 0.4 0.8 0.8 0.6 0.6 62470 0.2 1.0 0.8 0.8 1.0 62474 0.6 0.8 0.8 0.6 0.8 62476 0.6 0.4 0.2 0.8 0.4 62479 0.0 0.6 1.0 0.8 1.0 62480 0.0 0.8 1.0 1.0 0.4 62481 0.4 0.6 0.6 0.8 0.6 62486 0.4 0.6 0.8 0.2 0.4 62489 0.0 1.0 0.8 1.0 0.8 62491 0.4 1.0 1.0 1.0 0.6 62493 0.0 1.0 0.4 0.8 0.4 62494 0.0 0.2 0.0 0.6 0.2 62496 0.4 0.8 0.8 1.0 0.4 62497 0.6 1.0 0.8 1.0 1.0 62498 0.0 0.8 1.0 1.0 1.0 62499 0.4 0.8 0.8 1.0 0.8 62500 0.4 1.0 0.8 1.0 1.0 62502 0.0 1.0 1.0 1.0 1.0 62505 0.0 1.0 0.6 1.0 1.0 62508 0.4 1.0 0.8 1.0 0.8 62509 0.0 1.0 1.0 0.8 1.0 62512 0.8 NA 0.8 1.0 0.8 62514 0.8 NA 0.8 1.0 0.8 62518 0.2 1.0 1.0 1.0 1.0 62520 NA 1.0 1.0 1.0 1.0 62522 0.0 1.0 1.0 1.0 1.0 62526 0.2 1.0 0.8 1.0 0.8 62527 0.2 0.8 0.2 0.6 0.2 62528 0.2 0.8 0.2 0.8 0.8 62529 0.2 0.8 0.2 0.8 0.8 62530 0.2 0.8 0.2 0.8 0.8 62531 0.2 0.8 0.2 0.8 0.8 62532 0.2 0.8 0.8 0.8 0.8 62533 0.2 0.8 0.8 0.8 0.8 62535 0.2 0.8 0.8 0.8 0.8 62537 0.2 0.8 0.2 1.0 0.8 62538 0.6 0.8 1.0 1.0 0.4 62541 0.2 1.0 1.0 1.0 0.8 62542 0.8 0.6 0.8 1.0 0.6 62543 0.2 0.8 1.0 0.8 0.8 62545 0.0 0.6 0.6 0.8 0.6 62546 0.6 0.6 0.6 0.8 0.8 62547 0.0 1.0 1.0 1.0 0.8 62548 0.2 1.0 1.0 0.8 0.6 62550 0.2 0.8 1.0 1.0 0.8 62551 0.8 0.6 0.2 0.2 0.0 62552 1.0 0.0 0.0 0.6 0.0 62553 0.0 1.0 1.0 1.0 0.8 62555 0.6 0.6 0.4 0.6 0.2 62556 0.6 0.8 0.8 0.8 0.6 62557 0.4 1.0 1.0 1.0 0.8 62559 0.8 1.0 0.8 1.0 0.8 62561 0.0 1.0 0.8 1.0 0.8 62562 0.2 0.8 0.6 1.0 0.8 62565 0.0 1.0 1.0 1.0 1.0 62567 0.2 0.8 1.0 0.8 0.6 62570 0.2 0.2 0.2 0.0 0.4 62573 0.8 0.6 0.6 0.6 0.4 62574 0.2 0.8 0.6 0.6 1.0 62577 0.2 0.8 0.8 0.8 0.8 62578 0.0 0.8 1.0 1.0 0.8 62582 0.4 1.0 0.8 1.0 1.0 62589 0.0 0.8 0.0 0.8 0.8 62590 0.0 0.8 1.0 0.8 0.8 62594 0.2 1.0 NA 0.8 0.8 62597 0.2 0.8 0.8 0.8 0.6 62599 0.0 0.8 0.8 1.0 1.0 62604 0.0 0.8 1.0 1.0 1.0 62605 0.4 0.8 0.8 1.0 0.8 62606 0.2 1.0 1.0 1.0 1.0 62610 0.0 1.0 0.8 1.0 1.0 62611 0.2 0.2 0.6 0.4 0.2 62612 0.2 0.6 0.6 0.4 0.4 62613 0.6 0.2 0.0 0.4 0.0 62615 1.0 0.8 0.8 1.0 1.0 62617 0.2 1.0 1.0 1.0 1.0 62618 0.2 0.8 1.0 0.8 0.6 62622 0.0 1.0 1.0 1.0 1.0 62623 0.0 1.0 0.2 1.0 1.0 62625 0.0 1.0 1.0 1.0 1.0 62627 0.0 0.4 0.2 1.0 0.4 62635 0.4 0.8 0.2 0.8 0.8 62638 0.2 1.0 1.0 0.2 1.0 62640 0.0 0.8 1.0 1.0 1.0 62642 0.2 0.8 0.8 1.0 0.8 62643 0.0 0.6 0.6 1.0 0.6 62644 0.2 1.0 0.8 1.0 0.6 62645 0.0 0.8 0.8 1.0 0.8 62646 0.0 1.0 1.0 0.4 1.0 62647 0.6 0.8 0.8 1.0 0.8 62648 0.0 1.0 0.8 0.8 0.2 62650 0.2 0.8 0.0 1.0 1.0 62652 0.0 1.0 0.8 0.4 0.6 62653 0.6 0.4 0.8 0.8 0.8 62654 0.6 0.6 0.8 1.0 0.8 62657 0.2 1.0 0.6 0.2 0.8 62662 0.6 0.8 0.6 0.8 0.6 62664 0.0 0.8 1.0 0.8 1.0 62665 0.4 1.0 1.0 1.0 1.0 62667 0.0 1.0 1.0 1.0 1.0 62668 0.8 1.0 0.8 0.6 0.8 62669 0.8 1.0 1.0 0.8 1.0 62670 0.2 0.6 0.2 1.0 0.6 62673 0.2 1.0 0.8 0.8 0.6 62675 0.0 1.0 0.8 0.8 1.0 62677 0.0 1.0 1.0 1.0 1.0 62679 0.2 0.6 1.0 0.8 1.0 62681 0.0 0.8 1.0 1.0 0.8 62682 0.4 0.8 0.8 1.0 0.8 62683 1.0 0.0 1.0 1.0 1.0 62684 0.2 0.8 0.6 1.0 0.6 62685 0.4 0.2 0.6 1.0 1.0 62686 0.2 0.6 0.6 1.0 0.6 62687 0.2 0.6 1.0 0.8 0.8 62688 0.0 1.0 0.8 1.0 0.6 62690 0.0 0.8 0.6 1.0 0.6 62692 0.0 1.0 1.0 1.0 1.0 62694 0.4 0.8 0.8 1.0 0.8 62698 0.6 1.0 1.0 1.0 0.8 62700 0.0 0.6 0.6 0.6 1.0 62703 0.2 0.8 0.8 0.6 1.0 62706 0.0 1.0 0.8 1.0 0.8 62707 0.2 0.6 0.2 1.0 0.8 62708 1.0 1.0 1.0 1.0 1.0 62710 0.0 1.0 0.6 0.4 0.8 62712 0.0 1.0 0.8 0.8 1.0 62715 0.2 0.6 0.8 1.0 0.8 62716 0.6 0.8 0.6 0.8 0.6 62717 0.0 0.8 0.8 1.0 0.6 62718 0.4 0.6 0.6 0.8 0.8 62719 0.0 0.6 1.0 0.8 1.0 62720 0.2 0.8 0.8 1.0 0.8 62722 0.0 0.8 0.6 0.0 0.8 62726 0.0 0.8 1.0 1.0 0.8 62728 0.4 0.6 NA 0.8 0.4 62729 0.8 0.6 0.4 0.6 0.4 62731 0.8 0.6 0.6 0.6 0.2 62740 0.4 0.8 0.8 0.8 0.8 62741 0.4 0.6 0.6 1.0 0.8 62744 0.6 0.8 0.6 0.8 0.2 62745 0.0 1.0 1.0 1.0 0.8 62749 0.0 1.0 0.6 1.0 0.8 62750 0.2 0.6 0.6 1.0 0.8 62751 0.2 0.6 0.8 0.4 0.8 62757 0.8 0.6 0.6 1.0 0.6 62758 0.2 0.8 0.6 0.6 0.6 62761 0.0 1.0 0.8 0.6 0.8 62764 0.6 0.4 0.8 1.0 0.8 62765 0.0 1.0 1.0 1.0 0.0 62766 0.6 0.8 0.6 0.2 0.2 62767 0.6 0.8 0.6 1.0 0.6 62768 0.0 1.0 1.0 1.0 1.0 62770 0.4 1.0 1.0 1.0 0.8 62772 0.0 0.8 0.8 1.0 0.8 62776 0.2 0.8 0.4 0.2 0.6 62778 0.0 1.0 1.0 0.8 1.0 62779 0.0 0.6 0.8 0.6 0.8 62780 0.0 1.0 1.0 1.0 0.8 62781 0.2 0.8 0.6 1.0 0.6 62783 0.8 0.8 0.8 0.8 0.8 62785 0.4 1.0 0.8 1.0 0.8 62786 0.0 0.8 1.0 1.0 1.0 62787 0.0 0.0 0.8 1.0 0.4 62788 0.0 1.0 1.0 1.0 1.0 62789 0.0 1.0 0.6 1.0 1.0 62790 0.0 0.8 0.8 1.0 1.0 62792 0.8 0.6 0.6 0.8 0.8 62795 0.2 0.8 0.2 0.8 0.6 62796 0.0 0.8 0.8 0.8 0.8 62797 0.0 1.0 1.0 1.0 1.0 62800 0.4 0.8 1.0 1.0 0.6 62801 1.0 1.0 1.0 1.0 1.0 62803 0.0 1.0 0.8 0.8 0.8 62804 0.4 1.0 0.0 0.8 1.0 62805 0.0 1.0 0.8 1.0 0.8 62809 0.4 0.2 0.4 0.6 0.2 62810 0.0 0.8 0.8 0.8 1.0 62816 0.2 1.0 1.0 0.8 0.4 62817 0.6 0.8 0.8 1.0 1.0 62819 0.0 0.6 1.0 1.0 1.0 62821 0.4 1.0 1.0 1.0 0.8 62822 0.0 0.8 0.8 1.0 0.6 62825 0.2 0.8 0.2 0.0 0.4 62826 0.2 0.8 0.6 1.0 0.8 62827 0.6 0.8 0.8 1.0 0.8 62828 0.2 1.0 0.8 1.0 1.0 62831 1.0 0.2 0.8 1.0 0.2 62832 0.0 0.8 0.8 1.0 0.8 62834 0.0 1.0 0.6 0.6 0.6 62835 0.0 0.8 0.6 0.8 0.8 62837 0.2 0.8 1.0 0.8 0.6 62839 0.6 1.0 1.0 0.2 0.8 62840 0.0 0.8 0.8 1.0 0.8 62844 0.0 1.0 0.8 1.0 0.6 62846 0.8 1.0 0.2 1.0 0.8 62847 NA 1.0 1.0 NA 1.0 62849 0.2 1.0 0.8 1.0 0.8 62851 0.4 1.0 1.0 1.0 0.4 62853 0.0 1.0 1.0 1.0 1.0 62856 0.2 0.8 0.8 1.0 1.0 62857 0.4 0.6 0.6 1.0 0.6 62858 0.6 1.0 0.8 1.0 1.0 62861 0.2 1.0 0.6 1.0 0.8 62863 0.0 1.0 0.8 1.0 1.0 62864 0.4 0.6 0.2 0.6 0.6 62867 0.0 1.0 1.0 0.6 0.8 62869 0.0 0.8 1.0 1.0 0.8 62870 0.0 1.0 1.0 1.0 1.0 62872 0.0 1.0 1.0 1.0 1.0 62874 0.0 1.0 1.0 1.0 1.0 62876 0.0 0.8 0.6 0.8 0.6 62877 0.2 1.0 1.0 1.0 1.0 62878 0.2 0.8 0.8 1.0 1.0 62879 0.0 1.0 1.0 1.0 1.0 62881 0.2 1.0 1.0 1.0 0.8 62883 0.4 0.6 0.6 0.6 0.6 62887 0.0 0.6 0.0 0.6 0.8 62889 0.2 0.8 0.8 0.6 0.4 62890 0.0 1.0 1.0 0.6 0.6 62891 0.2 0.8 1.0 0.6 1.0 62897 0.2 1.0 0.8 1.0 0.4 62898 0.6 1.0 1.0 0.8 1.0 62899 0.2 0.0 1.0 1.0 1.0 62901 0.2 0.8 0.6 0.6 0.2 62903 0.2 0.8 0.8 0.8 0.0 62908 0.4 0.8 0.4 1.0 0.0 62910 0.4 0.8 0.8 1.0 0.8 62911 0.6 0.6 0.6 0.6 0.6 62916 0.2 1.0 1.0 1.0 0.8 62918 0.6 0.4 0.0 0.4 0.4 62920 0.2 1.0 1.0 1.0 1.0 62922 0.4 1.0 0.6 1.0 0.8 62926 0.0 0.4 0.2 0.4 0.2 62931 0.0 1.0 1.0 0.6 1.0 62933 0.0 0.8 0.6 0.8 0.8 62934 0.6 0.6 0.2 0.6 0.6 62936 0.0 0.6 0.6 0.8 0.8 62938 0.2 0.8 0.8 1.0 0.8 62939 0.4 0.8 0.8 0.8 0.6 62941 0.2 0.8 0.6 0.8 0.8 62942 0.0 0.8 0.8 1.0 1.0 62948 0.6 1.0 1.0 1.0 1.0 62949 0.4 0.6 1.0 0.4 0.4 62950 0.0 1.0 1.0 1.0 1.0 62951 0.0 0.6 0.8 0.8 0.8 62953 0.0 1.0 1.0 1.0 0.8 62954 0.2 0.6 0.6 0.8 0.2 62957 0.2 0.8 0.2 1.0 0.6 62962 0.0 0.8 1.0 1.0 0.8 62965 0.2 0.6 0.2 0.8 0.6 62968 0.6 0.6 0.6 1.0 0.2 62969 0.0 0.8 1.0 1.0 1.0 62971 0.4 0.8 0.8 1.0 1.0 62974 0.8 1.0 1.0 1.0 1.0 62976 0.0 1.0 1.0 1.0 1.0 62983 0.0 0.8 0.8 1.0 1.0 62984 0.0 1.0 0.8 1.0 0.6 62989 0.4 1.0 0.8 1.0 0.8 62990 1.0 0.0 0.0 0.0 0.0 62991 0.8 0.8 0.6 0.8 0.8 62994 0.0 1.0 0.8 0.2 0.8 62995 1.0 0.0 0.0 0.2 0.4 62996 0.2 0.8 0.8 0.8 0.6 62997 0.2 NA 0.8 0.4 1.0 63004 0.8 0.8 0.8 1.0 0.4 63007 0.6 1.0 1.0 1.0 0.6 63013 0.2 0.8 0.6 0.6 0.6 63017 0.4 0.6 0.8 1.0 0.8 63018 0.2 1.0 1.0 1.0 1.0 63021 0.8 0.4 0.6 0.0 0.6 63023 0.0 0.8 1.0 0.8 0.8 63026 0.2 1.0 0.4 1.0 0.8 63027 0.0 1.0 0.8 0.2 0.8 63030 0.0 NA NA NA 0.8 63034 0.6 1.0 1.0 1.0 1.0 63035 NA 1.0 0.4 0.8 0.6 63036 0.2 1.0 1.0 NA 1.0 63037 0.8 0.8 0.8 1.0 0.8 63038 0.0 0.8 0.6 0.2 0.8 63039 0.6 0.8 1.0 0.0 0.8 63040 0.6 0.6 0.8 0.6 0.6 63042 0.2 1.0 1.0 0.8 0.8 63047 0.2 0.8 0.8 1.0 0.6 63048 0.0 1.0 1.0 1.0 1.0 63049 0.0 1.0 0.8 1.0 1.0 63050 0.4 0.6 1.0 0.6 0.8 63051 0.2 1.0 1.0 1.0 1.0 63054 0.2 0.8 0.8 1.0 0.8 63055 0.4 1.0 1.0 1.0 0.8 63059 0.0 1.0 1.0 1.0 1.0 63062 0.4 0.6 0.6 0.6 0.6 63063 0.2 0.6 0.6 0.4 0.8 63069 0.2 1.0 1.0 1.0 0.8 63070 0.8 0.2 0.4 0.8 0.6 63071 0.0 1.0 1.0 1.0 1.0 63073 0.4 0.8 0.4 0.8 0.8 63075 0.0 1.0 0.8 1.0 0.8 63077 0.0 0.8 0.8 0.8 1.0 63081 0.4 0.6 0.8 1.0 0.8 63083 0.2 0.8 0.8 1.0 0.6 63084 0.0 1.0 1.0 0.6 0.8 63089 0.2 0.8 0.8 0.4 0.8 63090 0.8 1.0 1.0 0.6 0.8 63092 0.8 0.6 0.8 0.0 0.8 63094 0.4 0.8 0.2 NA 0.8 63096 0.0 1.0 0.8 1.0 0.8 63097 0.0 1.0 1.0 1.0 1.0 63099 0.0 0.8 1.0 1.0 0.0 63100 0.4 0.2 0.6 0.0 0.6 63102 0.0 1.0 0.6 0.8 0.6 63103 0.8 1.0 0.4 0.2 0.2 63104 0.6 0.6 0.4 0.4 0.4 63108 0.2 0.4 0.6 0.6 0.8 63109 0.2 1.0 0.6 0.2 0.8 63112 0.2 0.6 0.6 0.4 0.8 63115 0.2 0.8 0.8 1.0 0.8 63116 0.8 1.0 1.0 1.0 1.0 63120 0.2 0.8 0.8 0.4 0.2 63121 0.2 1.0 0.8 1.0 0.8 63122 0.2 0.6 0.6 0.6 0.8 63123 0.4 0.0 0.8 1.0 0.6 63125 0.2 0.6 0.2 0.2 0.4 63127 0.2 0.8 1.0 1.0 0.2 63128 0.2 1.0 1.0 1.0 0.8 63130 0.0 1.0 1.0 0.8 0.8 63131 0.0 1.0 NA 1.0 1.0 63135 0.2 0.8 0.6 0.8 0.8 63136 0.2 1.0 0.8 0.8 0.2 63139 0.0 1.0 0.8 1.0 1.0 63141 0.0 1.0 1.0 NA 0.8 63142 0.2 0.8 0.8 0.8 0.8 63146 0.0 1.0 1.0 1.0 0.0 63147 0.0 0.8 0.6 0.8 0.8 63152 0.0 0.4 0.6 0.6 0.6 63154 0.2 1.0 0.8 1.0 0.6 63155 0.4 1.0 0.8 0.6 0.6 63158 0.0 0.0 0.8 1.0 1.0 63161 0.2 1.0 0.6 0.2 0.8 63163 0.0 1.0 0.8 1.0 0.8 63165 0.0 0.8 0.8 1.0 0.8 63171 0.8 0.6 1.0 0.2 0.6 63172 0.8 1.0 1.0 1.0 1.0 63173 0.4 0.8 0.8 0.4 0.6 63176 0.0 1.0 1.0 0.8 1.0 63181 0.4 0.6 0.8 1.0 0.2 63182 0.2 0.6 0.6 1.0 0.6 63183 0.4 0.6 0.6 1.0 0.8 63187 0.4 0.4 0.4 0.6 0.8 63192 0.2 0.8 0.6 0.6 0.4 63193 0.4 0.8 0.2 0.0 0.2 63194 0.6 1.0 0.6 0.2 0.6 63197 0.2 0.8 0.8 0.2 0.6 63200 0.4 1.0 1.0 0.4 0.6 63201 0.0 0.8 1.0 0.8 0.8 63203 0.2 0.6 0.8 0.0 0.8 63209 0.0 0.8 0.6 0.8 0.8 63215 0.4 0.6 1.0 0.0 0.2 63222 0.4 0.6 0.2 0.4 0.6 63223 0.2 0.8 1.0 0.8 0.6 63225 0.2 0.8 1.0 0.2 0.6 63227 0.2 0.6 0.8 0.2 0.4 63228 0.0 0.8 0.8 0.8 0.8 63233 0.0 0.8 0.8 1.0 0.6 63237 0.2 1.0 1.0 1.0 1.0 63238 0.0 0.8 1.0 0.2 0.4 63239 0.4 1.0 1.0 1.0 1.0 63242 0.2 0.8 0.8 0.8 0.8 63245 0.0 1.0 1.0 1.0 0.8 63246 0.0 1.0 0.8 0.2 0.6 63250 0.2 0.8 0.8 0.6 0.8 63252 0.0 0.8 0.8 1.0 0.6 63253 0.2 1.0 0.4 0.4 0.4 63258 0.0 0.6 1.0 1.0 1.0 63261 0.6 0.8 0.8 1.0 0.8 63264 0.4 0.6 0.6 0.2 0.6 63266 0.0 0.8 1.0 1.0 0.6 63271 0.4 0.4 0.6 0.2 0.4 63277 0.0 1.0 0.8 0.8 0.8 63278 NA 0.4 0.6 0.4 0.4 63280 0.0 0.8 0.8 0.6 0.6 63281 0.0 0.8 0.6 0.8 0.8 63286 0.4 0.6 0.8 0.8 0.6 63287 0.8 1.0 1.0 0.8 0.8 63289 0.0 0.6 0.4 0.8 0.4 63292 0.6 0.4 0.6 0.6 0.4 63293 0.2 0.8 0.6 0.2 0.6 63294 0.8 0.4 0.6 0.6 0.8 63301 0.4 0.4 0.8 1.0 0.8 63302 0.0 0.8 0.8 0.2 0.8 63307 0.6 0.8 0.2 0.0 0.6 63309 0.0 0.8 0.0 0.8 1.0 63313 0.2 0.8 0.8 0.6 0.8 63317 0.0 1.0 1.0 1.0 1.0 63320 0.0 1.0 0.4 0.6 0.2 63321 0.4 0.8 1.0 1.0 0.8 63322 0.6 0.6 0.2 0.0 0.8 63324 0.0 0.0 0.0 0.4 0.0 63326 0.2 0.6 0.4 0.2 0.6 63328 0.6 1.0 1.0 0.0 1.0 63330 0.4 0.8 0.6 0.4 0.6 63331 0.0 1.0 0.8 0.8 0.6 63332 0.6 0.4 0.0 0.0 0.8 63334 0.0 1.0 NA 1.0 1.0 63335 0.2 0.8 0.8 1.0 0.8 63337 0.8 0.2 0.2 0.2 0.6 63338 0.2 0.8 0.8 0.8 0.2 63339 0.2 0.8 0.8 0.2 0.8 63340 0.2 1.0 1.0 1.0 1.0 63342 0.2 0.8 0.8 0.8 0.8 63346 0.0 1.0 0.4 0.8 1.0 63351 0.0 1.0 1.0 1.0 1.0 63352 1.0 0.8 0.8 0.6 0.8 63358 0.6 0.6 0.4 0.8 0.6 63361 0.4 0.8 0.8 1.0 0.8 63364 0.2 0.8 0.8 0.6 0.8 63369 0.2 0.8 0.8 0.8 NA 63370 0.2 1.0 0.6 0.6 0.6 63371 0.6 0.8 0.6 0.2 0.8 63372 0.2 0.8 0.2 0.6 1.0 63375 0.2 1.0 0.6 0.4 0.8 63376 0.0 1.0 1.0 1.0 1.0 63378 0.2 0.8 1.0 1.0 0.8 63379 0.8 0.8 0.6 1.0 0.6 63382 0.4 0.8 0.8 0.8 0.8 63383 0.8 0.8 0.8 0.6 0.8 63386 0.2 0.8 0.8 0.6 0.2 63389 0.0 0.8 0.8 1.0 0.8 63395 0.2 0.2 0.8 0.8 0.6 63399 0.0 1.0 1.0 1.0 0.0 63400 0.2 0.8 0.8 1.0 0.8 63402 0.0 0.8 0.8 1.0 0.6 63404 0.8 0.6 0.2 0.6 0.8 63406 0.2 0.6 1.0 0.0 0.6 63408 0.4 0.8 0.8 0.8 1.0 63412 0.2 1.0 1.0 1.0 1.0 63413 0.0 1.0 1.0 1.0 0.8 63414 0.6 1.0 0.6 1.0 0.6 63420 0.4 0.8 0.8 0.8 0.2 63421 0.4 0.8 0.8 0.8 0.8 63427 0.4 0.6 0.6 0.6 0.2 63428 1.0 0.4 0.2 0.6 0.6 63430 1.0 0.2 0.0 0.6 0.6 63431 0.4 0.6 0.8 0.6 1.0 63432 0.4 0.8 0.8 0.8 0.8 63435 0.6 0.6 0.8 0.6 1.0 63437 0.6 0.2 0.8 0.0 0.8 63438 0.0 1.0 1.0 0.8 1.0 63439 0.2 0.8 0.8 1.0 0.6 63441 0.2 0.8 0.6 0.6 0.8 63442 0.4 0.8 0.8 0.8 0.8 63444 0.0 0.8 0.6 1.0 0.8 63445 0.0 0.8 0.0 1.0 1.0 63446 0.2 0.8 0.8 0.2 0.6 63448 0.0 1.0 1.0 0.6 0.8 63450 0.2 0.8 0.8 0.8 0.4 63452 0.0 1.0 1.0 0.8 0.8 63454 0.8 0.8 0.6 0.8 0.8 63455 0.6 1.0 0.8 0.8 1.0 63458 0.0 0.6 0.2 0.4 0.6 63459 0.0 NA 0.6 1.0 0.4 63463 0.0 0.6 0.4 1.0 0.4 63464 0.0 0.6 0.8 1.0 1.0 63465 0.2 1.0 0.8 0.8 0.8 63466 0.0 1.0 1.0 1.0 1.0 63467 1.0 1.0 1.0 1.0 1.0 63468 0.4 0.8 0.4 0.6 0.2 63470 0.6 1.0 1.0 1.0 1.0 63472 1.0 0.2 0.6 0.0 0.8 63473 0.0 0.8 0.6 0.6 0.6 63476 0.4 0.8 0.8 1.0 0.8 63479 0.0 0.2 0.8 1.0 0.8 63480 0.0 1.0 1.0 0.6 0.8 63481 0.2 0.6 0.6 0.6 0.8 63485 0.2 0.6 0.4 0.6 0.4 63486 0.8 0.6 0.8 0.6 0.6 63488 0.0 0.8 0.8 1.0 0.8 63491 1.0 1.0 1.0 1.0 1.0 63493 0.6 1.0 0.8 1.0 0.8 63494 0.6 0.4 0.4 0.6 0.4 63496 0.2 0.8 0.8 1.0 0.8 63498 0.2 0.8 0.8 1.0 0.8 63499 0.0 1.0 0.8 0.6 1.0 63501 0.6 0.4 1.0 0.6 1.0 63502 0.4 0.6 0.6 1.0 0.6 63504 0.0 1.0 0.6 0.6 0.6 63505 0.0 0.8 0.8 0.0 1.0 63509 0.2 0.6 0.2 0.2 0.4 63510 1.0 1.0 0.0 0.4 0.8 63512 0.8 0.6 0.0 0.2 0.2 63513 0.4 0.6 0.6 0.2 0.4 63516 0.2 1.0 1.0 1.0 1.0 63518 0.8 1.0 1.0 1.0 0.4 63521 0.0 1.0 0.6 0.6 0.8 63525 0.4 0.6 0.6 0.2 0.6 63528 0.0 0.6 0.2 0.8 0.2 63530 0.4 0.8 0.6 1.0 0.6 63531 0.2 1.0 0.8 0.2 0.8 63533 0.2 0.4 0.6 0.2 0.8 63534 0.6 0.8 0.8 0.8 0.4 63536 0.0 1.0 1.0 0.4 0.8 63538 0.2 0.8 0.8 0.6 0.8 63547 0.2 0.6 0.8 0.6 0.6 63549 0.0 NA 0.8 0.2 0.6 63551 0.2 1.0 0.8 0.6 1.0 63554 0.6 0.8 0.6 1.0 0.6 63555 0.0 1.0 0.8 0.6 0.8 63556 0.0 0.4 0.8 1.0 1.0 63557 0.4 0.8 0.8 1.0 1.0 63558 0.8 1.0 1.0 0.8 0.6 63559 0.0 1.0 1.0 1.0 0.8 63560 0.0 0.8 0.4 0.6 0.4 63562 0.2 0.6 0.8 0.6 0.8 63567 0.2 0.4 0.4 0.8 0.4 63575 0.2 0.8 0.8 0.2 0.8 63578 0.0 0.8 1.0 1.0 1.0 63579 0.2 0.6 0.8 0.8 0.6 63580 0.0 0.6 0.6 0.8 0.6 63581 0.2 0.8 0.8 0.6 0.6 63582 0.0 0.6 0.6 0.8 0.4 63583 0.2 0.6 1.0 1.0 1.0 63584 0.4 0.6 0.8 0.6 0.6 63586 0.2 0.8 0.8 0.8 0.6 63587 0.2 0.8 1.0 1.0 1.0 63588 0.0 1.0 1.0 1.0 0.8 63589 0.2 0.4 0.6 0.6 0.6 63590 0.0 0.8 0.0 0.2 0.8 63591 0.2 0.8 0.8 1.0 0.6 63595 0.6 0.6 0.0 0.0 0.0 63596 0.2 0.8 0.6 0.8 0.2 63597 0.2 0.6 0.8 0.4 0.8 63602 0.0 0.6 0.6 1.0 1.0 63608 0.0 1.0 1.0 1.0 1.0 63609 0.2 0.2 0.6 0.0 0.6 63613 0.0 0.8 0.8 1.0 1.0 63616 0.2 0.8 0.8 0.6 0.8 63617 0.2 0.8 1.0 1.0 1.0 63618 0.2 0.8 1.0 1.0 0.8 63619 0.2 0.2 0.6 0.6 0.8 63621 0.6 0.6 0.6 0.6 0.6 63623 0.0 0.8 0.8 1.0 0.8 63625 0.2 1.0 0.6 0.0 0.6 63631 0.4 0.6 0.6 0.2 0.8 63636 0.0 1.0 1.0 0.6 1.0 63637 0.8 0.8 0.8 1.0 0.6 63638 0.2 0.8 0.6 0.6 0.6 63642 0.4 0.6 1.0 0.0 0.6 63644 0.0 0.8 1.0 0.6 1.0 63648 0.0 0.6 1.0 0.8 1.0 63649 0.0 0.8 0.8 0.4 0.8 63655 0.2 1.0 0.8 1.0 0.2 63657 0.2 0.6 0.6 0.6 1.0 63658 0.2 1.0 0.8 0.4 0.6 63659 0.6 0.6 0.6 0.2 0.8 63661 0.2 0.8 0.6 0.8 0.8 63668 0.6 0.8 0.6 0.6 0.8 63669 0.0 0.8 1.0 0.6 0.8 63670 0.6 0.6 0.6 1.0 0.4 63672 0.4 0.8 0.8 1.0 0.8 63675 0.4 0.6 1.0 1.0 NA 63676 0.2 0.8 1.0 0.8 0.8 63677 0.0 1.0 1.0 0.8 1.0 63681 0.2 0.8 0.6 0.2 0.6 63682 0.2 1.0 0.4 0.6 0.8 63683 0.2 1.0 0.8 0.8 0.8 63687 0.2 1.0 0.6 0.8 0.6 63690 0.8 0.8 0.2 0.4 0.4 63692 0.0 1.0 1.0 1.0 1.0 63693 0.0 1.0 1.0 0.8 0.6 63696 0.2 0.8 0.8 0.8 0.4 63697 0.0 0.8 1.0 1.0 0.8 63699 0.8 0.8 0.8 1.0 0.2 63700 0.0 0.8 0.6 1.0 0.4 63701 1.0 1.0 1.0 1.0 1.0 63704 0.0 1.0 1.0 1.0 0.8 63705 0.2 0.6 0.6 0.0 0.2 63706 0.0 0.8 1.0 1.0 0.8 63707 0.0 0.8 1.0 0.8 0.6 63709 0.2 0.8 0.8 0.8 0.8 63711 0.2 0.8 1.0 1.0 0.8 63712 0.0 0.8 1.0 1.0 1.0 63713 0.4 0.8 0.8 0.6 1.0 63721 0.6 1.0 1.0 1.0 1.0 63723 0.0 1.0 1.0 1.0 1.0 63724 0.0 0.8 0.8 1.0 0.8 63726 0.2 0.4 0.8 1.0 0.8 63727 0.6 0.6 0.4 0.8 0.8 63728 0.4 1.0 1.0 1.0 0.8 63729 0.4 0.8 1.0 0.6 0.8 63732 0.0 0.6 0.6 0.6 0.6 63734 0.6 0.2 0.2 0.0 0.8 63735 0.0 1.0 1.0 0.6 0.8 63738 0.6 1.0 0.8 1.0 0.6 63739 0.4 0.8 0.8 0.6 0.8 63742 0.2 0.6 0.6 1.0 1.0 63743 0.2 0.8 0.6 0.8 0.8 63744 0.2 0.8 0.8 0.6 0.8 63745 0.0 0.8 0.8 0.8 0.8 63746 1.0 0.8 0.8 0.6 0.8 63748 0.2 0.6 0.8 0.0 0.6 63750 0.6 0.6 0.6 0.4 0.4 63752 0.0 1.0 0.8 0.8 0.8 63760 0.2 0.6 1.0 1.0 0.8 63761 0.2 0.8 0.8 0.6 0.6 63762 0.0 1.0 1.0 0.0 1.0 63763 0.0 1.0 1.0 0.6 0.0 63766 0.4 0.8 1.0 1.0 0.8 63767 0.0 1.0 1.0 1.0 1.0 63768 0.2 0.8 0.8 0.6 0.8 63770 0.4 1.0 1.0 1.0 0.8 63773 0.2 1.0 0.8 1.0 1.0 63775 0.0 0.8 0.4 0.6 0.8 63776 0.0 NA 0.8 1.0 1.0 63778 0.4 0.8 0.2 0.2 0.6 63788 0.0 0.8 0.8 1.0 0.8 63789 0.2 0.6 0.4 1.0 0.6 63791 0.2 1.0 0.6 0.8 0.6 63792 0.6 1.0 1.0 1.0 0.8 63793 0.6 1.0 1.0 1.0 0.8 63794 0.8 0.8 0.8 1.0 0.6 63796 0.0 1.0 0.6 1.0 0.6 63798 1.0 1.0 1.0 1.0 1.0 63799 0.0 0.8 1.0 1.0 0.6 63801 0.0 0.8 1.0 0.8 0.8 63803 0.0 1.0 1.0 0.8 1.0 63807 0.2 0.4 0.6 0.8 0.4 63810 0.4 0.8 0.6 0.6 0.8 63811 0.8 0.8 0.8 1.0 0.8 63812 0.0 NA 1.0 1.0 1.0 63815 0.2 0.8 0.6 1.0 0.4 63816 0.2 0.8 0.6 0.8 0.8 63817 0.4 0.8 0.8 0.2 1.0 63818 0.0 0.6 0.6 1.0 0.4 63820 0.6 0.2 0.0 0.2 0.2 63822 0.0 0.8 0.8 0.2 0.8 63823 0.6 0.8 0.6 1.0 0.6 63824 0.0 1.0 1.0 1.0 1.0 63825 0.0 0.8 1.0 0.8 1.0 63826 0.6 0.6 0.2 0.8 0.4 63827 0.0 1.0 0.8 0.2 0.8 63828 0.4 0.4 0.6 0.8 0.8 63829 0.0 0.8 0.8 0.8 0.2 63834 0.8 1.0 1.0 0.6 1.0 63835 0.8 0.8 0.4 1.0 0.8 63837 0.8 0.4 0.8 0.6 0.6 63838 0.0 0.8 0.8 0.8 0.6 63839 0.6 0.8 0.0 0.8 0.0 63846 0.2 1.0 1.0 0.4 0.8 63847 0.8 0.8 0.2 0.8 0.0 63849 0.0 1.0 1.0 1.0 1.0 63851 0.4 0.6 0.8 0.0 0.6 63852 0.6 0.8 0.8 0.8 1.0 63853 0.4 0.6 0.4 0.2 0.6 63854 0.0 1.0 1.0 0.8 1.0 63855 0.2 0.8 0.8 0.8 0.8 63856 0.2 0.8 0.6 1.0 0.8 63862 0.8 0.2 0.0 0.0 0.0 63863 0.4 0.8 0.8 1.0 0.6 63866 0.6 0.8 0.4 0.8 0.6 63868 0.2 0.6 0.8 0.6 0.2 63871 0.0 1.0 1.0 0.8 0.8 63873 0.0 1.0 1.0 0.8 0.8 63875 0.8 1.0 0.8 1.0 0.8 63877 0.6 0.6 0.6 0.6 0.8 63879 0.6 0.6 0.0 0.0 0.2 63880 0.2 0.6 0.2 0.8 0.6 63881 0.8 0.8 0.8 0.2 1.0 63882 0.4 0.8 0.8 0.4 0.8 63883 0.4 0.6 1.0 1.0 0.6 63884 0.2 0.8 0.8 0.2 1.0 63885 0.8 0.6 0.0 0.2 0.2 63887 0.4 0.6 0.2 0.8 0.6 63888 1.0 0.0 0.8 0.6 0.2 63890 0.0 1.0 0.8 0.4 0.8 63897 0.6 0.8 0.6 0.2 0.6 63898 0.0 0.8 0.6 0.8 0.2 63899 0.0 1.0 0.8 0.6 0.8 63900 0.4 0.8 0.2 0.6 0.2 63902 0.0 1.0 0.8 1.0 0.8 63907 0.4 0.8 0.6 1.0 0.8 63909 0.4 0.4 0.6 0.6 0.4 63911 0.0 1.0 0.8 0.8 1.0 63912 0.0 1.0 1.0 1.0 1.0 63913 0.2 0.8 0.8 1.0 1.0 63918 0.0 1.0 1.0 1.0 1.0 63924 0.6 0.6 1.0 0.6 1.0 63925 0.2 0.8 0.8 0.8 0.4 63930 0.6 0.6 0.8 1.0 0.8 63931 0.2 0.4 0.8 0.6 0.6 63932 0.0 1.0 1.0 0.6 0.6 63934 0.0 NA 1.0 1.0 1.0 63937 0.0 0.8 0.4 0.0 0.6 63939 0.2 0.8 0.8 0.2 0.4 63942 0.4 0.8 0.8 0.6 0.8 63946 0.8 0.0 0.2 1.0 0.2 63947 0.2 1.0 1.0 1.0 1.0 63948 0.0 0.8 0.4 1.0 1.0 63950 0.0 0.8 0.2 1.0 0.6 63952 0.2 NA 1.0 1.0 1.0 63955 0.2 0.4 0.6 0.2 0.6 63956 0.0 1.0 0.8 1.0 1.0 63957 0.2 0.4 0.8 0.8 0.6 63958 0.2 0.6 0.6 0.6 0.8 63959 0.2 1.0 1.0 1.0 0.8 63961 0.6 0.0 0.2 0.6 0.8 63962 0.0 NA 0.8 1.0 1.0 63963 0.8 0.8 1.0 0.0 0.8 63966 0.0 1.0 0.8 1.0 1.0 63967 0.2 0.8 1.0 0.8 0.8 63971 0.4 0.8 0.8 0.8 0.4 63977 0.2 0.6 0.8 0.8 0.8 63978 0.8 0.8 1.0 1.0 0.8 63979 0.4 0.6 0.8 0.8 0.4 63980 0.2 0.6 0.6 1.0 0.4 63982 0.6 0.2 0.2 0.6 0.4 63983 0.2 0.8 0.8 0.2 0.6 63984 0.2 0.6 0.4 0.0 0.8 63985 1.0 1.0 0.4 1.0 1.0 63987 0.0 1.0 0.8 1.0 0.6 63990 0.4 0.8 0.6 0.6 0.8 63991 0.4 NA NA NA 0.4 63993 0.2 0.2 0.2 0.8 0.6 63997 0.2 0.6 0.6 0.4 0.6 63999 0.2 0.6 0.4 1.0 0.6 64000 0.4 0.8 0.4 1.0 0.6 64001 0.4 1.0 1.0 0.4 1.0 64003 0.2 0.8 0.8 1.0 0.8 64005 0.2 0.8 0.8 0.8 0.4 64006 0.0 1.0 0.8 0.8 0.8 64010 0.2 0.8 1.0 1.0 0.8 64012 0.6 1.0 0.2 1.0 1.0 64014 0.0 1.0 1.0 0.6 0.8 64016 0.6 0.8 0.8 1.0 0.6 64017 0.4 0.6 0.8 1.0 1.0 64018 0.0 0.6 1.0 1.0 0.4 64020 0.0 1.0 1.0 1.0 NA 64021 0.0 1.0 1.0 1.0 1.0 64024 0.0 1.0 1.0 1.0 1.0 64025 0.0 1.0 1.0 1.0 1.0 64028 0.6 0.8 0.4 1.0 0.4 64029 0.8 0.8 0.8 0.8 0.8 64030 0.8 0.4 0.6 0.6 0.6 64031 1.0 0.8 0.6 1.0 1.0 64032 0.6 0.6 0.6 0.6 0.6 64033 0.0 0.8 0.6 0.8 0.6 64036 0.2 1.0 0.6 1.0 0.8 64044 0.4 1.0 0.8 0.4 0.8 64046 0.2 0.8 0.6 0.4 0.6 64047 0.0 0.8 0.8 0.2 0.4 64049 0.4 0.6 0.0 1.0 0.6 64050 0.0 1.0 0.8 1.0 0.8 64051 0.2 0.8 1.0 0.8 1.0 64053 0.0 1.0 1.0 1.0 1.0 64056 0.6 1.0 1.0 1.0 1.0 64057 0.2 0.8 0.8 1.0 1.0 64065 0.6 0.8 0.6 0.0 0.6 64066 0.0 0.4 0.6 0.6 0.6 64069 0.2 1.0 0.8 0.4 0.6 64072 0.6 0.4 0.2 0.0 0.6 64074 0.0 0.6 0.8 1.0 0.8 64075 0.8 0.6 0.0 0.6 0.2 64076 0.2 0.8 1.0 0.8 0.8 64078 0.0 0.8 1.0 0.8 1.0 64079 0.0 0.0 0.0 0.0 0.8 64086 0.2 0.8 0.8 0.6 0.8 64087 0.6 0.6 0.2 0.2 0.4 64089 0.0 1.0 0.6 1.0 0.8 64091 0.0 0.6 0.2 0.0 0.8 64092 0.2 0.8 0.4 0.8 0.8 64094 0.4 0.6 0.6 0.4 0.6 64097 0.0 NA 1.0 0.8 1.0 64099 0.6 0.8 0.8 0.8 0.8 64100 0.4 0.6 0.6 1.0 0.6 64101 0.4 0.8 0.8 0.8 0.8 64102 0.2 0.8 1.0 1.0 0.8 64103 0.6 0.8 0.8 0.4 0.8 64108 0.2 1.0 1.0 1.0 1.0 64111 0.0 0.8 0.8 1.0 0.6 64113 0.0 1.0 1.0 1.0 0.8 64116 0.0 0.8 0.6 0.8 0.6 64119 0.2 0.8 0.6 0.8 0.6 64120 0.2 1.0 0.8 0.8 1.0 64122 0.4 1.0 1.0 1.0 1.0 64125 1.0 0.6 0.0 0.8 0.0 64128 0.4 0.6 0.6 0.6 0.6 64129 0.0 0.8 1.0 0.8 1.0 64132 0.4 0.2 0.6 0.4 0.6 64134 0.4 0.2 0.0 0.2 0.6 64136 0.4 0.4 0.6 0.8 0.2 64137 0.2 0.8 0.8 0.6 0.8 64138 0.2 0.8 0.6 0.8 0.8 64141 0.2 0.8 0.8 0.8 0.8 64142 0.2 1.0 0.8 1.0 0.6 64143 0.2 1.0 0.8 1.0 0.8 64144 0.4 0.4 0.0 0.4 0.2 64147 0.2 0.6 1.0 0.6 0.8 64148 0.4 0.6 0.2 0.6 0.6 64150 0.4 0.6 0.4 0.8 0.6 64151 0.0 0.8 1.0 0.8 0.8 64152 0.0 0.8 1.0 0.8 0.8 64154 0.2 0.8 1.0 0.8 1.0 64155 0.8 0.6 0.6 0.6 0.8 64156 0.0 0.6 1.0 1.0 1.0 64158 0.2 0.4 0.0 0.0 0.0 64163 0.0 1.0 1.0 1.0 0.6 64168 0.0 1.0 0.8 0.8 0.8 64169 0.2 0.8 1.0 1.0 1.0 64178 NA 0.8 0.6 1.0 0.6 64179 0.2 0.6 0.4 1.0 1.0 64180 0.6 0.2 0.6 0.6 0.6 64185 0.2 0.8 0.6 0.6 0.8 64192 1.0 0.8 1.0 0.8 1.0 64193 0.2 0.8 0.8 1.0 0.6 64195 0.2 0.8 0.8 1.0 1.0 64200 0.6 NA 0.8 1.0 1.0 64202 0.6 0.2 0.2 0.2 0.6 64204 0.2 0.8 0.8 1.0 0.8 64206 0.2 0.8 0.8 0.6 1.0 64212 0.2 0.8 0.4 0.2 0.4 64216 0.6 0.6 0.4 0.8 0.8 64217 0.2 0.8 1.0 0.4 0.8 64222 0.0 1.0 1.0 1.0 0.8 64226 0.2 0.6 0.6 0.2 0.2 64228 0.0 1.0 1.0 1.0 1.0 64231 0.2 1.0 1.0 1.0 1.0 64235 0.2 0.8 0.8 1.0 0.6 64236 0.2 0.8 0.4 0.2 0.2 64240 0.2 0.8 0.8 0.8 0.8 64241 0.6 0.8 0.4 0.8 0.6 64242 0.2 1.0 0.8 0.8 0.6 64243 0.2 0.8 0.4 0.8 0.2 64244 0.2 1.0 1.0 0.6 0.2 64248 0.2 0.8 0.6 1.0 0.6 64250 0.0 1.0 1.0 0.0 0.0 64251 0.0 0.8 0.4 0.6 0.6 64254 0.8 0.8 0.8 0.6 1.0 64256 0.0 1.0 0.8 0.6 1.0 64261 0.4 1.0 0.4 0.8 0.4 64263 0.2 1.0 1.0 0.8 0.8 64264 0.8 1.0 1.0 0.8 0.8 64266 0.2 0.8 0.2 0.6 0.2 64267 0.2 0.4 0.4 0.6 0.6 64274 0.2 1.0 1.0 0.8 0.8 64275 0.8 0.8 0.4 1.0 1.0 64279 0.4 0.4 0.6 0.2 0.2 64280 0.4 1.0 0.6 1.0 0.8 64284 0.0 1.0 1.0 1.0 1.0 64286 0.2 1.0 1.0 1.0 1.0 64289 0.2 0.6 0.6 0.8 0.6 64290 0.2 1.0 1.0 1.0 1.0 64295 0.2 0.6 0.6 0.6 0.4 64296 0.6 0.8 0.8 1.0 0.6 64297 0.2 0.6 0.4 0.4 0.4 64298 0.2 0.8 1.0 0.6 1.0 64299 0.2 0.6 0.8 0.6 1.0 64300 0.8 0.6 0.4 0.2 0.4 64303 0.2 0.6 0.6 0.4 0.8 64305 0.2 0.6 0.8 1.0 0.6 64308 0.0 1.0 1.0 1.0 1.0 64310 0.6 1.0 0.8 0.8 0.4 64311 0.4 0.4 0.4 0.6 0.6 64312 0.8 0.8 0.2 0.6 0.6 64315 0.6 0.6 0.6 0.0 0.8 64318 0.8 0.8 0.6 0.8 0.8 64320 0.0 0.8 0.8 0.8 0.8 64321 0.0 1.0 1.0 1.0 1.0 64322 0.4 0.8 0.2 0.0 0.8 64323 0.4 1.0 0.6 1.0 0.6 64324 0.0 1.0 1.0 0.8 1.0 64326 0.2 0.8 0.8 0.6 1.0 64327 0.0 0.8 0.8 1.0 0.8 64329 0.2 0.8 0.8 0.4 0.8 64332 0.2 0.6 0.8 0.2 0.8 64333 0.2 1.0 1.0 0.8 1.0 64334 0.0 0.8 1.0 0.6 0.8 64335 0.8 0.6 0.4 0.4 0.6 64338 0.2 0.6 1.0 0.8 0.8 64339 0.0 0.8 1.0 1.0 0.6 64340 0.2 1.0 0.8 0.6 0.8 64341 0.6 1.0 0.0 1.0 1.0 64342 0.2 0.8 0.8 0.8 0.8 64344 0.2 1.0 0.8 0.8 0.6 64345 0.4 0.8 0.6 0.6 0.6 64347 0.2 0.6 0.8 0.8 0.6 64349 1.0 1.0 0.8 0.8 0.6 64352 0.0 0.8 0.8 1.0 0.8 64356 0.2 1.0 0.6 0.2 0.6 64359 0.4 0.8 0.8 0.6 0.8 64363 0.2 0.8 0.8 0.8 0.8 64365 0.4 0.8 0.8 0.2 0.6 64367 0.6 0.6 0.8 1.0 NA 64368 0.6 0.8 NA 0.8 0.8 64370 0.6 0.6 0.6 0.8 0.8 64371 0.4 0.2 0.4 0.8 0.6 64372 0.2 0.6 0.6 0.4 0.8 64374 0.4 0.6 0.6 0.6 0.8 64375 0.8 0.8 0.8 0.8 0.8 64377 0.2 0.2 0.2 0.4 0.2 64378 0.2 0.8 0.6 1.0 0.6 64379 0.2 0.2 0.6 0.0 0.4 64382 0.2 0.8 0.6 0.8 0.6 64385 0.0 1.0 1.0 1.0 0.8 64389 0.0 1.0 1.0 1.0 0.0 64390 0.4 0.6 0.8 0.2 0.8 64392 0.0 0.0 1.0 1.0 0.8 64393 0.2 1.0 1.0 0.4 1.0 64396 0.6 0.4 0.0 1.0 0.4 64399 0.2 0.0 0.0 0.2 0.6 64400 0.0 1.0 1.0 1.0 1.0 64401 0.2 0.6 0.6 0.8 0.8 64403 0.0 0.8 0.8 1.0 0.8 64413 0.8 0.6 0.6 0.8 0.6 64414 0.0 1.0 0.8 1.0 0.8 64417 0.8 1.0 0.8 1.0 1.0 64418 0.4 0.6 0.6 0.8 0.8 64421 0.6 0.6 0.6 0.6 0.4 64422 0.4 0.8 0.8 0.8 0.8 64424 0.4 0.6 0.4 1.0 1.0 64425 0.0 1.0 1.0 1.0 1.0 64426 0.2 0.6 0.8 0.2 1.0 64427 0.6 1.0 1.0 1.0 1.0 64429 0.2 0.8 0.8 1.0 0.8 64430 0.4 0.4 0.4 0.8 0.8 64431 0.4 0.6 0.8 1.0 0.6 64432 0.2 0.4 0.4 0.8 0.8 64435 0.2 0.8 0.8 1.0 0.8 64436 0.2 0.2 0.8 0.4 0.6 64437 0.4 1.0 0.8 1.0 0.8 64438 0.6 0.4 0.6 0.4 0.0 64439 0.6 0.8 0.6 1.0 0.8 64440 0.4 0.6 0.8 0.6 0.8 64446 0.2 1.0 0.8 0.4 0.8 64450 0.2 0.4 0.6 1.0 0.6 64453 0.0 1.0 0.8 NA 0.6 64456 0.2 0.6 0.4 0.4 0.8 64458 0.0 0.2 1.0 0.6 1.0 64459 0.8 0.8 0.8 0.4 0.8 64460 0.2 0.8 0.8 1.0 0.6 64461 0.0 0.8 0.8 0.6 1.0 64462 0.4 0.8 0.6 0.8 1.0 64463 0.0 1.0 NA 1.0 0.8 64464 0.2 0.6 0.8 1.0 0.8 64466 0.0 1.0 0.6 0.6 0.6 64467 0.4 0.6 0.8 0.6 1.0 64468 0.4 0.8 0.8 0.8 0.8 64469 0.8 1.0 1.0 0.0 1.0 64471 0.6 1.0 0.8 0.2 0.8 64479 0.2 0.6 0.6 0.4 0.6 64481 0.0 1.0 1.0 1.0 0.6 64483 0.2 0.0 0.4 0.6 1.0 64484 0.0 1.0 0.8 1.0 0.6 64487 0.4 1.0 1.0 1.0 0.8 64488 NA 1.0 0.8 0.6 1.0 64490 0.0 0.8 0.8 0.8 0.8 64491 0.4 0.8 0.8 1.0 0.6 64493 0.2 0.6 0.8 0.8 0.4 64495 0.4 0.8 0.6 1.0 0.8 64496 0.2 0.6 0.2 0.0 0.2 64499 0.2 0.6 0.2 0.0 0.4 64501 0.8 0.8 0.6 0.4 0.6 64507 0.0 0.8 0.8 0.8 0.6 64508 0.4 0.6 0.4 0.8 0.8 64510 0.6 0.8 0.6 0.6 0.8 64511 0.0 1.0 0.8 1.0 1.0 64512 0.0 1.0 0.8 1.0 0.2 64513 0.2 1.0 1.0 0.6 1.0 64514 0.8 1.0 0.8 0.8 0.6 64516 0.4 0.8 0.8 0.6 0.8 64518 0.0 1.0 1.0 1.0 1.0 64519 0.2 0.8 0.6 1.0 0.8 64520 0.8 0.2 0.4 0.2 0.2 64522 0.0 1.0 0.8 1.0 0.8 64525 0.0 1.0 1.0 1.0 1.0 64528 0.4 0.8 0.8 1.0 0.8 64530 0.8 0.2 0.2 0.6 0.4 64532 0.6 0.8 0.8 0.0 1.0 64537 0.4 0.8 0.4 0.8 0.6 64540 0.0 1.0 1.0 1.0 1.0 64541 0.2 0.8 0.8 1.0 1.0 64543 0.0 1.0 0.8 1.0 1.0 64544 0.0 1.0 1.0 0.6 1.0 64545 0.8 0.8 0.8 1.0 1.0 64546 1.0 0.0 0.0 0.2 0.0 64547 0.0 1.0 1.0 0.8 1.0 64548 0.2 0.6 0.8 0.4 0.4 64549 0.0 1.0 1.0 1.0 1.0 64552 0.2 0.8 0.6 0.8 0.6 64555 0.2 1.0 1.0 1.0 1.0 64556 0.0 1.0 1.0 1.0 0.8 64557 0.2 0.6 0.8 0.2 0.6 64562 0.8 0.4 0.6 0.2 0.6 64563 0.0 1.0 1.0 0.8 1.0 64564 0.0 0.8 0.8 1.0 0.6 64565 0.0 1.0 1.0 1.0 0.6 64567 0.0 0.6 0.4 0.8 0.4 64568 0.2 0.8 0.6 1.0 0.8 64571 0.0 0.8 1.0 1.0 1.0 64572 0.6 0.6 0.0 0.2 0.2 64573 0.6 0.6 0.4 0.4 0.6 64577 0.4 0.2 0.6 0.6 0.6 64579 0.6 0.0 0.0 0.0 0.4 64581 1.0 0.6 1.0 1.0 0.8 64583 0.0 1.0 1.0 1.0 0.8 64585 0.4 0.8 0.8 1.0 NA 64586 0.0 0.6 0.6 0.6 0.6 64588 1.0 0.4 0.6 0.0 0.8 64591 0.0 0.8 0.6 0.4 0.6 64593 0.2 0.8 0.8 0.6 0.6 64595 0.6 0.2 0.6 0.6 0.2 64600 0.0 1.0 0.8 0.8 1.0 64603 0.2 0.6 0.6 0.8 0.4 64605 0.8 0.6 0.8 0.2 0.8 64606 0.0 1.0 0.8 0.2 0.8 64607 0.6 0.8 0.6 1.0 1.0 64612 0.0 0.8 0.8 1.0 0.8 64614 0.0 1.0 0.8 0.8 0.8 64618 0.2 0.6 1.0 1.0 1.0 64620 0.4 1.0 0.4 0.8 0.6 64621 0.4 1.0 0.4 1.0 0.0 64622 0.0 0.8 0.8 0.6 0.2 64623 0.4 0.8 1.0 1.0 0.8 64626 0.0 1.0 0.8 0.6 0.8 64627 0.2 0.6 0.4 0.6 0.6 64630 0.2 1.0 1.0 1.0 1.0 64634 0.4 0.4 0.2 0.6 0.4 64636 0.8 1.0 0.4 1.0 0.4 64638 0.4 0.2 0.6 0.2 0.2 64639 0.0 1.0 0.8 0.6 0.8 64642 0.0 0.0 0.0 0.0 0.0 64648 0.0 1.0 1.0 0.8 0.8 64652 0.0 1.0 1.0 1.0 1.0 64656 0.6 1.0 0.8 0.8 1.0 64658 0.2 0.8 0.6 NA 0.8 64660 0.2 0.8 0.8 1.0 0.8 64661 0.2 0.6 0.6 0.6 0.6 64664 0.6 0.8 0.4 0.6 0.2 64665 0.0 0.8 0.8 0.8 0.8 64668 0.4 0.4 0.2 0.2 0.4 64669 0.2 0.8 0.4 0.4 0.2 64670 0.2 0.6 0.4 0.2 0.4 64671 0.2 0.6 0.6 0.8 0.4 64673 0.2 0.4 0.0 0.0 0.6 64675 0.0 0.6 0.8 0.2 0.2 64679 0.6 0.8 0.4 0.6 0.6 64680 0.0 0.8 0.8 1.0 0.6 64682 0.2 0.6 1.0 1.0 0.8 64683 0.4 0.2 0.2 1.0 0.8 64685 0.0 0.8 0.8 1.0 1.0 64686 0.8 0.6 0.6 0.4 0.0 64688 0.2 0.8 0.4 0.8 0.8 64689 0.6 0.6 0.8 0.4 0.6 64693 0.6 0.8 0.4 0.6 0.4 64696 0.0 0.8 0.8 1.0 0.6 64697 0.0 0.4 0.4 0.8 0.4 64698 0.8 1.0 1.0 1.0 0.8 64702 0.0 0.6 0.4 1.0 0.6 64706 0.2 0.4 0.6 1.0 0.8 64707 0.6 0.6 0.2 0.0 0.4 64709 0.4 1.0 0.8 1.0 0.8 64712 0.6 0.8 0.8 0.8 1.0 64714 0.0 0.8 0.6 1.0 0.6 64715 1.0 0.2 0.6 1.0 0.4 64716 0.2 1.0 1.0 1.0 1.0 64718 0.2 0.6 0.2 0.6 0.6 64720 0.0 1.0 1.0 1.0 0.8 64724 0.4 1.0 1.0 0.6 0.4 64725 0.0 0.6 0.8 0.8 0.8 64727 0.4 0.6 0.6 0.6 0.6 64729 0.6 0.8 0.0 0.2 0.2 64730 0.2 1.0 0.6 1.0 0.8 64733 0.4 0.8 0.8 0.6 0.8 64735 0.2 0.8 0.6 1.0 0.4 64737 0.0 0.8 0.2 0.6 0.2 64738 0.6 0.8 0.6 0.4 0.4 64739 0.8 0.6 0.2 0.2 0.4 64742 0.2 1.0 0.8 0.6 1.0 64744 0.2 0.8 0.8 0.8 0.8 64747 0.0 0.8 0.6 0.6 0.6 64753 0.6 1.0 0.8 0.8 0.8 64754 0.2 0.8 1.0 1.0 0.8 64758 0.0 0.6 0.8 0.4 0.6 64759 0.0 0.8 0.8 0.6 0.8 64763 0.0 1.0 1.0 0.8 0.6 64768 0.2 0.6 0.8 1.0 0.8 64769 0.8 0.2 0.2 1.0 1.0 64770 0.2 0.6 0.6 0.0 0.4 64771 0.0 1.0 0.8 0.8 1.0 64772 0.4 0.6 0.8 0.6 0.8 64773 0.2 1.0 0.8 0.8 1.0 64774 0.2 0.8 0.6 0.0 1.0 64780 0.0 0.6 0.8 NA 0.8 64804 0.2 0.8 0.8 1.0 0.4 64807 0.6 1.0 1.0 1.0 0.8 64810 0.6 0.2 0.0 0.6 0.0 64814 0.2 0.6 0.2 0.2 0.6 64815 0.8 0.4 0.2 0.2 0.4 64817 0.8 0.6 0.6 1.0 0.6 64819 0.6 0.6 0.6 0.8 0.6 64822 0.2 1.0 1.0 1.0 0.8 64824 0.2 0.8 0.8 1.0 1.0 64825 0.6 0.8 0.6 0.8 0.6 64826 0.6 0.8 NA 1.0 0.8 64830 0.2 0.0 0.6 0.2 0.6 64831 0.2 0.0 0.6 0.0 0.6 64835 0.0 0.8 1.0 0.8 1.0 64838 0.0 1.0 1.0 0.8 1.0 64839 1.0 1.0 0.0 1.0 0.8 64842 0.4 1.0 1.0 0.8 1.0 64843 0.2 1.0 1.0 0.8 0.8 64844 0.6 0.2 0.8 1.0 0.0 64845 0.0 0.8 0.8 0.6 0.6 64846 0.2 1.0 0.6 0.0 0.8 64847 0.6 0.8 0.6 0.2 1.0 64849 0.0 0.4 0.8 0.8 0.8 64850 0.2 0.8 0.6 0.8 0.6 64852 0.0 1.0 0.6 0.6 0.8 64857 0.0 0.6 0.4 1.0 0.8 64861 0.6 1.0 0.6 0.8 0.8 64865 0.2 0.8 0.4 0.6 0.6 64866 0.0 0.8 0.2 0.6 0.2 64874 0.2 0.6 0.4 0.8 0.6 64876 0.2 0.8 1.0 1.0 0.8 64877 0.2 0.6 0.6 0.6 0.2 64880 0.6 0.2 0.0 0.0 0.2 64886 0.2 0.8 1.0 0.8 1.0 64887 0.4 0.8 0.6 0.6 0.6 64888 0.4 0.4 0.2 0.8 0.2 64890 0.6 0.8 0.8 0.8 0.6 64891 0.6 0.6 0.8 0.2 1.0 64894 0.6 0.6 0.8 0.0 0.8 64898 0.0 0.6 0.8 1.0 0.6 64902 0.8 0.2 0.6 0.4 0.4 64907 0.4 0.8 0.2 0.6 0.8 64912 0.0 1.0 0.8 1.0 0.6 64914 0.2 1.0 0.8 0.4 0.8 64915 0.4 0.8 0.6 1.0 0.8 64917 0.0 1.0 1.0 0.8 0.8 64918 0.0 0.8 0.8 1.0 0.8 64919 0.0 0.8 0.4 0.6 0.6 64920 0.4 0.6 0.8 1.0 0.4 64921 0.4 1.0 1.0 0.4 1.0 64926 0.6 0.8 0.2 0.0 0.6 64931 0.8 1.0 0.8 0.4 1.0 64936 0.0 1.0 0.8 1.0 1.0 64937 0.6 0.6 0.8 0.2 0.8 64938 0.0 1.0 1.0 1.0 1.0 64939 0.6 0.8 1.0 0.8 1.0 64940 0.6 0.4 0.4 0.2 0.2 64944 1.0 0.6 0.8 1.0 0.8 64946 1.0 NA 1.0 0.6 0.8 64949 0.0 0.8 0.8 0.6 1.0 64950 0.4 0.4 0.4 0.6 0.6 64952 0.0 1.0 0.6 0.4 0.8 64953 0.8 0.8 0.8 0.8 0.8 64954 0.0 1.0 1.0 0.4 1.0 64958 0.8 1.0 1.0 0.0 1.0 64960 0.0 1.0 1.0 1.0 1.0 64961 0.0 0.6 0.4 0.4 0.6 64962 0.0 1.0 1.0 0.0 0.0 64963 0.4 0.6 0.6 0.6 0.6 64967 0.0 1.0 1.0 1.0 1.0 64968 1.0 0.8 0.8 1.0 1.0 64969 1.0 0.8 0.8 1.0 1.0 64970 1.0 0.2 0.8 1.0 1.0 64971 1.0 0.2 0.8 1.0 1.0 64975 1.0 0.2 0.8 1.0 1.0 64978 1.0 0.2 0.8 1.0 1.0 64979 1.0 0.2 0.8 1.0 1.0 64980 1.0 0.2 0.8 1.0 1.0 64981 1.0 0.2 0.8 1.0 1.0 64982 1.0 0.2 0.8 1.0 1.0 64984 0.4 0.6 0.6 0.0 0.6 64987 0.0 0.8 0.0 1.0 0.2 64990 0.2 0.8 0.8 0.8 0.2 64991 0.2 0.8 0.8 1.0 0.8 64992 0.2 0.8 0.6 0.6 0.6 64993 0.6 0.8 0.6 0.6 0.4 64996 0.0 0.0 0.6 1.0 1.0 64997 0.4 1.0 0.8 1.0 1.0 65017 0.6 0.8 0.6 0.6 0.6 65018 0.0 1.0 1.0 0.6 0.8 65020 0.8 0.8 NA 0.8 0.6 65022 0.6 0.6 0.6 0.6 0.4 65025 0.2 0.8 0.8 1.0 0.8 65026 0.8 0.6 0.4 0.6 0.4 65027 0.6 0.6 0.6 0.8 0.8 65028 0.6 0.0 0.8 0.2 0.8 65033 0.4 0.8 0.4 1.0 0.6 65041 0.4 0.8 0.0 0.6 0.6 65042 0.2 0.6 1.0 0.8 0.8 65044 0.2 0.6 0.6 1.0 0.6 65047 0.4 0.2 0.2 0.2 0.6 65049 0.8 0.4 0.0 0.0 0.4 65050 0.0 1.0 0.6 1.0 0.6 65052 0.0 0.8 0.8 1.0 0.4 65054 0.2 0.8 0.6 1.0 0.6 65056 0.2 1.0 0.8 1.0 0.8 65058 0.4 0.6 0.4 0.0 0.4 65059 0.8 0.6 0.8 0.6 0.6 65060 0.2 0.8 1.0 1.0 1.0 65061 0.6 0.6 0.8 1.0 0.2 65070 0.2 0.8 1.0 0.8 0.8 65071 0.0 0.8 0.8 0.8 NA 65075 0.0 1.0 1.0 1.0 1.0 65078 0.4 1.0 NA 1.0 0.6 65079 0.2 1.0 0.8 1.0 0.8 65080 0.4 0.6 0.2 0.6 0.0 65081 0.0 0.8 0.8 0.8 0.8 65082 0.6 0.6 0.8 0.8 1.0 65083 0.0 0.8 0.6 0.8 0.6 65084 0.0 0.6 0.6 0.4 1.0 65086 0.2 0.6 0.6 0.2 1.0 65087 0.2 0.8 0.2 0.0 0.2 65088 0.2 0.8 0.6 0.8 0.8 65091 0.2 0.6 0.6 0.4 0.6 65092 0.2 0.6 0.6 0.6 0.8 65096 0.4 1.0 0.8 1.0 0.8 65097 0.0 1.0 1.0 1.0 1.0 65099 0.2 0.6 0.6 0.8 0.4 65100 0.2 0.6 0.4 0.4 0.4 65101 0.6 0.4 0.8 0.4 0.6 65105 0.4 0.4 0.6 0.4 0.8 65108 0.0 0.8 0.8 1.0 0.8 65109 0.2 0.4 0.6 0.8 0.8 65111 0.0 0.8 NA 0.8 0.8 65114 0.0 1.0 0.2 0.8 0.2 65117 0.0 0.2 0.4 0.6 0.2 65121 0.2 0.6 1.0 0.8 0.0 65122 0.8 0.6 0.0 0.6 0.4 65124 0.2 1.0 1.0 0.6 0.8 65126 0.8 0.8 0.8 1.0 1.0 65129 1.0 0.2 1.0 1.0 1.0 65130 0.2 1.0 1.0 1.0 1.0 65140 0.2 0.6 0.8 1.0 0.8 65142 0.4 0.8 0.6 0.8 0.4 65144 0.0 1.0 1.0 1.0 0.8 65147 0.0 0.8 0.8 1.0 0.6 65150 0.0 1.0 1.0 1.0 1.0 65151 0.2 1.0 1.0 1.0 1.0 65152 0.2 0.6 0.8 1.0 1.0 65156 0.0 1.0 1.0 0.8 0.8 65157 0.0 1.0 0.8 0.6 0.8 65162 0.6 0.6 0.4 0.6 0.2 65163 0.4 0.6 0.4 0.2 0.2 65164 0.4 0.8 0.4 0.8 0.4 65166 0.2 0.8 1.0 0.8 1.0 65168 0.4 0.4 NA NA 0.8 65169 0.0 0.8 0.8 0.6 0.8 65170 0.4 0.4 0.8 0.8 0.8 65172 0.4 0.8 0.8 0.6 0.6 65173 0.6 0.6 0.6 0.8 0.8 65174 0.0 1.0 1.0 1.0 1.0 65175 0.0 0.6 0.8 1.0 0.8 65179 0.4 NA 0.0 0.6 0.8 65180 0.0 0.8 1.0 1.0 1.0 65185 0.0 0.8 1.0 0.8 0.6 65186 0.4 1.0 1.0 0.8 1.0 65188 0.0 1.0 1.0 0.0 1.0 65190 0.6 0.4 0.6 0.6 0.0 65191 0.6 1.0 1.0 1.0 0.2 65193 0.2 0.6 0.8 0.6 0.0 65195 0.2 0.8 0.8 1.0 0.8 65196 0.2 0.8 0.4 0.6 0.4 65197 0.0 0.8 0.8 1.0 0.8 65199 0.0 1.0 1.0 1.0 1.0 65201 0.4 0.8 0.6 0.4 0.6 65202 0.2 0.8 0.6 0.8 0.8 65204 0.6 0.4 1.0 1.0 1.0 65206 0.4 0.4 0.6 0.8 1.0 65207 0.0 0.8 0.8 0.6 1.0 65212 0.2 0.4 0.6 0.8 0.2 65213 0.2 0.2 1.0 0.2 1.0 65215 0.0 1.0 1.0 1.0 1.0 65220 0.4 0.8 0.6 0.8 0.8 65223 0.4 0.8 0.0 0.4 0.6 65224 0.6 0.6 0.6 0.2 0.6 65229 0.2 1.0 1.0 1.0 1.0 65230 0.8 0.6 0.8 0.2 0.8 65232 0.6 0.6 0.8 0.0 0.8 65235 0.6 1.0 0.8 1.0 1.0 65236 0.2 0.8 0.8 0.6 0.8 65237 0.4 0.6 0.6 0.6 0.6 65238 0.0 1.0 0.8 0.8 1.0 65240 0.2 1.0 1.0 1.0 0.0 65241 0.0 1.0 1.0 1.0 1.0 65245 0.2 0.8 0.2 1.0 NA 65247 0.4 0.8 0.6 1.0 0.6 65249 0.0 0.6 0.2 1.0 0.8 65252 0.8 0.8 0.2 0.8 0.2 65254 0.4 0.4 0.8 0.0 0.8 65255 0.8 1.0 0.8 1.0 0.6 65256 0.4 0.4 0.0 0.0 0.4 65257 0.2 0.8 0.8 0.8 0.6 65258 0.0 1.0 1.0 1.0 1.0 65260 0.6 0.8 0.8 0.8 1.0 65262 0.0 0.8 0.8 0.8 1.0 65266 0.0 0.8 1.0 1.0 1.0 65267 0.2 0.8 0.8 1.0 0.8 65268 0.0 0.8 0.6 0.8 0.6 65271 0.0 0.8 0.2 0.8 0.4 65273 0.4 1.0 0.4 0.6 0.4 65274 0.0 0.6 0.8 0.6 0.8 65275 0.2 0.8 0.8 1.0 0.2 65278 0.2 1.0 0.6 0.6 0.4 65279 0.2 0.8 0.8 0.8 0.8 65281 0.4 0.8 0.2 0.2 0.8 65282 0.2 0.2 0.2 0.8 0.8 65285 0.6 0.6 0.4 0.2 0.8 65286 NA 1.0 0.8 1.0 1.0 65288 0.6 1.0 0.6 1.0 0.4 65289 0.0 1.0 1.0 0.6 1.0 65291 0.0 0.8 0.6 0.6 0.8 65295 0.2 0.8 0.6 0.8 0.8 65296 0.2 0.8 0.8 1.0 0.8 65297 0.2 0.6 0.4 1.0 0.6 65299 0.0 1.0 1.0 0.8 0.8 65300 0.0 1.0 1.0 1.0 1.0 65301 0.8 0.6 0.6 0.6 0.4 65307 0.6 0.8 0.6 0.8 0.8 65309 0.8 0.2 0.0 0.2 0.8 65311 0.0 1.0 1.0 1.0 1.0 65313 0.0 0.6 0.6 1.0 0.6 65314 0.2 1.0 0.6 0.8 0.8 65316 0.4 0.4 0.6 0.2 0.6 65319 0.2 0.6 0.6 0.8 0.6 65320 0.2 1.0 0.6 1.0 0.8 65322 0.2 0.2 0.6 0.8 0.2 65326 0.2 0.8 0.8 0.2 0.8 65327 0.2 0.8 0.8 0.8 0.8 65332 0.0 1.0 0.8 1.0 0.8 65335 0.0 1.0 1.0 1.0 0.8 65342 0.2 0.8 0.8 0.2 0.8 65343 0.2 0.8 0.8 0.2 0.8 65344 0.2 1.0 0.8 0.4 0.8 65346 0.2 0.8 0.8 0.2 0.8 65347 0.4 0.6 0.6 0.8 0.8 65348 0.0 0.6 0.6 0.6 0.6 65349 0.4 0.8 1.0 0.8 0.8 65352 0.8 0.8 0.2 0.8 0.4 65353 0.0 0.8 0.8 1.0 1.0 65356 0.0 1.0 0.8 0.8 0.8 65359 0.4 0.4 0.6 0.4 0.6 65361 0.0 1.0 0.4 0.8 0.6 65362 0.6 0.2 0.6 0.8 0.6 65363 0.0 1.0 0.8 0.6 1.0 65370 0.2 0.8 1.0 1.0 1.0 65372 0.0 1.0 1.0 0.6 1.0 65374 0.0 1.0 1.0 1.0 0.8 65377 0.0 1.0 1.0 1.0 0.8 65378 0.8 0.6 0.2 0.8 0.4 65381 0.8 0.2 0.0 0.2 0.2 65382 0.8 1.0 1.0 1.0 1.0 65385 0.0 1.0 0.6 0.8 1.0 65386 0.2 0.8 0.6 1.0 0.6 65387 0.4 0.8 1.0 1.0 0.8 65388 0.0 1.0 1.0 0.8 1.0 65391 0.0 1.0 1.0 1.0 1.0 65392 0.6 0.4 0.4 0.8 0.0 65395 0.2 0.8 0.8 0.8 1.0 65401 0.2 0.6 0.8 1.0 0.8 65402 0.0 1.0 1.0 0.6 1.0 65403 0.0 1.0 1.0 1.0 1.0 65404 0.6 0.8 0.6 1.0 0.8 65405 0.0 0.8 1.0 0.6 1.0 65407 0.0 0.8 1.0 1.0 0.0 65408 0.4 0.6 0.4 0.8 0.6 65411 0.4 1.0 1.0 0.8 0.8 65412 1.0 0.6 0.0 0.8 0.6 65413 0.2 0.6 0.6 0.8 0.8 65415 0.0 0.8 0.6 1.0 0.6 65417 0.0 0.8 0.8 1.0 0.8 65419 0.6 0.4 0.8 1.0 0.6 65420 0.0 1.0 1.0 1.0 0.4 65421 1.0 1.0 0.6 0.6 0.6 65424 0.0 0.8 1.0 0.8 0.8 65426 0.2 1.0 0.8 0.8 0.4 65428 0.0 1.0 1.0 0.6 1.0 65430 0.2 0.8 0.8 0.0 0.6 65432 0.8 0.6 0.8 1.0 0.2 65433 0.6 0.6 0.6 0.8 0.6 65434 0.2 1.0 1.0 1.0 0.8 65435 0.8 0.6 0.8 0.6 0.8 65437 1.0 1.0 1.0 0.8 0.8 65439 0.4 0.0 0.0 0.2 0.0 65440 0.2 1.0 1.0 0.2 0.6 65442 0.0 0.8 1.0 0.8 0.8 65445 0.0 1.0 1.0 1.0 0.6 65446 0.2 0.8 0.6 0.4 0.4 65448 0.0 1.0 1.0 1.0 1.0 65450 0.6 0.8 0.8 0.6 0.8 65452 0.4 0.6 0.4 0.0 0.4 65453 0.0 0.8 1.0 0.6 1.0 65455 0.4 0.8 0.8 0.4 0.8 65457 0.0 1.0 0.8 0.6 0.6 65458 0.0 0.8 1.0 0.8 0.2 65459 0.2 0.8 0.8 1.0 0.8 65460 0.0 0.8 1.0 0.8 1.0 65462 0.2 0.8 0.8 0.6 0.6 65466 0.8 0.8 0.2 0.2 0.6 65467 0.2 1.0 1.0 1.0 1.0 65470 0.0 0.8 0.6 0.6 0.8 65472 0.0 1.0 1.0 1.0 1.0 65473 0.0 0.8 0.8 1.0 0.6 65474 0.6 0.6 0.8 0.6 0.6 65475 0.6 0.6 0.6 0.6 0.6 65476 0.0 1.0 0.8 0.8 0.8 65477 0.0 0.6 1.0 0.2 0.8 65479 0.0 1.0 0.8 1.0 0.8 65484 0.8 0.4 0.8 1.0 0.6 65487 0.0 1.0 1.0 1.0 1.0 65488 0.0 1.0 0.6 0.2 1.0 65490 0.0 1.0 1.0 1.0 1.0 65491 0.4 1.0 0.8 1.0 1.0 65494 0.8 0.8 0.8 0.8 0.4 65496 0.4 0.6 0.4 0.6 0.4 65499 0.0 1.0 1.0 0.6 0.8 65501 0.0 0.6 0.6 1.0 0.6 65502 0.0 1.0 1.0 0.8 1.0 65503 0.8 0.2 0.2 0.6 0.6 65504 0.2 0.6 0.8 0.8 0.8 65506 0.2 1.0 0.8 0.8 0.8 65510 0.0 0.6 1.0 0.8 1.0 65511 0.2 1.0 0.6 0.4 0.6 65512 0.0 0.6 0.6 0.6 0.6 65515 0.2 0.6 0.6 1.0 0.6 65519 0.8 0.2 0.2 0.2 0.6 65522 0.0 0.8 0.8 1.0 0.8 65523 0.0 0.8 0.8 0.8 0.6 65524 0.2 0.8 0.8 0.8 1.0 65526 0.2 0.8 0.8 1.0 0.6 65527 0.6 0.8 0.8 1.0 0.6 65528 0.2 0.2 0.8 0.8 0.4 65529 0.2 0.8 0.6 0.6 0.6 65532 0.0 0.6 0.4 0.6 0.8 65536 0.6 0.8 0.8 1.0 0.8 65538 0.0 1.0 1.0 0.8 1.0 65545 0.4 0.6 0.6 1.0 0.4 65547 0.2 1.0 0.8 1.0 1.0 65548 0.2 0.6 0.8 0.6 0.6 65549 0.2 0.6 0.6 0.4 0.6 65551 0.2 0.8 0.8 0.6 0.8 65552 0.0 0.8 0.8 1.0 1.0 65556 0.0 1.0 1.0 1.0 1.0 65557 0.0 1.0 0.8 0.6 1.0 65563 0.0 1.0 0.8 0.6 0.8 65564 0.2 0.8 1.0 0.8 1.0 65565 0.8 0.2 0.4 0.6 0.2 65566 0.2 1.0 0.8 1.0 0.4 65568 0.0 1.0 0.6 1.0 0.6 65569 0.2 0.8 1.0 0.6 1.0 65571 0.0 1.0 1.0 1.0 1.0 65575 0.0 0.8 0.8 0.8 0.8 65577 0.4 0.2 0.4 0.8 0.4 65578 0.4 1.0 0.8 1.0 0.8 65580 0.0 1.0 1.0 0.6 1.0 65583 0.0 0.8 0.8 1.0 0.6 65584 0.0 1.0 1.0 0.8 1.0 65586 0.2 0.8 1.0 1.0 0.6 65589 0.0 1.0 1.0 1.0 1.0 65591 0.4 1.0 1.0 0.8 1.0 65592 0.4 0.8 1.0 1.0 0.8 65593 0.0 1.0 1.0 1.0 1.0 65595 0.2 1.0 0.2 1.0 0.8 65598 0.0 1.0 0.8 0.8 0.8 65599 0.0 0.8 0.2 0.6 0.2 65600 0.2 0.8 1.0 1.0 0.8 65602 0.8 0.8 0.8 0.2 1.0 65605 0.2 1.0 1.0 1.0 0.8 65606 0.2 1.0 1.0 1.0 0.6 65612 0.0 1.0 0.6 0.0 0.6 65613 0.0 0.8 1.0 1.0 0.6 65618 0.4 0.2 0.8 0.6 0.6 65620 0.0 1.0 1.0 0.6 1.0 65628 0.6 1.0 0.8 1.0 0.8 65629 0.2 0.8 0.8 0.2 0.8 65630 0.6 0.6 0.2 0.2 0.0 65631 0.0 0.8 1.0 0.8 1.0 65632 0.0 0.8 1.0 1.0 0.8 65633 0.4 0.6 0.2 0.0 0.0 65634 0.2 1.0 0.8 1.0 1.0 65635 0.0 1.0 1.0 1.0 1.0 65636 0.4 0.6 0.6 0.6 0.4 65641 0.0 0.8 0.8 1.0 0.8 65643 0.8 0.8 0.8 0.6 0.8 65646 0.2 0.6 0.8 1.0 0.6 65648 NA 0.8 1.0 1.0 0.8 65649 0.4 0.6 0.8 1.0 0.8 65651 0.2 1.0 1.0 1.0 0.8 65652 0.2 1.0 0.8 1.0 0.6 65653 0.4 0.6 0.6 0.8 0.6 65654 0.6 0.6 0.6 1.0 0.4 65656 NA 0.8 NA 1.0 1.0 65659 0.8 0.4 0.2 0.8 0.2 65664 0.4 0.6 0.6 1.0 1.0 65668 0.8 0.6 0.6 1.0 NA 65671 0.0 0.8 1.0 0.8 0.8 65673 0.4 0.6 0.8 0.8 0.8 65674 0.2 1.0 0.8 1.0 0.8 65675 0.2 0.8 0.8 0.8 0.8 65678 0.0 0.8 0.8 0.4 0.6 65679 0.0 1.0 1.0 1.0 1.0 65680 0.2 1.0 1.0 1.0 0.8 65682 0.0 1.0 1.0 1.0 0.8 65684 0.8 1.0 1.0 0.8 1.0 65687 0.8 1.0 0.4 1.0 0.4 65694 0.6 0.2 0.8 0.2 0.8 65695 0.0 1.0 NA 1.0 1.0 65696 0.0 1.0 1.0 1.0 1.0 65698 0.8 0.8 0.2 0.2 0.4 65699 0.8 0.8 0.2 0.2 0.4 65700 1.0 1.0 1.0 1.0 0.8 65702 0.8 1.0 1.0 1.0 1.0 65703 0.2 0.8 0.4 0.8 0.8 65704 0.0 0.8 0.8 1.0 1.0 65705 0.4 0.6 0.6 0.0 0.4 65706 1.0 1.0 1.0 1.0 1.0 65708 0.6 NA 0.4 1.0 0.6 65709 0.2 0.8 0.4 0.8 1.0 65710 0.8 0.6 0.4 0.6 0.4 65711 0.4 0.6 0.8 1.0 0.8 65712 0.4 1.0 0.8 1.0 0.6 65713 0.0 0.8 0.8 1.0 1.0 65714 0.4 0.8 1.0 1.0 1.0 65715 0.8 0.0 0.8 0.0 0.6 65720 0.0 1.0 1.0 1.0 1.0 65724 0.8 0.2 0.4 0.4 0.4 65726 0.2 0.8 0.8 1.0 0.8 65727 0.8 0.2 1.0 0.8 0.8 65728 0.8 0.4 0.4 0.6 0.6 65730 0.2 0.6 0.6 1.0 0.6 65732 NA 0.8 1.0 0.8 1.0 65733 0.6 0.4 0.2 0.6 0.4 65734 0.0 0.8 1.0 1.0 1.0 65738 0.0 1.0 0.8 1.0 0.8 65740 0.2 0.8 0.8 1.0 0.8 65742 0.6 0.6 0.8 0.8 0.8 65743 0.0 0.2 0.2 1.0 0.2 65744 0.4 1.0 0.4 0.4 0.6 65745 0.0 0.8 0.8 0.8 0.6 65746 0.8 0.8 0.8 1.0 1.0 65747 0.8 0.6 0.2 0.6 0.2 65748 0.2 1.0 1.0 1.0 1.0 65752 0.0 0.2 0.8 1.0 0.8 65758 0.0 1.0 0.8 1.0 1.0 65762 0.4 0.6 0.8 1.0 0.8 65763 0.0 1.0 0.8 0.8 1.0 65764 0.2 0.8 0.2 0.8 0.6 65765 0.2 0.8 0.6 1.0 0.8 65767 0.6 0.8 0.4 0.2 0.4 65777 0.2 0.8 0.8 0.8 0.6 65778 0.0 1.0 1.0 1.0 0.6 65779 1.0 0.8 1.0 0.8 NA 65793 0.2 0.8 0.8 0.6 0.8 65794 0.0 1.0 1.0 1.0 1.0 65798 0.0 1.0 0.8 0.8 0.6 65801 0.0 1.0 1.0 1.0 1.0 65802 0.0 1.0 1.0 1.0 1.0 65803 0.0 1.0 0.8 1.0 1.0 65804 0.8 0.6 0.8 1.0 0.6 65809 0.0 1.0 0.8 NA 0.8 65811 0.2 0.6 0.8 0.6 0.6 65813 0.0 1.0 0.8 1.0 0.8 65816 1.0 1.0 0.6 1.0 0.4 65818 0.4 0.8 0.8 1.0 1.0 65820 0.4 0.6 0.8 0.8 0.8 65822 0.6 0.8 0.8 0.6 0.8 65825 0.8 1.0 0.8 1.0 1.0 65826 0.8 1.0 0.4 1.0 1.0 65828 0.8 0.8 0.2 0.8 1.0 65831 1.0 1.0 1.0 1.0 1.0 65834 0.0 0.8 0.8 1.0 0.8 65836 0.4 1.0 1.0 1.0 1.0 65838 0.0 1.0 1.0 1.0 1.0 65839 0.0 1.0 0.8 1.0 0.8 65840 0.2 1.0 1.0 0.6 1.0 65841 0.4 1.0 1.0 1.0 1.0 65842 0.8 0.6 0.6 0.4 0.8 65844 0.0 1.0 1.0 0.8 1.0 65847 0.2 0.8 1.0 1.0 0.8 65848 0.2 0.6 0.6 1.0 0.6 65849 0.0 0.8 0.6 1.0 0.6 65851 0.0 0.6 0.6 0.8 0.6 65852 0.8 0.8 1.0 0.8 1.0 65856 0.4 0.8 0.8 1.0 1.0 65857 0.6 0.6 0.2 1.0 0.6 65862 0.0 0.8 0.6 1.0 0.6 65863 0.6 0.6 0.6 1.0 0.0 65864 0.0 1.0 1.0 1.0 0.0 65865 0.8 1.0 1.0 1.0 1.0 65870 0.2 0.8 0.8 0.6 1.0 65874 0.2 1.0 1.0 1.0 0.8 65876 0.0 0.8 1.0 1.0 1.0 65879 0.0 1.0 0.8 0.6 0.6 65880 0.4 0.2 0.6 1.0 0.8 65883 0.0 1.0 1.0 1.0 0.6 65886 0.4 0.6 0.8 1.0 0.8 65888 0.8 0.0 1.0 0.8 0.8 65890 0.0 0.8 0.8 0.8 0.8 65891 0.0 1.0 0.8 1.0 0.8 65892 0.2 1.0 0.6 1.0 0.2 65893 0.4 0.8 0.8 0.8 0.6 65894 0.0 1.0 0.6 1.0 1.0 65895 0.4 0.6 0.8 1.0 0.4 65896 0.0 0.8 0.8 1.0 0.8 65897 0.2 0.8 1.0 0.8 0.8 65899 1.0 0.8 1.0 1.0 1.0 65900 0.4 0.4 0.8 1.0 0.6 65901 0.0 NA NA 1.0 0.8 65902 0.6 0.8 1.0 1.0 1.0 65903 0.6 0.6 0.6 0.8 1.0 65905 0.4 1.0 1.0 1.0 1.0 65909 0.2 1.0 1.0 1.0 0.6 65913 0.2 0.8 0.8 0.4 0.8 65917 0.2 1.0 0.8 1.0 1.0 65918 0.0 1.0 1.0 1.0 1.0 65921 0.2 0.8 0.8 1.0 1.0 65924 NA 1.0 0.8 1.0 1.0 65925 0.2 0.8 0.2 1.0 0.2 65926 0.6 0.6 0.6 1.0 0.8 65929 0.8 0.8 0.8 1.0 0.8 65930 0.8 0.8 0.8 1.0 0.8 65932 0.0 1.0 1.0 1.0 1.0 65933 0.4 0.6 0.6 0.6 0.6 65936 0.4 0.6 0.6 0.6 0.6 65937 0.0 1.0 1.0 0.8 1.0 65938 0.8 0.8 1.0 1.0 0.2 65940 0.2 0.8 0.8 1.0 1.0 65941 0.2 1.0 1.0 1.0 0.8 65942 0.2 0.8 0.6 0.6 0.8 65946 0.6 0.8 0.8 1.0 0.2 65948 0.0 1.0 1.0 1.0 1.0 65950 0.2 0.4 1.0 0.8 0.8 65951 0.8 1.0 1.0 1.0 1.0 65959 0.4 0.8 0.8 0.8 0.8 65961 0.0 1.0 0.6 1.0 0.8 65962 0.0 0.8 0.8 1.0 0.8 65965 0.0 1.0 0.8 1.0 0.8 65969 0.2 1.0 1.0 1.0 1.0 65971 0.0 0.8 0.4 0.6 0.8 65972 0.0 0.8 0.6 0.8 0.6 65973 0.0 0.8 0.8 0.8 0.8 65974 0.0 0.0 0.0 0.0 0.0 65976 0.0 1.0 1.0 1.0 0.8 65977 0.0 0.8 0.8 1.0 0.8 65981 0.2 0.6 0.2 0.8 0.2 65986 0.4 0.8 0.8 1.0 0.8 65987 0.8 0.8 0.4 0.8 0.2 65988 0.2 1.0 0.8 1.0 0.8 65989 0.0 0.6 1.0 0.8 0.6 65990 0.2 0.8 0.8 1.0 0.8 65992 0.4 0.8 0.8 0.6 0.8 65995 0.2 0.4 0.8 0.0 0.8 65998 0.2 1.0 0.4 1.0 0.2 65999 0.2 0.6 0.4 0.4 0.4 66000 0.0 1.0 1.0 1.0 0.8 66001 0.8 0.8 0.6 1.0 0.8 66002 0.0 1.0 1.0 1.0 0.8 66003 0.8 1.0 1.0 1.0 1.0 66004 0.0 0.8 1.0 1.0 0.8 66006 0.2 0.8 0.0 1.0 1.0 66007 0.0 1.0 0.6 1.0 1.0 66013 0.2 0.8 0.8 1.0 1.0 66014 0.6 0.6 0.0 1.0 0.4 66015 0.4 0.8 0.8 0.8 0.4 66016 0.2 0.6 1.0 0.6 1.0 66017 1.0 0.0 1.0 1.0 0.8 66022 0.2 0.8 0.8 0.6 0.8 66024 0.6 0.8 0.6 0.8 0.8 66032 0.2 1.0 1.0 1.0 NA 66034 0.2 1.0 1.0 1.0 0.0 66037 0.4 0.6 0.4 1.0 0.4 66039 0.2 0.8 0.6 0.6 0.8 66042 0.2 0.8 0.8 0.4 0.6 66045 0.6 1.0 1.0 1.0 1.0 66047 0.0 1.0 1.0 1.0 1.0 66049 0.2 0.6 0.6 0.8 0.6 66050 0.0 1.0 1.0 1.0 1.0 66053 0.2 0.8 0.8 0.2 0.6 66057 0.8 0.2 1.0 0.6 1.0 66058 0.4 1.0 0.6 1.0 0.8 66060 0.0 1.0 1.0 1.0 1.0 66061 0.0 1.0 0.8 1.0 0.6 66062 0.2 0.8 0.8 0.8 0.6 66063 0.2 1.0 0.8 1.0 1.0 66064 0.0 0.8 0.0 0.8 0.8 66065 0.8 0.0 0.4 0.8 0.2 66067 0.0 1.0 1.0 1.0 1.0 66068 0.4 0.8 1.0 0.8 1.0 66070 0.0 1.0 1.0 1.0 1.0 66071 0.6 0.6 0.2 0.2 0.2 66072 0.6 1.0 0.8 0.8 0.6 66077 0.8 0.8 0.8 1.0 0.8 66080 0.2 0.8 1.0 1.0 0.8 66081 0.8 0.6 0.2 0.4 0.6 66082 0.0 1.0 1.0 1.0 0.8 66083 0.0 1.0 1.0 1.0 1.0 66086 0.2 0.8 0.6 0.2 0.8 66088 0.0 1.0 1.0 1.0 1.0 66092 0.0 1.0 1.0 1.0 1.0 66093 0.0 0.8 0.8 0.8 1.0 66094 0.2 0.8 0.8 1.0 0.8 66095 0.2 0.6 0.8 0.4 0.8 66096 0.2 0.6 1.0 1.0 1.0 66097 0.0 1.0 1.0 1.0 1.0 66098 0.0 1.0 1.0 0.8 1.0 66102 0.6 0.6 0.8 0.8 0.8 66106 0.0 0.6 0.8 1.0 1.0 66107 0.2 0.6 0.8 0.4 0.6 66108 0.0 0.8 1.0 0.0 0.6 66109 0.2 0.8 0.2 0.6 0.2 66114 0.6 0.6 0.2 0.2 0.2 66115 0.0 1.0 1.0 0.8 1.0 66116 0.0 0.6 0.6 0.8 0.6 66117 0.4 0.8 1.0 1.0 1.0 66119 0.2 1.0 1.0 1.0 1.0 66120 0.6 0.8 0.8 1.0 0.8 66121 0.2 1.0 0.6 1.0 0.8 66122 0.2 1.0 0.8 0.8 1.0 66125 0.0 0.8 0.8 1.0 0.8 66127 0.2 0.8 1.0 1.0 1.0 66135 0.2 1.0 1.0 0.6 0.8 66136 0.2 0.6 0.6 0.6 0.8 66138 1.0 1.0 0.6 0.8 0.6 66139 0.6 0.0 1.0 1.0 0.8 66141 1.0 0.4 0.4 1.0 1.0 66144 0.0 1.0 1.0 1.0 0.8 66150 0.4 0.8 0.8 0.8 0.8 66151 0.2 0.6 0.8 1.0 0.8 66153 0.0 0.8 0.8 1.0 0.8 66156 0.2 0.8 0.8 0.6 0.8 66157 0.6 1.0 NA 1.0 0.8 66158 0.6 0.8 0.8 0.8 0.4 66161 0.2 0.8 0.8 0.6 1.0 66163 0.0 0.8 0.8 1.0 1.0 66164 0.2 0.8 0.8 1.0 1.0 66169 0.0 0.8 1.0 1.0 0.6 66170 0.0 1.0 1.0 1.0 1.0 66171 0.6 0.6 0.6 0.8 0.6 66172 0.2 0.2 0.4 0.2 0.2 66173 0.0 1.0 0.8 1.0 0.6 66176 0.2 1.0 0.8 1.0 0.6 66181 0.0 1.0 1.0 1.0 1.0 66187 0.0 0.6 0.6 0.4 0.6 66188 0.2 0.8 0.8 0.8 0.8 66190 0.0 0.8 0.6 0.6 0.6 66191 0.4 0.8 0.6 0.2 0.2 66195 0.0 1.0 0.8 1.0 0.8 66198 0.6 0.8 0.8 0.8 1.0 66199 0.2 0.6 0.6 0.8 0.6 66201 0.8 1.0 NA 1.0 1.0 66204 0.2 0.8 0.6 1.0 0.6 66205 0.2 1.0 1.0 1.0 1.0 66210 0.0 1.0 0.8 1.0 1.0 66212 0.0 1.0 0.6 0.4 0.8 66215 0.0 0.8 0.8 0.8 1.0 66216 0.2 0.8 1.0 1.0 1.0 66218 0.0 0.8 0.8 0.6 0.6 66219 0.6 0.6 0.6 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0.4 0.8 0.8 1.0 0.6 66307 0.0 1.0 0.8 1.0 0.8 66309 0.6 0.6 0.8 0.6 0.6 66311 0.0 1.0 0.6 1.0 0.6 66312 0.8 1.0 1.0 1.0 0.8 66314 0.4 0.8 0.8 1.0 1.0 66317 0.4 0.4 0.2 1.0 0.2 66318 0.2 1.0 0.8 1.0 0.8 66323 0.4 1.0 0.8 1.0 0.8 66324 0.2 0.8 0.6 0.6 0.6 66325 0.0 0.8 1.0 0.6 1.0 66326 0.2 0.8 0.8 1.0 0.6 66329 0.2 0.8 0.6 1.0 0.8 66331 0.6 0.6 0.2 0.8 0.2 66337 0.0 1.0 1.0 0.8 0.8 66341 0.2 0.8 0.6 0.8 0.4 66344 0.0 1.0 1.0 1.0 0.8 66346 0.0 0.8 1.0 1.0 0.8 66348 0.6 1.0 0.6 1.0 0.6 66349 0.0 1.0 1.0 1.0 1.0 66357 0.2 0.8 0.6 0.6 0.4 66360 0.2 0.8 1.0 1.0 1.0 66364 0.0 1.0 1.0 1.0 1.0 66368 0.2 0.8 0.8 1.0 0.6 66370 0.2 0.8 0.8 1.0 0.6 66371 0.2 0.8 0.8 1.0 0.6 66372 0.2 0.8 0.8 1.0 0.6 66373 0.2 0.8 0.8 1.0 0.8 66376 0.6 1.0 0.8 0.6 0.8 66381 1.0 0.2 0.8 0.2 0.2 66382 0.2 0.4 0.6 0.4 0.6 66389 0.4 0.8 0.6 0.8 0.2 66391 0.0 1.0 0.8 0.0 0.4 66392 0.0 1.0 1.0 0.2 0.8 66396 0.2 0.8 0.6 0.8 0.6 66399 0.4 1.0 0.8 0.6 0.8 66402 1.0 0.8 0.8 0.4 0.4 66403 0.0 0.6 0.6 0.2 0.2 66404 0.0 1.0 1.0 0.6 0.6 66405 0.0 0.8 1.0 0.6 1.0 66406 0.2 1.0 0.6 0.8 0.6 66408 0.2 0.8 1.0 1.0 0.8 66409 0.4 1.0 1.0 0.4 0.8 66410 0.2 0.4 0.6 0.2 0.4 66413 0.2 0.2 0.4 0.8 0.8 66415 0.8 0.8 1.0 1.0 0.8 66419 0.6 0.6 0.8 0.6 0.8 66421 0.0 0.8 0.8 1.0 0.6 66425 0.0 1.0 0.4 0.4 0.6 66429 0.4 0.8 0.6 1.0 0.6 66431 0.0 1.0 0.8 1.0 0.8 66434 0.2 0.2 0.8 0.8 0.8 66437 0.0 1.0 1.0 1.0 1.0 66438 0.0 1.0 0.8 1.0 0.8 66439 0.6 0.8 0.8 1.0 1.0 66440 0.0 0.8 0.8 0.8 1.0 66441 0.2 0.8 1.0 1.0 0.8 66444 0.6 0.6 0.4 0.6 0.8 66445 0.4 1.0 1.0 1.0 0.2 66447 0.8 0.4 0.2 0.6 0.6 66450 0.6 0.8 0.8 0.6 0.8 66451 0.0 0.8 1.0 1.0 1.0 66459 0.4 0.8 0.6 0.6 0.8 66463 0.0 0.8 1.0 0.6 0.8 66464 0.0 0.6 0.8 1.0 0.6 66469 0.0 1.0 1.0 0.8 1.0 66472 0.6 0.6 1.0 1.0 0.8 66474 0.8 1.0 0.8 1.0 1.0 66475 0.0 0.6 0.8 0.4 0.4 66476 0.0 1.0 0.4 0.8 1.0 66483 0.0 0.8 0.8 1.0 1.0 66484 0.0 1.0 1.0 1.0 1.0 66485 0.6 0.8 0.6 1.0 0.8 66486 0.0 1.0 1.0 1.0 0.8 66487 0.2 0.8 0.2 0.0 0.2 66488 1.0 0.8 0.8 0.6 0.8 66491 0.2 0.8 0.8 0.8 1.0 66492 0.2 0.8 0.8 1.0 0.6 66493 0.2 0.6 0.2 1.0 0.6 66495 0.0 1.0 1.0 1.0 1.0 66496 0.2 0.6 0.8 0.8 1.0 66497 0.4 0.6 0.8 0.8 0.4 66498 0.0 0.8 0.8 0.8 1.0 66499 0.2 0.2 0.2 0.6 0.0 66500 0.2 0.2 0.0 1.0 0.6 66501 0.0 1.0 0.8 1.0 0.6 66503 0.0 0.8 1.0 1.0 0.8 66504 0.4 0.6 0.6 0.2 0.8 66506 0.2 0.4 0.8 0.6 0.8 66507 0.6 0.6 0.2 0.8 0.4 66509 0.0 1.0 1.0 1.0 1.0 66511 0.0 1.0 0.8 1.0 0.6 66512 0.2 1.0 0.8 1.0 0.8 66513 0.2 NA 1.0 0.6 1.0 66514 0.2 0.8 0.4 1.0 0.4 66517 0.2 0.6 0.6 0.8 0.8 66520 0.6 0.8 1.0 0.6 0.8 66522 0.4 0.8 0.6 1.0 0.8 66525 0.6 0.6 0.8 0.6 0.8 66526 0.0 0.8 1.0 0.8 0.6 66528 0.2 0.8 0.6 0.4 0.8 66529 0.0 0.6 0.6 0.8 1.0 66531 0.4 0.8 0.6 0.6 0.2 66532 0.4 0.6 0.6 0.8 0.4 66533 0.2 1.0 0.8 0.4 1.0 66534 0.2 1.0 0.6 1.0 0.6 66538 0.4 0.6 0.4 1.0 0.6 66540 0.0 1.0 1.0 1.0 1.0 66545 0.2 0.6 0.4 0.2 0.6 66546 1.0 NA NA NA 0.6 66549 0.2 0.8 1.0 1.0 1.0 66550 0.0 0.8 0.8 0.8 0.8 66551 0.2 0.8 0.8 0.2 0.8 66552 0.0 0.6 1.0 1.0 0.2 66553 0.6 1.0 0.8 1.0 1.0 66554 1.0 0.0 0.0 1.0 0.0 66560 0.4 0.8 NA 0.6 0.6 66562 0.0 1.0 1.0 1.0 1.0 66563 0.6 1.0 0.2 0.2 0.6 66564 0.0 0.6 0.6 0.0 1.0 66566 0.4 0.6 0.4 0.0 0.6 66568 0.8 0.6 0.8 1.0 0.4 66569 0.2 0.6 1.0 0.6 1.0 66570 0.6 0.6 0.6 0.6 0.6 66572 0.8 0.8 0.6 1.0 0.6 66574 0.6 0.6 0.4 0.6 0.2 66575 1.0 1.0 1.0 1.0 1.0 66576 0.0 1.0 0.8 0.8 0.8 66582 0.0 0.8 0.6 0.8 0.2 66583 0.0 0.6 0.6 0.2 0.2 66585 0.2 0.8 0.6 1.0 0.6 66588 0.0 1.0 1.0 1.0 0.8 66589 0.4 0.6 0.8 1.0 NA 66591 0.2 0.4 0.4 0.8 0.6 66596 0.0 0.8 0.8 1.0 0.8 66598 0.2 1.0 1.0 1.0 1.0 66601 0.8 0.6 0.6 1.0 0.4 66604 0.0 1.0 1.0 0.8 1.0 66605 0.6 0.6 1.0 0.4 1.0 66606 0.6 0.6 0.8 0.6 0.8 66607 0.2 1.0 0.8 1.0 0.8 66609 0.4 0.8 0.4 0.4 0.6 66610 0.8 0.6 0.6 0.8 0.2 66612 0.2 0.8 0.6 0.6 0.8 66615 0.2 0.6 0.8 1.0 0.6 66617 0.6 0.8 1.0 1.0 0.8 66619 0.4 0.8 0.2 1.0 0.2 66620 0.0 0.8 0.8 0.8 0.8 66621 0.0 1.0 1.0 1.0 1.0 66622 0.4 0.6 0.8 0.8 0.6 66623 0.4 0.4 0.4 0.6 0.8 66630 0.2 0.8 0.8 1.0 0.8 66635 0.8 0.2 0.8 1.0 0.2 66637 0.4 0.4 0.4 1.0 0.6 66638 0.6 0.2 0.0 0.2 0.2 66642 0.4 1.0 1.0 0.6 0.4 66645 0.2 0.8 0.8 0.8 0.8 66646 0.0 1.0 1.0 1.0 1.0 66647 0.0 1.0 1.0 1.0 0.8 66648 0.0 1.0 0.8 1.0 0.6 66649 0.6 0.6 0.6 0.4 0.4 66651 0.0 0.6 0.8 0.0 0.8 66653 0.4 0.6 0.8 0.0 0.8 66657 0.2 1.0 1.0 1.0 1.0 66660 0.6 0.6 0.8 0.2 0.4 66662 0.2 0.8 1.0 0.8 1.0 66663 0.4 0.8 0.6 1.0 0.8 66664 0.2 0.8 0.6 0.8 0.8 66665 0.2 0.8 0.8 0.0 0.8 66666 0.2 0.8 0.8 1.0 0.8 66668 0.0 NA 0.6 1.0 0.6 66669 0.0 0.8 0.8 0.6 0.6 66671 0.2 0.8 0.8 0.6 0.8 66674 0.0 0.8 0.6 1.0 0.8 66676 0.0 0.8 NA 1.0 0.6 66679 0.6 0.6 0.8 1.0 0.4 66680 0.0 0.6 0.8 0.2 0.4 66681 0.4 0.8 1.0 1.0 1.0 66682 0.8 0.4 0.4 1.0 0.2 66683 0.0 0.8 0.8 0.6 0.8 66684 0.0 0.8 0.6 1.0 0.8 66685 0.0 1.0 1.0 0.8 0.8 66689 0.8 0.8 0.4 1.0 0.6 66690 0.2 0.6 0.0 0.0 0.8 66692 0.4 NA 1.0 0.6 1.0 66694 0.6 1.0 1.0 1.0 1.0 66700 0.6 0.4 0.8 0.6 0.2 66701 0.0 0.8 1.0 1.0 1.0 66703 0.0 0.8 0.6 1.0 0.8 66704 0.2 0.8 0.4 0.8 0.6 66708 0.6 1.0 0.2 0.8 0.8 66711 0.0 1.0 1.0 1.0 1.0 66712 0.2 0.6 0.8 0.4 0.6 66713 0.2 0.6 0.0 0.2 0.2 66716 0.6 0.8 0.6 0.2 0.6 66717 0.6 0.6 0.4 0.4 0.4 66718 0.2 0.8 0.4 1.0 0.8 66722 0.4 0.8 1.0 1.0 1.0 66723 NA 1.0 1.0 1.0 NA 66724 0.0 1.0 0.8 0.6 0.8 66727 0.0 1.0 1.0 0.6 0.8 66728 0.0 0.8 0.8 0.4 0.6 66729 0.6 0.8 1.0 0.0 1.0 66730 0.0 0.8 0.8 1.0 1.0 66731 0.4 0.8 0.8 0.4 0.4 66733 0.0 0.6 0.6 0.6 0.8 66734 0.2 0.8 0.8 0.0 0.8 66737 0.0 1.0 1.0 1.0 1.0 66739 0.2 1.0 0.8 1.0 1.0 66741 0.2 0.8 0.8 0.4 0.8 66743 0.2 0.8 0.8 0.4 1.0 66744 0.0 0.8 0.8 0.6 0.8 66745 0.0 1.0 0.8 1.0 0.8 66748 0.4 0.2 0.8 1.0 1.0 66749 0.2 0.8 0.4 0.0 0.6 66751 0.0 1.0 1.0 1.0 1.0 66755 0.2 0.8 0.8 0.8 0.8 66756 0.8 0.4 1.0 1.0 NA 66757 0.0 0.8 0.6 0.2 0.4 66760 0.2 0.2 0.6 1.0 0.6 66764 0.2 0.8 0.8 1.0 0.8 66766 0.2 0.8 1.0 0.8 1.0 66768 0.4 0.8 0.2 1.0 0.2 66769 0.8 1.0 0.8 1.0 1.0 66770 0.6 0.8 0.8 0.4 1.0 66771 0.2 0.6 0.6 0.8 0.6 66772 0.8 0.6 0.6 0.6 0.4 66775 0.0 1.0 1.0 0.8 1.0 66778 0.0 1.0 0.8 0.8 0.8 66779 0.0 1.0 0.8 1.0 1.0 66785 0.0 1.0 1.0 0.8 1.0 66788 0.8 1.0 0.8 1.0 0.8 66790 0.0 0.8 0.8 0.8 0.6 66792 0.4 0.6 0.8 1.0 0.8 66793 0.2 1.0 1.0 1.0 0.4 66797 0.0 0.8 0.6 1.0 0.8 66800 0.0 1.0 1.0 1.0 1.0 66801 0.0 0.8 0.8 1.0 0.8 66804 0.0 1.0 1.0 1.0 1.0 66806 0.4 0.8 0.8 0.8 0.8 66807 0.0 1.0 0.8 1.0 1.0 66810 0.2 0.6 0.4 0.8 0.8 66812 0.0 0.8 1.0 0.8 1.0 66813 0.4 0.8 1.0 0.8 0.8 66814 0.0 1.0 1.0 0.8 1.0 66817 0.0 0.8 0.8 1.0 0.8 66819 0.8 1.0 1.0 0.8 0.8 66820 0.2 0.8 0.6 0.8 0.4 66824 0.4 0.6 0.6 1.0 0.4 66826 0.2 0.8 0.8 0.6 1.0 66830 0.0 0.8 0.8 1.0 0.8 66831 0.8 0.8 0.8 0.8 0.6 66832 0.2 0.6 0.6 0.8 0.6 66833 0.0 0.8 0.6 1.0 0.6 66838 0.0 1.0 1.0 1.0 0.8 66839 0.2 0.8 0.8 0.6 0.8 66840 0.6 1.0 0.8 1.0 0.6 66842 0.4 1.0 0.8 0.8 0.8 66843 0.4 0.6 0.6 0.2 1.0 66845 0.0 0.6 1.0 0.2 1.0 66847 0.0 0.6 0.6 0.8 0.6 66848 0.0 1.0 1.0 0.8 1.0 66850 0.6 0.6 0.6 0.6 0.6 66853 0.0 1.0 0.8 0.8 0.8 66855 0.0 0.8 0.8 1.0 0.8 66856 0.0 1.0 0.8 1.0 1.0 66857 0.4 0.2 0.2 0.6 0.6 66858 0.0 0.8 0.8 0.2 0.8 66860 0.2 0.8 0.8 0.6 0.8 66863 0.0 0.8 0.8 0.6 0.8 66864 0.8 0.8 0.6 0.8 0.6 66867 0.0 0.6 0.8 0.6 0.8 66868 0.4 0.2 0.2 0.0 0.8 66869 0.2 0.6 0.8 0.8 0.8 66872 0.2 0.8 0.8 1.0 0.8 66873 0.0 0.6 0.2 0.6 1.0 66879 0.0 1.0 1.0 0.0 1.0 66886 0.0 1.0 1.0 1.0 1.0 66887 0.6 0.0 0.2 0.6 0.2 66890 0.0 1.0 1.0 0.8 1.0 66892 0.2 0.8 0.8 1.0 0.6 66896 0.4 0.4 0.0 0.6 0.4 66897 0.6 0.6 0.4 0.0 0.4 66898 0.2 0.8 1.0 0.6 0.6 66899 0.8 0.2 0.0 0.0 0.6 66902 0.4 0.6 0.2 0.2 0.4 66903 0.4 0.6 0.4 0.6 0.8 66905 0.6 0.6 0.6 0.8 0.6 66911 0.6 1.0 0.6 0.2 0.8 66916 0.4 0.2 0.2 0.2 0.2 66917 0.6 0.0 0.4 0.4 0.8 66918 0.0 1.0 0.8 0.8 0.8 66919 0.2 0.6 0.6 0.2 0.2 66920 0.2 0.8 0.8 0.8 0.6 66921 0.2 0.8 0.8 0.8 1.0 66924 0.6 0.6 0.4 1.0 0.8 66925 0.0 1.0 0.8 0.0 0.8 66926 0.0 0.8 1.0 1.0 1.0 66927 0.0 1.0 1.0 1.0 1.0 66929 0.0 0.8 0.6 1.0 0.6 66930 0.2 1.0 0.8 1.0 0.6 66932 0.6 0.2 0.0 0.0 0.2 66934 0.2 0.8 0.6 1.0 0.2 66935 0.0 0.8 0.8 0.0 0.2 66937 0.0 1.0 1.0 0.4 1.0 66944 0.6 0.6 0.6 0.8 0.8 66945 0.2 0.6 0.4 1.0 0.8 66946 0.0 0.8 0.4 1.0 0.4 66947 0.0 1.0 0.8 0.8 0.6 66949 0.0 1.0 0.6 0.6 0.2 66953 0.0 0.6 0.6 0.6 0.8 66960 0.2 0.8 0.8 0.8 0.8 66962 0.2 0.8 0.8 0.8 0.6 66965 0.6 0.6 0.2 0.6 1.0 66968 0.4 0.4 0.6 0.2 0.6 66970 0.6 0.2 1.0 1.0 0.2 66982 0.0 1.0 1.0 0.8 0.8 66983 0.4 0.8 0.6 0.8 0.8 66985 0.2 0.6 0.8 0.8 0.6 66986 0.8 0.6 0.4 0.6 0.8 66987 0.0 0.8 0.6 0.8 0.2 66988 0.0 0.8 0.8 0.8 1.0 66994 0.6 0.6 0.6 0.6 0.6 66995 0.0 1.0 0.8 0.6 0.8 66997 0.2 0.0 0.0 0.0 0.2 67000 0.0 0.8 0.8 0.8 0.6 67003 0.0 0.8 0.6 0.2 0.2 67007 0.4 0.2 0.2 0.2 0.4 67008 0.8 0.8 0.8 1.0 0.8 67013 0.0 1.0 0.8 0.8 1.0 67014 0.2 1.0 0.8 0.6 0.8 67017 0.0 0.8 0.8 0.2 1.0 67018 0.0 1.0 1.0 0.6 1.0 67019 0.6 0.8 0.6 1.0 0.8 67022 0.0 0.6 0.8 0.8 0.6 67023 0.2 0.6 0.8 0.2 0.8 67024 0.8 0.2 0.4 0.4 0.2 67025 0.0 1.0 1.0 1.0 1.0 67026 0.4 0.8 0.4 0.8 0.8 67027 0.2 0.8 0.8 0.6 0.8 67029 0.8 0.6 0.2 1.0 0.6 67031 0.2 0.6 0.4 0.6 0.6 67033 0.0 1.0 0.8 0.4 0.8 67034 0.6 0.6 0.8 0.6 0.8 67036 0.4 0.8 0.6 NA 1.0 67039 0.2 1.0 0.8 0.6 0.6 67040 0.2 0.2 0.0 0.2 0.4 67041 0.0 0.8 0.8 1.0 1.0 67042 0.2 0.6 0.6 0.4 0.6 67043 0.4 0.8 0.8 1.0 0.8 67045 0.0 0.8 0.6 0.8 1.0 67046 0.6 0.6 0.8 1.0 0.8 67049 0.2 0.8 1.0 0.6 0.8 67055 0.0 0.8 0.8 0.8 0.8 67056 0.0 1.0 1.0 1.0 1.0 67057 0.2 0.4 0.6 0.8 0.6 67059 0.2 1.0 0.6 0.6 0.6 67064 0.2 0.8 1.0 0.8 0.8 67065 0.0 1.0 0.8 0.2 0.8 67066 0.0 1.0 1.0 0.8 1.0 67070 0.2 0.6 0.6 1.0 0.4 67072 0.4 0.4 0.6 0.6 0.8 67073 1.0 1.0 1.0 1.0 1.0 67075 0.2 0.8 0.8 0.8 0.8 67078 0.0 1.0 1.0 0.8 0.4 67079 0.0 0.8 0.8 1.0 1.0 67080 0.0 0.8 1.0 0.8 1.0 67082 0.6 0.8 0.8 0.8 0.8 67085 0.0 1.0 1.0 0.8 0.6 67087 0.4 0.6 0.6 0.8 0.6 67091 0.8 0.0 0.6 0.4 0.6 67093 0.6 1.0 0.8 0.8 0.8 67095 1.0 1.0 0.8 0.0 1.0 67096 0.0 0.8 0.8 1.0 0.8 67098 0.0 1.0 1.0 1.0 1.0 67101 0.6 0.8 0.2 1.0 0.6 67104 0.2 1.0 0.8 1.0 0.8 67107 0.8 0.2 0.4 0.2 0.6 67109 0.2 1.0 0.8 1.0 0.8 67115 0.4 0.6 0.6 0.4 0.8 67116 0.4 0.6 0.6 0.6 0.6 67118 0.6 0.4 0.4 0.2 0.4 67119 0.2 0.8 0.6 1.0 0.6 67121 0.0 0.8 1.0 0.8 0.4 67123 0.0 0.6 0.4 0.8 0.4 67124 0.0 0.6 1.0 0.6 1.0 67126 0.0 0.8 0.8 1.0 1.0 67132 0.6 1.0 0.6 0.8 0.8 67134 0.6 0.6 1.0 0.2 1.0 67135 0.0 1.0 1.0 1.0 0.8 67136 0.8 0.8 1.0 0.2 0.8 67140 0.2 0.6 0.4 0.6 0.6 67143 0.2 1.0 1.0 1.0 0.8 67144 0.6 1.0 0.8 0.8 1.0 67145 0.0 1.0 0.8 1.0 0.6 67146 0.6 0.8 0.8 0.6 0.2 67148 0.4 0.4 0.6 0.6 0.4 67149 0.4 0.4 0.8 1.0 0.8 67152 0.6 0.8 0.8 0.8 0.8 67154 0.0 1.0 1.0 1.0 0.8 67155 0.8 0.2 0.2 0.8 0.6 67156 0.0 0.2 0.4 0.8 0.0 67157 0.6 0.8 0.6 1.0 0.6 67158 0.6 0.4 1.0 1.0 1.0 67161 0.6 0.8 0.0 0.8 0.6 67162 0.4 0.4 0.6 0.8 0.6 67168 0.6 0.8 0.8 1.0 0.6 67174 0.4 0.4 0.8 0.8 0.8 67179 0.8 0.6 0.0 0.0 0.0 67180 NA 0.2 0.2 0.8 0.2 67183 0.2 0.8 0.2 1.0 0.4 67184 1.0 0.6 0.6 0.0 1.0 67185 0.8 0.2 0.2 1.0 0.2 67186 0.8 0.8 1.0 1.0 0.6 67188 0.0 0.0 0.0 0.2 0.0 67189 0.4 0.4 0.2 0.2 1.0 67190 0.6 0.6 0.8 0.4 0.8 67191 0.0 0.6 0.8 0.2 0.6 67197 0.4 0.4 0.8 0.8 0.6 67202 0.8 0.2 0.0 0.2 0.2 67203 0.8 0.4 0.6 1.0 0.2 67206 0.6 1.0 0.8 1.0 0.8 67213 0.6 0.6 0.6 0.8 0.6 67215 0.4 0.2 0.4 0.0 0.4 67220 0.2 0.8 0.6 0.4 0.4 67222 0.0 0.8 0.8 0.8 0.6 67223 0.0 NA 1.0 1.0 1.0 67224 0.2 0.4 0.6 0.4 0.4 67225 0.4 0.8 0.8 1.0 0.2 67226 0.8 0.8 0.4 0.2 0.8 67229 0.0 0.8 0.8 0.2 0.8 67230 0.8 0.6 0.8 0.2 0.6 67232 1.0 1.0 0.8 1.0 1.0 67234 0.6 1.0 0.8 1.0 0.6 67237 0.2 0.8 0.8 0.8 0.6 67238 0.8 0.4 0.8 0.4 0.4 67239 0.6 0.2 0.2 0.6 0.4 67240 0.2 1.0 0.6 0.8 0.6 67242 0.6 0.2 0.6 0.6 0.6 67246 0.2 0.8 0.8 0.8 0.6 67247 0.6 1.0 0.8 0.8 0.6 67251 1.0 0.8 0.8 1.0 0.8 67254 0.2 0.8 0.6 1.0 0.2 67255 0.8 1.0 1.0 1.0 1.0 67257 0.2 0.8 1.0 1.0 0.8 67258 0.2 0.8 1.0 0.6 1.0 67259 NA 1.0 0.6 NA 0.6 67260 0.0 0.0 0.2 0.2 0.0 67262 0.0 0.8 0.8 0.8 0.6 67263 0.4 0.4 0.4 0.2 0.2 67269 0.0 0.8 1.0 1.0 0.8 67270 0.2 0.8 0.2 0.6 0.4 67271 0.4 0.4 0.6 0.2 0.8 67272 0.6 0.4 0.0 0.4 0.4 67274 0.6 0.8 1.0 1.0 1.0 67275 0.4 0.4 0.8 1.0 0.8 67277 0.0 1.0 1.0 0.8 1.0 67278 0.0 0.8 1.0 1.0 0.8 67279 0.2 0.8 0.6 0.6 0.4 67283 0.0 1.0 0.4 0.2 0.8 67284 0.0 1.0 0.6 0.8 0.8 67286 1.0 0.4 0.2 0.4 0.8 67287 0.2 0.0 1.0 1.0 1.0 67288 1.0 1.0 1.0 1.0 1.0 67289 0.0 1.0 0.8 1.0 1.0 67294 0.6 0.8 1.0 0.8 0.8 67295 0.2 1.0 0.8 0.8 1.0 67298 0.0 1.0 1.0 1.0 1.0 67299 0.2 1.0 0.8 0.8 0.8 67303 0.8 0.8 0.6 0.8 0.6 67307 0.8 0.2 0.4 0.2 0.2 67309 0.0 1.0 1.0 1.0 1.0 67310 0.4 0.6 0.8 1.0 0.6 67312 0.6 0.8 0.8 1.0 0.6 67313 0.0 1.0 1.0 0.8 0.2 67315 0.4 0.6 0.6 1.0 0.8 67316 0.6 0.4 0.6 0.6 0.4 67322 0.2 1.0 1.0 0.8 1.0 67324 0.6 0.6 0.6 0.4 0.6 67325 0.0 1.0 1.0 1.0 1.0 67328 0.2 0.0 0.4 0.0 0.4 67331 0.2 0.2 0.8 0.8 0.6 67332 0.2 1.0 0.8 0.4 0.8 67334 0.4 1.0 1.0 0.8 1.0 67335 0.2 0.8 0.6 1.0 0.8 67336 0.2 0.6 0.4 0.4 NA 67338 0.0 1.0 1.0 0.8 0.8 67339 0.2 0.8 0.8 0.8 0.8 67341 0.0 1.0 1.0 1.0 0.8 67342 0.2 1.0 0.8 0.8 0.8 67344 0.2 0.2 0.0 0.4 0.4 67345 0.8 0.0 0.2 0.0 0.2 67346 0.2 0.8 0.8 0.8 0.8 67347 0.0 0.8 0.8 NA 0.8 67352 0.4 1.0 1.0 0.4 0.2 67355 0.0 1.0 1.0 0.2 1.0 67356 0.8 0.6 0.6 0.0 1.0 67357 0.6 0.2 0.8 0.8 0.2 67360 0.2 0.6 0.4 0.8 0.6 67363 0.2 0.8 0.8 0.8 1.0 67364 0.0 0.6 0.8 0.8 0.2 67365 0.0 1.0 1.0 1.0 1.0 67366 0.4 0.8 0.2 1.0 0.8 67367 0.0 0.8 0.8 1.0 1.0 67368 0.8 1.0 0.6 NA 1.0 67370 0.6 0.6 1.0 1.0 1.0 67371 0.6 0.6 1.0 1.0 1.0 67374 0.2 0.6 0.4 0.4 0.2 67376 0.0 0.8 0.8 0.8 1.0 67377 0.2 0.6 0.8 0.6 0.6 67378 0.4 0.8 0.8 0.4 0.8 67379 0.2 0.8 0.8 1.0 0.6 67380 0.0 1.0 0.6 0.6 1.0 67382 0.4 0.8 0.8 0.6 0.6 67388 0.2 0.6 0.8 0.8 0.8 67390 0.0 0.8 0.6 0.8 0.0 67392 1.0 0.6 0.6 1.0 1.0 67393 0.6 0.8 0.8 1.0 0.8 67395 0.6 0.6 0.6 1.0 0.6 67396 0.2 0.8 0.4 0.4 0.4 67397 0.6 0.8 0.8 1.0 0.8 67399 0.0 0.8 0.8 0.8 0.8 67401 0.0 0.8 0.8 1.0 0.6 67403 0.4 0.2 0.0 0.2 0.2 67406 0.0 0.8 1.0 1.0 0.8 67408 0.4 0.6 0.6 0.4 0.6 67413 0.0 0.0 1.0 0.0 0.0 67415 0.4 0.6 0.6 0.8 0.8 67417 0.0 0.8 0.8 1.0 0.6 67418 0.2 0.4 0.2 0.8 0.6 67420 0.0 1.0 0.8 0.2 0.8 67421 0.6 0.8 1.0 0.6 0.8 67426 0.0 0.8 0.6 0.8 0.6 67427 0.2 0.6 0.4 0.6 0.8 67429 0.4 0.4 0.2 0.0 0.2 67431 0.6 0.6 0.8 0.8 0.6 67436 0.2 0.6 0.8 0.8 0.8 67438 1.0 0.4 0.4 0.4 0.4 67439 0.8 1.0 0.8 0.8 1.0 67440 0.2 0.4 0.6 0.2 0.8 67441 0.2 0.6 0.6 1.0 0.8 67442 0.4 0.2 0.8 0.8 0.4 67445 0.2 0.6 0.6 1.0 0.6 67446 0.0 0.8 0.6 0.2 0.8 67447 0.0 1.0 0.8 0.8 0.8 67449 1.0 0.8 0.2 0.6 0.8 67451 0.0 1.0 1.0 0.2 1.0 67453 0.4 1.0 0.8 1.0 0.8 67454 0.0 1.0 1.0 0.8 1.0 67455 0.0 1.0 1.0 1.0 1.0 67458 0.4 0.2 0.4 0.4 0.0 67460 0.2 1.0 1.0 1.0 1.0 67462 1.0 0.8 0.8 0.6 0.6 67465 1.0 0.8 1.0 1.0 1.0 67471 0.0 1.0 0.4 0.0 0.6 67473 0.2 0.8 0.8 0.8 0.8 67479 0.2 0.8 0.8 1.0 0.4 67480 1.0 0.0 0.2 0.2 0.2 67483 0.2 0.8 0.8 0.8 0.8 67485 0.2 0.2 0.8 1.0 1.0 67487 0.2 0.8 0.8 0.6 1.0 67488 0.2 0.8 0.4 1.0 0.4 67489 0.2 0.6 0.6 0.8 0.6 67490 0.4 0.8 0.4 0.8 0.6 67495 0.2 0.6 NA 0.8 0.8 67497 0.2 0.8 0.6 1.0 0.8 67502 0.4 0.6 0.4 0.6 0.6 67503 0.2 0.4 0.6 1.0 0.2 67504 0.6 0.6 0.8 0.0 0.6 67505 0.2 0.8 0.8 0.8 0.8 67506 0.0 1.0 0.8 1.0 0.6 67507 0.4 0.6 0.4 1.0 0.6 67508 0.2 0.8 0.6 0.6 0.8 67509 1.0 0.6 0.8 0.8 0.8 67510 0.4 0.6 0.4 0.8 0.8 67512 0.0 0.8 0.8 1.0 1.0 67516 0.2 0.8 0.6 0.6 0.4 67518 0.4 0.8 0.6 0.4 0.6 67521 0.2 0.8 0.8 0.8 0.8 67522 0.0 1.0 1.0 1.0 1.0 67523 0.0 1.0 1.0 1.0 1.0 67524 0.8 1.0 0.6 0.0 0.8 67525 0.0 1.0 0.0 1.0 1.0 67526 0.0 1.0 0.6 1.0 0.4 67528 0.4 0.8 1.0 1.0 1.0 67529 0.0 NA 1.0 NA 0.2 67530 0.0 0.8 1.0 0.8 1.0 67531 0.2 0.8 0.6 0.8 0.6 67533 0.2 1.0 1.0 1.0 0.8 67534 0.0 0.6 0.4 0.8 0.8 67535 0.2 1.0 0.8 1.0 1.0 67537 0.2 1.0 0.8 0.6 0.8 67539 0.4 0.6 0.6 0.4 0.8 67540 0.2 0.8 0.4 0.8 0.6 67541 0.6 0.6 0.8 0.0 0.2 67544 0.8 0.8 0.8 1.0 0.8 67547 0.4 0.6 0.4 0.0 0.4 67549 0.8 0.8 0.8 0.8 0.4 67551 1.0 0.0 0.4 0.4 0.4 67552 0.2 0.6 0.6 0.4 0.8 67556 0.2 0.4 0.8 0.2 0.8 67559 0.8 0.2 0.2 0.6 0.6 67560 0.2 0.4 0.0 0.6 0.2 > pomps(data = psych::bfi, vrb.nm = vrb_nm, min = 1, max = 6, unit = 50) # unit = 50 A1_p50 A2_p50 A3_p50 A4_p50 A5_p50 61617 0.4 1.2 0.8 1.2 1.2 61618 0.4 1.2 1.6 0.4 1.6 61620 1.6 1.2 1.6 1.2 1.2 61621 1.2 1.2 2.0 1.6 1.6 61622 0.4 0.8 0.8 1.2 1.6 61623 2.0 2.0 1.6 2.0 1.6 61624 0.4 1.6 1.6 0.8 1.6 61629 1.2 0.8 0.0 1.6 0.0 61630 1.2 0.8 2.0 0.8 0.8 61633 0.4 1.6 2.0 2.0 1.6 61634 1.2 1.2 1.6 2.0 1.6 61636 0.4 1.6 1.6 1.6 1.6 61637 1.6 1.6 1.6 2.0 1.2 61639 1.6 1.6 1.6 2.0 2.0 61640 1.2 1.6 0.4 0.4 0.0 61643 1.2 0.8 2.0 2.0 0.8 61650 1.2 2.0 2.0 0.4 1.6 61651 1.6 1.6 1.6 1.2 1.6 61653 1.2 1.2 1.6 1.2 0.8 61654 1.2 1.2 2.0 1.6 1.6 61656 1.6 1.2 0.4 0.0 0.4 61659 0.0 2.0 2.0 0.0 1.6 61661 0.0 1.6 2.0 1.6 2.0 61664 0.4 2.0 1.6 2.0 1.6 61667 1.2 1.6 1.6 2.0 1.6 61668 0.0 2.0 2.0 0.0 2.0 61669 0.4 1.2 1.2 1.2 0.8 61670 0.4 1.6 2.0 2.0 2.0 61672 0.4 1.6 0.0 0.8 1.6 61673 1.2 1.6 2.0 1.6 1.6 61678 0.0 2.0 1.6 2.0 0.8 61679 0.4 1.6 2.0 2.0 2.0 61682 0.0 1.6 2.0 1.6 1.2 61683 0.4 1.2 1.6 2.0 1.6 61684 1.2 1.2 1.2 1.2 1.2 61685 1.6 0.8 1.6 1.2 0.4 61686 0.0 2.0 1.2 2.0 1.2 61687 0.0 1.2 1.2 0.4 0.8 61688 0.0 2.0 2.0 2.0 2.0 61691 0.0 1.6 1.2 0.8 1.6 61692 0.0 1.6 1.6 2.0 1.6 61693 1.6 1.2 0.8 2.0 1.2 61696 0.0 1.6 1.2 1.2 1.6 61698 1.6 2.0 1.2 0.8 1.6 61700 0.4 2.0 2.0 2.0 2.0 61701 0.0 2.0 2.0 2.0 2.0 61702 1.6 1.6 0.8 1.2 0.8 61703 0.4 2.0 1.2 1.6 1.6 61713 0.0 1.6 0.8 0.4 0.8 61715 0.0 2.0 2.0 2.0 2.0 61716 0.0 2.0 2.0 2.0 1.2 61723 0.0 1.6 2.0 1.6 1.2 61724 0.8 2.0 1.2 1.2 1.2 61725 1.2 0.8 1.6 2.0 0.8 61728 0.0 2.0 2.0 2.0 2.0 61730 0.0 1.2 0.8 1.6 1.6 61731 0.0 1.2 0.4 0.4 0.4 61732 0.8 1.2 1.6 0.4 1.2 61740 0.0 2.0 1.6 1.2 1.2 61742 0.8 0.8 1.6 1.2 1.6 61748 0.4 0.8 1.2 1.2 1.6 61749 0.4 1.2 1.2 1.6 0.8 61754 0.0 1.2 2.0 2.0 2.0 61756 1.2 1.6 0.8 1.6 1.2 61757 0.4 2.0 2.0 2.0 2.0 61759 0.4 NA 1.2 2.0 1.2 61761 0.0 1.6 1.2 0.4 1.6 61762 1.2 0.8 0.4 0.4 0.4 61763 0.4 0.8 1.2 1.6 2.0 61764 0.0 2.0 2.0 0.8 2.0 61771 1.2 1.6 2.0 2.0 1.2 61772 0.4 1.2 2.0 0.4 1.6 61773 1.2 1.2 1.2 1.6 0.8 61775 0.0 1.6 1.6 1.6 1.2 61776 0.8 1.6 1.6 1.6 1.2 61777 0.4 1.6 1.6 2.0 1.6 61778 0.4 2.0 2.0 2.0 2.0 61780 1.6 2.0 2.0 2.0 1.6 61782 0.0 0.4 0.4 1.2 0.4 61783 0.4 1.6 1.6 1.6 1.6 61784 0.0 1.6 2.0 1.6 0.8 61788 0.0 2.0 0.8 0.8 0.0 61789 0.0 1.2 2.0 0.4 2.0 61793 0.0 2.0 2.0 1.6 2.0 61794 0.0 2.0 1.6 0.4 2.0 61797 1.2 1.2 1.2 2.0 1.6 61798 0.0 1.6 1.6 1.6 1.6 61801 0.0 2.0 1.6 1.2 2.0 61808 0.0 2.0 1.2 2.0 1.2 61812 0.8 1.2 1.6 1.6 1.2 61813 0.0 1.6 1.6 1.6 1.6 61816 0.0 1.6 2.0 0.4 1.6 61818 0.4 1.2 1.6 1.2 1.6 61819 0.0 2.0 2.0 0.8 2.0 61821 0.4 2.0 1.2 2.0 1.2 61822 0.4 0.8 0.8 1.2 0.8 61825 0.8 0.0 0.4 0.4 0.4 61826 0.0 1.2 1.6 1.6 2.0 61829 1.2 0.8 0.4 2.0 1.6 61831 0.4 2.0 0.4 0.8 1.2 61834 0.4 1.6 1.6 0.8 1.6 61835 0.8 1.2 1.2 0.4 1.2 61838 0.4 1.6 1.6 1.6 0.4 61839 0.0 1.2 1.6 0.4 2.0 61840 0.0 2.0 1.2 0.0 0.0 61841 0.0 2.0 1.6 2.0 2.0 61847 0.0 1.6 1.6 1.6 0.4 61848 2.0 0.0 0.0 0.0 0.4 61851 0.4 2.0 1.6 2.0 1.6 61852 0.8 2.0 1.6 2.0 1.2 61854 0.0 2.0 2.0 2.0 2.0 61856 NA 1.2 1.6 2.0 1.2 61857 0.0 2.0 2.0 1.6 1.2 61861 0.0 2.0 2.0 2.0 2.0 61862 0.0 2.0 1.6 1.2 1.6 61865 1.2 0.4 0.0 0.4 0.4 61868 0.0 1.6 1.2 0.8 1.2 61873 1.6 0.4 0.4 0.4 0.0 61874 0.4 1.6 1.2 0.8 1.2 61880 0.0 1.6 2.0 2.0 2.0 61886 1.2 2.0 1.6 1.6 2.0 61888 0.4 1.6 1.2 1.2 1.2 61889 2.0 1.6 1.6 0.4 1.6 61890 0.4 1.6 1.6 0.0 2.0 61891 0.4 1.2 1.6 1.6 1.6 61895 0.4 1.6 1.6 1.6 0.4 61896 0.4 1.6 2.0 1.6 2.0 61900 0.4 1.6 1.2 1.2 1.6 61901 0.4 1.6 1.6 0.8 0.4 61907 1.2 1.6 1.2 NA 0.8 61908 0.4 1.2 1.2 1.2 1.2 61909 0.8 1.6 1.2 1.6 1.2 61911 1.2 0.8 0.8 0.8 0.8 61913 0.4 1.2 0.8 0.8 0.8 61915 0.0 2.0 2.0 2.0 2.0 61918 0.0 2.0 2.0 1.6 2.0 61921 0.0 2.0 2.0 1.2 2.0 61922 0.0 2.0 1.6 2.0 1.6 61923 0.0 2.0 1.6 2.0 1.6 61925 0.8 2.0 2.0 1.2 2.0 61926 0.4 0.8 0.0 0.8 0.0 61928 0.4 1.2 1.6 1.6 1.6 61932 0.4 1.6 0.8 1.6 0.0 61935 0.0 2.0 1.6 2.0 2.0 61936 1.6 1.2 1.2 1.6 2.0 61939 0.4 1.6 1.2 0.8 0.8 61944 0.0 2.0 2.0 0.8 2.0 61945 0.4 2.0 1.2 1.6 1.2 61949 0.4 2.0 0.8 1.6 0.4 61952 0.0 1.6 1.6 2.0 1.6 61953 0.0 1.6 1.6 0.8 0.8 61954 0.0 2.0 2.0 1.6 2.0 61957 0.4 1.6 1.6 2.0 1.6 61958 1.2 1.2 0.8 0.0 1.2 61965 0.4 2.0 1.6 1.6 0.8 61967 0.8 1.6 2.0 1.6 1.6 61968 0.0 2.0 2.0 2.0 1.6 61969 0.4 1.6 1.6 1.6 1.2 61971 0.4 1.2 1.2 0.0 0.8 61972 0.4 1.2 1.6 1.6 1.2 61973 0.0 1.6 1.6 2.0 1.6 61974 0.0 2.0 2.0 0.4 1.6 61975 0.4 2.0 1.2 1.6 1.2 61976 0.0 1.6 0.4 0.8 1.2 61978 0.0 2.0 2.0 2.0 2.0 61979 0.4 1.2 1.6 1.6 1.6 61983 0.4 1.2 1.6 2.0 1.6 61986 0.4 2.0 1.6 1.2 1.2 61987 0.0 1.6 1.6 1.6 1.6 61989 0.0 2.0 2.0 0.0 0.8 61990 0.8 1.2 1.2 0.8 1.2 61992 1.2 1.2 1.6 2.0 1.2 61993 0.0 1.6 1.6 1.6 1.6 61994 0.4 2.0 1.6 1.6 1.2 61999 0.4 2.0 2.0 2.0 1.6 62001 0.0 0.4 0.4 1.2 0.8 62003 0.0 2.0 1.6 2.0 2.0 62004 2.0 1.6 2.0 2.0 1.6 62005 1.2 0.8 1.2 0.8 1.2 62007 0.8 1.2 0.4 0.4 0.8 62009 1.2 0.8 1.2 0.8 0.8 62011 0.0 2.0 1.6 2.0 2.0 62013 0.8 1.6 1.2 1.2 1.2 62014 1.2 0.4 0.4 0.8 0.8 62015 0.8 1.2 1.2 0.8 0.8 62022 0.0 2.0 1.6 2.0 0.8 62023 0.0 1.6 1.6 1.2 1.6 62024 0.4 0.4 0.0 2.0 0.8 62025 0.4 1.2 1.2 1.6 1.6 62026 0.0 2.0 2.0 0.4 1.6 62029 0.4 0.8 1.2 1.2 1.2 62031 0.0 2.0 1.2 0.8 2.0 62032 0.4 0.8 1.2 1.6 0.8 62033 1.6 1.2 0.8 1.6 1.2 62034 0.0 1.6 1.2 1.2 1.2 62038 0.0 2.0 2.0 1.6 0.8 62039 0.4 1.6 2.0 0.8 1.6 62041 0.4 1.2 1.6 2.0 1.6 62042 0.4 2.0 1.6 1.6 1.6 62043 0.0 1.6 2.0 1.2 1.6 62044 0.4 1.6 1.6 1.6 1.6 62047 0.0 1.6 0.4 0.4 0.4 62048 0.8 0.8 1.6 1.6 1.6 62051 0.4 1.2 1.6 2.0 1.6 62052 0.0 2.0 1.6 1.6 1.6 62054 0.4 2.0 1.2 2.0 0.4 62055 0.4 1.6 1.6 2.0 1.2 62056 0.0 1.2 NA 2.0 2.0 62059 0.0 1.6 2.0 1.6 1.6 62060 0.4 0.8 0.4 0.0 1.2 62063 0.4 1.6 1.6 1.6 1.6 62064 0.0 2.0 1.6 2.0 2.0 62067 1.6 1.2 1.2 1.2 1.6 62070 0.8 1.2 0.8 0.8 0.8 62073 0.0 2.0 2.0 1.2 2.0 62075 0.4 1.2 1.2 0.0 1.2 62077 0.0 2.0 2.0 2.0 2.0 62079 0.4 1.2 1.2 1.2 0.8 62082 1.6 1.6 1.2 1.6 1.2 62084 1.6 0.8 0.0 0.4 0.8 62090 0.4 1.6 1.6 1.6 1.2 62092 0.0 1.6 NA 1.6 2.0 62094 0.0 1.6 1.6 2.0 2.0 62099 0.0 1.6 2.0 2.0 1.2 62101 0.0 1.6 2.0 1.6 2.0 62102 0.0 2.0 2.0 2.0 1.2 62103 0.8 1.6 1.6 0.8 1.2 62105 0.4 1.6 1.2 0.8 0.4 62106 0.0 1.6 2.0 1.6 1.6 62107 0.4 2.0 2.0 1.6 1.6 62111 1.6 1.2 1.6 2.0 1.2 62115 0.4 2.0 1.2 0.8 1.6 62118 1.6 1.2 1.6 2.0 0.4 62119 0.8 1.6 0.4 1.2 1.6 62120 0.8 2.0 1.6 2.0 2.0 62121 0.4 1.6 1.2 1.2 1.2 62124 0.0 NA 1.2 2.0 1.6 62128 0.0 2.0 2.0 2.0 1.6 62130 0.8 1.2 1.2 2.0 0.8 62132 0.8 1.6 2.0 1.6 2.0 62133 0.0 1.6 NA 2.0 1.6 62136 0.4 1.2 1.2 2.0 0.8 62137 1.2 1.6 1.6 1.6 1.6 62142 0.0 2.0 1.2 2.0 2.0 62144 1.2 0.8 1.2 0.4 1.6 62147 0.8 1.6 1.6 1.6 1.6 62151 1.6 0.4 0.0 0.0 0.4 62156 0.8 0.8 0.8 1.2 1.2 62160 1.2 0.4 1.2 0.0 1.2 62161 0.8 1.6 1.2 1.6 1.6 62162 0.8 1.6 2.0 2.0 1.6 62163 1.2 1.6 1.6 2.0 1.2 62164 2.0 0.0 0.0 1.2 2.0 62165 0.8 1.6 1.6 2.0 2.0 62166 1.6 1.6 1.6 2.0 2.0 62168 0.8 1.2 1.2 1.6 0.8 62170 1.6 2.0 1.6 1.6 2.0 62171 0.0 2.0 1.6 2.0 1.6 62173 0.0 2.0 2.0 2.0 1.6 62176 1.2 1.2 2.0 1.6 1.2 62179 1.6 1.6 1.6 0.0 1.6 62180 0.8 1.2 1.2 0.8 0.4 62181 2.0 2.0 2.0 2.0 2.0 62182 0.4 2.0 1.6 2.0 1.6 62183 1.2 0.0 1.2 2.0 2.0 62189 0.8 1.6 1.2 1.6 1.6 62192 0.4 1.2 1.6 1.6 1.6 62197 0.0 1.6 1.6 1.6 1.6 62198 0.0 2.0 2.0 1.6 1.6 62199 1.2 1.6 1.2 0.8 1.2 62201 0.8 1.6 1.2 1.6 1.6 62202 1.2 1.6 2.0 1.2 2.0 62203 0.0 2.0 2.0 2.0 2.0 62204 0.0 1.6 2.0 2.0 2.0 62205 0.4 1.2 1.2 1.2 1.6 62206 0.8 1.6 2.0 2.0 2.0 62208 0.0 1.6 2.0 1.6 2.0 62209 0.0 1.2 1.2 0.4 1.6 62212 1.2 2.0 2.0 2.0 1.6 62213 0.4 2.0 1.6 2.0 0.8 62214 0.8 1.2 1.6 1.2 1.2 62215 0.4 2.0 2.0 2.0 2.0 62216 0.4 0.4 0.4 1.2 0.8 62219 0.4 1.6 1.2 2.0 1.2 62220 0.8 1.2 2.0 2.0 1.2 62224 0.0 1.6 1.6 2.0 1.6 62225 0.4 1.6 2.0 1.6 1.2 62226 0.4 1.2 0.4 1.2 1.2 62227 0.0 2.0 2.0 2.0 2.0 62228 0.0 2.0 2.0 2.0 1.6 62231 0.0 1.6 1.6 2.0 1.6 62233 0.4 1.6 1.6 1.6 1.6 62237 0.8 1.2 1.6 1.6 1.6 62239 0.8 2.0 1.6 1.6 1.6 62240 0.0 1.6 2.0 1.2 2.0 62242 1.6 2.0 2.0 2.0 2.0 62244 0.0 2.0 2.0 0.0 1.6 62245 0.8 1.2 1.2 1.2 0.8 62246 1.2 1.6 1.6 2.0 2.0 62252 0.8 1.2 1.2 0.8 1.2 62259 0.0 1.6 1.6 2.0 1.6 62260 0.0 1.6 1.6 1.6 1.2 62261 0.4 2.0 2.0 1.6 1.6 62263 0.8 1.6 2.0 1.2 1.6 62264 1.2 1.6 1.2 1.6 1.2 62265 0.4 2.0 1.2 0.4 1.6 62266 1.6 0.4 0.8 0.4 0.8 62267 0.0 2.0 1.6 2.0 1.6 62272 0.8 1.2 1.2 1.2 1.2 62276 0.0 2.0 2.0 1.2 2.0 62278 0.8 1.6 2.0 2.0 2.0 62279 0.0 2.0 1.6 1.2 1.6 62280 0.4 1.6 1.6 2.0 1.6 62281 0.0 2.0 2.0 2.0 2.0 62282 0.8 1.6 1.6 2.0 0.0 62287 0.4 1.2 1.2 2.0 0.4 62288 0.8 1.6 2.0 1.2 2.0 62289 0.8 1.6 1.2 0.4 0.4 62290 0.4 1.6 0.8 0.4 0.4 62293 0.4 2.0 2.0 2.0 2.0 62295 0.4 0.4 2.0 0.4 0.4 62296 0.4 2.0 2.0 2.0 2.0 62298 1.6 0.4 0.4 0.4 1.6 62299 1.2 1.2 1.2 1.2 1.2 62300 0.8 0.8 1.2 2.0 0.8 62301 0.0 2.0 1.6 2.0 1.6 62303 0.4 NA 1.6 1.6 0.4 62305 1.2 1.6 1.6 2.0 1.2 62307 0.0 1.6 1.2 1.6 2.0 62312 0.4 0.4 1.6 1.2 1.2 62313 0.0 2.0 2.0 2.0 1.2 62316 0.4 2.0 1.6 1.6 2.0 62317 1.2 1.2 0.8 0.4 0.8 62325 0.0 1.2 2.0 2.0 2.0 62327 0.4 2.0 2.0 2.0 1.6 62328 0.8 0.4 2.0 0.0 2.0 62330 0.8 1.6 1.6 1.6 1.6 62333 0.0 2.0 2.0 2.0 2.0 62335 0.4 1.6 1.6 2.0 1.2 62336 0.0 1.6 1.2 0.8 0.4 62339 0.8 1.6 0.8 1.2 1.2 62342 0.0 1.6 1.6 1.6 1.6 62343 0.4 1.2 0.4 2.0 1.6 62344 0.0 2.0 1.6 2.0 1.6 62345 0.4 1.2 1.2 2.0 1.6 62346 0.0 1.6 1.6 1.6 1.6 62347 0.4 1.2 1.6 2.0 1.6 62348 1.6 1.6 1.6 2.0 1.6 62349 0.4 1.6 1.6 2.0 1.6 62350 0.4 1.6 0.0 2.0 1.2 62351 1.2 2.0 2.0 1.2 1.2 62352 0.4 1.6 1.6 2.0 1.6 62353 0.8 1.6 0.0 1.2 1.2 62354 0.4 1.6 1.6 1.2 1.6 62358 0.4 0.8 2.0 1.2 1.6 62359 0.0 1.6 1.6 2.0 1.6 62360 1.2 0.4 1.6 1.6 1.6 62362 0.0 1.6 1.6 1.6 1.2 62363 0.4 1.6 1.6 2.0 2.0 62366 1.2 0.8 2.0 2.0 2.0 62367 0.8 1.2 2.0 2.0 2.0 62368 0.0 2.0 0.0 2.0 1.6 62369 0.0 1.6 0.8 2.0 1.6 62370 0.0 2.0 2.0 1.2 1.6 62371 1.2 1.6 0.4 2.0 1.6 62375 0.0 2.0 1.6 0.8 2.0 62376 NA 1.6 1.2 0.8 1.2 62377 1.2 2.0 1.6 2.0 2.0 62380 0.0 2.0 1.6 0.4 0.4 62382 2.0 2.0 2.0 2.0 2.0 62384 0.0 1.2 0.4 1.6 0.8 62387 0.8 1.6 1.2 1.2 0.8 62390 0.0 2.0 2.0 2.0 2.0 62391 0.4 1.6 1.6 1.6 0.8 62394 0.0 1.6 0.8 1.6 1.2 62397 0.0 2.0 1.6 2.0 2.0 62401 0.0 1.6 1.6 2.0 1.2 62408 1.2 1.2 1.6 0.0 1.6 62412 0.4 0.8 1.2 0.0 0.4 62416 0.4 2.0 2.0 1.2 2.0 62419 1.2 2.0 2.0 2.0 1.6 62421 1.2 1.2 1.2 1.2 1.6 62423 0.4 2.0 1.6 2.0 0.8 62426 0.4 1.2 1.2 2.0 NA 62433 1.6 2.0 2.0 1.6 2.0 62434 0.0 1.6 2.0 2.0 2.0 62435 0.4 1.6 1.6 1.2 2.0 62438 1.2 1.2 1.2 NA 1.6 62440 0.0 2.0 1.6 1.6 2.0 62443 1.2 0.4 1.2 2.0 1.6 62444 0.4 1.6 1.6 1.2 1.2 62447 0.4 2.0 2.0 2.0 2.0 62448 0.0 2.0 0.4 2.0 1.6 62450 0.0 2.0 2.0 2.0 2.0 62453 0.4 1.6 1.6 2.0 1.2 62454 0.8 0.8 2.0 0.4 2.0 62457 1.2 1.2 NA 0.8 1.2 62462 0.0 1.6 0.8 1.2 1.2 62463 0.4 2.0 1.6 1.2 1.6 62464 0.0 1.6 1.6 2.0 1.6 62467 0.8 1.2 0.8 1.6 1.6 62468 0.0 2.0 2.0 1.6 0.4 62469 0.8 1.6 1.6 1.2 1.2 62470 0.4 2.0 1.6 1.6 2.0 62474 1.2 1.6 1.6 1.2 1.6 62476 1.2 0.8 0.4 1.6 0.8 62479 0.0 1.2 2.0 1.6 2.0 62480 0.0 1.6 2.0 2.0 0.8 62481 0.8 1.2 1.2 1.6 1.2 62486 0.8 1.2 1.6 0.4 0.8 62489 0.0 2.0 1.6 2.0 1.6 62491 0.8 2.0 2.0 2.0 1.2 62493 0.0 2.0 0.8 1.6 0.8 62494 0.0 0.4 0.0 1.2 0.4 62496 0.8 1.6 1.6 2.0 0.8 62497 1.2 2.0 1.6 2.0 2.0 62498 0.0 1.6 2.0 2.0 2.0 62499 0.8 1.6 1.6 2.0 1.6 62500 0.8 2.0 1.6 2.0 2.0 62502 0.0 2.0 2.0 2.0 2.0 62505 0.0 2.0 1.2 2.0 2.0 62508 0.8 2.0 1.6 2.0 1.6 62509 0.0 2.0 2.0 1.6 2.0 62512 1.6 NA 1.6 2.0 1.6 62514 1.6 NA 1.6 2.0 1.6 62518 0.4 2.0 2.0 2.0 2.0 62520 NA 2.0 2.0 2.0 2.0 62522 0.0 2.0 2.0 2.0 2.0 62526 0.4 2.0 1.6 2.0 1.6 62527 0.4 1.6 0.4 1.2 0.4 62528 0.4 1.6 0.4 1.6 1.6 62529 0.4 1.6 0.4 1.6 1.6 62530 0.4 1.6 0.4 1.6 1.6 62531 0.4 1.6 0.4 1.6 1.6 62532 0.4 1.6 1.6 1.6 1.6 62533 0.4 1.6 1.6 1.6 1.6 62535 0.4 1.6 1.6 1.6 1.6 62537 0.4 1.6 0.4 2.0 1.6 62538 1.2 1.6 2.0 2.0 0.8 62541 0.4 2.0 2.0 2.0 1.6 62542 1.6 1.2 1.6 2.0 1.2 62543 0.4 1.6 2.0 1.6 1.6 62545 0.0 1.2 1.2 1.6 1.2 62546 1.2 1.2 1.2 1.6 1.6 62547 0.0 2.0 2.0 2.0 1.6 62548 0.4 2.0 2.0 1.6 1.2 62550 0.4 1.6 2.0 2.0 1.6 62551 1.6 1.2 0.4 0.4 0.0 62552 2.0 0.0 0.0 1.2 0.0 62553 0.0 2.0 2.0 2.0 1.6 62555 1.2 1.2 0.8 1.2 0.4 62556 1.2 1.6 1.6 1.6 1.2 62557 0.8 2.0 2.0 2.0 1.6 62559 1.6 2.0 1.6 2.0 1.6 62561 0.0 2.0 1.6 2.0 1.6 62562 0.4 1.6 1.2 2.0 1.6 62565 0.0 2.0 2.0 2.0 2.0 62567 0.4 1.6 2.0 1.6 1.2 62570 0.4 0.4 0.4 0.0 0.8 62573 1.6 1.2 1.2 1.2 0.8 62574 0.4 1.6 1.2 1.2 2.0 62577 0.4 1.6 1.6 1.6 1.6 62578 0.0 1.6 2.0 2.0 1.6 62582 0.8 2.0 1.6 2.0 2.0 62589 0.0 1.6 0.0 1.6 1.6 62590 0.0 1.6 2.0 1.6 1.6 62594 0.4 2.0 NA 1.6 1.6 62597 0.4 1.6 1.6 1.6 1.2 62599 0.0 1.6 1.6 2.0 2.0 62604 0.0 1.6 2.0 2.0 2.0 62605 0.8 1.6 1.6 2.0 1.6 62606 0.4 2.0 2.0 2.0 2.0 62610 0.0 2.0 1.6 2.0 2.0 62611 0.4 0.4 1.2 0.8 0.4 62612 0.4 1.2 1.2 0.8 0.8 62613 1.2 0.4 0.0 0.8 0.0 62615 2.0 1.6 1.6 2.0 2.0 62617 0.4 2.0 2.0 2.0 2.0 62618 0.4 1.6 2.0 1.6 1.2 62622 0.0 2.0 2.0 2.0 2.0 62623 0.0 2.0 0.4 2.0 2.0 62625 0.0 2.0 2.0 2.0 2.0 62627 0.0 0.8 0.4 2.0 0.8 62635 0.8 1.6 0.4 1.6 1.6 62638 0.4 2.0 2.0 0.4 2.0 62640 0.0 1.6 2.0 2.0 2.0 62642 0.4 1.6 1.6 2.0 1.6 62643 0.0 1.2 1.2 2.0 1.2 62644 0.4 2.0 1.6 2.0 1.2 62645 0.0 1.6 1.6 2.0 1.6 62646 0.0 2.0 2.0 0.8 2.0 62647 1.2 1.6 1.6 2.0 1.6 62648 0.0 2.0 1.6 1.6 0.4 62650 0.4 1.6 0.0 2.0 2.0 62652 0.0 2.0 1.6 0.8 1.2 62653 1.2 0.8 1.6 1.6 1.6 62654 1.2 1.2 1.6 2.0 1.6 62657 0.4 2.0 1.2 0.4 1.6 62662 1.2 1.6 1.2 1.6 1.2 62664 0.0 1.6 2.0 1.6 2.0 62665 0.8 2.0 2.0 2.0 2.0 62667 0.0 2.0 2.0 2.0 2.0 62668 1.6 2.0 1.6 1.2 1.6 62669 1.6 2.0 2.0 1.6 2.0 62670 0.4 1.2 0.4 2.0 1.2 62673 0.4 2.0 1.6 1.6 1.2 62675 0.0 2.0 1.6 1.6 2.0 62677 0.0 2.0 2.0 2.0 2.0 62679 0.4 1.2 2.0 1.6 2.0 62681 0.0 1.6 2.0 2.0 1.6 62682 0.8 1.6 1.6 2.0 1.6 62683 2.0 0.0 2.0 2.0 2.0 62684 0.4 1.6 1.2 2.0 1.2 62685 0.8 0.4 1.2 2.0 2.0 62686 0.4 1.2 1.2 2.0 1.2 62687 0.4 1.2 2.0 1.6 1.6 62688 0.0 2.0 1.6 2.0 1.2 62690 0.0 1.6 1.2 2.0 1.2 62692 0.0 2.0 2.0 2.0 2.0 62694 0.8 1.6 1.6 2.0 1.6 62698 1.2 2.0 2.0 2.0 1.6 62700 0.0 1.2 1.2 1.2 2.0 62703 0.4 1.6 1.6 1.2 2.0 62706 0.0 2.0 1.6 2.0 1.6 62707 0.4 1.2 0.4 2.0 1.6 62708 2.0 2.0 2.0 2.0 2.0 62710 0.0 2.0 1.2 0.8 1.6 62712 0.0 2.0 1.6 1.6 2.0 62715 0.4 1.2 1.6 2.0 1.6 62716 1.2 1.6 1.2 1.6 1.2 62717 0.0 1.6 1.6 2.0 1.2 62718 0.8 1.2 1.2 1.6 1.6 62719 0.0 1.2 2.0 1.6 2.0 62720 0.4 1.6 1.6 2.0 1.6 62722 0.0 1.6 1.2 0.0 1.6 62726 0.0 1.6 2.0 2.0 1.6 62728 0.8 1.2 NA 1.6 0.8 62729 1.6 1.2 0.8 1.2 0.8 62731 1.6 1.2 1.2 1.2 0.4 62740 0.8 1.6 1.6 1.6 1.6 62741 0.8 1.2 1.2 2.0 1.6 62744 1.2 1.6 1.2 1.6 0.4 62745 0.0 2.0 2.0 2.0 1.6 62749 0.0 2.0 1.2 2.0 1.6 62750 0.4 1.2 1.2 2.0 1.6 62751 0.4 1.2 1.6 0.8 1.6 62757 1.6 1.2 1.2 2.0 1.2 62758 0.4 1.6 1.2 1.2 1.2 62761 0.0 2.0 1.6 1.2 1.6 62764 1.2 0.8 1.6 2.0 1.6 62765 0.0 2.0 2.0 2.0 0.0 62766 1.2 1.6 1.2 0.4 0.4 62767 1.2 1.6 1.2 2.0 1.2 62768 0.0 2.0 2.0 2.0 2.0 62770 0.8 2.0 2.0 2.0 1.6 62772 0.0 1.6 1.6 2.0 1.6 62776 0.4 1.6 0.8 0.4 1.2 62778 0.0 2.0 2.0 1.6 2.0 62779 0.0 1.2 1.6 1.2 1.6 62780 0.0 2.0 2.0 2.0 1.6 62781 0.4 1.6 1.2 2.0 1.2 62783 1.6 1.6 1.6 1.6 1.6 62785 0.8 2.0 1.6 2.0 1.6 62786 0.0 1.6 2.0 2.0 2.0 62787 0.0 0.0 1.6 2.0 0.8 62788 0.0 2.0 2.0 2.0 2.0 62789 0.0 2.0 1.2 2.0 2.0 62790 0.0 1.6 1.6 2.0 2.0 62792 1.6 1.2 1.2 1.6 1.6 62795 0.4 1.6 0.4 1.6 1.2 62796 0.0 1.6 1.6 1.6 1.6 62797 0.0 2.0 2.0 2.0 2.0 62800 0.8 1.6 2.0 2.0 1.2 62801 2.0 2.0 2.0 2.0 2.0 62803 0.0 2.0 1.6 1.6 1.6 62804 0.8 2.0 0.0 1.6 2.0 62805 0.0 2.0 1.6 2.0 1.6 62809 0.8 0.4 0.8 1.2 0.4 62810 0.0 1.6 1.6 1.6 2.0 62816 0.4 2.0 2.0 1.6 0.8 62817 1.2 1.6 1.6 2.0 2.0 62819 0.0 1.2 2.0 2.0 2.0 62821 0.8 2.0 2.0 2.0 1.6 62822 0.0 1.6 1.6 2.0 1.2 62825 0.4 1.6 0.4 0.0 0.8 62826 0.4 1.6 1.2 2.0 1.6 62827 1.2 1.6 1.6 2.0 1.6 62828 0.4 2.0 1.6 2.0 2.0 62831 2.0 0.4 1.6 2.0 0.4 62832 0.0 1.6 1.6 2.0 1.6 62834 0.0 2.0 1.2 1.2 1.2 62835 0.0 1.6 1.2 1.6 1.6 62837 0.4 1.6 2.0 1.6 1.2 62839 1.2 2.0 2.0 0.4 1.6 62840 0.0 1.6 1.6 2.0 1.6 62844 0.0 2.0 1.6 2.0 1.2 62846 1.6 2.0 0.4 2.0 1.6 62847 NA 2.0 2.0 NA 2.0 62849 0.4 2.0 1.6 2.0 1.6 62851 0.8 2.0 2.0 2.0 0.8 62853 0.0 2.0 2.0 2.0 2.0 62856 0.4 1.6 1.6 2.0 2.0 62857 0.8 1.2 1.2 2.0 1.2 62858 1.2 2.0 1.6 2.0 2.0 62861 0.4 2.0 1.2 2.0 1.6 62863 0.0 2.0 1.6 2.0 2.0 62864 0.8 1.2 0.4 1.2 1.2 62867 0.0 2.0 2.0 1.2 1.6 62869 0.0 1.6 2.0 2.0 1.6 62870 0.0 2.0 2.0 2.0 2.0 62872 0.0 2.0 2.0 2.0 2.0 62874 0.0 2.0 2.0 2.0 2.0 62876 0.0 1.6 1.2 1.6 1.2 62877 0.4 2.0 2.0 2.0 2.0 62878 0.4 1.6 1.6 2.0 2.0 62879 0.0 2.0 2.0 2.0 2.0 62881 0.4 2.0 2.0 2.0 1.6 62883 0.8 1.2 1.2 1.2 1.2 62887 0.0 1.2 0.0 1.2 1.6 62889 0.4 1.6 1.6 1.2 0.8 62890 0.0 2.0 2.0 1.2 1.2 62891 0.4 1.6 2.0 1.2 2.0 62897 0.4 2.0 1.6 2.0 0.8 62898 1.2 2.0 2.0 1.6 2.0 62899 0.4 0.0 2.0 2.0 2.0 62901 0.4 1.6 1.2 1.2 0.4 62903 0.4 1.6 1.6 1.6 0.0 62908 0.8 1.6 0.8 2.0 0.0 62910 0.8 1.6 1.6 2.0 1.6 62911 1.2 1.2 1.2 1.2 1.2 62916 0.4 2.0 2.0 2.0 1.6 62918 1.2 0.8 0.0 0.8 0.8 62920 0.4 2.0 2.0 2.0 2.0 62922 0.8 2.0 1.2 2.0 1.6 62926 0.0 0.8 0.4 0.8 0.4 62931 0.0 2.0 2.0 1.2 2.0 62933 0.0 1.6 1.2 1.6 1.6 62934 1.2 1.2 0.4 1.2 1.2 62936 0.0 1.2 1.2 1.6 1.6 62938 0.4 1.6 1.6 2.0 1.6 62939 0.8 1.6 1.6 1.6 1.2 62941 0.4 1.6 1.2 1.6 1.6 62942 0.0 1.6 1.6 2.0 2.0 62948 1.2 2.0 2.0 2.0 2.0 62949 0.8 1.2 2.0 0.8 0.8 62950 0.0 2.0 2.0 2.0 2.0 62951 0.0 1.2 1.6 1.6 1.6 62953 0.0 2.0 2.0 2.0 1.6 62954 0.4 1.2 1.2 1.6 0.4 62957 0.4 1.6 0.4 2.0 1.2 62962 0.0 1.6 2.0 2.0 1.6 62965 0.4 1.2 0.4 1.6 1.2 62968 1.2 1.2 1.2 2.0 0.4 62969 0.0 1.6 2.0 2.0 2.0 62971 0.8 1.6 1.6 2.0 2.0 62974 1.6 2.0 2.0 2.0 2.0 62976 0.0 2.0 2.0 2.0 2.0 62983 0.0 1.6 1.6 2.0 2.0 62984 0.0 2.0 1.6 2.0 1.2 62989 0.8 2.0 1.6 2.0 1.6 62990 2.0 0.0 0.0 0.0 0.0 62991 1.6 1.6 1.2 1.6 1.6 62994 0.0 2.0 1.6 0.4 1.6 62995 2.0 0.0 0.0 0.4 0.8 62996 0.4 1.6 1.6 1.6 1.2 62997 0.4 NA 1.6 0.8 2.0 63004 1.6 1.6 1.6 2.0 0.8 63007 1.2 2.0 2.0 2.0 1.2 63013 0.4 1.6 1.2 1.2 1.2 63017 0.8 1.2 1.6 2.0 1.6 63018 0.4 2.0 2.0 2.0 2.0 63021 1.6 0.8 1.2 0.0 1.2 63023 0.0 1.6 2.0 1.6 1.6 63026 0.4 2.0 0.8 2.0 1.6 63027 0.0 2.0 1.6 0.4 1.6 63030 0.0 NA NA NA 1.6 63034 1.2 2.0 2.0 2.0 2.0 63035 NA 2.0 0.8 1.6 1.2 63036 0.4 2.0 2.0 NA 2.0 63037 1.6 1.6 1.6 2.0 1.6 63038 0.0 1.6 1.2 0.4 1.6 63039 1.2 1.6 2.0 0.0 1.6 63040 1.2 1.2 1.6 1.2 1.2 63042 0.4 2.0 2.0 1.6 1.6 63047 0.4 1.6 1.6 2.0 1.2 63048 0.0 2.0 2.0 2.0 2.0 63049 0.0 2.0 1.6 2.0 2.0 63050 0.8 1.2 2.0 1.2 1.6 63051 0.4 2.0 2.0 2.0 2.0 63054 0.4 1.6 1.6 2.0 1.6 63055 0.8 2.0 2.0 2.0 1.6 63059 0.0 2.0 2.0 2.0 2.0 63062 0.8 1.2 1.2 1.2 1.2 63063 0.4 1.2 1.2 0.8 1.6 63069 0.4 2.0 2.0 2.0 1.6 63070 1.6 0.4 0.8 1.6 1.2 63071 0.0 2.0 2.0 2.0 2.0 63073 0.8 1.6 0.8 1.6 1.6 63075 0.0 2.0 1.6 2.0 1.6 63077 0.0 1.6 1.6 1.6 2.0 63081 0.8 1.2 1.6 2.0 1.6 63083 0.4 1.6 1.6 2.0 1.2 63084 0.0 2.0 2.0 1.2 1.6 63089 0.4 1.6 1.6 0.8 1.6 63090 1.6 2.0 2.0 1.2 1.6 63092 1.6 1.2 1.6 0.0 1.6 63094 0.8 1.6 0.4 NA 1.6 63096 0.0 2.0 1.6 2.0 1.6 63097 0.0 2.0 2.0 2.0 2.0 63099 0.0 1.6 2.0 2.0 0.0 63100 0.8 0.4 1.2 0.0 1.2 63102 0.0 2.0 1.2 1.6 1.2 63103 1.6 2.0 0.8 0.4 0.4 63104 1.2 1.2 0.8 0.8 0.8 63108 0.4 0.8 1.2 1.2 1.6 63109 0.4 2.0 1.2 0.4 1.6 63112 0.4 1.2 1.2 0.8 1.6 63115 0.4 1.6 1.6 2.0 1.6 63116 1.6 2.0 2.0 2.0 2.0 63120 0.4 1.6 1.6 0.8 0.4 63121 0.4 2.0 1.6 2.0 1.6 63122 0.4 1.2 1.2 1.2 1.6 63123 0.8 0.0 1.6 2.0 1.2 63125 0.4 1.2 0.4 0.4 0.8 63127 0.4 1.6 2.0 2.0 0.4 63128 0.4 2.0 2.0 2.0 1.6 63130 0.0 2.0 2.0 1.6 1.6 63131 0.0 2.0 NA 2.0 2.0 63135 0.4 1.6 1.2 1.6 1.6 63136 0.4 2.0 1.6 1.6 0.4 63139 0.0 2.0 1.6 2.0 2.0 63141 0.0 2.0 2.0 NA 1.6 63142 0.4 1.6 1.6 1.6 1.6 63146 0.0 2.0 2.0 2.0 0.0 63147 0.0 1.6 1.2 1.6 1.6 63152 0.0 0.8 1.2 1.2 1.2 63154 0.4 2.0 1.6 2.0 1.2 63155 0.8 2.0 1.6 1.2 1.2 63158 0.0 0.0 1.6 2.0 2.0 63161 0.4 2.0 1.2 0.4 1.6 63163 0.0 2.0 1.6 2.0 1.6 63165 0.0 1.6 1.6 2.0 1.6 63171 1.6 1.2 2.0 0.4 1.2 63172 1.6 2.0 2.0 2.0 2.0 63173 0.8 1.6 1.6 0.8 1.2 63176 0.0 2.0 2.0 1.6 2.0 63181 0.8 1.2 1.6 2.0 0.4 63182 0.4 1.2 1.2 2.0 1.2 63183 0.8 1.2 1.2 2.0 1.6 63187 0.8 0.8 0.8 1.2 1.6 63192 0.4 1.6 1.2 1.2 0.8 63193 0.8 1.6 0.4 0.0 0.4 63194 1.2 2.0 1.2 0.4 1.2 63197 0.4 1.6 1.6 0.4 1.2 63200 0.8 2.0 2.0 0.8 1.2 63201 0.0 1.6 2.0 1.6 1.6 63203 0.4 1.2 1.6 0.0 1.6 63209 0.0 1.6 1.2 1.6 1.6 63215 0.8 1.2 2.0 0.0 0.4 63222 0.8 1.2 0.4 0.8 1.2 63223 0.4 1.6 2.0 1.6 1.2 63225 0.4 1.6 2.0 0.4 1.2 63227 0.4 1.2 1.6 0.4 0.8 63228 0.0 1.6 1.6 1.6 1.6 63233 0.0 1.6 1.6 2.0 1.2 63237 0.4 2.0 2.0 2.0 2.0 63238 0.0 1.6 2.0 0.4 0.8 63239 0.8 2.0 2.0 2.0 2.0 63242 0.4 1.6 1.6 1.6 1.6 63245 0.0 2.0 2.0 2.0 1.6 63246 0.0 2.0 1.6 0.4 1.2 63250 0.4 1.6 1.6 1.2 1.6 63252 0.0 1.6 1.6 2.0 1.2 63253 0.4 2.0 0.8 0.8 0.8 63258 0.0 1.2 2.0 2.0 2.0 63261 1.2 1.6 1.6 2.0 1.6 63264 0.8 1.2 1.2 0.4 1.2 63266 0.0 1.6 2.0 2.0 1.2 63271 0.8 0.8 1.2 0.4 0.8 63277 0.0 2.0 1.6 1.6 1.6 63278 NA 0.8 1.2 0.8 0.8 63280 0.0 1.6 1.6 1.2 1.2 63281 0.0 1.6 1.2 1.6 1.6 63286 0.8 1.2 1.6 1.6 1.2 63287 1.6 2.0 2.0 1.6 1.6 63289 0.0 1.2 0.8 1.6 0.8 63292 1.2 0.8 1.2 1.2 0.8 63293 0.4 1.6 1.2 0.4 1.2 63294 1.6 0.8 1.2 1.2 1.6 63301 0.8 0.8 1.6 2.0 1.6 63302 0.0 1.6 1.6 0.4 1.6 63307 1.2 1.6 0.4 0.0 1.2 63309 0.0 1.6 0.0 1.6 2.0 63313 0.4 1.6 1.6 1.2 1.6 63317 0.0 2.0 2.0 2.0 2.0 63320 0.0 2.0 0.8 1.2 0.4 63321 0.8 1.6 2.0 2.0 1.6 63322 1.2 1.2 0.4 0.0 1.6 63324 0.0 0.0 0.0 0.8 0.0 63326 0.4 1.2 0.8 0.4 1.2 63328 1.2 2.0 2.0 0.0 2.0 63330 0.8 1.6 1.2 0.8 1.2 63331 0.0 2.0 1.6 1.6 1.2 63332 1.2 0.8 0.0 0.0 1.6 63334 0.0 2.0 NA 2.0 2.0 63335 0.4 1.6 1.6 2.0 1.6 63337 1.6 0.4 0.4 0.4 1.2 63338 0.4 1.6 1.6 1.6 0.4 63339 0.4 1.6 1.6 0.4 1.6 63340 0.4 2.0 2.0 2.0 2.0 63342 0.4 1.6 1.6 1.6 1.6 63346 0.0 2.0 0.8 1.6 2.0 63351 0.0 2.0 2.0 2.0 2.0 63352 2.0 1.6 1.6 1.2 1.6 63358 1.2 1.2 0.8 1.6 1.2 63361 0.8 1.6 1.6 2.0 1.6 63364 0.4 1.6 1.6 1.2 1.6 63369 0.4 1.6 1.6 1.6 NA 63370 0.4 2.0 1.2 1.2 1.2 63371 1.2 1.6 1.2 0.4 1.6 63372 0.4 1.6 0.4 1.2 2.0 63375 0.4 2.0 1.2 0.8 1.6 63376 0.0 2.0 2.0 2.0 2.0 63378 0.4 1.6 2.0 2.0 1.6 63379 1.6 1.6 1.2 2.0 1.2 63382 0.8 1.6 1.6 1.6 1.6 63383 1.6 1.6 1.6 1.2 1.6 63386 0.4 1.6 1.6 1.2 0.4 63389 0.0 1.6 1.6 2.0 1.6 63395 0.4 0.4 1.6 1.6 1.2 63399 0.0 2.0 2.0 2.0 0.0 63400 0.4 1.6 1.6 2.0 1.6 63402 0.0 1.6 1.6 2.0 1.2 63404 1.6 1.2 0.4 1.2 1.6 63406 0.4 1.2 2.0 0.0 1.2 63408 0.8 1.6 1.6 1.6 2.0 63412 0.4 2.0 2.0 2.0 2.0 63413 0.0 2.0 2.0 2.0 1.6 63414 1.2 2.0 1.2 2.0 1.2 63420 0.8 1.6 1.6 1.6 0.4 63421 0.8 1.6 1.6 1.6 1.6 63427 0.8 1.2 1.2 1.2 0.4 63428 2.0 0.8 0.4 1.2 1.2 63430 2.0 0.4 0.0 1.2 1.2 63431 0.8 1.2 1.6 1.2 2.0 63432 0.8 1.6 1.6 1.6 1.6 63435 1.2 1.2 1.6 1.2 2.0 63437 1.2 0.4 1.6 0.0 1.6 63438 0.0 2.0 2.0 1.6 2.0 63439 0.4 1.6 1.6 2.0 1.2 63441 0.4 1.6 1.2 1.2 1.6 63442 0.8 1.6 1.6 1.6 1.6 63444 0.0 1.6 1.2 2.0 1.6 63445 0.0 1.6 0.0 2.0 2.0 63446 0.4 1.6 1.6 0.4 1.2 63448 0.0 2.0 2.0 1.2 1.6 63450 0.4 1.6 1.6 1.6 0.8 63452 0.0 2.0 2.0 1.6 1.6 63454 1.6 1.6 1.2 1.6 1.6 63455 1.2 2.0 1.6 1.6 2.0 63458 0.0 1.2 0.4 0.8 1.2 63459 0.0 NA 1.2 2.0 0.8 63463 0.0 1.2 0.8 2.0 0.8 63464 0.0 1.2 1.6 2.0 2.0 63465 0.4 2.0 1.6 1.6 1.6 63466 0.0 2.0 2.0 2.0 2.0 63467 2.0 2.0 2.0 2.0 2.0 63468 0.8 1.6 0.8 1.2 0.4 63470 1.2 2.0 2.0 2.0 2.0 63472 2.0 0.4 1.2 0.0 1.6 63473 0.0 1.6 1.2 1.2 1.2 63476 0.8 1.6 1.6 2.0 1.6 63479 0.0 0.4 1.6 2.0 1.6 63480 0.0 2.0 2.0 1.2 1.6 63481 0.4 1.2 1.2 1.2 1.6 63485 0.4 1.2 0.8 1.2 0.8 63486 1.6 1.2 1.6 1.2 1.2 63488 0.0 1.6 1.6 2.0 1.6 63491 2.0 2.0 2.0 2.0 2.0 63493 1.2 2.0 1.6 2.0 1.6 63494 1.2 0.8 0.8 1.2 0.8 63496 0.4 1.6 1.6 2.0 1.6 63498 0.4 1.6 1.6 2.0 1.6 63499 0.0 2.0 1.6 1.2 2.0 63501 1.2 0.8 2.0 1.2 2.0 63502 0.8 1.2 1.2 2.0 1.2 63504 0.0 2.0 1.2 1.2 1.2 63505 0.0 1.6 1.6 0.0 2.0 63509 0.4 1.2 0.4 0.4 0.8 63510 2.0 2.0 0.0 0.8 1.6 63512 1.6 1.2 0.0 0.4 0.4 63513 0.8 1.2 1.2 0.4 0.8 63516 0.4 2.0 2.0 2.0 2.0 63518 1.6 2.0 2.0 2.0 0.8 63521 0.0 2.0 1.2 1.2 1.6 63525 0.8 1.2 1.2 0.4 1.2 63528 0.0 1.2 0.4 1.6 0.4 63530 0.8 1.6 1.2 2.0 1.2 63531 0.4 2.0 1.6 0.4 1.6 63533 0.4 0.8 1.2 0.4 1.6 63534 1.2 1.6 1.6 1.6 0.8 63536 0.0 2.0 2.0 0.8 1.6 63538 0.4 1.6 1.6 1.2 1.6 63547 0.4 1.2 1.6 1.2 1.2 63549 0.0 NA 1.6 0.4 1.2 63551 0.4 2.0 1.6 1.2 2.0 63554 1.2 1.6 1.2 2.0 1.2 63555 0.0 2.0 1.6 1.2 1.6 63556 0.0 0.8 1.6 2.0 2.0 63557 0.8 1.6 1.6 2.0 2.0 63558 1.6 2.0 2.0 1.6 1.2 63559 0.0 2.0 2.0 2.0 1.6 63560 0.0 1.6 0.8 1.2 0.8 63562 0.4 1.2 1.6 1.2 1.6 63567 0.4 0.8 0.8 1.6 0.8 63575 0.4 1.6 1.6 0.4 1.6 63578 0.0 1.6 2.0 2.0 2.0 63579 0.4 1.2 1.6 1.6 1.2 63580 0.0 1.2 1.2 1.6 1.2 63581 0.4 1.6 1.6 1.2 1.2 63582 0.0 1.2 1.2 1.6 0.8 63583 0.4 1.2 2.0 2.0 2.0 63584 0.8 1.2 1.6 1.2 1.2 63586 0.4 1.6 1.6 1.6 1.2 63587 0.4 1.6 2.0 2.0 2.0 63588 0.0 2.0 2.0 2.0 1.6 63589 0.4 0.8 1.2 1.2 1.2 63590 0.0 1.6 0.0 0.4 1.6 63591 0.4 1.6 1.6 2.0 1.2 63595 1.2 1.2 0.0 0.0 0.0 63596 0.4 1.6 1.2 1.6 0.4 63597 0.4 1.2 1.6 0.8 1.6 63602 0.0 1.2 1.2 2.0 2.0 63608 0.0 2.0 2.0 2.0 2.0 63609 0.4 0.4 1.2 0.0 1.2 63613 0.0 1.6 1.6 2.0 2.0 63616 0.4 1.6 1.6 1.2 1.6 63617 0.4 1.6 2.0 2.0 2.0 63618 0.4 1.6 2.0 2.0 1.6 63619 0.4 0.4 1.2 1.2 1.6 63621 1.2 1.2 1.2 1.2 1.2 63623 0.0 1.6 1.6 2.0 1.6 63625 0.4 2.0 1.2 0.0 1.2 63631 0.8 1.2 1.2 0.4 1.6 63636 0.0 2.0 2.0 1.2 2.0 63637 1.6 1.6 1.6 2.0 1.2 63638 0.4 1.6 1.2 1.2 1.2 63642 0.8 1.2 2.0 0.0 1.2 63644 0.0 1.6 2.0 1.2 2.0 63648 0.0 1.2 2.0 1.6 2.0 63649 0.0 1.6 1.6 0.8 1.6 63655 0.4 2.0 1.6 2.0 0.4 63657 0.4 1.2 1.2 1.2 2.0 63658 0.4 2.0 1.6 0.8 1.2 63659 1.2 1.2 1.2 0.4 1.6 63661 0.4 1.6 1.2 1.6 1.6 63668 1.2 1.6 1.2 1.2 1.6 63669 0.0 1.6 2.0 1.2 1.6 63670 1.2 1.2 1.2 2.0 0.8 63672 0.8 1.6 1.6 2.0 1.6 63675 0.8 1.2 2.0 2.0 NA 63676 0.4 1.6 2.0 1.6 1.6 63677 0.0 2.0 2.0 1.6 2.0 63681 0.4 1.6 1.2 0.4 1.2 63682 0.4 2.0 0.8 1.2 1.6 63683 0.4 2.0 1.6 1.6 1.6 63687 0.4 2.0 1.2 1.6 1.2 63690 1.6 1.6 0.4 0.8 0.8 63692 0.0 2.0 2.0 2.0 2.0 63693 0.0 2.0 2.0 1.6 1.2 63696 0.4 1.6 1.6 1.6 0.8 63697 0.0 1.6 2.0 2.0 1.6 63699 1.6 1.6 1.6 2.0 0.4 63700 0.0 1.6 1.2 2.0 0.8 63701 2.0 2.0 2.0 2.0 2.0 63704 0.0 2.0 2.0 2.0 1.6 63705 0.4 1.2 1.2 0.0 0.4 63706 0.0 1.6 2.0 2.0 1.6 63707 0.0 1.6 2.0 1.6 1.2 63709 0.4 1.6 1.6 1.6 1.6 63711 0.4 1.6 2.0 2.0 1.6 63712 0.0 1.6 2.0 2.0 2.0 63713 0.8 1.6 1.6 1.2 2.0 63721 1.2 2.0 2.0 2.0 2.0 63723 0.0 2.0 2.0 2.0 2.0 63724 0.0 1.6 1.6 2.0 1.6 63726 0.4 0.8 1.6 2.0 1.6 63727 1.2 1.2 0.8 1.6 1.6 63728 0.8 2.0 2.0 2.0 1.6 63729 0.8 1.6 2.0 1.2 1.6 63732 0.0 1.2 1.2 1.2 1.2 63734 1.2 0.4 0.4 0.0 1.6 63735 0.0 2.0 2.0 1.2 1.6 63738 1.2 2.0 1.6 2.0 1.2 63739 0.8 1.6 1.6 1.2 1.6 63742 0.4 1.2 1.2 2.0 2.0 63743 0.4 1.6 1.2 1.6 1.6 63744 0.4 1.6 1.6 1.2 1.6 63745 0.0 1.6 1.6 1.6 1.6 63746 2.0 1.6 1.6 1.2 1.6 63748 0.4 1.2 1.6 0.0 1.2 63750 1.2 1.2 1.2 0.8 0.8 63752 0.0 2.0 1.6 1.6 1.6 63760 0.4 1.2 2.0 2.0 1.6 63761 0.4 1.6 1.6 1.2 1.2 63762 0.0 2.0 2.0 0.0 2.0 63763 0.0 2.0 2.0 1.2 0.0 63766 0.8 1.6 2.0 2.0 1.6 63767 0.0 2.0 2.0 2.0 2.0 63768 0.4 1.6 1.6 1.2 1.6 63770 0.8 2.0 2.0 2.0 1.6 63773 0.4 2.0 1.6 2.0 2.0 63775 0.0 1.6 0.8 1.2 1.6 63776 0.0 NA 1.6 2.0 2.0 63778 0.8 1.6 0.4 0.4 1.2 63788 0.0 1.6 1.6 2.0 1.6 63789 0.4 1.2 0.8 2.0 1.2 63791 0.4 2.0 1.2 1.6 1.2 63792 1.2 2.0 2.0 2.0 1.6 63793 1.2 2.0 2.0 2.0 1.6 63794 1.6 1.6 1.6 2.0 1.2 63796 0.0 2.0 1.2 2.0 1.2 63798 2.0 2.0 2.0 2.0 2.0 63799 0.0 1.6 2.0 2.0 1.2 63801 0.0 1.6 2.0 1.6 1.6 63803 0.0 2.0 2.0 1.6 2.0 63807 0.4 0.8 1.2 1.6 0.8 63810 0.8 1.6 1.2 1.2 1.6 63811 1.6 1.6 1.6 2.0 1.6 63812 0.0 NA 2.0 2.0 2.0 63815 0.4 1.6 1.2 2.0 0.8 63816 0.4 1.6 1.2 1.6 1.6 63817 0.8 1.6 1.6 0.4 2.0 63818 0.0 1.2 1.2 2.0 0.8 63820 1.2 0.4 0.0 0.4 0.4 63822 0.0 1.6 1.6 0.4 1.6 63823 1.2 1.6 1.2 2.0 1.2 63824 0.0 2.0 2.0 2.0 2.0 63825 0.0 1.6 2.0 1.6 2.0 63826 1.2 1.2 0.4 1.6 0.8 63827 0.0 2.0 1.6 0.4 1.6 63828 0.8 0.8 1.2 1.6 1.6 63829 0.0 1.6 1.6 1.6 0.4 63834 1.6 2.0 2.0 1.2 2.0 63835 1.6 1.6 0.8 2.0 1.6 63837 1.6 0.8 1.6 1.2 1.2 63838 0.0 1.6 1.6 1.6 1.2 63839 1.2 1.6 0.0 1.6 0.0 63846 0.4 2.0 2.0 0.8 1.6 63847 1.6 1.6 0.4 1.6 0.0 63849 0.0 2.0 2.0 2.0 2.0 63851 0.8 1.2 1.6 0.0 1.2 63852 1.2 1.6 1.6 1.6 2.0 63853 0.8 1.2 0.8 0.4 1.2 63854 0.0 2.0 2.0 1.6 2.0 63855 0.4 1.6 1.6 1.6 1.6 63856 0.4 1.6 1.2 2.0 1.6 63862 1.6 0.4 0.0 0.0 0.0 63863 0.8 1.6 1.6 2.0 1.2 63866 1.2 1.6 0.8 1.6 1.2 63868 0.4 1.2 1.6 1.2 0.4 63871 0.0 2.0 2.0 1.6 1.6 63873 0.0 2.0 2.0 1.6 1.6 63875 1.6 2.0 1.6 2.0 1.6 63877 1.2 1.2 1.2 1.2 1.6 63879 1.2 1.2 0.0 0.0 0.4 63880 0.4 1.2 0.4 1.6 1.2 63881 1.6 1.6 1.6 0.4 2.0 63882 0.8 1.6 1.6 0.8 1.6 63883 0.8 1.2 2.0 2.0 1.2 63884 0.4 1.6 1.6 0.4 2.0 63885 1.6 1.2 0.0 0.4 0.4 63887 0.8 1.2 0.4 1.6 1.2 63888 2.0 0.0 1.6 1.2 0.4 63890 0.0 2.0 1.6 0.8 1.6 63897 1.2 1.6 1.2 0.4 1.2 63898 0.0 1.6 1.2 1.6 0.4 63899 0.0 2.0 1.6 1.2 1.6 63900 0.8 1.6 0.4 1.2 0.4 63902 0.0 2.0 1.6 2.0 1.6 63907 0.8 1.6 1.2 2.0 1.6 63909 0.8 0.8 1.2 1.2 0.8 63911 0.0 2.0 1.6 1.6 2.0 63912 0.0 2.0 2.0 2.0 2.0 63913 0.4 1.6 1.6 2.0 2.0 63918 0.0 2.0 2.0 2.0 2.0 63924 1.2 1.2 2.0 1.2 2.0 63925 0.4 1.6 1.6 1.6 0.8 63930 1.2 1.2 1.6 2.0 1.6 63931 0.4 0.8 1.6 1.2 1.2 63932 0.0 2.0 2.0 1.2 1.2 63934 0.0 NA 2.0 2.0 2.0 63937 0.0 1.6 0.8 0.0 1.2 63939 0.4 1.6 1.6 0.4 0.8 63942 0.8 1.6 1.6 1.2 1.6 63946 1.6 0.0 0.4 2.0 0.4 63947 0.4 2.0 2.0 2.0 2.0 63948 0.0 1.6 0.8 2.0 2.0 63950 0.0 1.6 0.4 2.0 1.2 63952 0.4 NA 2.0 2.0 2.0 63955 0.4 0.8 1.2 0.4 1.2 63956 0.0 2.0 1.6 2.0 2.0 63957 0.4 0.8 1.6 1.6 1.2 63958 0.4 1.2 1.2 1.2 1.6 63959 0.4 2.0 2.0 2.0 1.6 63961 1.2 0.0 0.4 1.2 1.6 63962 0.0 NA 1.6 2.0 2.0 63963 1.6 1.6 2.0 0.0 1.6 63966 0.0 2.0 1.6 2.0 2.0 63967 0.4 1.6 2.0 1.6 1.6 63971 0.8 1.6 1.6 1.6 0.8 63977 0.4 1.2 1.6 1.6 1.6 63978 1.6 1.6 2.0 2.0 1.6 63979 0.8 1.2 1.6 1.6 0.8 63980 0.4 1.2 1.2 2.0 0.8 63982 1.2 0.4 0.4 1.2 0.8 63983 0.4 1.6 1.6 0.4 1.2 63984 0.4 1.2 0.8 0.0 1.6 63985 2.0 2.0 0.8 2.0 2.0 63987 0.0 2.0 1.6 2.0 1.2 63990 0.8 1.6 1.2 1.2 1.6 63991 0.8 NA NA NA 0.8 63993 0.4 0.4 0.4 1.6 1.2 63997 0.4 1.2 1.2 0.8 1.2 63999 0.4 1.2 0.8 2.0 1.2 64000 0.8 1.6 0.8 2.0 1.2 64001 0.8 2.0 2.0 0.8 2.0 64003 0.4 1.6 1.6 2.0 1.6 64005 0.4 1.6 1.6 1.6 0.8 64006 0.0 2.0 1.6 1.6 1.6 64010 0.4 1.6 2.0 2.0 1.6 64012 1.2 2.0 0.4 2.0 2.0 64014 0.0 2.0 2.0 1.2 1.6 64016 1.2 1.6 1.6 2.0 1.2 64017 0.8 1.2 1.6 2.0 2.0 64018 0.0 1.2 2.0 2.0 0.8 64020 0.0 2.0 2.0 2.0 NA 64021 0.0 2.0 2.0 2.0 2.0 64024 0.0 2.0 2.0 2.0 2.0 64025 0.0 2.0 2.0 2.0 2.0 64028 1.2 1.6 0.8 2.0 0.8 64029 1.6 1.6 1.6 1.6 1.6 64030 1.6 0.8 1.2 1.2 1.2 64031 2.0 1.6 1.2 2.0 2.0 64032 1.2 1.2 1.2 1.2 1.2 64033 0.0 1.6 1.2 1.6 1.2 64036 0.4 2.0 1.2 2.0 1.6 64044 0.8 2.0 1.6 0.8 1.6 64046 0.4 1.6 1.2 0.8 1.2 64047 0.0 1.6 1.6 0.4 0.8 64049 0.8 1.2 0.0 2.0 1.2 64050 0.0 2.0 1.6 2.0 1.6 64051 0.4 1.6 2.0 1.6 2.0 64053 0.0 2.0 2.0 2.0 2.0 64056 1.2 2.0 2.0 2.0 2.0 64057 0.4 1.6 1.6 2.0 2.0 64065 1.2 1.6 1.2 0.0 1.2 64066 0.0 0.8 1.2 1.2 1.2 64069 0.4 2.0 1.6 0.8 1.2 64072 1.2 0.8 0.4 0.0 1.2 64074 0.0 1.2 1.6 2.0 1.6 64075 1.6 1.2 0.0 1.2 0.4 64076 0.4 1.6 2.0 1.6 1.6 64078 0.0 1.6 2.0 1.6 2.0 64079 0.0 0.0 0.0 0.0 1.6 64086 0.4 1.6 1.6 1.2 1.6 64087 1.2 1.2 0.4 0.4 0.8 64089 0.0 2.0 1.2 2.0 1.6 64091 0.0 1.2 0.4 0.0 1.6 64092 0.4 1.6 0.8 1.6 1.6 64094 0.8 1.2 1.2 0.8 1.2 64097 0.0 NA 2.0 1.6 2.0 64099 1.2 1.6 1.6 1.6 1.6 64100 0.8 1.2 1.2 2.0 1.2 64101 0.8 1.6 1.6 1.6 1.6 64102 0.4 1.6 2.0 2.0 1.6 64103 1.2 1.6 1.6 0.8 1.6 64108 0.4 2.0 2.0 2.0 2.0 64111 0.0 1.6 1.6 2.0 1.2 64113 0.0 2.0 2.0 2.0 1.6 64116 0.0 1.6 1.2 1.6 1.2 64119 0.4 1.6 1.2 1.6 1.2 64120 0.4 2.0 1.6 1.6 2.0 64122 0.8 2.0 2.0 2.0 2.0 64125 2.0 1.2 0.0 1.6 0.0 64128 0.8 1.2 1.2 1.2 1.2 64129 0.0 1.6 2.0 1.6 2.0 64132 0.8 0.4 1.2 0.8 1.2 64134 0.8 0.4 0.0 0.4 1.2 64136 0.8 0.8 1.2 1.6 0.4 64137 0.4 1.6 1.6 1.2 1.6 64138 0.4 1.6 1.2 1.6 1.6 64141 0.4 1.6 1.6 1.6 1.6 64142 0.4 2.0 1.6 2.0 1.2 64143 0.4 2.0 1.6 2.0 1.6 64144 0.8 0.8 0.0 0.8 0.4 64147 0.4 1.2 2.0 1.2 1.6 64148 0.8 1.2 0.4 1.2 1.2 64150 0.8 1.2 0.8 1.6 1.2 64151 0.0 1.6 2.0 1.6 1.6 64152 0.0 1.6 2.0 1.6 1.6 64154 0.4 1.6 2.0 1.6 2.0 64155 1.6 1.2 1.2 1.2 1.6 64156 0.0 1.2 2.0 2.0 2.0 64158 0.4 0.8 0.0 0.0 0.0 64163 0.0 2.0 2.0 2.0 1.2 64168 0.0 2.0 1.6 1.6 1.6 64169 0.4 1.6 2.0 2.0 2.0 64178 NA 1.6 1.2 2.0 1.2 64179 0.4 1.2 0.8 2.0 2.0 64180 1.2 0.4 1.2 1.2 1.2 64185 0.4 1.6 1.2 1.2 1.6 64192 2.0 1.6 2.0 1.6 2.0 64193 0.4 1.6 1.6 2.0 1.2 64195 0.4 1.6 1.6 2.0 2.0 64200 1.2 NA 1.6 2.0 2.0 64202 1.2 0.4 0.4 0.4 1.2 64204 0.4 1.6 1.6 2.0 1.6 64206 0.4 1.6 1.6 1.2 2.0 64212 0.4 1.6 0.8 0.4 0.8 64216 1.2 1.2 0.8 1.6 1.6 64217 0.4 1.6 2.0 0.8 1.6 64222 0.0 2.0 2.0 2.0 1.6 64226 0.4 1.2 1.2 0.4 0.4 64228 0.0 2.0 2.0 2.0 2.0 64231 0.4 2.0 2.0 2.0 2.0 64235 0.4 1.6 1.6 2.0 1.2 64236 0.4 1.6 0.8 0.4 0.4 64240 0.4 1.6 1.6 1.6 1.6 64241 1.2 1.6 0.8 1.6 1.2 64242 0.4 2.0 1.6 1.6 1.2 64243 0.4 1.6 0.8 1.6 0.4 64244 0.4 2.0 2.0 1.2 0.4 64248 0.4 1.6 1.2 2.0 1.2 64250 0.0 2.0 2.0 0.0 0.0 64251 0.0 1.6 0.8 1.2 1.2 64254 1.6 1.6 1.6 1.2 2.0 64256 0.0 2.0 1.6 1.2 2.0 64261 0.8 2.0 0.8 1.6 0.8 64263 0.4 2.0 2.0 1.6 1.6 64264 1.6 2.0 2.0 1.6 1.6 64266 0.4 1.6 0.4 1.2 0.4 64267 0.4 0.8 0.8 1.2 1.2 64274 0.4 2.0 2.0 1.6 1.6 64275 1.6 1.6 0.8 2.0 2.0 64279 0.8 0.8 1.2 0.4 0.4 64280 0.8 2.0 1.2 2.0 1.6 64284 0.0 2.0 2.0 2.0 2.0 64286 0.4 2.0 2.0 2.0 2.0 64289 0.4 1.2 1.2 1.6 1.2 64290 0.4 2.0 2.0 2.0 2.0 64295 0.4 1.2 1.2 1.2 0.8 64296 1.2 1.6 1.6 2.0 1.2 64297 0.4 1.2 0.8 0.8 0.8 64298 0.4 1.6 2.0 1.2 2.0 64299 0.4 1.2 1.6 1.2 2.0 64300 1.6 1.2 0.8 0.4 0.8 64303 0.4 1.2 1.2 0.8 1.6 64305 0.4 1.2 1.6 2.0 1.2 64308 0.0 2.0 2.0 2.0 2.0 64310 1.2 2.0 1.6 1.6 0.8 64311 0.8 0.8 0.8 1.2 1.2 64312 1.6 1.6 0.4 1.2 1.2 64315 1.2 1.2 1.2 0.0 1.6 64318 1.6 1.6 1.2 1.6 1.6 64320 0.0 1.6 1.6 1.6 1.6 64321 0.0 2.0 2.0 2.0 2.0 64322 0.8 1.6 0.4 0.0 1.6 64323 0.8 2.0 1.2 2.0 1.2 64324 0.0 2.0 2.0 1.6 2.0 64326 0.4 1.6 1.6 1.2 2.0 64327 0.0 1.6 1.6 2.0 1.6 64329 0.4 1.6 1.6 0.8 1.6 64332 0.4 1.2 1.6 0.4 1.6 64333 0.4 2.0 2.0 1.6 2.0 64334 0.0 1.6 2.0 1.2 1.6 64335 1.6 1.2 0.8 0.8 1.2 64338 0.4 1.2 2.0 1.6 1.6 64339 0.0 1.6 2.0 2.0 1.2 64340 0.4 2.0 1.6 1.2 1.6 64341 1.2 2.0 0.0 2.0 2.0 64342 0.4 1.6 1.6 1.6 1.6 64344 0.4 2.0 1.6 1.6 1.2 64345 0.8 1.6 1.2 1.2 1.2 64347 0.4 1.2 1.6 1.6 1.2 64349 2.0 2.0 1.6 1.6 1.2 64352 0.0 1.6 1.6 2.0 1.6 64356 0.4 2.0 1.2 0.4 1.2 64359 0.8 1.6 1.6 1.2 1.6 64363 0.4 1.6 1.6 1.6 1.6 64365 0.8 1.6 1.6 0.4 1.2 64367 1.2 1.2 1.6 2.0 NA 64368 1.2 1.6 NA 1.6 1.6 64370 1.2 1.2 1.2 1.6 1.6 64371 0.8 0.4 0.8 1.6 1.2 64372 0.4 1.2 1.2 0.8 1.6 64374 0.8 1.2 1.2 1.2 1.6 64375 1.6 1.6 1.6 1.6 1.6 64377 0.4 0.4 0.4 0.8 0.4 64378 0.4 1.6 1.2 2.0 1.2 64379 0.4 0.4 1.2 0.0 0.8 64382 0.4 1.6 1.2 1.6 1.2 64385 0.0 2.0 2.0 2.0 1.6 64389 0.0 2.0 2.0 2.0 0.0 64390 0.8 1.2 1.6 0.4 1.6 64392 0.0 0.0 2.0 2.0 1.6 64393 0.4 2.0 2.0 0.8 2.0 64396 1.2 0.8 0.0 2.0 0.8 64399 0.4 0.0 0.0 0.4 1.2 64400 0.0 2.0 2.0 2.0 2.0 64401 0.4 1.2 1.2 1.6 1.6 64403 0.0 1.6 1.6 2.0 1.6 64413 1.6 1.2 1.2 1.6 1.2 64414 0.0 2.0 1.6 2.0 1.6 64417 1.6 2.0 1.6 2.0 2.0 64418 0.8 1.2 1.2 1.6 1.6 64421 1.2 1.2 1.2 1.2 0.8 64422 0.8 1.6 1.6 1.6 1.6 64424 0.8 1.2 0.8 2.0 2.0 64425 0.0 2.0 2.0 2.0 2.0 64426 0.4 1.2 1.6 0.4 2.0 64427 1.2 2.0 2.0 2.0 2.0 64429 0.4 1.6 1.6 2.0 1.6 64430 0.8 0.8 0.8 1.6 1.6 64431 0.8 1.2 1.6 2.0 1.2 64432 0.4 0.8 0.8 1.6 1.6 64435 0.4 1.6 1.6 2.0 1.6 64436 0.4 0.4 1.6 0.8 1.2 64437 0.8 2.0 1.6 2.0 1.6 64438 1.2 0.8 1.2 0.8 0.0 64439 1.2 1.6 1.2 2.0 1.6 64440 0.8 1.2 1.6 1.2 1.6 64446 0.4 2.0 1.6 0.8 1.6 64450 0.4 0.8 1.2 2.0 1.2 64453 0.0 2.0 1.6 NA 1.2 64456 0.4 1.2 0.8 0.8 1.6 64458 0.0 0.4 2.0 1.2 2.0 64459 1.6 1.6 1.6 0.8 1.6 64460 0.4 1.6 1.6 2.0 1.2 64461 0.0 1.6 1.6 1.2 2.0 64462 0.8 1.6 1.2 1.6 2.0 64463 0.0 2.0 NA 2.0 1.6 64464 0.4 1.2 1.6 2.0 1.6 64466 0.0 2.0 1.2 1.2 1.2 64467 0.8 1.2 1.6 1.2 2.0 64468 0.8 1.6 1.6 1.6 1.6 64469 1.6 2.0 2.0 0.0 2.0 64471 1.2 2.0 1.6 0.4 1.6 64479 0.4 1.2 1.2 0.8 1.2 64481 0.0 2.0 2.0 2.0 1.2 64483 0.4 0.0 0.8 1.2 2.0 64484 0.0 2.0 1.6 2.0 1.2 64487 0.8 2.0 2.0 2.0 1.6 64488 NA 2.0 1.6 1.2 2.0 64490 0.0 1.6 1.6 1.6 1.6 64491 0.8 1.6 1.6 2.0 1.2 64493 0.4 1.2 1.6 1.6 0.8 64495 0.8 1.6 1.2 2.0 1.6 64496 0.4 1.2 0.4 0.0 0.4 64499 0.4 1.2 0.4 0.0 0.8 64501 1.6 1.6 1.2 0.8 1.2 64507 0.0 1.6 1.6 1.6 1.2 64508 0.8 1.2 0.8 1.6 1.6 64510 1.2 1.6 1.2 1.2 1.6 64511 0.0 2.0 1.6 2.0 2.0 64512 0.0 2.0 1.6 2.0 0.4 64513 0.4 2.0 2.0 1.2 2.0 64514 1.6 2.0 1.6 1.6 1.2 64516 0.8 1.6 1.6 1.2 1.6 64518 0.0 2.0 2.0 2.0 2.0 64519 0.4 1.6 1.2 2.0 1.6 64520 1.6 0.4 0.8 0.4 0.4 64522 0.0 2.0 1.6 2.0 1.6 64525 0.0 2.0 2.0 2.0 2.0 64528 0.8 1.6 1.6 2.0 1.6 64530 1.6 0.4 0.4 1.2 0.8 64532 1.2 1.6 1.6 0.0 2.0 64537 0.8 1.6 0.8 1.6 1.2 64540 0.0 2.0 2.0 2.0 2.0 64541 0.4 1.6 1.6 2.0 2.0 64543 0.0 2.0 1.6 2.0 2.0 64544 0.0 2.0 2.0 1.2 2.0 64545 1.6 1.6 1.6 2.0 2.0 64546 2.0 0.0 0.0 0.4 0.0 64547 0.0 2.0 2.0 1.6 2.0 64548 0.4 1.2 1.6 0.8 0.8 64549 0.0 2.0 2.0 2.0 2.0 64552 0.4 1.6 1.2 1.6 1.2 64555 0.4 2.0 2.0 2.0 2.0 64556 0.0 2.0 2.0 2.0 1.6 64557 0.4 1.2 1.6 0.4 1.2 64562 1.6 0.8 1.2 0.4 1.2 64563 0.0 2.0 2.0 1.6 2.0 64564 0.0 1.6 1.6 2.0 1.2 64565 0.0 2.0 2.0 2.0 1.2 64567 0.0 1.2 0.8 1.6 0.8 64568 0.4 1.6 1.2 2.0 1.6 64571 0.0 1.6 2.0 2.0 2.0 64572 1.2 1.2 0.0 0.4 0.4 64573 1.2 1.2 0.8 0.8 1.2 64577 0.8 0.4 1.2 1.2 1.2 64579 1.2 0.0 0.0 0.0 0.8 64581 2.0 1.2 2.0 2.0 1.6 64583 0.0 2.0 2.0 2.0 1.6 64585 0.8 1.6 1.6 2.0 NA 64586 0.0 1.2 1.2 1.2 1.2 64588 2.0 0.8 1.2 0.0 1.6 64591 0.0 1.6 1.2 0.8 1.2 64593 0.4 1.6 1.6 1.2 1.2 64595 1.2 0.4 1.2 1.2 0.4 64600 0.0 2.0 1.6 1.6 2.0 64603 0.4 1.2 1.2 1.6 0.8 64605 1.6 1.2 1.6 0.4 1.6 64606 0.0 2.0 1.6 0.4 1.6 64607 1.2 1.6 1.2 2.0 2.0 64612 0.0 1.6 1.6 2.0 1.6 64614 0.0 2.0 1.6 1.6 1.6 64618 0.4 1.2 2.0 2.0 2.0 64620 0.8 2.0 0.8 1.6 1.2 64621 0.8 2.0 0.8 2.0 0.0 64622 0.0 1.6 1.6 1.2 0.4 64623 0.8 1.6 2.0 2.0 1.6 64626 0.0 2.0 1.6 1.2 1.6 64627 0.4 1.2 0.8 1.2 1.2 64630 0.4 2.0 2.0 2.0 2.0 64634 0.8 0.8 0.4 1.2 0.8 64636 1.6 2.0 0.8 2.0 0.8 64638 0.8 0.4 1.2 0.4 0.4 64639 0.0 2.0 1.6 1.2 1.6 64642 0.0 0.0 0.0 0.0 0.0 64648 0.0 2.0 2.0 1.6 1.6 64652 0.0 2.0 2.0 2.0 2.0 64656 1.2 2.0 1.6 1.6 2.0 64658 0.4 1.6 1.2 NA 1.6 64660 0.4 1.6 1.6 2.0 1.6 64661 0.4 1.2 1.2 1.2 1.2 64664 1.2 1.6 0.8 1.2 0.4 64665 0.0 1.6 1.6 1.6 1.6 64668 0.8 0.8 0.4 0.4 0.8 64669 0.4 1.6 0.8 0.8 0.4 64670 0.4 1.2 0.8 0.4 0.8 64671 0.4 1.2 1.2 1.6 0.8 64673 0.4 0.8 0.0 0.0 1.2 64675 0.0 1.2 1.6 0.4 0.4 64679 1.2 1.6 0.8 1.2 1.2 64680 0.0 1.6 1.6 2.0 1.2 64682 0.4 1.2 2.0 2.0 1.6 64683 0.8 0.4 0.4 2.0 1.6 64685 0.0 1.6 1.6 2.0 2.0 64686 1.6 1.2 1.2 0.8 0.0 64688 0.4 1.6 0.8 1.6 1.6 64689 1.2 1.2 1.6 0.8 1.2 64693 1.2 1.6 0.8 1.2 0.8 64696 0.0 1.6 1.6 2.0 1.2 64697 0.0 0.8 0.8 1.6 0.8 64698 1.6 2.0 2.0 2.0 1.6 64702 0.0 1.2 0.8 2.0 1.2 64706 0.4 0.8 1.2 2.0 1.6 64707 1.2 1.2 0.4 0.0 0.8 64709 0.8 2.0 1.6 2.0 1.6 64712 1.2 1.6 1.6 1.6 2.0 64714 0.0 1.6 1.2 2.0 1.2 64715 2.0 0.4 1.2 2.0 0.8 64716 0.4 2.0 2.0 2.0 2.0 64718 0.4 1.2 0.4 1.2 1.2 64720 0.0 2.0 2.0 2.0 1.6 64724 0.8 2.0 2.0 1.2 0.8 64725 0.0 1.2 1.6 1.6 1.6 64727 0.8 1.2 1.2 1.2 1.2 64729 1.2 1.6 0.0 0.4 0.4 64730 0.4 2.0 1.2 2.0 1.6 64733 0.8 1.6 1.6 1.2 1.6 64735 0.4 1.6 1.2 2.0 0.8 64737 0.0 1.6 0.4 1.2 0.4 64738 1.2 1.6 1.2 0.8 0.8 64739 1.6 1.2 0.4 0.4 0.8 64742 0.4 2.0 1.6 1.2 2.0 64744 0.4 1.6 1.6 1.6 1.6 64747 0.0 1.6 1.2 1.2 1.2 64753 1.2 2.0 1.6 1.6 1.6 64754 0.4 1.6 2.0 2.0 1.6 64758 0.0 1.2 1.6 0.8 1.2 64759 0.0 1.6 1.6 1.2 1.6 64763 0.0 2.0 2.0 1.6 1.2 64768 0.4 1.2 1.6 2.0 1.6 64769 1.6 0.4 0.4 2.0 2.0 64770 0.4 1.2 1.2 0.0 0.8 64771 0.0 2.0 1.6 1.6 2.0 64772 0.8 1.2 1.6 1.2 1.6 64773 0.4 2.0 1.6 1.6 2.0 64774 0.4 1.6 1.2 0.0 2.0 64780 0.0 1.2 1.6 NA 1.6 64804 0.4 1.6 1.6 2.0 0.8 64807 1.2 2.0 2.0 2.0 1.6 64810 1.2 0.4 0.0 1.2 0.0 64814 0.4 1.2 0.4 0.4 1.2 64815 1.6 0.8 0.4 0.4 0.8 64817 1.6 1.2 1.2 2.0 1.2 64819 1.2 1.2 1.2 1.6 1.2 64822 0.4 2.0 2.0 2.0 1.6 64824 0.4 1.6 1.6 2.0 2.0 64825 1.2 1.6 1.2 1.6 1.2 64826 1.2 1.6 NA 2.0 1.6 64830 0.4 0.0 1.2 0.4 1.2 64831 0.4 0.0 1.2 0.0 1.2 64835 0.0 1.6 2.0 1.6 2.0 64838 0.0 2.0 2.0 1.6 2.0 64839 2.0 2.0 0.0 2.0 1.6 64842 0.8 2.0 2.0 1.6 2.0 64843 0.4 2.0 2.0 1.6 1.6 64844 1.2 0.4 1.6 2.0 0.0 64845 0.0 1.6 1.6 1.2 1.2 64846 0.4 2.0 1.2 0.0 1.6 64847 1.2 1.6 1.2 0.4 2.0 64849 0.0 0.8 1.6 1.6 1.6 64850 0.4 1.6 1.2 1.6 1.2 64852 0.0 2.0 1.2 1.2 1.6 64857 0.0 1.2 0.8 2.0 1.6 64861 1.2 2.0 1.2 1.6 1.6 64865 0.4 1.6 0.8 1.2 1.2 64866 0.0 1.6 0.4 1.2 0.4 64874 0.4 1.2 0.8 1.6 1.2 64876 0.4 1.6 2.0 2.0 1.6 64877 0.4 1.2 1.2 1.2 0.4 64880 1.2 0.4 0.0 0.0 0.4 64886 0.4 1.6 2.0 1.6 2.0 64887 0.8 1.6 1.2 1.2 1.2 64888 0.8 0.8 0.4 1.6 0.4 64890 1.2 1.6 1.6 1.6 1.2 64891 1.2 1.2 1.6 0.4 2.0 64894 1.2 1.2 1.6 0.0 1.6 64898 0.0 1.2 1.6 2.0 1.2 64902 1.6 0.4 1.2 0.8 0.8 64907 0.8 1.6 0.4 1.2 1.6 64912 0.0 2.0 1.6 2.0 1.2 64914 0.4 2.0 1.6 0.8 1.6 64915 0.8 1.6 1.2 2.0 1.6 64917 0.0 2.0 2.0 1.6 1.6 64918 0.0 1.6 1.6 2.0 1.6 64919 0.0 1.6 0.8 1.2 1.2 64920 0.8 1.2 1.6 2.0 0.8 64921 0.8 2.0 2.0 0.8 2.0 64926 1.2 1.6 0.4 0.0 1.2 64931 1.6 2.0 1.6 0.8 2.0 64936 0.0 2.0 1.6 2.0 2.0 64937 1.2 1.2 1.6 0.4 1.6 64938 0.0 2.0 2.0 2.0 2.0 64939 1.2 1.6 2.0 1.6 2.0 64940 1.2 0.8 0.8 0.4 0.4 64944 2.0 1.2 1.6 2.0 1.6 64946 2.0 NA 2.0 1.2 1.6 64949 0.0 1.6 1.6 1.2 2.0 64950 0.8 0.8 0.8 1.2 1.2 64952 0.0 2.0 1.2 0.8 1.6 64953 1.6 1.6 1.6 1.6 1.6 64954 0.0 2.0 2.0 0.8 2.0 64958 1.6 2.0 2.0 0.0 2.0 64960 0.0 2.0 2.0 2.0 2.0 64961 0.0 1.2 0.8 0.8 1.2 64962 0.0 2.0 2.0 0.0 0.0 64963 0.8 1.2 1.2 1.2 1.2 64967 0.0 2.0 2.0 2.0 2.0 64968 2.0 1.6 1.6 2.0 2.0 64969 2.0 1.6 1.6 2.0 2.0 64970 2.0 0.4 1.6 2.0 2.0 64971 2.0 0.4 1.6 2.0 2.0 64975 2.0 0.4 1.6 2.0 2.0 64978 2.0 0.4 1.6 2.0 2.0 64979 2.0 0.4 1.6 2.0 2.0 64980 2.0 0.4 1.6 2.0 2.0 64981 2.0 0.4 1.6 2.0 2.0 64982 2.0 0.4 1.6 2.0 2.0 64984 0.8 1.2 1.2 0.0 1.2 64987 0.0 1.6 0.0 2.0 0.4 64990 0.4 1.6 1.6 1.6 0.4 64991 0.4 1.6 1.6 2.0 1.6 64992 0.4 1.6 1.2 1.2 1.2 64993 1.2 1.6 1.2 1.2 0.8 64996 0.0 0.0 1.2 2.0 2.0 64997 0.8 2.0 1.6 2.0 2.0 65017 1.2 1.6 1.2 1.2 1.2 65018 0.0 2.0 2.0 1.2 1.6 65020 1.6 1.6 NA 1.6 1.2 65022 1.2 1.2 1.2 1.2 0.8 65025 0.4 1.6 1.6 2.0 1.6 65026 1.6 1.2 0.8 1.2 0.8 65027 1.2 1.2 1.2 1.6 1.6 65028 1.2 0.0 1.6 0.4 1.6 65033 0.8 1.6 0.8 2.0 1.2 65041 0.8 1.6 0.0 1.2 1.2 65042 0.4 1.2 2.0 1.6 1.6 65044 0.4 1.2 1.2 2.0 1.2 65047 0.8 0.4 0.4 0.4 1.2 65049 1.6 0.8 0.0 0.0 0.8 65050 0.0 2.0 1.2 2.0 1.2 65052 0.0 1.6 1.6 2.0 0.8 65054 0.4 1.6 1.2 2.0 1.2 65056 0.4 2.0 1.6 2.0 1.6 65058 0.8 1.2 0.8 0.0 0.8 65059 1.6 1.2 1.6 1.2 1.2 65060 0.4 1.6 2.0 2.0 2.0 65061 1.2 1.2 1.6 2.0 0.4 65070 0.4 1.6 2.0 1.6 1.6 65071 0.0 1.6 1.6 1.6 NA 65075 0.0 2.0 2.0 2.0 2.0 65078 0.8 2.0 NA 2.0 1.2 65079 0.4 2.0 1.6 2.0 1.6 65080 0.8 1.2 0.4 1.2 0.0 65081 0.0 1.6 1.6 1.6 1.6 65082 1.2 1.2 1.6 1.6 2.0 65083 0.0 1.6 1.2 1.6 1.2 65084 0.0 1.2 1.2 0.8 2.0 65086 0.4 1.2 1.2 0.4 2.0 65087 0.4 1.6 0.4 0.0 0.4 65088 0.4 1.6 1.2 1.6 1.6 65091 0.4 1.2 1.2 0.8 1.2 65092 0.4 1.2 1.2 1.2 1.6 65096 0.8 2.0 1.6 2.0 1.6 65097 0.0 2.0 2.0 2.0 2.0 65099 0.4 1.2 1.2 1.6 0.8 65100 0.4 1.2 0.8 0.8 0.8 65101 1.2 0.8 1.6 0.8 1.2 65105 0.8 0.8 1.2 0.8 1.6 65108 0.0 1.6 1.6 2.0 1.6 65109 0.4 0.8 1.2 1.6 1.6 65111 0.0 1.6 NA 1.6 1.6 65114 0.0 2.0 0.4 1.6 0.4 65117 0.0 0.4 0.8 1.2 0.4 65121 0.4 1.2 2.0 1.6 0.0 65122 1.6 1.2 0.0 1.2 0.8 65124 0.4 2.0 2.0 1.2 1.6 65126 1.6 1.6 1.6 2.0 2.0 65129 2.0 0.4 2.0 2.0 2.0 65130 0.4 2.0 2.0 2.0 2.0 65140 0.4 1.2 1.6 2.0 1.6 65142 0.8 1.6 1.2 1.6 0.8 65144 0.0 2.0 2.0 2.0 1.6 65147 0.0 1.6 1.6 2.0 1.2 65150 0.0 2.0 2.0 2.0 2.0 65151 0.4 2.0 2.0 2.0 2.0 65152 0.4 1.2 1.6 2.0 2.0 65156 0.0 2.0 2.0 1.6 1.6 65157 0.0 2.0 1.6 1.2 1.6 65162 1.2 1.2 0.8 1.2 0.4 65163 0.8 1.2 0.8 0.4 0.4 65164 0.8 1.6 0.8 1.6 0.8 65166 0.4 1.6 2.0 1.6 2.0 65168 0.8 0.8 NA NA 1.6 65169 0.0 1.6 1.6 1.2 1.6 65170 0.8 0.8 1.6 1.6 1.6 65172 0.8 1.6 1.6 1.2 1.2 65173 1.2 1.2 1.2 1.6 1.6 65174 0.0 2.0 2.0 2.0 2.0 65175 0.0 1.2 1.6 2.0 1.6 65179 0.8 NA 0.0 1.2 1.6 65180 0.0 1.6 2.0 2.0 2.0 65185 0.0 1.6 2.0 1.6 1.2 65186 0.8 2.0 2.0 1.6 2.0 65188 0.0 2.0 2.0 0.0 2.0 65190 1.2 0.8 1.2 1.2 0.0 65191 1.2 2.0 2.0 2.0 0.4 65193 0.4 1.2 1.6 1.2 0.0 65195 0.4 1.6 1.6 2.0 1.6 65196 0.4 1.6 0.8 1.2 0.8 65197 0.0 1.6 1.6 2.0 1.6 65199 0.0 2.0 2.0 2.0 2.0 65201 0.8 1.6 1.2 0.8 1.2 65202 0.4 1.6 1.2 1.6 1.6 65204 1.2 0.8 2.0 2.0 2.0 65206 0.8 0.8 1.2 1.6 2.0 65207 0.0 1.6 1.6 1.2 2.0 65212 0.4 0.8 1.2 1.6 0.4 65213 0.4 0.4 2.0 0.4 2.0 65215 0.0 2.0 2.0 2.0 2.0 65220 0.8 1.6 1.2 1.6 1.6 65223 0.8 1.6 0.0 0.8 1.2 65224 1.2 1.2 1.2 0.4 1.2 65229 0.4 2.0 2.0 2.0 2.0 65230 1.6 1.2 1.6 0.4 1.6 65232 1.2 1.2 1.6 0.0 1.6 65235 1.2 2.0 1.6 2.0 2.0 65236 0.4 1.6 1.6 1.2 1.6 65237 0.8 1.2 1.2 1.2 1.2 65238 0.0 2.0 1.6 1.6 2.0 65240 0.4 2.0 2.0 2.0 0.0 65241 0.0 2.0 2.0 2.0 2.0 65245 0.4 1.6 0.4 2.0 NA 65247 0.8 1.6 1.2 2.0 1.2 65249 0.0 1.2 0.4 2.0 1.6 65252 1.6 1.6 0.4 1.6 0.4 65254 0.8 0.8 1.6 0.0 1.6 65255 1.6 2.0 1.6 2.0 1.2 65256 0.8 0.8 0.0 0.0 0.8 65257 0.4 1.6 1.6 1.6 1.2 65258 0.0 2.0 2.0 2.0 2.0 65260 1.2 1.6 1.6 1.6 2.0 65262 0.0 1.6 1.6 1.6 2.0 65266 0.0 1.6 2.0 2.0 2.0 65267 0.4 1.6 1.6 2.0 1.6 65268 0.0 1.6 1.2 1.6 1.2 65271 0.0 1.6 0.4 1.6 0.8 65273 0.8 2.0 0.8 1.2 0.8 65274 0.0 1.2 1.6 1.2 1.6 65275 0.4 1.6 1.6 2.0 0.4 65278 0.4 2.0 1.2 1.2 0.8 65279 0.4 1.6 1.6 1.6 1.6 65281 0.8 1.6 0.4 0.4 1.6 65282 0.4 0.4 0.4 1.6 1.6 65285 1.2 1.2 0.8 0.4 1.6 65286 NA 2.0 1.6 2.0 2.0 65288 1.2 2.0 1.2 2.0 0.8 65289 0.0 2.0 2.0 1.2 2.0 65291 0.0 1.6 1.2 1.2 1.6 65295 0.4 1.6 1.2 1.6 1.6 65296 0.4 1.6 1.6 2.0 1.6 65297 0.4 1.2 0.8 2.0 1.2 65299 0.0 2.0 2.0 1.6 1.6 65300 0.0 2.0 2.0 2.0 2.0 65301 1.6 1.2 1.2 1.2 0.8 65307 1.2 1.6 1.2 1.6 1.6 65309 1.6 0.4 0.0 0.4 1.6 65311 0.0 2.0 2.0 2.0 2.0 65313 0.0 1.2 1.2 2.0 1.2 65314 0.4 2.0 1.2 1.6 1.6 65316 0.8 0.8 1.2 0.4 1.2 65319 0.4 1.2 1.2 1.6 1.2 65320 0.4 2.0 1.2 2.0 1.6 65322 0.4 0.4 1.2 1.6 0.4 65326 0.4 1.6 1.6 0.4 1.6 65327 0.4 1.6 1.6 1.6 1.6 65332 0.0 2.0 1.6 2.0 1.6 65335 0.0 2.0 2.0 2.0 1.6 65342 0.4 1.6 1.6 0.4 1.6 65343 0.4 1.6 1.6 0.4 1.6 65344 0.4 2.0 1.6 0.8 1.6 65346 0.4 1.6 1.6 0.4 1.6 65347 0.8 1.2 1.2 1.6 1.6 65348 0.0 1.2 1.2 1.2 1.2 65349 0.8 1.6 2.0 1.6 1.6 65352 1.6 1.6 0.4 1.6 0.8 65353 0.0 1.6 1.6 2.0 2.0 65356 0.0 2.0 1.6 1.6 1.6 65359 0.8 0.8 1.2 0.8 1.2 65361 0.0 2.0 0.8 1.6 1.2 65362 1.2 0.4 1.2 1.6 1.2 65363 0.0 2.0 1.6 1.2 2.0 65370 0.4 1.6 2.0 2.0 2.0 65372 0.0 2.0 2.0 1.2 2.0 65374 0.0 2.0 2.0 2.0 1.6 65377 0.0 2.0 2.0 2.0 1.6 65378 1.6 1.2 0.4 1.6 0.8 65381 1.6 0.4 0.0 0.4 0.4 65382 1.6 2.0 2.0 2.0 2.0 65385 0.0 2.0 1.2 1.6 2.0 65386 0.4 1.6 1.2 2.0 1.2 65387 0.8 1.6 2.0 2.0 1.6 65388 0.0 2.0 2.0 1.6 2.0 65391 0.0 2.0 2.0 2.0 2.0 65392 1.2 0.8 0.8 1.6 0.0 65395 0.4 1.6 1.6 1.6 2.0 65401 0.4 1.2 1.6 2.0 1.6 65402 0.0 2.0 2.0 1.2 2.0 65403 0.0 2.0 2.0 2.0 2.0 65404 1.2 1.6 1.2 2.0 1.6 65405 0.0 1.6 2.0 1.2 2.0 65407 0.0 1.6 2.0 2.0 0.0 65408 0.8 1.2 0.8 1.6 1.2 65411 0.8 2.0 2.0 1.6 1.6 65412 2.0 1.2 0.0 1.6 1.2 65413 0.4 1.2 1.2 1.6 1.6 65415 0.0 1.6 1.2 2.0 1.2 65417 0.0 1.6 1.6 2.0 1.6 65419 1.2 0.8 1.6 2.0 1.2 65420 0.0 2.0 2.0 2.0 0.8 65421 2.0 2.0 1.2 1.2 1.2 65424 0.0 1.6 2.0 1.6 1.6 65426 0.4 2.0 1.6 1.6 0.8 65428 0.0 2.0 2.0 1.2 2.0 65430 0.4 1.6 1.6 0.0 1.2 65432 1.6 1.2 1.6 2.0 0.4 65433 1.2 1.2 1.2 1.6 1.2 65434 0.4 2.0 2.0 2.0 1.6 65435 1.6 1.2 1.6 1.2 1.6 65437 2.0 2.0 2.0 1.6 1.6 65439 0.8 0.0 0.0 0.4 0.0 65440 0.4 2.0 2.0 0.4 1.2 65442 0.0 1.6 2.0 1.6 1.6 65445 0.0 2.0 2.0 2.0 1.2 65446 0.4 1.6 1.2 0.8 0.8 65448 0.0 2.0 2.0 2.0 2.0 65450 1.2 1.6 1.6 1.2 1.6 65452 0.8 1.2 0.8 0.0 0.8 65453 0.0 1.6 2.0 1.2 2.0 65455 0.8 1.6 1.6 0.8 1.6 65457 0.0 2.0 1.6 1.2 1.2 65458 0.0 1.6 2.0 1.6 0.4 65459 0.4 1.6 1.6 2.0 1.6 65460 0.0 1.6 2.0 1.6 2.0 65462 0.4 1.6 1.6 1.2 1.2 65466 1.6 1.6 0.4 0.4 1.2 65467 0.4 2.0 2.0 2.0 2.0 65470 0.0 1.6 1.2 1.2 1.6 65472 0.0 2.0 2.0 2.0 2.0 65473 0.0 1.6 1.6 2.0 1.2 65474 1.2 1.2 1.6 1.2 1.2 65475 1.2 1.2 1.2 1.2 1.2 65476 0.0 2.0 1.6 1.6 1.6 65477 0.0 1.2 2.0 0.4 1.6 65479 0.0 2.0 1.6 2.0 1.6 65484 1.6 0.8 1.6 2.0 1.2 65487 0.0 2.0 2.0 2.0 2.0 65488 0.0 2.0 1.2 0.4 2.0 65490 0.0 2.0 2.0 2.0 2.0 65491 0.8 2.0 1.6 2.0 2.0 65494 1.6 1.6 1.6 1.6 0.8 65496 0.8 1.2 0.8 1.2 0.8 65499 0.0 2.0 2.0 1.2 1.6 65501 0.0 1.2 1.2 2.0 1.2 65502 0.0 2.0 2.0 1.6 2.0 65503 1.6 0.4 0.4 1.2 1.2 65504 0.4 1.2 1.6 1.6 1.6 65506 0.4 2.0 1.6 1.6 1.6 65510 0.0 1.2 2.0 1.6 2.0 65511 0.4 2.0 1.2 0.8 1.2 65512 0.0 1.2 1.2 1.2 1.2 65515 0.4 1.2 1.2 2.0 1.2 65519 1.6 0.4 0.4 0.4 1.2 65522 0.0 1.6 1.6 2.0 1.6 65523 0.0 1.6 1.6 1.6 1.2 65524 0.4 1.6 1.6 1.6 2.0 65526 0.4 1.6 1.6 2.0 1.2 65527 1.2 1.6 1.6 2.0 1.2 65528 0.4 0.4 1.6 1.6 0.8 65529 0.4 1.6 1.2 1.2 1.2 65532 0.0 1.2 0.8 1.2 1.6 65536 1.2 1.6 1.6 2.0 1.6 65538 0.0 2.0 2.0 1.6 2.0 65545 0.8 1.2 1.2 2.0 0.8 65547 0.4 2.0 1.6 2.0 2.0 65548 0.4 1.2 1.6 1.2 1.2 65549 0.4 1.2 1.2 0.8 1.2 65551 0.4 1.6 1.6 1.2 1.6 65552 0.0 1.6 1.6 2.0 2.0 65556 0.0 2.0 2.0 2.0 2.0 65557 0.0 2.0 1.6 1.2 2.0 65563 0.0 2.0 1.6 1.2 1.6 65564 0.4 1.6 2.0 1.6 2.0 65565 1.6 0.4 0.8 1.2 0.4 65566 0.4 2.0 1.6 2.0 0.8 65568 0.0 2.0 1.2 2.0 1.2 65569 0.4 1.6 2.0 1.2 2.0 65571 0.0 2.0 2.0 2.0 2.0 65575 0.0 1.6 1.6 1.6 1.6 65577 0.8 0.4 0.8 1.6 0.8 65578 0.8 2.0 1.6 2.0 1.6 65580 0.0 2.0 2.0 1.2 2.0 65583 0.0 1.6 1.6 2.0 1.2 65584 0.0 2.0 2.0 1.6 2.0 65586 0.4 1.6 2.0 2.0 1.2 65589 0.0 2.0 2.0 2.0 2.0 65591 0.8 2.0 2.0 1.6 2.0 65592 0.8 1.6 2.0 2.0 1.6 65593 0.0 2.0 2.0 2.0 2.0 65595 0.4 2.0 0.4 2.0 1.6 65598 0.0 2.0 1.6 1.6 1.6 65599 0.0 1.6 0.4 1.2 0.4 65600 0.4 1.6 2.0 2.0 1.6 65602 1.6 1.6 1.6 0.4 2.0 65605 0.4 2.0 2.0 2.0 1.6 65606 0.4 2.0 2.0 2.0 1.2 65612 0.0 2.0 1.2 0.0 1.2 65613 0.0 1.6 2.0 2.0 1.2 65618 0.8 0.4 1.6 1.2 1.2 65620 0.0 2.0 2.0 1.2 2.0 65628 1.2 2.0 1.6 2.0 1.6 65629 0.4 1.6 1.6 0.4 1.6 65630 1.2 1.2 0.4 0.4 0.0 65631 0.0 1.6 2.0 1.6 2.0 65632 0.0 1.6 2.0 2.0 1.6 65633 0.8 1.2 0.4 0.0 0.0 65634 0.4 2.0 1.6 2.0 2.0 65635 0.0 2.0 2.0 2.0 2.0 65636 0.8 1.2 1.2 1.2 0.8 65641 0.0 1.6 1.6 2.0 1.6 65643 1.6 1.6 1.6 1.2 1.6 65646 0.4 1.2 1.6 2.0 1.2 65648 NA 1.6 2.0 2.0 1.6 65649 0.8 1.2 1.6 2.0 1.6 65651 0.4 2.0 2.0 2.0 1.6 65652 0.4 2.0 1.6 2.0 1.2 65653 0.8 1.2 1.2 1.6 1.2 65654 1.2 1.2 1.2 2.0 0.8 65656 NA 1.6 NA 2.0 2.0 65659 1.6 0.8 0.4 1.6 0.4 65664 0.8 1.2 1.2 2.0 2.0 65668 1.6 1.2 1.2 2.0 NA 65671 0.0 1.6 2.0 1.6 1.6 65673 0.8 1.2 1.6 1.6 1.6 65674 0.4 2.0 1.6 2.0 1.6 65675 0.4 1.6 1.6 1.6 1.6 65678 0.0 1.6 1.6 0.8 1.2 65679 0.0 2.0 2.0 2.0 2.0 65680 0.4 2.0 2.0 2.0 1.6 65682 0.0 2.0 2.0 2.0 1.6 65684 1.6 2.0 2.0 1.6 2.0 65687 1.6 2.0 0.8 2.0 0.8 65694 1.2 0.4 1.6 0.4 1.6 65695 0.0 2.0 NA 2.0 2.0 65696 0.0 2.0 2.0 2.0 2.0 65698 1.6 1.6 0.4 0.4 0.8 65699 1.6 1.6 0.4 0.4 0.8 65700 2.0 2.0 2.0 2.0 1.6 65702 1.6 2.0 2.0 2.0 2.0 65703 0.4 1.6 0.8 1.6 1.6 65704 0.0 1.6 1.6 2.0 2.0 65705 0.8 1.2 1.2 0.0 0.8 65706 2.0 2.0 2.0 2.0 2.0 65708 1.2 NA 0.8 2.0 1.2 65709 0.4 1.6 0.8 1.6 2.0 65710 1.6 1.2 0.8 1.2 0.8 65711 0.8 1.2 1.6 2.0 1.6 65712 0.8 2.0 1.6 2.0 1.2 65713 0.0 1.6 1.6 2.0 2.0 65714 0.8 1.6 2.0 2.0 2.0 65715 1.6 0.0 1.6 0.0 1.2 65720 0.0 2.0 2.0 2.0 2.0 65724 1.6 0.4 0.8 0.8 0.8 65726 0.4 1.6 1.6 2.0 1.6 65727 1.6 0.4 2.0 1.6 1.6 65728 1.6 0.8 0.8 1.2 1.2 65730 0.4 1.2 1.2 2.0 1.2 65732 NA 1.6 2.0 1.6 2.0 65733 1.2 0.8 0.4 1.2 0.8 65734 0.0 1.6 2.0 2.0 2.0 65738 0.0 2.0 1.6 2.0 1.6 65740 0.4 1.6 1.6 2.0 1.6 65742 1.2 1.2 1.6 1.6 1.6 65743 0.0 0.4 0.4 2.0 0.4 65744 0.8 2.0 0.8 0.8 1.2 65745 0.0 1.6 1.6 1.6 1.2 65746 1.6 1.6 1.6 2.0 2.0 65747 1.6 1.2 0.4 1.2 0.4 65748 0.4 2.0 2.0 2.0 2.0 65752 0.0 0.4 1.6 2.0 1.6 65758 0.0 2.0 1.6 2.0 2.0 65762 0.8 1.2 1.6 2.0 1.6 65763 0.0 2.0 1.6 1.6 2.0 65764 0.4 1.6 0.4 1.6 1.2 65765 0.4 1.6 1.2 2.0 1.6 65767 1.2 1.6 0.8 0.4 0.8 65777 0.4 1.6 1.6 1.6 1.2 65778 0.0 2.0 2.0 2.0 1.2 65779 2.0 1.6 2.0 1.6 NA 65793 0.4 1.6 1.6 1.2 1.6 65794 0.0 2.0 2.0 2.0 2.0 65798 0.0 2.0 1.6 1.6 1.2 65801 0.0 2.0 2.0 2.0 2.0 65802 0.0 2.0 2.0 2.0 2.0 65803 0.0 2.0 1.6 2.0 2.0 65804 1.6 1.2 1.6 2.0 1.2 65809 0.0 2.0 1.6 NA 1.6 65811 0.4 1.2 1.6 1.2 1.2 65813 0.0 2.0 1.6 2.0 1.6 65816 2.0 2.0 1.2 2.0 0.8 65818 0.8 1.6 1.6 2.0 2.0 65820 0.8 1.2 1.6 1.6 1.6 65822 1.2 1.6 1.6 1.2 1.6 65825 1.6 2.0 1.6 2.0 2.0 65826 1.6 2.0 0.8 2.0 2.0 65828 1.6 1.6 0.4 1.6 2.0 65831 2.0 2.0 2.0 2.0 2.0 65834 0.0 1.6 1.6 2.0 1.6 65836 0.8 2.0 2.0 2.0 2.0 65838 0.0 2.0 2.0 2.0 2.0 65839 0.0 2.0 1.6 2.0 1.6 65840 0.4 2.0 2.0 1.2 2.0 65841 0.8 2.0 2.0 2.0 2.0 65842 1.6 1.2 1.2 0.8 1.6 65844 0.0 2.0 2.0 1.6 2.0 65847 0.4 1.6 2.0 2.0 1.6 65848 0.4 1.2 1.2 2.0 1.2 65849 0.0 1.6 1.2 2.0 1.2 65851 0.0 1.2 1.2 1.6 1.2 65852 1.6 1.6 2.0 1.6 2.0 65856 0.8 1.6 1.6 2.0 2.0 65857 1.2 1.2 0.4 2.0 1.2 65862 0.0 1.6 1.2 2.0 1.2 65863 1.2 1.2 1.2 2.0 0.0 65864 0.0 2.0 2.0 2.0 0.0 65865 1.6 2.0 2.0 2.0 2.0 65870 0.4 1.6 1.6 1.2 2.0 65874 0.4 2.0 2.0 2.0 1.6 65876 0.0 1.6 2.0 2.0 2.0 65879 0.0 2.0 1.6 1.2 1.2 65880 0.8 0.4 1.2 2.0 1.6 65883 0.0 2.0 2.0 2.0 1.2 65886 0.8 1.2 1.6 2.0 1.6 65888 1.6 0.0 2.0 1.6 1.6 65890 0.0 1.6 1.6 1.6 1.6 65891 0.0 2.0 1.6 2.0 1.6 65892 0.4 2.0 1.2 2.0 0.4 65893 0.8 1.6 1.6 1.6 1.2 65894 0.0 2.0 1.2 2.0 2.0 65895 0.8 1.2 1.6 2.0 0.8 65896 0.0 1.6 1.6 2.0 1.6 65897 0.4 1.6 2.0 1.6 1.6 65899 2.0 1.6 2.0 2.0 2.0 65900 0.8 0.8 1.6 2.0 1.2 65901 0.0 NA NA 2.0 1.6 65902 1.2 1.6 2.0 2.0 2.0 65903 1.2 1.2 1.2 1.6 2.0 65905 0.8 2.0 2.0 2.0 2.0 65909 0.4 2.0 2.0 2.0 1.2 65913 0.4 1.6 1.6 0.8 1.6 65917 0.4 2.0 1.6 2.0 2.0 65918 0.0 2.0 2.0 2.0 2.0 65921 0.4 1.6 1.6 2.0 2.0 65924 NA 2.0 1.6 2.0 2.0 65925 0.4 1.6 0.4 2.0 0.4 65926 1.2 1.2 1.2 2.0 1.6 65929 1.6 1.6 1.6 2.0 1.6 65930 1.6 1.6 1.6 2.0 1.6 65932 0.0 2.0 2.0 2.0 2.0 65933 0.8 1.2 1.2 1.2 1.2 65936 0.8 1.2 1.2 1.2 1.2 65937 0.0 2.0 2.0 1.6 2.0 65938 1.6 1.6 2.0 2.0 0.4 65940 0.4 1.6 1.6 2.0 2.0 65941 0.4 2.0 2.0 2.0 1.6 65942 0.4 1.6 1.2 1.2 1.6 65946 1.2 1.6 1.6 2.0 0.4 65948 0.0 2.0 2.0 2.0 2.0 65950 0.4 0.8 2.0 1.6 1.6 65951 1.6 2.0 2.0 2.0 2.0 65959 0.8 1.6 1.6 1.6 1.6 65961 0.0 2.0 1.2 2.0 1.6 65962 0.0 1.6 1.6 2.0 1.6 65965 0.0 2.0 1.6 2.0 1.6 65969 0.4 2.0 2.0 2.0 2.0 65971 0.0 1.6 0.8 1.2 1.6 65972 0.0 1.6 1.2 1.6 1.2 65973 0.0 1.6 1.6 1.6 1.6 65974 0.0 0.0 0.0 0.0 0.0 65976 0.0 2.0 2.0 2.0 1.6 65977 0.0 1.6 1.6 2.0 1.6 65981 0.4 1.2 0.4 1.6 0.4 65986 0.8 1.6 1.6 2.0 1.6 65987 1.6 1.6 0.8 1.6 0.4 65988 0.4 2.0 1.6 2.0 1.6 65989 0.0 1.2 2.0 1.6 1.2 65990 0.4 1.6 1.6 2.0 1.6 65992 0.8 1.6 1.6 1.2 1.6 65995 0.4 0.8 1.6 0.0 1.6 65998 0.4 2.0 0.8 2.0 0.4 65999 0.4 1.2 0.8 0.8 0.8 66000 0.0 2.0 2.0 2.0 1.6 66001 1.6 1.6 1.2 2.0 1.6 66002 0.0 2.0 2.0 2.0 1.6 66003 1.6 2.0 2.0 2.0 2.0 66004 0.0 1.6 2.0 2.0 1.6 66006 0.4 1.6 0.0 2.0 2.0 66007 0.0 2.0 1.2 2.0 2.0 66013 0.4 1.6 1.6 2.0 2.0 66014 1.2 1.2 0.0 2.0 0.8 66015 0.8 1.6 1.6 1.6 0.8 66016 0.4 1.2 2.0 1.2 2.0 66017 2.0 0.0 2.0 2.0 1.6 66022 0.4 1.6 1.6 1.2 1.6 66024 1.2 1.6 1.2 1.6 1.6 66032 0.4 2.0 2.0 2.0 NA 66034 0.4 2.0 2.0 2.0 0.0 66037 0.8 1.2 0.8 2.0 0.8 66039 0.4 1.6 1.2 1.2 1.6 66042 0.4 1.6 1.6 0.8 1.2 66045 1.2 2.0 2.0 2.0 2.0 66047 0.0 2.0 2.0 2.0 2.0 66049 0.4 1.2 1.2 1.6 1.2 66050 0.0 2.0 2.0 2.0 2.0 66053 0.4 1.6 1.6 0.4 1.2 66057 1.6 0.4 2.0 1.2 2.0 66058 0.8 2.0 1.2 2.0 1.6 66060 0.0 2.0 2.0 2.0 2.0 66061 0.0 2.0 1.6 2.0 1.2 66062 0.4 1.6 1.6 1.6 1.2 66063 0.4 2.0 1.6 2.0 2.0 66064 0.0 1.6 0.0 1.6 1.6 66065 1.6 0.0 0.8 1.6 0.4 66067 0.0 2.0 2.0 2.0 2.0 66068 0.8 1.6 2.0 1.6 2.0 66070 0.0 2.0 2.0 2.0 2.0 66071 1.2 1.2 0.4 0.4 0.4 66072 1.2 2.0 1.6 1.6 1.2 66077 1.6 1.6 1.6 2.0 1.6 66080 0.4 1.6 2.0 2.0 1.6 66081 1.6 1.2 0.4 0.8 1.2 66082 0.0 2.0 2.0 2.0 1.6 66083 0.0 2.0 2.0 2.0 2.0 66086 0.4 1.6 1.2 0.4 1.6 66088 0.0 2.0 2.0 2.0 2.0 66092 0.0 2.0 2.0 2.0 2.0 66093 0.0 1.6 1.6 1.6 2.0 66094 0.4 1.6 1.6 2.0 1.6 66095 0.4 1.2 1.6 0.8 1.6 66096 0.4 1.2 2.0 2.0 2.0 66097 0.0 2.0 2.0 2.0 2.0 66098 0.0 2.0 2.0 1.6 2.0 66102 1.2 1.2 1.6 1.6 1.6 66106 0.0 1.2 1.6 2.0 2.0 66107 0.4 1.2 1.6 0.8 1.2 66108 0.0 1.6 2.0 0.0 1.2 66109 0.4 1.6 0.4 1.2 0.4 66114 1.2 1.2 0.4 0.4 0.4 66115 0.0 2.0 2.0 1.6 2.0 66116 0.0 1.2 1.2 1.6 1.2 66117 0.8 1.6 2.0 2.0 2.0 66119 0.4 2.0 2.0 2.0 2.0 66120 1.2 1.6 1.6 2.0 1.6 66121 0.4 2.0 1.2 2.0 1.6 66122 0.4 2.0 1.6 1.6 2.0 66125 0.0 1.6 1.6 2.0 1.6 66127 0.4 1.6 2.0 2.0 2.0 66135 0.4 2.0 2.0 1.2 1.6 66136 0.4 1.2 1.2 1.2 1.6 66138 2.0 2.0 1.2 1.6 1.2 66139 1.2 0.0 2.0 2.0 1.6 66141 2.0 0.8 0.8 2.0 2.0 66144 0.0 2.0 2.0 2.0 1.6 66150 0.8 1.6 1.6 1.6 1.6 66151 0.4 1.2 1.6 2.0 1.6 66153 0.0 1.6 1.6 2.0 1.6 66156 0.4 1.6 1.6 1.2 1.6 66157 1.2 2.0 NA 2.0 1.6 66158 1.2 1.6 1.6 1.6 0.8 66161 0.4 1.6 1.6 1.2 2.0 66163 0.0 1.6 1.6 2.0 2.0 66164 0.4 1.6 1.6 2.0 2.0 66169 0.0 1.6 2.0 2.0 1.2 66170 0.0 2.0 2.0 2.0 2.0 66171 1.2 1.2 1.2 1.6 1.2 66172 0.4 0.4 0.8 0.4 0.4 66173 0.0 2.0 1.6 2.0 1.2 66176 0.4 2.0 1.6 2.0 1.2 66181 0.0 2.0 2.0 2.0 2.0 66187 0.0 1.2 1.2 0.8 1.2 66188 0.4 1.6 1.6 1.6 1.6 66190 0.0 1.6 1.2 1.2 1.2 66191 0.8 1.6 1.2 0.4 0.4 66195 0.0 2.0 1.6 2.0 1.6 66198 1.2 1.6 1.6 1.6 2.0 66199 0.4 1.2 1.2 1.6 1.2 66201 1.6 2.0 NA 2.0 2.0 66204 0.4 1.6 1.2 2.0 1.2 66205 0.4 2.0 2.0 2.0 2.0 66210 0.0 2.0 1.6 2.0 2.0 66212 0.0 2.0 1.2 0.8 1.6 66215 0.0 1.6 1.6 1.6 2.0 66216 0.4 1.6 2.0 2.0 2.0 66218 0.0 1.6 1.6 1.2 1.2 66219 1.2 1.2 1.2 1.2 0.4 66224 1.2 0.8 0.4 0.8 1.2 66225 0.0 1.6 0.8 1.2 1.6 66227 1.6 0.8 0.4 1.6 0.8 66231 1.2 1.6 2.0 2.0 1.6 66233 0.0 2.0 2.0 2.0 2.0 66234 0.8 1.6 1.6 2.0 1.6 66237 1.6 1.6 2.0 2.0 1.6 66244 0.0 2.0 1.6 2.0 1.6 66245 0.8 1.2 1.2 1.2 1.2 66246 0.4 1.6 1.6 1.2 0.8 66247 0.4 2.0 1.6 2.0 1.2 66251 0.4 1.6 1.6 1.2 0.8 66253 1.2 2.0 1.2 2.0 1.6 66255 0.0 1.6 1.6 2.0 1.6 66256 0.8 1.6 1.6 0.8 1.6 66257 0.0 2.0 2.0 2.0 1.6 66259 0.4 1.6 1.6 2.0 NA 66264 0.4 1.6 2.0 1.6 2.0 66265 1.6 2.0 2.0 2.0 1.6 66266 0.0 1.6 1.2 1.2 1.2 66271 0.0 2.0 2.0 2.0 1.6 66272 0.4 2.0 0.0 2.0 1.6 66273 0.0 2.0 2.0 2.0 1.6 66274 0.8 1.2 2.0 2.0 2.0 66277 0.4 1.6 1.2 2.0 1.6 66278 0.0 2.0 1.6 0.0 1.6 66279 0.4 1.6 1.2 2.0 1.2 66281 0.0 2.0 2.0 2.0 1.6 66283 0.8 1.6 0.4 1.2 1.6 66286 0.0 2.0 1.6 2.0 1.2 66288 1.2 1.2 1.6 0.8 1.2 66289 1.6 1.6 1.2 1.6 1.6 66291 0.0 1.6 2.0 2.0 2.0 66294 0.0 1.6 2.0 1.6 1.6 66295 0.4 0.8 1.2 1.2 1.2 66297 0.0 2.0 1.6 2.0 1.6 66299 0.0 2.0 1.6 2.0 2.0 66304 0.4 1.6 1.6 1.6 1.6 66306 0.8 1.6 1.6 2.0 1.2 66307 0.0 2.0 1.6 2.0 1.6 66309 1.2 1.2 1.6 1.2 1.2 66311 0.0 2.0 1.2 2.0 1.2 66312 1.6 2.0 2.0 2.0 1.6 66314 0.8 1.6 1.6 2.0 2.0 66317 0.8 0.8 0.4 2.0 0.4 66318 0.4 2.0 1.6 2.0 1.6 66323 0.8 2.0 1.6 2.0 1.6 66324 0.4 1.6 1.2 1.2 1.2 66325 0.0 1.6 2.0 1.2 2.0 66326 0.4 1.6 1.6 2.0 1.2 66329 0.4 1.6 1.2 2.0 1.6 66331 1.2 1.2 0.4 1.6 0.4 66337 0.0 2.0 2.0 1.6 1.6 66341 0.4 1.6 1.2 1.6 0.8 66344 0.0 2.0 2.0 2.0 1.6 66346 0.0 1.6 2.0 2.0 1.6 66348 1.2 2.0 1.2 2.0 1.2 66349 0.0 2.0 2.0 2.0 2.0 66357 0.4 1.6 1.2 1.2 0.8 66360 0.4 1.6 2.0 2.0 2.0 66364 0.0 2.0 2.0 2.0 2.0 66368 0.4 1.6 1.6 2.0 1.2 66370 0.4 1.6 1.6 2.0 1.2 66371 0.4 1.6 1.6 2.0 1.2 66372 0.4 1.6 1.6 2.0 1.2 66373 0.4 1.6 1.6 2.0 1.6 66376 1.2 2.0 1.6 1.2 1.6 66381 2.0 0.4 1.6 0.4 0.4 66382 0.4 0.8 1.2 0.8 1.2 66389 0.8 1.6 1.2 1.6 0.4 66391 0.0 2.0 1.6 0.0 0.8 66392 0.0 2.0 2.0 0.4 1.6 66396 0.4 1.6 1.2 1.6 1.2 66399 0.8 2.0 1.6 1.2 1.6 66402 2.0 1.6 1.6 0.8 0.8 66403 0.0 1.2 1.2 0.4 0.4 66404 0.0 2.0 2.0 1.2 1.2 66405 0.0 1.6 2.0 1.2 2.0 66406 0.4 2.0 1.2 1.6 1.2 66408 0.4 1.6 2.0 2.0 1.6 66409 0.8 2.0 2.0 0.8 1.6 66410 0.4 0.8 1.2 0.4 0.8 66413 0.4 0.4 0.8 1.6 1.6 66415 1.6 1.6 2.0 2.0 1.6 66419 1.2 1.2 1.6 1.2 1.6 66421 0.0 1.6 1.6 2.0 1.2 66425 0.0 2.0 0.8 0.8 1.2 66429 0.8 1.6 1.2 2.0 1.2 66431 0.0 2.0 1.6 2.0 1.6 66434 0.4 0.4 1.6 1.6 1.6 66437 0.0 2.0 2.0 2.0 2.0 66438 0.0 2.0 1.6 2.0 1.6 66439 1.2 1.6 1.6 2.0 2.0 66440 0.0 1.6 1.6 1.6 2.0 66441 0.4 1.6 2.0 2.0 1.6 66444 1.2 1.2 0.8 1.2 1.6 66445 0.8 2.0 2.0 2.0 0.4 66447 1.6 0.8 0.4 1.2 1.2 66450 1.2 1.6 1.6 1.2 1.6 66451 0.0 1.6 2.0 2.0 2.0 66459 0.8 1.6 1.2 1.2 1.6 66463 0.0 1.6 2.0 1.2 1.6 66464 0.0 1.2 1.6 2.0 1.2 66469 0.0 2.0 2.0 1.6 2.0 66472 1.2 1.2 2.0 2.0 1.6 66474 1.6 2.0 1.6 2.0 2.0 66475 0.0 1.2 1.6 0.8 0.8 66476 0.0 2.0 0.8 1.6 2.0 66483 0.0 1.6 1.6 2.0 2.0 66484 0.0 2.0 2.0 2.0 2.0 66485 1.2 1.6 1.2 2.0 1.6 66486 0.0 2.0 2.0 2.0 1.6 66487 0.4 1.6 0.4 0.0 0.4 66488 2.0 1.6 1.6 1.2 1.6 66491 0.4 1.6 1.6 1.6 2.0 66492 0.4 1.6 1.6 2.0 1.2 66493 0.4 1.2 0.4 2.0 1.2 66495 0.0 2.0 2.0 2.0 2.0 66496 0.4 1.2 1.6 1.6 2.0 66497 0.8 1.2 1.6 1.6 0.8 66498 0.0 1.6 1.6 1.6 2.0 66499 0.4 0.4 0.4 1.2 0.0 66500 0.4 0.4 0.0 2.0 1.2 66501 0.0 2.0 1.6 2.0 1.2 66503 0.0 1.6 2.0 2.0 1.6 66504 0.8 1.2 1.2 0.4 1.6 66506 0.4 0.8 1.6 1.2 1.6 66507 1.2 1.2 0.4 1.6 0.8 66509 0.0 2.0 2.0 2.0 2.0 66511 0.0 2.0 1.6 2.0 1.2 66512 0.4 2.0 1.6 2.0 1.6 66513 0.4 NA 2.0 1.2 2.0 66514 0.4 1.6 0.8 2.0 0.8 66517 0.4 1.2 1.2 1.6 1.6 66520 1.2 1.6 2.0 1.2 1.6 66522 0.8 1.6 1.2 2.0 1.6 66525 1.2 1.2 1.6 1.2 1.6 66526 0.0 1.6 2.0 1.6 1.2 66528 0.4 1.6 1.2 0.8 1.6 66529 0.0 1.2 1.2 1.6 2.0 66531 0.8 1.6 1.2 1.2 0.4 66532 0.8 1.2 1.2 1.6 0.8 66533 0.4 2.0 1.6 0.8 2.0 66534 0.4 2.0 1.2 2.0 1.2 66538 0.8 1.2 0.8 2.0 1.2 66540 0.0 2.0 2.0 2.0 2.0 66545 0.4 1.2 0.8 0.4 1.2 66546 2.0 NA NA NA 1.2 66549 0.4 1.6 2.0 2.0 2.0 66550 0.0 1.6 1.6 1.6 1.6 66551 0.4 1.6 1.6 0.4 1.6 66552 0.0 1.2 2.0 2.0 0.4 66553 1.2 2.0 1.6 2.0 2.0 66554 2.0 0.0 0.0 2.0 0.0 66560 0.8 1.6 NA 1.2 1.2 66562 0.0 2.0 2.0 2.0 2.0 66563 1.2 2.0 0.4 0.4 1.2 66564 0.0 1.2 1.2 0.0 2.0 66566 0.8 1.2 0.8 0.0 1.2 66568 1.6 1.2 1.6 2.0 0.8 66569 0.4 1.2 2.0 1.2 2.0 66570 1.2 1.2 1.2 1.2 1.2 66572 1.6 1.6 1.2 2.0 1.2 66574 1.2 1.2 0.8 1.2 0.4 66575 2.0 2.0 2.0 2.0 2.0 66576 0.0 2.0 1.6 1.6 1.6 66582 0.0 1.6 1.2 1.6 0.4 66583 0.0 1.2 1.2 0.4 0.4 66585 0.4 1.6 1.2 2.0 1.2 66588 0.0 2.0 2.0 2.0 1.6 66589 0.8 1.2 1.6 2.0 NA 66591 0.4 0.8 0.8 1.6 1.2 66596 0.0 1.6 1.6 2.0 1.6 66598 0.4 2.0 2.0 2.0 2.0 66601 1.6 1.2 1.2 2.0 0.8 66604 0.0 2.0 2.0 1.6 2.0 66605 1.2 1.2 2.0 0.8 2.0 66606 1.2 1.2 1.6 1.2 1.6 66607 0.4 2.0 1.6 2.0 1.6 66609 0.8 1.6 0.8 0.8 1.2 66610 1.6 1.2 1.2 1.6 0.4 66612 0.4 1.6 1.2 1.2 1.6 66615 0.4 1.2 1.6 2.0 1.2 66617 1.2 1.6 2.0 2.0 1.6 66619 0.8 1.6 0.4 2.0 0.4 66620 0.0 1.6 1.6 1.6 1.6 66621 0.0 2.0 2.0 2.0 2.0 66622 0.8 1.2 1.6 1.6 1.2 66623 0.8 0.8 0.8 1.2 1.6 66630 0.4 1.6 1.6 2.0 1.6 66635 1.6 0.4 1.6 2.0 0.4 66637 0.8 0.8 0.8 2.0 1.2 66638 1.2 0.4 0.0 0.4 0.4 66642 0.8 2.0 2.0 1.2 0.8 66645 0.4 1.6 1.6 1.6 1.6 66646 0.0 2.0 2.0 2.0 2.0 66647 0.0 2.0 2.0 2.0 1.6 66648 0.0 2.0 1.6 2.0 1.2 66649 1.2 1.2 1.2 0.8 0.8 66651 0.0 1.2 1.6 0.0 1.6 66653 0.8 1.2 1.6 0.0 1.6 66657 0.4 2.0 2.0 2.0 2.0 66660 1.2 1.2 1.6 0.4 0.8 66662 0.4 1.6 2.0 1.6 2.0 66663 0.8 1.6 1.2 2.0 1.6 66664 0.4 1.6 1.2 1.6 1.6 66665 0.4 1.6 1.6 0.0 1.6 66666 0.4 1.6 1.6 2.0 1.6 66668 0.0 NA 1.2 2.0 1.2 66669 0.0 1.6 1.6 1.2 1.2 66671 0.4 1.6 1.6 1.2 1.6 66674 0.0 1.6 1.2 2.0 1.6 66676 0.0 1.6 NA 2.0 1.2 66679 1.2 1.2 1.6 2.0 0.8 66680 0.0 1.2 1.6 0.4 0.8 66681 0.8 1.6 2.0 2.0 2.0 66682 1.6 0.8 0.8 2.0 0.4 66683 0.0 1.6 1.6 1.2 1.6 66684 0.0 1.6 1.2 2.0 1.6 66685 0.0 2.0 2.0 1.6 1.6 66689 1.6 1.6 0.8 2.0 1.2 66690 0.4 1.2 0.0 0.0 1.6 66692 0.8 NA 2.0 1.2 2.0 66694 1.2 2.0 2.0 2.0 2.0 66700 1.2 0.8 1.6 1.2 0.4 66701 0.0 1.6 2.0 2.0 2.0 66703 0.0 1.6 1.2 2.0 1.6 66704 0.4 1.6 0.8 1.6 1.2 66708 1.2 2.0 0.4 1.6 1.6 66711 0.0 2.0 2.0 2.0 2.0 66712 0.4 1.2 1.6 0.8 1.2 66713 0.4 1.2 0.0 0.4 0.4 66716 1.2 1.6 1.2 0.4 1.2 66717 1.2 1.2 0.8 0.8 0.8 66718 0.4 1.6 0.8 2.0 1.6 66722 0.8 1.6 2.0 2.0 2.0 66723 NA 2.0 2.0 2.0 NA 66724 0.0 2.0 1.6 1.2 1.6 66727 0.0 2.0 2.0 1.2 1.6 66728 0.0 1.6 1.6 0.8 1.2 66729 1.2 1.6 2.0 0.0 2.0 66730 0.0 1.6 1.6 2.0 2.0 66731 0.8 1.6 1.6 0.8 0.8 66733 0.0 1.2 1.2 1.2 1.6 66734 0.4 1.6 1.6 0.0 1.6 66737 0.0 2.0 2.0 2.0 2.0 66739 0.4 2.0 1.6 2.0 2.0 66741 0.4 1.6 1.6 0.8 1.6 66743 0.4 1.6 1.6 0.8 2.0 66744 0.0 1.6 1.6 1.2 1.6 66745 0.0 2.0 1.6 2.0 1.6 66748 0.8 0.4 1.6 2.0 2.0 66749 0.4 1.6 0.8 0.0 1.2 66751 0.0 2.0 2.0 2.0 2.0 66755 0.4 1.6 1.6 1.6 1.6 66756 1.6 0.8 2.0 2.0 NA 66757 0.0 1.6 1.2 0.4 0.8 66760 0.4 0.4 1.2 2.0 1.2 66764 0.4 1.6 1.6 2.0 1.6 66766 0.4 1.6 2.0 1.6 2.0 66768 0.8 1.6 0.4 2.0 0.4 66769 1.6 2.0 1.6 2.0 2.0 66770 1.2 1.6 1.6 0.8 2.0 66771 0.4 1.2 1.2 1.6 1.2 66772 1.6 1.2 1.2 1.2 0.8 66775 0.0 2.0 2.0 1.6 2.0 66778 0.0 2.0 1.6 1.6 1.6 66779 0.0 2.0 1.6 2.0 2.0 66785 0.0 2.0 2.0 1.6 2.0 66788 1.6 2.0 1.6 2.0 1.6 66790 0.0 1.6 1.6 1.6 1.2 66792 0.8 1.2 1.6 2.0 1.6 66793 0.4 2.0 2.0 2.0 0.8 66797 0.0 1.6 1.2 2.0 1.6 66800 0.0 2.0 2.0 2.0 2.0 66801 0.0 1.6 1.6 2.0 1.6 66804 0.0 2.0 2.0 2.0 2.0 66806 0.8 1.6 1.6 1.6 1.6 66807 0.0 2.0 1.6 2.0 2.0 66810 0.4 1.2 0.8 1.6 1.6 66812 0.0 1.6 2.0 1.6 2.0 66813 0.8 1.6 2.0 1.6 1.6 66814 0.0 2.0 2.0 1.6 2.0 66817 0.0 1.6 1.6 2.0 1.6 66819 1.6 2.0 2.0 1.6 1.6 66820 0.4 1.6 1.2 1.6 0.8 66824 0.8 1.2 1.2 2.0 0.8 66826 0.4 1.6 1.6 1.2 2.0 66830 0.0 1.6 1.6 2.0 1.6 66831 1.6 1.6 1.6 1.6 1.2 66832 0.4 1.2 1.2 1.6 1.2 66833 0.0 1.6 1.2 2.0 1.2 66838 0.0 2.0 2.0 2.0 1.6 66839 0.4 1.6 1.6 1.2 1.6 66840 1.2 2.0 1.6 2.0 1.2 66842 0.8 2.0 1.6 1.6 1.6 66843 0.8 1.2 1.2 0.4 2.0 66845 0.0 1.2 2.0 0.4 2.0 66847 0.0 1.2 1.2 1.6 1.2 66848 0.0 2.0 2.0 1.6 2.0 66850 1.2 1.2 1.2 1.2 1.2 66853 0.0 2.0 1.6 1.6 1.6 66855 0.0 1.6 1.6 2.0 1.6 66856 0.0 2.0 1.6 2.0 2.0 66857 0.8 0.4 0.4 1.2 1.2 66858 0.0 1.6 1.6 0.4 1.6 66860 0.4 1.6 1.6 1.2 1.6 66863 0.0 1.6 1.6 1.2 1.6 66864 1.6 1.6 1.2 1.6 1.2 66867 0.0 1.2 1.6 1.2 1.6 66868 0.8 0.4 0.4 0.0 1.6 66869 0.4 1.2 1.6 1.6 1.6 66872 0.4 1.6 1.6 2.0 1.6 66873 0.0 1.2 0.4 1.2 2.0 66879 0.0 2.0 2.0 0.0 2.0 66886 0.0 2.0 2.0 2.0 2.0 66887 1.2 0.0 0.4 1.2 0.4 66890 0.0 2.0 2.0 1.6 2.0 66892 0.4 1.6 1.6 2.0 1.2 66896 0.8 0.8 0.0 1.2 0.8 66897 1.2 1.2 0.8 0.0 0.8 66898 0.4 1.6 2.0 1.2 1.2 66899 1.6 0.4 0.0 0.0 1.2 66902 0.8 1.2 0.4 0.4 0.8 66903 0.8 1.2 0.8 1.2 1.6 66905 1.2 1.2 1.2 1.6 1.2 66911 1.2 2.0 1.2 0.4 1.6 66916 0.8 0.4 0.4 0.4 0.4 66917 1.2 0.0 0.8 0.8 1.6 66918 0.0 2.0 1.6 1.6 1.6 66919 0.4 1.2 1.2 0.4 0.4 66920 0.4 1.6 1.6 1.6 1.2 66921 0.4 1.6 1.6 1.6 2.0 66924 1.2 1.2 0.8 2.0 1.6 66925 0.0 2.0 1.6 0.0 1.6 66926 0.0 1.6 2.0 2.0 2.0 66927 0.0 2.0 2.0 2.0 2.0 66929 0.0 1.6 1.2 2.0 1.2 66930 0.4 2.0 1.6 2.0 1.2 66932 1.2 0.4 0.0 0.0 0.4 66934 0.4 1.6 1.2 2.0 0.4 66935 0.0 1.6 1.6 0.0 0.4 66937 0.0 2.0 2.0 0.8 2.0 66944 1.2 1.2 1.2 1.6 1.6 66945 0.4 1.2 0.8 2.0 1.6 66946 0.0 1.6 0.8 2.0 0.8 66947 0.0 2.0 1.6 1.6 1.2 66949 0.0 2.0 1.2 1.2 0.4 66953 0.0 1.2 1.2 1.2 1.6 66960 0.4 1.6 1.6 1.6 1.6 66962 0.4 1.6 1.6 1.6 1.2 66965 1.2 1.2 0.4 1.2 2.0 66968 0.8 0.8 1.2 0.4 1.2 66970 1.2 0.4 2.0 2.0 0.4 66982 0.0 2.0 2.0 1.6 1.6 66983 0.8 1.6 1.2 1.6 1.6 66985 0.4 1.2 1.6 1.6 1.2 66986 1.6 1.2 0.8 1.2 1.6 66987 0.0 1.6 1.2 1.6 0.4 66988 0.0 1.6 1.6 1.6 2.0 66994 1.2 1.2 1.2 1.2 1.2 66995 0.0 2.0 1.6 1.2 1.6 66997 0.4 0.0 0.0 0.0 0.4 67000 0.0 1.6 1.6 1.6 1.2 67003 0.0 1.6 1.2 0.4 0.4 67007 0.8 0.4 0.4 0.4 0.8 67008 1.6 1.6 1.6 2.0 1.6 67013 0.0 2.0 1.6 1.6 2.0 67014 0.4 2.0 1.6 1.2 1.6 67017 0.0 1.6 1.6 0.4 2.0 67018 0.0 2.0 2.0 1.2 2.0 67019 1.2 1.6 1.2 2.0 1.6 67022 0.0 1.2 1.6 1.6 1.2 67023 0.4 1.2 1.6 0.4 1.6 67024 1.6 0.4 0.8 0.8 0.4 67025 0.0 2.0 2.0 2.0 2.0 67026 0.8 1.6 0.8 1.6 1.6 67027 0.4 1.6 1.6 1.2 1.6 67029 1.6 1.2 0.4 2.0 1.2 67031 0.4 1.2 0.8 1.2 1.2 67033 0.0 2.0 1.6 0.8 1.6 67034 1.2 1.2 1.6 1.2 1.6 67036 0.8 1.6 1.2 NA 2.0 67039 0.4 2.0 1.6 1.2 1.2 67040 0.4 0.4 0.0 0.4 0.8 67041 0.0 1.6 1.6 2.0 2.0 67042 0.4 1.2 1.2 0.8 1.2 67043 0.8 1.6 1.6 2.0 1.6 67045 0.0 1.6 1.2 1.6 2.0 67046 1.2 1.2 1.6 2.0 1.6 67049 0.4 1.6 2.0 1.2 1.6 67055 0.0 1.6 1.6 1.6 1.6 67056 0.0 2.0 2.0 2.0 2.0 67057 0.4 0.8 1.2 1.6 1.2 67059 0.4 2.0 1.2 1.2 1.2 67064 0.4 1.6 2.0 1.6 1.6 67065 0.0 2.0 1.6 0.4 1.6 67066 0.0 2.0 2.0 1.6 2.0 67070 0.4 1.2 1.2 2.0 0.8 67072 0.8 0.8 1.2 1.2 1.6 67073 2.0 2.0 2.0 2.0 2.0 67075 0.4 1.6 1.6 1.6 1.6 67078 0.0 2.0 2.0 1.6 0.8 67079 0.0 1.6 1.6 2.0 2.0 67080 0.0 1.6 2.0 1.6 2.0 67082 1.2 1.6 1.6 1.6 1.6 67085 0.0 2.0 2.0 1.6 1.2 67087 0.8 1.2 1.2 1.6 1.2 67091 1.6 0.0 1.2 0.8 1.2 67093 1.2 2.0 1.6 1.6 1.6 67095 2.0 2.0 1.6 0.0 2.0 67096 0.0 1.6 1.6 2.0 1.6 67098 0.0 2.0 2.0 2.0 2.0 67101 1.2 1.6 0.4 2.0 1.2 67104 0.4 2.0 1.6 2.0 1.6 67107 1.6 0.4 0.8 0.4 1.2 67109 0.4 2.0 1.6 2.0 1.6 67115 0.8 1.2 1.2 0.8 1.6 67116 0.8 1.2 1.2 1.2 1.2 67118 1.2 0.8 0.8 0.4 0.8 67119 0.4 1.6 1.2 2.0 1.2 67121 0.0 1.6 2.0 1.6 0.8 67123 0.0 1.2 0.8 1.6 0.8 67124 0.0 1.2 2.0 1.2 2.0 67126 0.0 1.6 1.6 2.0 2.0 67132 1.2 2.0 1.2 1.6 1.6 67134 1.2 1.2 2.0 0.4 2.0 67135 0.0 2.0 2.0 2.0 1.6 67136 1.6 1.6 2.0 0.4 1.6 67140 0.4 1.2 0.8 1.2 1.2 67143 0.4 2.0 2.0 2.0 1.6 67144 1.2 2.0 1.6 1.6 2.0 67145 0.0 2.0 1.6 2.0 1.2 67146 1.2 1.6 1.6 1.2 0.4 67148 0.8 0.8 1.2 1.2 0.8 67149 0.8 0.8 1.6 2.0 1.6 67152 1.2 1.6 1.6 1.6 1.6 67154 0.0 2.0 2.0 2.0 1.6 67155 1.6 0.4 0.4 1.6 1.2 67156 0.0 0.4 0.8 1.6 0.0 67157 1.2 1.6 1.2 2.0 1.2 67158 1.2 0.8 2.0 2.0 2.0 67161 1.2 1.6 0.0 1.6 1.2 67162 0.8 0.8 1.2 1.6 1.2 67168 1.2 1.6 1.6 2.0 1.2 67174 0.8 0.8 1.6 1.6 1.6 67179 1.6 1.2 0.0 0.0 0.0 67180 NA 0.4 0.4 1.6 0.4 67183 0.4 1.6 0.4 2.0 0.8 67184 2.0 1.2 1.2 0.0 2.0 67185 1.6 0.4 0.4 2.0 0.4 67186 1.6 1.6 2.0 2.0 1.2 67188 0.0 0.0 0.0 0.4 0.0 67189 0.8 0.8 0.4 0.4 2.0 67190 1.2 1.2 1.6 0.8 1.6 67191 0.0 1.2 1.6 0.4 1.2 67197 0.8 0.8 1.6 1.6 1.2 67202 1.6 0.4 0.0 0.4 0.4 67203 1.6 0.8 1.2 2.0 0.4 67206 1.2 2.0 1.6 2.0 1.6 67213 1.2 1.2 1.2 1.6 1.2 67215 0.8 0.4 0.8 0.0 0.8 67220 0.4 1.6 1.2 0.8 0.8 67222 0.0 1.6 1.6 1.6 1.2 67223 0.0 NA 2.0 2.0 2.0 67224 0.4 0.8 1.2 0.8 0.8 67225 0.8 1.6 1.6 2.0 0.4 67226 1.6 1.6 0.8 0.4 1.6 67229 0.0 1.6 1.6 0.4 1.6 67230 1.6 1.2 1.6 0.4 1.2 67232 2.0 2.0 1.6 2.0 2.0 67234 1.2 2.0 1.6 2.0 1.2 67237 0.4 1.6 1.6 1.6 1.2 67238 1.6 0.8 1.6 0.8 0.8 67239 1.2 0.4 0.4 1.2 0.8 67240 0.4 2.0 1.2 1.6 1.2 67242 1.2 0.4 1.2 1.2 1.2 67246 0.4 1.6 1.6 1.6 1.2 67247 1.2 2.0 1.6 1.6 1.2 67251 2.0 1.6 1.6 2.0 1.6 67254 0.4 1.6 1.2 2.0 0.4 67255 1.6 2.0 2.0 2.0 2.0 67257 0.4 1.6 2.0 2.0 1.6 67258 0.4 1.6 2.0 1.2 2.0 67259 NA 2.0 1.2 NA 1.2 67260 0.0 0.0 0.4 0.4 0.0 67262 0.0 1.6 1.6 1.6 1.2 67263 0.8 0.8 0.8 0.4 0.4 67269 0.0 1.6 2.0 2.0 1.6 67270 0.4 1.6 0.4 1.2 0.8 67271 0.8 0.8 1.2 0.4 1.6 67272 1.2 0.8 0.0 0.8 0.8 67274 1.2 1.6 2.0 2.0 2.0 67275 0.8 0.8 1.6 2.0 1.6 67277 0.0 2.0 2.0 1.6 2.0 67278 0.0 1.6 2.0 2.0 1.6 67279 0.4 1.6 1.2 1.2 0.8 67283 0.0 2.0 0.8 0.4 1.6 67284 0.0 2.0 1.2 1.6 1.6 67286 2.0 0.8 0.4 0.8 1.6 67287 0.4 0.0 2.0 2.0 2.0 67288 2.0 2.0 2.0 2.0 2.0 67289 0.0 2.0 1.6 2.0 2.0 67294 1.2 1.6 2.0 1.6 1.6 67295 0.4 2.0 1.6 1.6 2.0 67298 0.0 2.0 2.0 2.0 2.0 67299 0.4 2.0 1.6 1.6 1.6 67303 1.6 1.6 1.2 1.6 1.2 67307 1.6 0.4 0.8 0.4 0.4 67309 0.0 2.0 2.0 2.0 2.0 67310 0.8 1.2 1.6 2.0 1.2 67312 1.2 1.6 1.6 2.0 1.2 67313 0.0 2.0 2.0 1.6 0.4 67315 0.8 1.2 1.2 2.0 1.6 67316 1.2 0.8 1.2 1.2 0.8 67322 0.4 2.0 2.0 1.6 2.0 67324 1.2 1.2 1.2 0.8 1.2 67325 0.0 2.0 2.0 2.0 2.0 67328 0.4 0.0 0.8 0.0 0.8 67331 0.4 0.4 1.6 1.6 1.2 67332 0.4 2.0 1.6 0.8 1.6 67334 0.8 2.0 2.0 1.6 2.0 67335 0.4 1.6 1.2 2.0 1.6 67336 0.4 1.2 0.8 0.8 NA 67338 0.0 2.0 2.0 1.6 1.6 67339 0.4 1.6 1.6 1.6 1.6 67341 0.0 2.0 2.0 2.0 1.6 67342 0.4 2.0 1.6 1.6 1.6 67344 0.4 0.4 0.0 0.8 0.8 67345 1.6 0.0 0.4 0.0 0.4 67346 0.4 1.6 1.6 1.6 1.6 67347 0.0 1.6 1.6 NA 1.6 67352 0.8 2.0 2.0 0.8 0.4 67355 0.0 2.0 2.0 0.4 2.0 67356 1.6 1.2 1.2 0.0 2.0 67357 1.2 0.4 1.6 1.6 0.4 67360 0.4 1.2 0.8 1.6 1.2 67363 0.4 1.6 1.6 1.6 2.0 67364 0.0 1.2 1.6 1.6 0.4 67365 0.0 2.0 2.0 2.0 2.0 67366 0.8 1.6 0.4 2.0 1.6 67367 0.0 1.6 1.6 2.0 2.0 67368 1.6 2.0 1.2 NA 2.0 67370 1.2 1.2 2.0 2.0 2.0 67371 1.2 1.2 2.0 2.0 2.0 67374 0.4 1.2 0.8 0.8 0.4 67376 0.0 1.6 1.6 1.6 2.0 67377 0.4 1.2 1.6 1.2 1.2 67378 0.8 1.6 1.6 0.8 1.6 67379 0.4 1.6 1.6 2.0 1.2 67380 0.0 2.0 1.2 1.2 2.0 67382 0.8 1.6 1.6 1.2 1.2 67388 0.4 1.2 1.6 1.6 1.6 67390 0.0 1.6 1.2 1.6 0.0 67392 2.0 1.2 1.2 2.0 2.0 67393 1.2 1.6 1.6 2.0 1.6 67395 1.2 1.2 1.2 2.0 1.2 67396 0.4 1.6 0.8 0.8 0.8 67397 1.2 1.6 1.6 2.0 1.6 67399 0.0 1.6 1.6 1.6 1.6 67401 0.0 1.6 1.6 2.0 1.2 67403 0.8 0.4 0.0 0.4 0.4 67406 0.0 1.6 2.0 2.0 1.6 67408 0.8 1.2 1.2 0.8 1.2 67413 0.0 0.0 2.0 0.0 0.0 67415 0.8 1.2 1.2 1.6 1.6 67417 0.0 1.6 1.6 2.0 1.2 67418 0.4 0.8 0.4 1.6 1.2 67420 0.0 2.0 1.6 0.4 1.6 67421 1.2 1.6 2.0 1.2 1.6 67426 0.0 1.6 1.2 1.6 1.2 67427 0.4 1.2 0.8 1.2 1.6 67429 0.8 0.8 0.4 0.0 0.4 67431 1.2 1.2 1.6 1.6 1.2 67436 0.4 1.2 1.6 1.6 1.6 67438 2.0 0.8 0.8 0.8 0.8 67439 1.6 2.0 1.6 1.6 2.0 67440 0.4 0.8 1.2 0.4 1.6 67441 0.4 1.2 1.2 2.0 1.6 67442 0.8 0.4 1.6 1.6 0.8 67445 0.4 1.2 1.2 2.0 1.2 67446 0.0 1.6 1.2 0.4 1.6 67447 0.0 2.0 1.6 1.6 1.6 67449 2.0 1.6 0.4 1.2 1.6 67451 0.0 2.0 2.0 0.4 2.0 67453 0.8 2.0 1.6 2.0 1.6 67454 0.0 2.0 2.0 1.6 2.0 67455 0.0 2.0 2.0 2.0 2.0 67458 0.8 0.4 0.8 0.8 0.0 67460 0.4 2.0 2.0 2.0 2.0 67462 2.0 1.6 1.6 1.2 1.2 67465 2.0 1.6 2.0 2.0 2.0 67471 0.0 2.0 0.8 0.0 1.2 67473 0.4 1.6 1.6 1.6 1.6 67479 0.4 1.6 1.6 2.0 0.8 67480 2.0 0.0 0.4 0.4 0.4 67483 0.4 1.6 1.6 1.6 1.6 67485 0.4 0.4 1.6 2.0 2.0 67487 0.4 1.6 1.6 1.2 2.0 67488 0.4 1.6 0.8 2.0 0.8 67489 0.4 1.2 1.2 1.6 1.2 67490 0.8 1.6 0.8 1.6 1.2 67495 0.4 1.2 NA 1.6 1.6 67497 0.4 1.6 1.2 2.0 1.6 67502 0.8 1.2 0.8 1.2 1.2 67503 0.4 0.8 1.2 2.0 0.4 67504 1.2 1.2 1.6 0.0 1.2 67505 0.4 1.6 1.6 1.6 1.6 67506 0.0 2.0 1.6 2.0 1.2 67507 0.8 1.2 0.8 2.0 1.2 67508 0.4 1.6 1.2 1.2 1.6 67509 2.0 1.2 1.6 1.6 1.6 67510 0.8 1.2 0.8 1.6 1.6 67512 0.0 1.6 1.6 2.0 2.0 67516 0.4 1.6 1.2 1.2 0.8 67518 0.8 1.6 1.2 0.8 1.2 67521 0.4 1.6 1.6 1.6 1.6 67522 0.0 2.0 2.0 2.0 2.0 67523 0.0 2.0 2.0 2.0 2.0 67524 1.6 2.0 1.2 0.0 1.6 67525 0.0 2.0 0.0 2.0 2.0 67526 0.0 2.0 1.2 2.0 0.8 67528 0.8 1.6 2.0 2.0 2.0 67529 0.0 NA 2.0 NA 0.4 67530 0.0 1.6 2.0 1.6 2.0 67531 0.4 1.6 1.2 1.6 1.2 67533 0.4 2.0 2.0 2.0 1.6 67534 0.0 1.2 0.8 1.6 1.6 67535 0.4 2.0 1.6 2.0 2.0 67537 0.4 2.0 1.6 1.2 1.6 67539 0.8 1.2 1.2 0.8 1.6 67540 0.4 1.6 0.8 1.6 1.2 67541 1.2 1.2 1.6 0.0 0.4 67544 1.6 1.6 1.6 2.0 1.6 67547 0.8 1.2 0.8 0.0 0.8 67549 1.6 1.6 1.6 1.6 0.8 67551 2.0 0.0 0.8 0.8 0.8 67552 0.4 1.2 1.2 0.8 1.6 67556 0.4 0.8 1.6 0.4 1.6 67559 1.6 0.4 0.4 1.2 1.2 67560 0.4 0.8 0.0 1.2 0.4 > pomps(data = psych::bfi, vrb.nm = vrb_nm, min = 1, max = 6, suffix = "_pomp") A1_pomp A2_pomp A3_pomp A4_pomp A5_pomp 61617 20 60 40 60 60 61618 20 60 80 20 80 61620 80 60 80 60 60 61621 60 60 100 80 80 61622 20 40 40 60 80 61623 100 100 80 100 80 61624 20 80 80 40 80 61629 60 40 0 80 0 61630 60 40 100 40 40 61633 20 80 100 100 80 61634 60 60 80 100 80 61636 20 80 80 80 80 61637 80 80 80 100 60 61639 80 80 80 100 100 61640 60 80 20 20 0 61643 60 40 100 100 40 61650 60 100 100 20 80 61651 80 80 80 60 80 61653 60 60 80 60 40 61654 60 60 100 80 80 61656 80 60 20 0 20 61659 0 100 100 0 80 61661 0 80 100 80 100 61664 20 100 80 100 80 61667 60 80 80 100 80 61668 0 100 100 0 100 61669 20 60 60 60 40 61670 20 80 100 100 100 61672 20 80 0 40 80 61673 60 80 100 80 80 61678 0 100 80 100 40 61679 20 80 100 100 100 61682 0 80 100 80 60 61683 20 60 80 100 80 61684 60 60 60 60 60 61685 80 40 80 60 20 61686 0 100 60 100 60 61687 0 60 60 20 40 61688 0 100 100 100 100 61691 0 80 60 40 80 61692 0 80 80 100 80 61693 80 60 40 100 60 61696 0 80 60 60 80 61698 80 100 60 40 80 61700 20 100 100 100 100 61701 0 100 100 100 100 61702 80 80 40 60 40 61703 20 100 60 80 80 61713 0 80 40 20 40 61715 0 100 100 100 100 61716 0 100 100 100 60 61723 0 80 100 80 60 61724 40 100 60 60 60 61725 60 40 80 100 40 61728 0 100 100 100 100 61730 0 60 40 80 80 61731 0 60 20 20 20 61732 40 60 80 20 60 61740 0 100 80 60 60 61742 40 40 80 60 80 61748 20 40 60 60 80 61749 20 60 60 80 40 61754 0 60 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67397 60 80 80 100 80 67399 0 80 80 80 80 67401 0 80 80 100 60 67403 40 20 0 20 20 67406 0 80 100 100 80 67408 40 60 60 40 60 67413 0 0 100 0 0 67415 40 60 60 80 80 67417 0 80 80 100 60 67418 20 40 20 80 60 67420 0 100 80 20 80 67421 60 80 100 60 80 67426 0 80 60 80 60 67427 20 60 40 60 80 67429 40 40 20 0 20 67431 60 60 80 80 60 67436 20 60 80 80 80 67438 100 40 40 40 40 67439 80 100 80 80 100 67440 20 40 60 20 80 67441 20 60 60 100 80 67442 40 20 80 80 40 67445 20 60 60 100 60 67446 0 80 60 20 80 67447 0 100 80 80 80 67449 100 80 20 60 80 67451 0 100 100 20 100 67453 40 100 80 100 80 67454 0 100 100 80 100 67455 0 100 100 100 100 67458 40 20 40 40 0 67460 20 100 100 100 100 67462 100 80 80 60 60 67465 100 80 100 100 100 67471 0 100 40 0 60 67473 20 80 80 80 80 67479 20 80 80 100 40 67480 100 0 20 20 20 67483 20 80 80 80 80 67485 20 20 80 100 100 67487 20 80 80 60 100 67488 20 80 40 100 40 67489 20 60 60 80 60 67490 40 80 40 80 60 67495 20 60 NA 80 80 67497 20 80 60 100 80 67502 40 60 40 60 60 67503 20 40 60 100 20 67504 60 60 80 0 60 67505 20 80 80 80 80 67506 0 100 80 100 60 67507 40 60 40 100 60 67508 20 80 60 60 80 67509 100 60 80 80 80 67510 40 60 40 80 80 67512 0 80 80 100 100 67516 20 80 60 60 40 67518 40 80 60 40 60 67521 20 80 80 80 80 67522 0 100 100 100 100 67523 0 100 100 100 100 67524 80 100 60 0 80 67525 0 100 0 100 100 67526 0 100 60 100 40 67528 40 80 100 100 100 67529 0 NA 100 NA 20 67530 0 80 100 80 100 67531 20 80 60 80 60 67533 20 100 100 100 80 67534 0 60 40 80 80 67535 20 100 80 100 100 67537 20 100 80 60 80 67539 40 60 60 40 80 67540 20 80 40 80 60 67541 60 60 80 0 20 67544 80 80 80 100 80 67547 40 60 40 0 40 67549 80 80 80 80 40 67551 100 0 40 40 40 67552 20 60 60 40 80 67556 20 40 80 20 80 67559 80 20 20 60 60 67560 20 40 0 60 20 > > > > cleanEx() > nameEx("recode2other") > ### * recode2other > > flush(stderr()); flush(stdout()) > > ### Name: recode2other > ### Title: Recode Unique Values in a Character Vector to 0ther (or NA) > ### Aliases: recode2other > > ### ** Examples > > > # based on minimum frequency unique values > state_region <- as.character(state.region) > recode2other(state_region, freq.min = 13) # freq.min as a count [1] "South" "West" "West" "South" "West" "West" "other" "South" "South" [10] "South" "West" "West" "other" "other" "other" "other" "South" "South" [19] "other" "South" "other" "other" "other" "South" "other" "West" "other" [28] "West" "other" "other" "West" "other" "South" "other" "other" "South" [37] "West" "other" "other" "South" "other" "South" "South" "West" "other" [46] "South" "West" "South" "other" "West" > recode2other(state_region, freq.min = 0.26, prop = TRUE) # freq.min as a proportion [1] "South" "West" "West" "South" "West" "West" "other" "South" "South" [10] "South" "West" "West" "other" "other" "other" "other" "South" "South" [19] "other" "South" "other" "other" "other" "South" "other" "West" "other" [28] "West" "other" "other" "West" "other" "South" "other" "other" "South" [37] "West" "other" "other" "South" "other" "South" "South" "West" "other" [46] "South" "West" "South" "other" "West" > recode2other(state_region, freq.min = 13, other.nm = "_blank_") [1] "South" "West" "West" "South" "West" "West" "_blank_" [8] "South" "South" "South" "West" "West" "_blank_" "_blank_" [15] "_blank_" "_blank_" "South" "South" "_blank_" "South" "_blank_" [22] "_blank_" "_blank_" "South" "_blank_" "West" "_blank_" "West" [29] "_blank_" "_blank_" "West" "_blank_" "South" "_blank_" "_blank_" [36] "South" "West" "_blank_" "_blank_" "South" "_blank_" "South" [43] "South" "West" "_blank_" "South" "West" "South" "_blank_" [50] "West" > recode2other(state_region, freq.min = 13, + other.nm = NA) # allows for other.nm to be NA [1] "South" "West" "West" "South" "West" "West" NA "South" "South" [10] "South" "West" "West" NA NA NA NA "South" "South" [19] NA "South" NA NA NA "South" NA "West" NA [28] "West" NA NA "West" NA "South" NA NA "South" [37] "West" NA NA "South" NA "South" "South" "West" NA [46] "South" "West" "South" NA "West" > recode2other(state_region, freq.min = 13, + extra.nm = "South") # add an extra unique value to recode [1] "other" "West" "West" "other" "West" "West" "other" "other" "other" [10] "other" "West" "West" "other" "other" "other" "other" "other" "other" [19] "other" "other" "other" "other" "other" "other" "other" "West" "other" [28] "West" "other" "other" "West" "other" "other" "other" "other" "other" [37] "West" "other" "other" "other" "other" "other" "other" "West" "other" [46] "other" "West" "other" "other" "West" > recode2other(state_region, freq.min = 13, + inclusive = FALSE) # recodes "West" to "other" [1] "South" "other" "other" "South" "other" "other" "other" "South" "South" [10] "South" "other" "other" "other" "other" "other" "other" "South" "South" [19] "other" "South" "other" "other" "other" "South" "other" "other" "other" [28] "other" "other" "other" "other" "other" "South" "other" "other" "South" [37] "other" "other" "other" "South" "other" "South" "South" "other" "other" [46] "South" "other" "South" "other" "other" > > # based on user given unique values > recode2other(state_region, freq.min = 0, + extra.nm = c("South","West")) # recodes manually rather than by freq.min [1] "other" "other" "other" "other" [5] "other" "other" "Northeast" "other" [9] "other" "other" "other" "other" [13] "North Central" "North Central" "North Central" "North Central" [17] "other" "other" "Northeast" "other" [21] "Northeast" "North Central" "North Central" "other" [25] "North Central" "other" "North Central" "other" [29] "Northeast" "Northeast" "other" "Northeast" [33] "other" "North Central" "North Central" "other" [37] "other" "Northeast" "Northeast" "other" [41] "North Central" "other" "other" "other" [45] "Northeast" "other" "other" "other" [49] "North Central" "other" > # current version does NOT allow for NA to be a unique value that is converted to other > state_region2 <- c(NA, state_region, NA) > recode2other(state_region2, freq.min = 13) # NA remains in the character vector [1] NA "South" "West" "West" "South" "West" "West" "other" "South" [10] "South" "South" "West" "West" "other" "other" "other" "other" "South" [19] "South" "other" "South" "other" "other" "other" "South" "other" "West" [28] "other" "West" "other" "other" "West" "other" "South" "other" "other" [37] "South" "West" "other" "other" "South" "other" "South" "South" "West" [46] "other" "South" "West" "South" "other" "West" NA > recode2other(state_region2, freq.min = 0, + extra.nm = c("South","West",NA)) # NA remains in the character vector [1] NA "other" "other" "other" [5] "other" "other" "other" "Northeast" [9] "other" "other" "other" "other" [13] "other" "North Central" "North Central" "North Central" [17] "North Central" "other" "other" "Northeast" [21] "other" "Northeast" "North Central" "North Central" [25] "other" "North Central" "other" "North Central" [29] "other" "Northeast" "Northeast" "other" [33] "Northeast" "other" "North Central" "North Central" [37] "other" "other" "Northeast" "Northeast" [41] "other" "North Central" "other" "other" [45] "other" "Northeast" "other" "other" [49] "other" "North Central" "other" NA > > > > > cleanEx() > nameEx("recodes") > ### * recodes > > flush(stderr()); flush(stdout()) > > ### Name: recodes > ### Title: Recode Data > ### Aliases: recodes > > ### ** Examples > > recodes(data = psych::bfi, vrb.nm = c("A1","C4","C5","E1","E2","O2","O5"), + recodes = "1=6; 2=5; 3=4; 4=3; 5=2; 6=1") A1_r C4_r C5_r E1_r E2_r O2_r O5_r 1 5 3 3 4 4 1 4 2 5 4 3 6 6 5 4 3 2 5 2 5 3 5 5 4 3 2 2 2 4 4 2 5 5 4 5 5 5 4 4 6 1 6 4 5 6 4 6 7 5 5 4 3 4 5 6 8 3 5 3 4 1 5 4 9 3 3 2 2 4 1 6 10 5 5 6 5 5 6 5 11 3 4 5 6 4 4 4 12 5 3 2 4 4 1 3 13 2 5 5 4 4 5 5 14 2 5 6 5 5 4 3 15 3 5 5 4 3 5 2 16 3 4 2 6 6 1 5 17 3 3 3 6 5 6 4 18 2 3 4 5 5 6 3 19 3 3 1 6 5 5 5 20 3 2 1 6 6 6 5 21 2 2 3 4 4 6 3 22 6 5 4 6 5 6 5 23 6 3 2 5 6 6 5 24 5 4 1 5 5 6 6 25 3 6 6 4 5 5 5 26 6 6 6 6 6 3 4 27 5 6 6 5 3 5 6 28 5 4 3 6 5 6 4 29 5 5 2 6 5 3 6 30 3 2 3 6 5 3 6 31 6 3 4 5 2 4 2 32 5 5 3 6 5 5 5 33 6 3 1 1 1 1 6 34 5 5 3 5 5 2 5 35 3 4 3 5 4 4 4 36 2 4 3 4 3 2 4 37 6 6 2 1 1 2 4 38 6 4 3 4 3 2 5 39 6 6 6 6 6 6 6 40 6 5 5 4 5 6 6 41 6 4 3 3 4 4 5 42 2 5 3 1 3 4 2 43 6 4 4 4 4 4 4 44 2 3 2 1 3 2 3 45 5 4 3 5 5 5 6 46 6 5 6 1 2 2 6 47 2 3 2 3 3 2 5 48 5 3 1 5 3 3 5 49 6 6 4 2 2 4 6 50 6 6 5 6 6 4 6 51 6 3 5 5 5 6 5 52 6 1 5 3 3 1 4 53 4 5 2 6 6 6 1 54 3 2 5 4 2 2 3 55 6 5 5 6 5 4 5 56 6 3 2 5 2 6 6 57 6 2 6 3 2 3 4 58 4 5 3 2 2 5 2 59 6 6 3 3 3 3 6 60 4 5 5 5 6 4 5 61 5 3 1 3 3 3 2 62 5 4 3 1 2 1 5 63 6 5 4 6 6 4 4 64 3 5 5 6 6 6 6 65 5 6 3 4 1 6 4 66 5 5 6 2 2 6 6 67 6 5 1 5 2 3 6 68 3 3 2 3 4 6 6 69 5 5 5 6 5 5 5 70 6 6 3 3 3 5 5 71 3 6 5 3 4 6 5 72 5 6 6 6 6 5 4 73 3 5 3 5 5 6 4 74 6 6 5 3 3 6 5 75 4 6 6 5 4 6 5 76 5 4 5 6 6 6 3 77 5 6 5 6 6 4 6 78 2 4 3 6 5 3 4 79 6 2 6 5 5 4 3 80 5 2 3 3 3 3 6 81 6 3 4 3 2 5 5 82 6 6 1 3 2 6 6 83 6 3 2 1 3 5 6 84 6 6 6 4 5 2 5 85 6 5 4 4 3 2 6 86 3 3 4 2 4 3 3 87 6 5 5 2 5 2 3 88 6 3 2 5 5 6 5 89 6 5 3 6 5 5 5 90 4 4 4 3 3 5 4 91 6 6 5 3 6 6 5 92 6 4 3 4 2 6 5 93 5 3 3 6 2 5 2 94 6 6 6 6 4 5 6 95 5 4 6 5 3 3 1 96 5 6 3 2 1 5 3 97 4 1 1 1 1 6 6 98 6 6 6 2 6 6 6 99 3 5 1 2 2 6 3 100 5 4 2 2 4 6 4 101 5 4 2 4 2 5 6 102 4 3 5 5 4 5 5 103 5 6 2 4 5 5 6 104 6 3 1 1 2 4 1 105 6 5 6 1 1 4 4 106 6 1 2 5 3 3 5 107 6 6 6 4 5 6 6 108 1 4 3 6 3 6 5 109 5 3 1 6 4 2 5 110 4 4 3 5 2 3 3 111 6 4 3 4 5 6 6 112 NA 6 2 4 3 1 4 113 6 6 5 5 3 5 5 114 6 6 1 2 2 6 5 115 6 5 5 6 6 6 6 116 3 1 1 2 2 1 2 117 6 6 3 4 5 6 6 118 2 4 2 2 1 6 5 119 5 5 4 6 5 6 5 120 6 5 1 6 5 6 6 121 3 2 5 6 4 4 4 122 5 2 2 2 1 3 3 123 1 6 6 6 5 5 6 124 5 4 1 5 3 4 5 125 5 1 1 2 2 5 6 126 5 5 1 2 2 6 6 127 5 4 5 5 5 4 5 128 5 4 2 5 4 5 5 129 5 2 1 5 2 5 5 130 3 5 2 6 3 3 4 131 5 3 2 3 4 5 4 132 4 4 2 5 3 4 5 133 3 5 4 2 NA 6 1 134 5 4 1 5 2 2 4 135 6 1 2 1 2 1 5 136 6 3 2 4 3 3 6 137 6 6 6 6 6 6 5 138 6 6 6 6 5 6 4 139 6 6 3 5 4 5 3 140 4 2 2 6 6 4 5 141 5 5 1 1 1 5 4 142 5 3 5 6 5 3 2 143 5 5 5 6 6 5 4 144 6 5 4 4 5 3 4 145 2 4 1 3 3 5 5 146 5 5 3 1 3 6 6 147 6 6 2 6 3 6 6 148 5 4 2 4 4 3 5 149 5 3 2 5 6 5 6 150 6 5 5 5 5 5 6 151 6 2 1 5 4 5 5 152 6 6 6 6 5 6 6 153 5 2 3 3 4 2 5 154 3 6 6 6 6 6 5 155 5 5 6 5 2 5 2 156 4 5 3 5 5 5 4 157 6 3 3 6 5 3 5 158 5 3 2 2 2 3 6 159 5 3 2 3 3 6 6 160 5 5 6 4 3 2 2 161 6 6 6 3 5 6 3 162 6 4 3 5 5 5 5 163 5 3 2 5 4 2 5 164 6 2 5 2 5 1 1 165 6 5 5 5 5 1 3 166 5 3 2 6 5 6 1 167 5 6 5 6 6 6 5 168 5 2 2 5 NA 3 5 169 6 5 5 3 6 6 6 170 6 6 3 2 1 6 6 171 4 4 1 5 2 3 2 172 3 3 5 5 5 5 4 173 6 3 2 5 5 5 2 174 5 4 3 3 1 1 6 175 5 5 6 5 6 6 4 176 6 2 1 3 2 3 3 177 6 4 3 1 4 6 6 178 1 6 4 1 1 6 4 179 3 3 3 5 3 3 3 180 4 3 6 5 5 4 6 181 3 3 2 4 2 6 6 182 6 5 3 4 3 2 4 183 4 3 3 2 3 3 3 184 3 3 2 3 2 5 5 185 4 4 5 4 3 6 6 186 6 6 1 6 4 1 1 187 6 3 3 3 6 3 6 188 5 6 6 5 5 6 5 189 5 6 5 1 2 5 5 190 6 6 5 5 5 6 6 191 5 4 5 4 4 5 5 192 6 5 2 5 2 5 6 193 5 5 2 2 4 4 4 194 2 3 2 6 2 3 2 195 6 5 3 4 3 5 5 196 6 5 2 1 1 6 3 197 5 4 3 6 5 3 5 198 5 4 5 3 3 6 4 199 5 3 1 6 4 4 4 200 6 5 6 6 6 6 6 201 5 6 5 5 4 4 2 202 6 5 4 2 1 2 6 203 4 4 3 4 5 3 4 204 5 6 5 5 6 4 5 205 6 4 3 4 5 5 4 206 5 6 6 1 3 5 6 207 5 5 3 2 3 5 4 208 6 6 6 1 1 6 6 209 6 3 3 6 6 4 4 210 5 6 2 6 2 6 6 211 5 5 5 5 5 6 6 212 6 2 1 4 3 6 5 213 2 3 3 3 3 4 3 214 4 3 1 2 2 3 3 215 6 6 4 4 6 6 6 216 5 5 5 3 2 6 5 217 6 6 5 6 6 1 6 218 5 5 6 6 6 4 4 219 2 6 1 5 5 3 5 220 2 3 2 2 3 5 5 221 5 3 4 5 3 2 4 222 6 6 6 1 6 1 6 223 6 4 4 6 6 5 2 224 6 4 2 5 5 6 4 225 6 5 5 6 4 6 6 226 6 4 5 3 5 2 6 227 4 4 4 4 3 5 4 228 5 5 3 5 3 6 6 229 6 6 2 5 5 4 5 230 5 5 6 5 4 6 5 231 2 5 5 4 4 1 4 232 5 5 3 3 3 3 4 233 2 6 6 6 6 5 5 234 4 4 4 4 2 5 5 235 4 6 6 6 5 5 6 236 5 2 3 5 5 2 3 237 6 4 4 5 5 4 5 238 6 2 1 5 5 4 4 239 4 5 3 1 3 5 5 240 4 5 6 4 6 6 4 241 6 6 6 6 5 6 5 242 5 5 3 1 3 5 5 243 3 5 3 5 5 3 6 244 6 6 5 2 6 5 4 245 3 5 5 5 5 6 6 246 4 4 2 4 3 3 2 247 2 2 1 5 2 1 3 248 4 5 4 3 5 4 2 249 3 6 2 5 5 3 5 250 4 3 3 4 3 6 4 251 4 4 4 4 3 3 4 252 3 5 5 2 2 5 5 253 1 6 6 6 6 6 6 254 4 6 6 6 5 5 6 255 2 5 5 3 6 2 3 256 4 5 5 3 4 3 4 257 2 6 5 6 4 2 5 258 6 3 3 3 2 4 5 259 6 6 2 6 6 1 6 260 3 2 3 5 3 3 3 261 2 1 1 3 3 1 3 262 4 6 4 5 2 6 6 263 1 1 6 6 6 5 1 264 5 6 6 2 6 6 5 265 3 5 1 6 6 4 2 266 4 5 6 4 4 2 4 267 5 5 4 4 3 3 5 268 6 6 6 5 5 5 5 269 6 5 2 3 3 5 4 270 3 3 2 3 3 5 1 271 4 3 1 6 1 1 5 272 3 4 4 6 6 5 4 273 6 6 4 6 6 6 4 274 6 2 6 3 6 2 5 275 5 5 3 6 6 4 6 276 4 4 6 5 6 2 3 277 6 6 5 5 6 6 5 278 6 5 3 2 2 5 3 279 3 5 2 6 6 6 6 280 5 4 2 6 3 3 4 281 4 3 2 2 3 4 4 282 5 6 6 6 6 6 5 283 5 3 1 3 3 1 3 284 5 4 3 6 4 3 5 285 4 2 3 5 3 3 3 286 6 5 6 6 4 4 5 287 5 5 6 3 2 5 5 288 5 5 4 1 3 5 3 289 6 4 3 6 6 6 6 290 6 5 5 5 3 2 6 291 6 2 2 4 2 3 3 292 5 5 6 5 5 6 5 293 4 3 3 2 3 2 3 294 4 3 3 6 5 5 4 295 6 3 2 6 6 5 6 296 2 4 2 6 5 1 5 297 6 2 1 6 4 4 2 298 4 3 1 2 4 5 4 299 3 5 6 1 1 4 3 300 4 3 4 4 3 4 4 301 6 6 2 4 4 6 6 302 6 5 5 3 3 5 5 303 5 2 2 1 2 2 4 304 4 4 3 6 5 4 2 305 3 6 6 5 5 6 6 306 5 5 3 5 3 6 6 307 2 5 4 3 2 6 4 308 6 5 6 2 5 2 2 309 4 6 5 5 5 1 3 310 6 5 4 6 6 2 NA 311 4 5 5 5 3 2 NA 312 6 6 5 6 3 6 3 313 5 6 5 4 5 5 5 314 6 6 5 6 5 3 3 315 4 3 1 6 6 6 6 316 5 5 6 4 1 5 6 317 4 1 1 6 6 5 6 318 4 5 5 3 3 6 5 319 5 3 3 5 5 5 6 320 5 6 5 5 5 5 6 321 5 6 5 1 5 6 6 322 5 5 5 6 6 6 3 323 2 5 2 3 2 6 6 324 3 2 2 4 4 4 4 325 4 3 3 2 2 5 4 326 6 5 3 5 5 6 6 327 5 6 5 1 2 6 3 328 3 4 3 1 1 3 5 329 6 1 1 6 6 2 6 330 5 4 2 2 2 5 5 331 6 3 2 3 3 6 6 332 5 6 5 4 5 5 5 333 3 5 4 5 3 6 3 334 6 6 6 4 3 6 6 335 5 5 2 1 1 3 3 336 4 5 2 4 2 5 6 337 4 5 6 6 6 2 3 338 6 6 6 2 6 6 6 339 5 3 5 2 2 2 3 340 6 6 2 5 5 4 4 341 4 3 2 5 2 2 3 342 6 1 4 5 6 6 6 343 5 1 2 2 1 5 3 344 6 5 3 6 6 2 6 345 5 4 3 6 6 6 5 346 6 4 4 4 5 3 5 347 5 2 2 2 3 5 5 348 2 3 4 2 3 1 3 349 5 6 4 6 6 5 6 350 5 3 5 3 4 3 3 351 3 6 3 6 2 4 5 352 5 6 6 5 5 4 4 353 4 3 3 3 5 6 6 354 5 4 5 3 2 2 4 355 5 4 3 4 6 2 3 356 6 5 3 3 NA 2 4 357 3 3 1 3 5 4 3 358 6 3 2 2 2 5 6 359 5 3 3 1 2 5 4 360 3 6 4 6 6 6 5 361 4 6 5 3 6 3 5 362 6 2 1 6 6 6 6 363 6 6 5 5 3 6 6 364 6 4 1 4 3 6 5 365 3 4 1 6 2 5 4 366 6 6 5 3 3 6 6 367 NA 4 3 3 5 5 4 368 3 2 3 6 6 5 5 369 6 1 1 1 1 1 5 370 1 2 2 5 5 2 5 371 6 4 3 3 2 5 5 372 4 5 3 2 5 6 5 373 6 6 6 6 6 6 6 374 5 6 3 3 3 6 6 375 6 6 3 4 5 3 6 376 6 6 6 6 6 6 6 377 6 6 6 4 4 6 6 378 3 5 2 6 3 4 2 379 5 6 6 1 1 5 4 380 5 4 5 6 6 6 1 381 3 NA 6 6 4 6 6 382 3 5 4 3 5 6 4 383 5 5 4 4 5 5 5 384 5 3 3 1 3 6 2 385 2 5 3 4 3 3 3 386 6 6 6 5 3 6 5 387 5 5 5 2 3 3 5 388 3 NA 4 3 5 3 5 389 6 2 1 3 2 6 4 390 3 6 5 4 5 3 5 391 5 3 4 6 1 5 5 392 5 6 2 2 6 5 6 393 6 3 5 5 1 2 2 394 6 4 3 5 6 6 5 395 5 5 4 5 4 6 5 396 4 3 3 6 6 3 5 397 3 4 4 5 5 5 2 398 6 6 6 4 3 3 4 399 5 4 5 5 3 3 4 400 6 3 3 6 5 5 5 401 4 4 5 3 5 4 3 402 6 2 3 1 6 6 4 403 4 6 6 5 6 6 6 404 5 5 3 5 3 5 4 405 3 6 6 1 6 6 6 406 3 3 2 2 3 6 4 407 6 6 6 5 5 6 5 408 6 5 3 6 4 5 6 409 4 6 5 4 4 4 3 410 4 6 6 1 2 2 2 411 6 5 2 4 5 6 5 412 4 3 4 3 5 1 3 413 6 4 3 3 5 6 6 414 6 6 6 1 1 6 6 415 4 6 2 6 6 6 6 416 3 4 2 6 5 4 2 417 6 3 6 1 1 2 2 418 4 5 4 5 3 5 3 419 4 6 4 6 5 4 4 420 6 6 6 6 1 6 6 421 6 4 3 4 5 1 6 422 4 6 5 6 5 2 3 423 6 5 5 2 3 2 6 424 2 NA 5 2 5 6 6 425 2 6 5 2 5 6 6 426 5 6 6 6 6 6 6 427 NA 6 6 3 6 6 6 428 6 5 4 6 6 6 4 429 5 4 6 3 6 6 2 430 5 4 2 2 2 4 5 431 5 6 4 5 5 5 6 432 5 6 4 5 5 5 6 433 5 6 4 5 5 5 6 434 5 6 4 5 5 5 6 435 5 5 3 5 5 5 6 436 5 5 3 5 5 5 6 437 5 5 3 5 5 4 5 438 5 3 2 5 6 2 3 439 3 6 6 5 3 6 6 440 5 3 3 3 6 6 6 441 2 6 4 2 2 1 6 442 5 6 NA 1 2 6 6 443 6 3 4 1 2 1 5 444 3 5 3 3 2 2 5 445 6 2 3 2 6 3 5 446 5 6 5 2 5 1 5 447 5 5 3 4 4 5 4 448 2 1 1 3 6 4 6 449 1 6 6 1 1 2 3 450 6 6 6 5 4 6 6 451 3 2 2 1 2 2 4 452 3 4 4 4 5 6 5 453 4 6 5 6 6 6 6 454 2 3 4 4 5 6 6 455 6 6 6 6 6 1 5 456 5 6 5 3 4 5 3 457 6 6 6 6 6 6 6 458 5 6 2 6 5 6 6 459 5 2 2 2 1 5 6 460 2 6 1 4 5 4 4 461 5 5 3 4 5 3 4 462 5 2 3 2 2 1 5 463 6 6 4 4 4 5 5 464 4 5 5 6 4 6 6 465 6 5 5 5 3 5 5 466 6 6 3 6 5 6 5 467 5 5 4 6 6 1 5 468 5 5 2 6 5 5 6 469 6 4 6 1 1 2 3 470 6 6 5 6 6 3 6 471 4 3 6 3 4 5 4 472 5 6 6 6 3 6 3 473 6 6 6 4 3 4 3 474 5 4 3 3 5 5 3 475 5 3 5 2 4 5 4 476 3 3 5 4 3 3 5 477 1 2 5 4 6 1 4 478 5 6 6 4 1 6 6 479 5 2 3 6 5 4 2 480 6 6 3 6 6 2 6 481 6 6 5 6 6 6 6 482 6 6 5 5 6 5 5 483 6 5 3 2 1 1 4 484 4 3 2 3 5 4 5 485 5 6 5 6 6 6 6 486 6 2 6 1 2 1 2 487 5 4 4 5 4 2 6 488 6 5 5 5 3 3 4 489 5 6 5 6 3 6 6 490 6 6 4 1 3 5 6 491 6 2 5 1 6 5 6 492 3 5 5 3 5 1 4 493 6 3 NA 4 6 5 5 494 5 6 6 5 6 6 6 495 6 6 6 1 4 4 3 496 3 5 4 3 6 1 6 497 3 6 5 4 3 6 5 498 5 5 6 4 5 5 5 499 3 2 2 6 3 5 2 500 6 6 5 5 6 3 6 501 4 6 6 6 6 4 6 502 6 6 6 2 5 1 5 503 2 4 3 3 6 3 2 504 2 5 3 4 4 5 4 505 5 5 5 4 4 5 5 506 5 NA 5 6 3 3 5 507 6 6 5 5 3 3 5 508 6 3 6 2 6 1 1 509 5 6 5 3 4 6 6 510 6 6 5 6 3 1 6 511 4 4 6 3 5 4 3 512 1 6 6 3 6 3 2 513 5 6 5 6 6 6 5 514 4 6 5 4 5 5 4 515 5 5 5 2 2 2 5 516 5 4 5 3 5 5 3 517 6 6 5 6 2 2 5 518 6 3 6 1 3 6 6 519 6 6 6 6 6 6 6 520 4 6 6 4 5 3 5 521 3 3 4 5 3 2 5 522 6 4 4 3 5 5 5 523 5 5 4 4 5 3 4 524 6 6 6 6 6 1 2 525 5 5 5 NA 3 3 3 526 1 6 6 6 6 1 6 527 6 4 3 6 5 5 5 528 6 6 4 6 6 6 6 529 5 5 3 4 3 2 4 530 3 4 1 5 4 5 5 531 6 3 5 3 3 5 5 532 4 4 3 5 5 5 3 533 6 6 6 6 4 6 6 534 5 6 5 6 2 6 2 535 6 5 2 1 1 5 5 536 6 5 6 6 6 1 4 537 4 5 2 4 2 5 6 538 2 3 3 4 4 6 4 539 2 3 5 4 3 5 3 540 4 5 3 5 5 3 5 541 4 5 6 2 4 3 4 542 3 4 5 4 5 4 6 543 6 3 1 1 2 1 4 544 6 6 5 6 4 6 5 545 5 6 5 5 5 4 4 546 5 3 2 2 1 2 2 547 2 5 5 3 5 2 5 548 5 5 2 6 6 6 6 549 6 6 5 6 6 5 6 550 3 6 5 4 3 4 4 551 6 6 6 3 6 6 6 552 3 4 2 2 1 1 3 553 3 3 2 6 6 2 1 554 6 6 5 6 6 3 3 555 4 5 5 4 3 2 3 556 6 3 3 5 6 2 5 557 5 3 3 4 4 1 6 558 6 6 6 5 6 4 6 559 6 5 3 3 4 3 4 560 6 6 5 4 4 6 4 561 5 5 6 4 5 2 5 562 2 2 2 2 2 2 2 563 4 6 3 5 5 6 3 564 6 6 3 4 5 6 5 565 6 5 2 3 4 2 1 566 6 5 4 6 5 4 5 567 6 2 5 6 6 6 5 568 6 6 6 4 6 6 3 569 2 3 4 4 6 1 1 570 5 2 2 5 2 1 1 571 6 1 2 3 1 1 3 572 6 6 3 6 6 1 4 573 4 6 6 6 6 6 5 574 1 6 6 2 2 2 4 575 6 3 5 5 4 5 3 576 4 5 3 6 6 4 6 577 6 5 3 5 4 6 5 578 4 5 4 1 1 1 4 579 6 6 6 6 5 1 5 580 5 6 1 6 2 6 5 581 3 2 4 1 1 1 2 582 6 5 2 3 4 4 1 583 4 6 5 4 6 6 6 584 6 4 5 6 5 5 4 585 5 3 2 3 3 5 5 586 5 4 3 6 4 3 3 587 3 3 3 6 2 3 2 588 5 3 5 6 5 3 4 589 1 2 4 2 5 2 1 590 6 4 4 5 6 2 3 591 6 5 5 1 1 3 5 592 6 NA 3 4 5 1 5 593 5 6 3 3 6 6 6 594 3 5 6 2 5 1 3 595 6 6 5 3 5 6 5 596 6 5 5 5 5 1 5 597 2 5 3 2 5 5 6 598 NA 6 6 6 6 6 3 599 5 6 5 3 3 6 3 600 4 3 5 6 6 1 1 601 6 5 3 1 3 6 5 602 5 6 5 6 5 3 4 603 4 3 3 6 2 2 3 604 3 5 5 6 6 3 3 605 5 3 3 5 5 5 6 606 6 5 NA 6 6 5 5 607 4 5 6 6 3 1 6 608 6 3 3 4 1 4 6 609 6 5 2 4 2 3 4 610 6 6 NA 1 2 6 6 611 6 6 NA 1 2 6 6 612 6 6 6 6 6 5 6 613 6 NA 4 4 3 3 6 614 5 6 3 1 3 6 6 615 5 6 4 2 3 3 4 616 6 5 6 2 1 6 6 617 5 6 6 3 2 6 6 618 4 5 3 2 4 5 2 619 6 6 6 5 5 3 2 620 5 3 4 4 4 6 5 621 6 3 2 5 3 6 5 622 5 6 5 3 4 6 6 623 5 6 6 4 1 5 6 624 3 6 6 6 6 3 5 625 5 6 6 2 2 5 2 626 5 3 4 3 3 5 3 627 5 3 5 5 5 6 5 628 4 2 2 4 1 1 3 629 4 5 5 5 5 5 5 630 3 6 5 3 4 2 4 631 5 3 6 5 5 6 5 632 3 5 5 4 3 2 4 633 5 5 4 2 2 5 5 634 4 5 3 2 5 3 3 635 6 3 2 2 2 5 5 636 6 6 5 2 5 6 1 637 6 6 6 3 3 4 5 638 3 4 3 4 2 5 5 639 6 5 5 3 2 3 5 640 5 5 5 6 6 5 5 641 4 4 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2288 6 6 6 5 6 6 6 2289 6 5 3 5 1 4 6 2290 5 6 6 6 6 5 4 2291 5 6 6 6 6 4 4 2292 5 2 3 1 1 5 5 2293 5 5 3 4 3 5 6 2294 3 6 5 6 5 1 6 2295 4 5 4 6 6 3 4 2296 3 4 2 5 2 3 3 2297 6 6 4 5 5 3 5 2298 5 5 5 5 5 4 3 2299 6 6 6 5 5 6 6 2300 4 4 4 6 4 3 4 2301 4 4 4 4 3 4 4 2302 5 2 3 3 2 4 6 2303 5 3 1 4 3 2 3 2304 4 4 5 NA 3 5 5 2305 6 6 5 6 5 3 1 2306 5 4 2 3 3 4 4 2307 1 NA 3 NA 3 3 NA 2308 5 3 3 6 6 2 6 2309 6 5 3 1 5 6 4 2310 5 4 4 5 4 4 4 2311 6 5 6 1 1 5 4 2312 3 6 6 5 5 6 3 2313 1 6 6 1 1 6 6 2314 4 4 3 1 3 4 3 2315 6 6 3 6 6 6 6 2316 3 5 3 5 NA 5 5 2317 6 3 1 6 6 6 3 2318 4 3 2 3 2 6 6 2319 2 5 6 6 4 1 3 2320 5 6 5 5 6 6 6 2321 3 3 2 5 3 2 4 2322 2 4 6 1 4 6 6 2323 3 2 2 4 2 3 3 2324 1 5 6 6 6 6 6 2325 6 3 2 6 2 3 5 2326 6 6 3 5 2 5 6 2327 6 5 1 2 1 2 5 2328 5 4 3 4 2 3 2 2329 6 6 4 3 3 3 5 2330 4 3 4 2 3 3 4 2331 5 4 3 3 3 4 5 2332 6 5 6 4 2 6 5 2333 5 6 5 6 5 4 4 2334 2 4 NA 3 4 4 4 2335 6 6 5 5 4 5 5 2336 3 3 1 5 4 2 6 2337 3 5 5 3 2 6 6 2338 5 4 4 5 4 4 4 2339 4 3 3 3 3 5 5 2340 2 2 1 5 1 5 5 2341 5 5 1 2 2 6 6 2342 5 4 4 5 NA 6 4 2343 3 5 3 5 3 6 6 2344 4 6 6 2 2 5 6 2345 6 4 1 2 1 5 5 2346 6 6 6 1 1 3 6 2347 4 2 2 6 6 6 5 2348 4 4 2 4 5 5 3 2349 5 4 5 6 5 2 5 2350 2 2 1 2 2 6 2 2351 4 4 5 4 4 3 4 2352 3 5 5 4 1 6 5 2353 4 3 4 1 2 2 2 2354 5 4 4 3 1 5 5 2355 6 5 2 5 5 5 6 2356 6 3 3 2 4 2 1 2357 6 5 5 3 5 5 6 2358 3 6 3 6 5 4 6 2359 6 4 4 5 5 3 3 2360 4 5 1 2 3 5 3 2361 5 4 6 6 6 3 5 2362 3 4 4 5 5 4 3 2363 5 5 6 5 6 5 6 2364 4 3 3 5 2 5 3 2365 5 5 5 1 3 2 2 2366 5 5 4 4 3 5 5 2367 5 5 6 3 3 6 5 2368 6 6 6 3 3 3 3 2369 6 5 3 6 5 3 5 2370 5 5 5 5 5 5 4 2371 6 6 6 6 5 6 6 2372 6 4 5 6 6 3 4 2373 3 3 3 4 4 6 6 2374 6 5 3 6 5 2 5 2375 4 5 5 5 6 1 5 2376 2 6 5 6 5 5 5 2377 6 4 4 5 5 6 2 2378 6 5 4 2 5 5 4 2379 6 5 5 4 4 5 5 2380 2 3 3 5 3 3 3 2381 5 4 3 6 3 2 5 2382 4 2 6 6 6 4 1 2383 3 5 4 5 6 5 6 2384 3 6 5 1 1 6 6 2385 6 4 3 5 4 3 2 2386 6 3 3 6 6 3 5 2387 5 2 2 5 2 3 3 2388 3 5 4 5 5 5 5 2389 6 4 2 4 2 2 4 2390 5 3 4 5 2 6 6 2391 5 6 1 5 5 3 3 2392 3 5 1 2 3 1 1 2393 3 2 1 3 2 4 6 2394 5 4 3 NA 2 4 5 2395 4 6 5 3 5 6 4 2396 NA 4 1 6 1 2 4 2397 6 4 4 3 4 4 6 2398 6 6 6 6 6 6 6 2399 6 5 4 2 2 3 5 2400 3 6 2 6 6 6 6 2401 6 5 1 NA 5 1 6 2402 4 4 4 5 2 6 5 2403 6 5 3 5 5 1 5 2404 5 5 3 5 3 5 5 2405 6 6 6 6 6 5 6 2406 5 3 2 6 1 6 6 2407 5 4 3 5 5 5 4 2408 5 5 5 3 2 6 5 2409 6 5 2 3 3 5 6 2410 6 6 3 5 3 1 6 2411 4 6 6 1 2 6 6 2412 5 6 6 5 3 6 6 2413 6 6 6 3 6 3 4 2414 5 4 4 3 4 5 3 2415 2 2 6 2 2 4 3 2416 6 3 3 5 2 5 5 2417 5 5 5 3 1 4 3 2418 5 NA 5 6 5 4 5 2419 5 2 2 5 5 1 1 2420 4 6 6 2 3 3 3 2421 2 3 5 NA 5 2 5 2422 3 3 3 3 5 6 4 2423 5 2 2 2 2 2 3 2424 2 3 5 2 4 6 6 2425 6 6 5 3 4 5 5 2426 6 6 6 3 4 5 5 2427 6 6 4 5 6 6 5 2428 6 6 6 5 1 5 6 2429 2 5 1 5 6 5 5 2430 6 6 2 6 6 5 5 2431 4 5 6 2 3 2 5 2432 5 6 5 3 3 3 6 2433 6 5 2 4 5 6 5 2434 6 6 5 6 6 6 5 2435 6 6 4 6 5 6 2 2436 6 6 6 5 1 6 5 2437 4 4 4 1 4 2 6 2438 6 3 4 6 6 2 6 2439 5 5 2 1 2 2 2 2440 6 5 1 4 1 5 2 2441 4 3 3 6 6 4 4 2442 6 5 3 5 6 2 4 2443 6 2 3 3 4 3 4 2444 2 4 1 4 4 6 6 2445 5 5 3 4 5 6 6 2446 4 4 3 3 3 2 4 2447 5 4 3 5 6 4 5 2448 6 3 3 6 6 4 5 2449 2 4 5 5 2 3 4 2450 5 2 2 2 3 2 2 2451 6 6 4 1 1 2 2 2452 6 5 5 1 1 6 6 2453 5 5 4 6 5 5 5 2454 3 5 5 4 5 3 3 2455 4 6 6 1 4 5 5 2456 4 2 2 5 2 5 2 2457 6 6 4 6 6 1 5 2458 6 5 3 5 4 6 5 2459 6 6 5 5 6 3 6 2460 3 5 5 3 2 6 5 2461 6 5 5 5 5 4 4 2462 6 5 2 6 4 4 5 2463 6 5 3 6 3 4 6 2464 4 5 2 4 1 4 4 2465 6 5 1 5 3 2 5 2466 5 4 3 3 4 3 2 2467 6 4 1 1 1 3 3 2468 2 4 1 3 5 5 2 2469 6 NA 5 3 5 4 5 2470 4 3 4 2 2 6 6 2471 5 5 5 5 4 5 5 2472 5 5 4 5 3 5 3 2473 6 6 6 4 3 6 6 2474 6 5 5 6 6 6 4 2475 6 6 6 6 6 4 5 2476 3 6 5 2 2 6 1 2477 6 2 1 3 6 6 6 2478 5 6 5 3 3 5 5 2479 4 4 5 4 5 6 5 2480 3 6 5 5 3 4 5 2481 5 4 2 2 2 2 3 2482 2 2 1 2 3 1 3 2483 4 6 3 4 4 3 3 2484 4 3 2 4 2 1 3 2485 3 3 5 2 3 3 3 2486 3 5 2 NA 3 5 5 2487 4 4 5 1 3 3 3 2488 3 3 3 3 2 6 5 2489 6 6 1 5 3 6 6 2490 5 5 3 3 3 6 6 2491 5 6 2 5 3 5 5 2492 5 3 3 4 5 5 5 2493 3 6 4 5 4 4 3 2494 6 5 6 4 4 6 6 2495 6 6 2 5 3 3 4 2496 6 5 3 2 3 5 5 2497 6 4 3 2 2 5 5 2498 5 5 5 4 3 6 4 2499 3 5 3 1 1 5 5 2500 5 5 4 4 2 5 6 2501 6 5 2 5 2 6 5 2502 6 5 5 3 5 5 6 2503 3 3 4 4 5 3 3 2504 5 6 5 5 5 2 2 2505 6 2 3 2 5 5 5 2506 6 5 3 5 4 6 6 2507 6 4 2 6 6 6 6 2508 6 1 2 5 5 4 2 2509 5 3 3 3 1 4 5 2510 5 4 2 5 3 2 2 2511 3 4 3 5 3 6 5 2512 4 2 1 6 4 4 4 2513 3 4 6 5 6 5 4 2514 6 6 6 6 5 6 6 2515 4 2 6 2 NA 5 2 2516 5 5 5 3 3 3 5 2517 2 4 2 4 4 5 5 2518 6 6 3 3 1 3 5 2519 6 5 2 6 5 5 5 2520 3 3 1 1 1 6 6 2521 6 3 2 3 2 6 5 2522 5 5 3 2 1 2 5 2523 6 6 3 5 3 5 6 2524 6 3 1 1 1 5 3 2525 4 5 3 1 1 6 6 2526 2 3 4 3 3 3 4 2527 6 3 3 5 5 2 3 2528 5 2 5 3 6 4 3 2529 6 5 1 2 2 5 5 2530 6 6 4 6 6 6 6 2531 3 6 5 4 5 6 5 2532 6 3 5 2 3 3 4 2533 5 6 4 6 5 6 5 2534 2 5 4 3 2 6 6 2535 6 6 5 6 4 4 3 2536 4 4 2 5 6 4 2 2537 5 5 3 5 2 3 5 2538 2 5 2 6 1 3 2 2539 5 5 4 3 2 5 6 2540 6 4 5 3 2 6 4 2541 3 4 5 5 5 3 3 2542 4 6 5 5 6 3 3 2543 5 3 1 6 3 2 5 2544 5 4 3 3 1 6 6 2545 6 6 6 3 6 3 6 2546 5 4 5 2 2 5 5 2547 4 5 5 6 3 4 5 2548 6 6 5 6 5 6 5 2549 3 6 3 1 1 6 3 2550 5 6 6 3 5 5 6 2551 6 6 5 5 3 6 2 2552 6 5 2 2 6 6 6 2553 5 6 5 5 2 6 6 2554 5 6 4 1 2 2 6 2555 5 3 3 5 5 2 3 2556 6 5 5 3 5 4 3 2557 6 5 1 6 6 4 6 2558 5 5 5 6 6 2 5 2559 4 5 6 2 4 2 3 2560 1 6 6 1 6 6 6 2561 5 5 4 5 5 5 5 2562 6 5 2 5 3 6 6 2563 6 6 5 6 6 6 5 2564 6 1 1 5 3 6 6 2565 3 2 2 6 4 2 2 2566 6 5 6 2 2 6 5 2567 4 5 5 4 5 4 4 2568 2 4 3 1 3 6 4 2569 3 3 3 5 3 3 4 2570 1 4 1 6 2 1 5 2571 6 4 1 3 5 5 6 2572 6 6 6 5 6 3 6 2573 3 5 5 5 6 6 6 2574 5 3 3 6 5 3 6 2575 2 5 3 1 2 2 5 2576 5 6 5 6 6 6 6 2577 4 4 3 3 2 3 3 2578 4 5 1 6 3 3 1 2579 3 1 1 4 2 1 1 2580 5 6 3 NA 3 6 1 2581 6 5 3 1 3 1 4 2582 6 6 6 2 3 6 2 2583 6 3 2 6 6 6 6 2584 6 6 3 4 6 4 1 2585 3 5 1 2 3 1 4 2586 3 3 1 6 5 1 4 2587 6 5 3 5 5 6 4 2588 2 1 5 3 6 3 5 2589 5 4 3 3 4 5 2 2590 5 6 3 6 5 6 5 2591 3 4 5 6 5 2 2 2592 6 2 1 5 2 5 6 2593 3 4 6 2 2 6 5 2594 4 5 3 6 3 3 3 2595 4 1 3 5 5 5 4 2596 3 6 6 6 3 3 5 2597 6 2 5 6 6 6 5 2598 2 4 6 5 6 2 5 2599 6 3 4 5 6 5 3 2600 3 4 4 2 3 5 4 2601 3 4 4 4 2 3 6 2602 3 5 6 5 6 4 5 2603 4 3 5 2 4 2 2 2604 3 3 5 6 2 2 6 2605 4 5 5 5 5 5 4 2606 2 6 6 1 6 6 5 2607 NA 3 2 6 6 5 6 2608 5 6 3 6 4 5 5 2609 1 4 3 5 3 3 5 2610 2 5 3 2 1 6 5 2611 2 4 5 6 2 6 4 2612 6 2 2 1 1 1 1 2613 4 6 6 2 6 6 2 2614 3 3 2 5 6 1 5 2615 6 2 1 3 2 5 4 2616 4 3 5 3 2 3 3 2617 2 1 1 6 5 1 5 2618 2 4 4 4 3 2 4 2619 3 4 3 6 6 3 2 2620 3 3 4 5 3 3 4 2621 4 3 3 6 3 1 4 2622 5 3 3 2 2 5 3 2623 6 5 3 5 4 5 5 2624 6 6 6 3 6 6 2 2625 5 4 2 2 1 4 3 2626 4 6 3 5 2 5 6 2627 2 2 1 1 2 5 5 2628 6 2 1 6 1 4 6 2629 2 4 4 2 4 5 5 2630 1 6 6 4 6 5 5 2631 3 5 1 1 3 5 4 2632 5 5 5 6 3 2 2 2633 2 2 2 1 3 5 5 2634 3 5 3 2 2 5 6 2635 5 5 1 3 2 3 6 2636 3 4 4 5 5 4 3 2637 5 5 5 5 5 3 5 2638 3 3 3 5 6 3 5 2639 1 6 6 2 4 1 3 2640 5 3 2 2 1 5 6 2641 2 6 6 5 6 6 6 2642 5 3 2 5 5 3 4 2643 5 5 2 3 2 5 6 2644 NA NA 6 6 2 2 NA 2645 6 5 5 6 5 6 6 2646 6 6 5 6 6 6 6 2647 4 4 3 5 3 3 4 2648 6 3 6 2 5 6 3 2649 5 5 3 3 3 5 5 2650 4 4 3 3 5 3 3 2651 3 2 5 6 6 4 4 2652 3 1 1 3 2 5 2 2653 4 6 6 6 6 6 4 2654 6 5 5 5 5 6 3 2655 6 3 1 6 6 6 5 2656 5 5 5 2 3 5 4 2657 6 6 3 5 4 5 4 2658 6 5 3 5 3 5 6 2659 1 6 6 5 2 5 5 2660 5 6 5 6 2 6 1 2661 1 6 6 1 1 2 6 2662 6 5 3 6 5 5 6 2663 3 3 2 6 5 3 4 2664 5 6 6 5 6 6 6 2665 6 4 6 2 5 5 3 2666 5 4 4 5 2 5 3 2667 2 4 4 5 5 2 4 2668 2 1 1 2 1 4 4 2669 6 4 4 6 6 3 4 2670 4 4 3 3 3 4 4 2671 3 3 2 4 3 3 3 2672 6 6 3 3 2 6 6 2673 4 6 6 3 4 4 4 2674 3 5 4 2 5 2 3 2675 5 5 2 6 6 6 5 2676 3 3 3 4 3 4 4 2677 6 6 6 6 6 6 6 2678 5 2 4 3 3 6 5 2679 5 3 2 3 1 6 1 2680 5 4 5 6 6 4 4 2681 4 6 4 6 6 6 5 2682 5 5 5 4 4 4 5 2683 5 5 2 3 5 6 5 2684 6 5 3 4 5 3 5 2685 5 6 5 5 5 6 5 2686 6 5 6 6 6 5 2 2687 5 6 6 5 5 5 3 2688 5 1 2 2 1 6 3 2689 2 2 2 1 1 5 6 2690 5 5 1 4 1 5 5 2691 6 3 3 6 6 6 6 2692 4 6 3 1 2 6 6 2693 6 6 5 3 6 6 6 2694 2 6 5 1 2 4 3 2695 3 5 2 3 5 6 5 2696 5 5 2 6 5 5 3 2697 5 3 6 6 6 5 6 2698 6 3 3 2 1 5 6 2699 6 6 6 6 6 4 6 2700 4 5 5 1 1 5 6 2701 6 6 5 3 5 5 5 2702 2 NA 4 4 6 5 NA 2703 3 2 3 6 4 5 3 2704 3 2 3 6 4 5 3 2705 5 5 1 2 2 2 2 2706 6 4 4 6 3 5 5 2707 5 4 3 3 4 4 5 2708 4 4 2 5 2 2 5 2709 5 4 5 3 4 4 4 2710 6 1 3 5 6 5 4 2711 4 6 4 5 3 6 5 2712 5 5 5 5 5 4 4 2713 6 3 2 1 1 4 2 2714 1 4 3 4 2 4 3 2715 3 5 2 2 1 6 5 2716 3 2 2 5 4 3 2 2717 5 3 2 1 1 2 2 2718 3 4 6 6 2 4 3 2719 6 3 3 5 1 3 5 2720 6 6 6 4 5 6 5 2721 4 2 2 5 3 6 6 2722 6 2 1 3 3 5 5 2723 4 6 4 2 3 6 6 2724 6 6 1 6 6 6 NA 2725 4 6 6 6 6 6 6 2726 6 2 1 6 3 2 6 2727 5 5 2 2 2 5 6 2728 6 4 2 4 5 4 5 2729 3 5 6 3 4 5 5 2730 6 5 2 3 1 2 3 2731 5 1 3 5 5 1 6 2732 4 4 2 1 2 5 3 2733 3 5 3 2 2 6 5 2734 5 4 3 6 5 5 4 2735 1 4 5 3 5 5 4 2736 2 4 5 3 4 6 3 2737 5 3 2 4 4 4 6 2738 5 5 4 4 2 5 4 2739 4 4 4 2 6 6 6 2740 5 5 5 4 5 4 5 2741 6 5 5 2 5 5 5 2742 6 4 3 4 3 4 4 2743 1 2 1 3 5 6 6 2744 6 6 5 4 6 4 2 2745 4 6 3 6 6 6 6 2746 6 5 3 6 2 6 6 2747 6 5 6 3 4 6 6 2748 4 4 3 1 1 5 5 2749 5 4 1 6 3 2 5 2750 1 4 2 1 3 5 6 2751 1 1 1 1 3 1 1 2752 6 5 2 6 2 5 5 2753 5 5 5 3 5 5 5 2754 5 3 5 6 3 6 5 2755 1 2 1 1 1 2 5 2756 5 5 4 3 4 3 4 2757 5 6 2 2 1 5 2 2758 5 6 6 2 6 5 5 2759 5 4 4 5 1 6 4 2760 5 4 3 4 4 4 4 2761 4 2 4 3 3 6 6 2762 5 5 5 3 6 3 4 2763 5 6 5 5 6 6 6 2764 4 3 4 1 3 6 5 2765 5 4 4 1 1 3 3 2766 3 2 2 4 4 6 5 2767 5 6 5 5 3 2 5 2768 6 6 6 2 3 1 3 2769 4 6 5 3 1 4 4 2770 5 5 5 4 3 5 6 2771 1 5 1 4 4 6 6 2772 4 2 6 6 3 6 1 2773 6 3 6 2 5 6 5 2774 5 3 3 4 1 6 4 2775 4 4 4 5 3 4 3 2776 5 5 5 3 3 4 3 2777 6 6 6 6 6 6 6 2778 6 6 6 2 6 6 5 2779 2 5 5 4 5 6 4 2780 6 6 4 6 4 1 6 2781 6 5 2 1 1 6 6 2782 4 3 2 5 6 5 6 2783 6 5 4 6 NA 6 4 2784 6 5 4 6 5 6 4 2785 5 5 3 1 2 6 6 2786 5 6 5 6 6 5 5 2787 6 3 4 2 2 6 5 2788 5 3 6 5 6 6 5 2789 5 3 6 6 2 3 6 2790 4 5 6 5 5 3 3 2791 5 3 3 2 5 5 4 2792 3 1 3 3 3 3 3 2793 2 3 2 2 6 2 3 2794 4 4 3 4 2 6 5 2795 2 4 2 4 2 6 5 2796 1 6 6 6 3 6 6 2797 5 3 4 5 5 4 5 2798 5 6 6 5 5 6 4 2799 2 5 1 5 5 5 6 2800 5 4 4 4 4 6 6 > re_codes <- "'Quebec' = 'canada'; 'Mississippi' = 'usa'; 'nonchilled' = 'no'; 'chilled' = 'yes'" > recodes(data = CO2, vrb.nm = c("Type","Treatment"), recodes = re_codes, + as.factor = FALSE) # convert from factors to characters Type_r Treatment_r 1 canada no 2 canada no 3 canada no 4 canada no 5 canada no 6 canada no 7 canada no 8 canada no 9 canada no 10 canada no 11 canada no 12 canada no 13 canada no 14 canada no 15 canada no 16 canada no 17 canada no 18 canada no 19 canada no 20 canada no 21 canada no 22 canada yes 23 canada yes 24 canada yes 25 canada yes 26 canada yes 27 canada yes 28 canada yes 29 canada yes 30 canada yes 31 canada yes 32 canada yes 33 canada yes 34 canada yes 35 canada yes 36 canada yes 37 canada yes 38 canada yes 39 canada yes 40 canada yes 41 canada yes 42 canada yes 43 usa no 44 usa no 45 usa no 46 usa no 47 usa no 48 usa no 49 usa no 50 usa no 51 usa no 52 usa no 53 usa no 54 usa no 55 usa no 56 usa no 57 usa no 58 usa no 59 usa no 60 usa no 61 usa no 62 usa no 63 usa no 64 usa yes 65 usa yes 66 usa yes 67 usa yes 68 usa yes 69 usa yes 70 usa yes 71 usa yes 72 usa yes 73 usa yes 74 usa yes 75 usa yes 76 usa yes 77 usa yes 78 usa yes 79 usa yes 80 usa yes 81 usa yes 82 usa yes 83 usa yes 84 usa yes > > > > cleanEx() > nameEx("renames") > ### * renames > > flush(stderr()); flush(stdout()) > > ### Name: renames > ### Title: Rename Data Columns from a Codebook > ### Aliases: renames > > ### ** Examples > > code_book <- list( + data.frame("old" = c("rating","complaints"), "new" = c("RATING","COMPLAINTS")), + data.frame("old" = c("privileges","learning"), "new" = c("PRIVILEGES","LEARNING")) + ) > renames(data = attitude, codebook = code_book, old = "old", new = "new") RATING COMPLAINTS PRIVILEGES LEARNING raises critical advance 1 43 51 30 39 61 92 45 2 63 64 51 54 63 73 47 3 71 70 68 69 76 86 48 4 61 63 45 47 54 84 35 5 81 78 56 66 71 83 47 6 43 55 49 44 54 49 34 7 58 67 42 56 66 68 35 8 71 75 50 55 70 66 41 9 72 82 72 67 71 83 31 10 67 61 45 47 62 80 41 11 64 53 53 58 58 67 34 12 67 60 47 39 59 74 41 13 69 62 57 42 55 63 25 14 68 83 83 45 59 77 35 15 77 77 54 72 79 77 46 16 81 90 50 72 60 54 36 17 74 85 64 69 79 79 63 18 65 60 65 75 55 80 60 19 65 70 46 57 75 85 46 20 50 58 68 54 64 78 52 21 50 40 33 34 43 64 33 22 64 61 52 62 66 80 41 23 53 66 52 50 63 80 37 24 40 37 42 58 50 57 49 25 63 54 42 48 66 75 33 26 66 77 66 63 88 76 72 27 78 75 58 74 80 78 49 28 48 57 44 45 51 83 38 29 85 85 71 71 77 74 55 30 82 82 39 59 64 78 39 > > > > cleanEx() > nameEx("reorders") > ### * reorders > > flush(stderr()); flush(stdout()) > > ### Name: reorders > ### Title: Reorder Levels of Factor Data > ### Aliases: reorders > > ### ** Examples > > > # factor vector > reorder(x = state.region, X = state.region, + FUN = length) # least frequent to most frequent [1] South West West South West [6] West Northeast South South South [11] West West North Central North Central North Central [16] North Central South South Northeast South [21] Northeast North Central North Central South North Central [26] West North Central West Northeast Northeast [31] West Northeast South North Central North Central [36] South West Northeast Northeast South [41] North Central South South West Northeast [46] South West South North Central West attr(,"scores") Northeast South North Central West 9 16 12 13 Levels: Northeast North Central West South > reorder(x = state.region, X = state.region, + FUN = function(vec) {-1 * length(vec)}) # most frequent to least frequent [1] South West West South West [6] West Northeast South South South [11] West West North Central North Central North Central [16] North Central South South Northeast South [21] Northeast North Central North Central South North Central [26] West North Central West Northeast Northeast [31] West Northeast South North Central North Central [36] South West Northeast Northeast South [41] North Central South South West Northeast [46] South West South North Central West attr(,"scores") Northeast South North Central West -9 -16 -12 -13 Levels: South West North Central Northeast > > # data.frame of factors > infert_fct <- infert > fct_nm <- c("education","parity","induced","case","spontaneous") > infert_fct[fct_nm] <- lapply(X = infert[fct_nm], FUN = as.factor) > x <- reorders(data = infert_fct, fct.nm = fct_nm, + fun = length) # least frequent to most frequent > lapply(X = x, FUN = levels) $education_r [1] "0-5yrs" "12+ yrs" "6-11yrs" $parity_r [1] "5" "6" "4" "3" "2" "1" $induced_r [1] "2" "1" "0" $case_r [1] "1" "0" $spontaneous_r [1] "2" "1" "0" > y <- reorders(data = infert_fct, fct.nm = fct_nm, + fun = function(vec) {-1 * length(vec)}) # most frequent to least frequent > lapply(X = y, FUN = levels) $education_r [1] "6-11yrs" "12+ yrs" "0-5yrs" $parity_r [1] "1" "2" "3" "4" "6" "5" $induced_r [1] "0" "1" "2" $case_r [1] "0" "1" $spontaneous_r [1] "0" "1" "2" > # ord.nm specified as a different column in data.frame > z <- reorders(data = infert_fct, fct.nm = fct_nm, ord.nm = "pooled.stratum", + fun = mean) # category with highest mean for pooled.stratum to > # category with lowest mean for pooled.stratum > lapply(X = z, FUN = levels) $education_r [1] "0-5yrs" "6-11yrs" "12+ yrs" $parity_r [1] "6" "1" "4" "2" "3" "5" $induced_r [1] "0" "2" "1" $case_r [1] "0" "1" $spontaneous_r [1] "0" "1" "2" > > > > > cleanEx() > nameEx("revalid") > ### * revalid > > flush(stderr()); flush(stdout()) > > ### Name: revalid > ### Title: Recode Invalid Values from a Vector > ### Aliases: revalid > > ### ** Examples > > revalid(x = attitude[[1]], valid = 25:75, undefined = NA) # numeric vector [1] 43 63 71 61 NA 43 58 71 72 67 64 67 69 68 NA NA 74 65 65 50 50 64 53 40 63 [26] 66 NA 48 NA NA > revalid(x = as.character(ToothGrowth[["supp"]]), valid = c('VC'), + undefined = NA) # character vector [1] "VC" "VC" "VC" "VC" "VC" "VC" "VC" "VC" "VC" "VC" "VC" "VC" "VC" "VC" "VC" [16] "VC" "VC" "VC" "VC" "VC" "VC" "VC" "VC" "VC" "VC" "VC" "VC" "VC" "VC" "VC" [31] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA [46] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA > revalid(x = ToothGrowth[["supp"]], valid = c('VC'), + undefined = NA) # factor [1] VC VC VC VC VC VC VC VC VC VC VC VC VC VC VC [16] VC VC VC VC VC VC VC VC VC VC VC VC VC VC VC [31] [46] Levels: VC > > > > cleanEx() > nameEx("revalids") > ### * revalids > > flush(stderr()); flush(stdout()) > > ### Name: revalids > ### Title: Recode Invalid Values from Data > ### Aliases: revalids > > ### ** Examples > > revalids(data = attitude, vrb.nm = names(attitude), + valid = 25:75) # numeric data rating_v complaints_v privileges_v learning_v raises_v critical_v advance_v 1 43 51 30 39 61 NA 45 2 63 64 51 54 63 73 47 3 71 70 68 69 NA NA 48 4 61 63 45 47 54 NA 35 5 NA NA 56 66 71 NA 47 6 43 55 49 44 54 49 34 7 58 67 42 56 66 68 35 8 71 75 50 55 70 66 41 9 72 NA 72 67 71 NA 31 10 67 61 45 47 62 NA 41 11 64 53 53 58 58 67 34 12 67 60 47 39 59 74 41 13 69 62 57 42 55 63 25 14 68 NA NA 45 59 NA 35 15 NA NA 54 72 NA NA 46 16 NA NA 50 72 60 54 36 17 74 NA 64 69 NA NA 63 18 65 60 65 75 55 NA 60 19 65 70 46 57 75 NA 46 20 50 58 68 54 64 NA 52 21 50 40 33 34 43 64 33 22 64 61 52 62 66 NA 41 23 53 66 52 50 63 NA 37 24 40 37 42 58 50 57 49 25 63 54 42 48 66 75 33 26 66 NA 66 63 NA NA 72 27 NA 75 58 74 NA NA 49 28 48 57 44 45 51 NA 38 29 NA NA 71 71 NA 74 55 30 NA NA 39 59 64 NA 39 > revalids(data = as.data.frame(CO2), vrb.nm = c("Type","Treatment"), + valid = c('Quebec','nonchilled')) # factors Type_v Treatment_v 1 Quebec nonchilled 2 Quebec nonchilled 3 Quebec nonchilled 4 Quebec nonchilled 5 Quebec nonchilled 6 Quebec nonchilled 7 Quebec nonchilled 8 Quebec nonchilled 9 Quebec nonchilled 10 Quebec nonchilled 11 Quebec nonchilled 12 Quebec nonchilled 13 Quebec nonchilled 14 Quebec nonchilled 15 Quebec nonchilled 16 Quebec nonchilled 17 Quebec nonchilled 18 Quebec nonchilled 19 Quebec nonchilled 20 Quebec nonchilled 21 Quebec nonchilled 22 Quebec 23 Quebec 24 Quebec 25 Quebec 26 Quebec 27 Quebec 28 Quebec 29 Quebec 30 Quebec 31 Quebec 32 Quebec 33 Quebec 34 Quebec 35 Quebec 36 Quebec 37 Quebec 38 Quebec 39 Quebec 40 Quebec 41 Quebec 42 Quebec 43 nonchilled 44 nonchilled 45 nonchilled 46 nonchilled 47 nonchilled 48 nonchilled 49 nonchilled 50 nonchilled 51 nonchilled 52 nonchilled 53 nonchilled 54 nonchilled 55 nonchilled 56 nonchilled 57 nonchilled 58 nonchilled 59 nonchilled 60 nonchilled 61 nonchilled 62 nonchilled 63 nonchilled 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 > > > > cleanEx() > nameEx("reverse") > ### * reverse > > flush(stderr()); flush(stdout()) > > ### Name: reverse > ### Title: Reverse Code a Numeric Vector > ### Aliases: reverse > > ### ** Examples > > x <- psych::bfi[[1]] > head(x, n = 15) [1] 2 2 5 4 2 6 2 4 4 2 4 2 5 5 4 > y <- reverse(x = psych::bfi[[1]], min = 1, max = 6) > head(y, n = 15) [1] 5 5 2 3 5 1 5 3 3 5 3 5 2 2 3 > cor(x, y, use = "complete.obs") [1] -1 > > > > cleanEx() > nameEx("reverses") > ### * reverses > > flush(stderr()); flush(stdout()) > > ### Name: reverses > ### Title: Reverse Code Numeric Data > ### Aliases: reverses > > ### ** Examples > > tmp <- !(is.element(el = names(psych::bfi) , set = c("gender","education","age"))) > vrb_nm <- names(psych::bfi)[tmp] > reverses(data = psych::bfi, vrb.nm = vrb_nm, mini = 1, maxi = 6) A1_r A2_r A3_r A4_r A5_r C1_r C2_r C3_r C4_r C5_r E1_r E2_r E3_r E4_r E5_r 1 5 3 4 3 3 5 4 4 3 3 4 4 4 3 3 2 5 3 2 5 2 2 3 3 4 3 6 6 1 3 4 3 2 3 2 3 3 3 2 3 5 2 5 3 3 3 2 4 3 3 1 2 2 3 3 4 2 2 2 4 3 3 3 5 5 4 4 3 2 3 3 2 4 5 5 5 2 3 2 6 1 1 2 1 2 1 1 1 6 4 5 6 1 2 1 7 5 2 2 4 2 2 3 3 5 4 3 4 3 2 2 8 3 4 6 2 6 4 5 3 5 3 4 1 3 5 6 9 3 4 1 4 4 1 1 4 3 2 2 4 NA 3 4 10 5 2 1 1 2 1 2 1 5 6 5 5 3 2 2 11 3 3 2 1 2 3 4 2 4 5 6 4 5 2 3 12 5 2 2 2 2 2 3 2 3 2 4 4 3 2 3 13 2 2 2 1 3 2 3 4 5 5 4 4 4 5 3 14 2 2 2 1 1 3 3 3 5 6 5 5 3 1 2 15 3 2 5 5 6 2 2 2 5 5 4 3 4 1 2 16 3 4 1 1 4 2 2 2 4 2 6 6 1 1 3 17 3 1 1 5 2 3 3 3 3 3 6 5 2 2 2 18 2 2 2 3 2 2 2 2 3 4 5 5 3 1 1 19 3 3 2 3 4 2 3 2 3 1 6 5 3 2 2 20 3 3 1 2 2 6 6 6 2 1 6 6 3 2 1 21 2 3 5 6 5 3 1 2 2 3 4 4 2 2 3 22 6 1 1 6 2 2 3 3 5 4 6 5 3 4 3 23 6 2 1 2 1 3 4 5 3 2 5 6 5 2 5 24 5 1 2 1 2 4 2 1 4 1 5 5 3 1 1 25 3 2 2 1 2 2 2 3 6 6 4 5 2 2 1 26 6 1 1 6 1 2 5 2 6 6 6 6 1 1 1 27 5 3 3 3 4 1 2 1 6 6 5 3 3 5 1 28 5 2 1 1 1 3 2 3 4 3 6 5 1 1 1 29 5 2 6 4 2 2 3 2 5 2 6 5 1 2 3 30 3 2 1 2 2 2 2 4 2 3 6 5 1 2 2 31 6 1 2 1 4 2 2 2 3 4 5 2 6 2 4 32 5 2 1 1 1 2 2 2 5 3 6 5 3 2 2 33 6 2 1 2 3 6 2 1 3 1 1 1 5 6 6 34 5 3 2 1 2 3 1 3 5 3 5 5 4 2 4 35 3 3 3 3 3 3 4 4 4 3 5 4 3 5 4 36 2 4 2 3 5 5 5 3 4 3 4 3 4 5 4 37 6 1 3 1 3 2 1 4 6 2 1 1 4 5 5 38 6 3 3 5 4 1 2 1 4 3 4 3 4 4 2 39 6 1 1 1 1 1 1 1 6 6 6 6 6 1 1 40 6 2 3 4 2 1 2 2 5 5 4 5 4 1 2 41 6 2 2 1 2 3 3 3 4 3 3 4 3 3 3 42 2 3 4 1 3 2 5 2 5 3 1 3 5 3 3 43 6 2 3 3 2 3 2 3 4 4 4 4 5 2 3 44 2 1 3 4 2 2 4 4 3 2 1 3 3 3 4 45 5 1 1 1 1 2 3 2 4 3 5 5 3 2 2 46 6 1 1 1 1 2 5 6 5 6 1 2 1 1 2 47 2 2 4 3 4 3 3 4 3 2 3 3 2 5 3 48 5 1 3 2 2 4 5 4 3 1 5 3 5 3 3 49 6 2 4 5 4 4 1 4 6 4 2 2 2 2 4 50 6 1 1 1 1 2 2 3 6 5 6 6 1 1 1 51 6 1 1 1 3 3 4 6 3 5 5 5 2 3 3 52 6 2 1 2 3 1 1 1 1 5 3 3 3 4 1 53 4 1 3 3 3 2 2 4 5 2 6 6 3 1 2 54 3 4 2 1 4 2 1 5 2 5 4 2 5 1 5 55 6 1 1 1 1 1 2 2 5 5 6 5 2 1 2 56 6 3 4 2 2 2 2 3 3 2 5 2 3 2 2 57 6 3 5 5 5 2 2 2 2 6 3 2 3 4 3 58 4 3 2 5 3 2 3 2 5 3 2 2 2 3 2 59 6 1 2 3 3 1 1 1 6 3 3 3 6 5 2 60 4 4 2 3 2 1 3 3 5 5 5 6 3 1 3 61 5 4 3 3 2 2 4 5 3 1 3 3 4 2 5 62 5 3 3 2 4 2 2 3 4 3 1 2 3 4 3 63 6 3 1 1 1 NA 1 1 5 4 6 6 2 1 1 64 3 2 4 2 3 1 2 2 5 5 6 6 2 1 1 65 5 1 1 1 1 3 1 1 6 3 4 1 2 3 3 66 5 NA 3 1 3 2 3 2 5 6 2 2 4 4 4 67 6 2 3 5 2 6 5 5 5 1 5 2 5 5 6 68 3 4 5 5 5 3 5 5 3 2 3 4 3 5 3 69 5 4 3 2 1 2 2 3 5 5 6 5 3 3 3 70 6 1 1 4 1 1 2 1 6 3 3 3 5 4 4 71 3 2 1 1 3 3 1 1 6 5 3 4 2 2 2 72 5 3 1 5 2 5 3 3 6 6 6 6 3 3 NA 73 3 3 3 2 4 2 3 1 5 3 5 5 3 1 3 74 6 2 2 2 3 2 2 2 6 5 3 3 4 5 2 75 4 2 2 2 3 2 2 2 6 6 5 4 2 2 3 76 5 2 2 1 2 2 3 1 4 5 6 6 3 1 2 77 5 1 1 1 1 2 3 2 6 5 6 6 1 1 2 78 2 1 1 1 2 2 2 1 4 3 6 5 2 1 1 79 6 5 5 3 5 5 3 5 2 6 5 5 3 5 5 80 5 2 2 2 2 3 5 4 2 3 3 3 3 2 3 81 6 2 1 2 4 1 2 3 3 4 3 2 3 3 2 82 6 1 4 4 6 1 1 2 6 1 3 2 4 2 6 83 6 3 1 5 1 4 4 3 3 2 1 3 2 1 5 84 6 1 1 2 1 2 1 2 6 6 4 5 2 2 2 85 6 1 2 5 1 3 2 2 5 4 4 3 3 2 4 86 3 3 3 1 2 5 2 3 3 4 2 4 4 4 2 87 6 2 2 2 2 3 3 2 5 5 2 5 5 2 2 88 6 1 2 3 1 3 2 3 3 2 5 5 3 2 2 89 6 1 3 1 3 3 3 2 5 3 6 5 3 2 2 90 4 3 2 2 3 4 4 NA 4 4 3 3 3 4 3 91 6 2 2 2 2 2 5 3 6 5 3 6 2 2 2 92 6 2 1 5 2 2 3 2 4 3 4 2 3 3 3 93 5 3 2 3 2 5 5 4 3 3 6 2 5 1 5 94 6 1 1 4 1 1 1 2 6 6 6 4 2 1 1 95 5 1 3 1 3 3 2 1 4 6 5 3 5 2 3 96 5 4 4 3 4 2 1 3 6 3 2 1 5 5 3 97 4 6 5 5 5 5 6 5 1 1 1 1 6 6 6 98 6 3 2 2 1 1 3 3 6 6 2 6 5 4 2 99 3 4 5 1 2 3 2 1 5 1 2 2 6 2 2 100 5 1 5 4 3 3 5 2 4 2 2 4 5 4 2 101 5 2 2 4 2 3 4 3 4 2 4 2 3 2 2 102 4 3 3 5 3 3 4 3 3 5 5 4 3 4 2 103 5 2 2 2 5 1 1 3 6 2 4 5 5 5 2 104 6 3 2 5 1 5 6 5 3 1 1 2 4 4 4 105 6 1 3 6 6 1 3 2 5 6 1 1 4 6 6 106 6 1 2 1 1 2 4 3 1 2 5 3 4 3 2 107 6 2 2 2 5 1 NA 1 6 6 4 5 6 5 1 108 1 6 6 6 5 4 1 4 4 3 6 3 2 5 1 109 5 1 2 1 2 2 3 3 3 1 6 4 3 1 3 110 4 1 2 1 3 4 2 5 4 3 5 2 1 1 2 111 6 1 1 1 1 3 3 2 4 3 4 5 3 2 3 112 NA 3 2 1 3 2 2 2 6 2 4 3 4 5 4 113 6 1 1 2 3 2 4 2 6 5 5 3 3 2 3 114 6 1 1 1 1 1 1 2 6 1 2 2 5 2 2 115 6 1 2 3 2 2 3 2 5 5 6 6 1 1 2 116 3 5 6 5 5 3 6 5 1 1 2 2 5 5 6 117 6 2 3 4 3 2 1 3 6 3 4 5 3 2 1 118 2 5 5 5 6 5 5 5 4 2 2 1 5 6 5 119 5 2 3 4 3 2 3 2 5 4 6 5 3 2 2 120 6 2 1 1 1 1 2 3 5 1 6 5 2 1 1 121 3 1 2 2 1 3 1 2 2 5 6 4 1 1 1 122 5 2 3 3 3 3 2 2 2 2 2 1 4 5 5 123 1 2 2 5 2 2 2 1 6 6 6 5 2 2 1 124 5 2 2 6 1 2 2 2 4 1 5 3 1 1 2 125 5 3 2 2 2 2 2 3 1 1 2 2 2 2 4 126 5 2 2 2 5 2 2 2 5 1 2 2 4 5 5 127 5 2 1 2 1 2 3 2 4 5 5 5 3 2 2 128 5 2 3 3 2 2 3 2 4 2 5 4 2 3 2 129 5 2 2 4 5 5 4 3 2 1 5 2 5 5 5 130 3 2 3 NA 4 2 4 3 5 2 6 3 4 2 3 131 5 3 3 3 3 3 3 3 3 2 3 4 3 1 2 132 4 2 3 2 3 3 3 2 4 2 5 3 2 2 3 133 3 4 4 4 4 1 2 2 5 4 2 NA 4 5 4 134 5 3 4 4 4 3 3 3 4 1 5 2 4 5 2 135 6 1 1 1 1 1 1 4 1 2 1 2 2 1 1 136 6 1 1 2 1 1 6 4 3 2 4 3 3 1 4 137 6 1 1 3 1 6 6 4 6 6 6 6 1 1 1 138 6 1 2 1 2 1 1 3 6 6 6 5 2 3 2 139 6 1 2 1 2 2 2 3 6 3 5 4 1 1 2 140 4 1 1 3 1 5 4 5 2 2 6 6 1 1 1 141 5 4 6 4 6 4 4 4 5 1 1 1 6 6 4 142 5 3 2 2 2 3 3 1 3 5 6 5 3 1 2 143 5 2 4 2 6 1 2 3 5 5 6 6 2 1 5 144 6 1 2 1 1 4 5 3 5 4 4 5 2 2 4 145 2 3 3 2 1 4 2 4 4 1 3 3 1 2 2 146 5 2 3 4 4 1 3 3 5 3 1 3 4 4 3 147 6 1 1 4 1 1 1 1 6 2 6 3 2 2 1 148 5 1 3 2 3 3 3 2 4 2 4 4 3 3 3 149 5 1 4 2 5 3 3 1 3 2 5 6 2 3 1 150 6 2 2 1 2 2 2 2 5 5 5 5 6 2 4 151 6 2 2 4 4 2 5 5 2 1 5 4 5 4 3 152 6 1 1 2 1 1 2 4 6 6 6 5 2 2 2 153 5 2 2 1 2 3 3 5 2 3 3 4 2 2 3 154 3 3 4 6 3 2 3 3 6 6 6 6 3 1 1 155 5 1 2 2 4 2 2 1 5 6 5 2 4 3 2 156 4 2 1 2 2 3 2 3 5 3 5 5 2 2 2 157 6 1 1 1 2 2 4 3 3 3 6 5 2 1 2 158 5 2 2 2 3 4 6 3 3 2 2 2 2 4 2 159 5 3 3 6 4 4 2 3 3 2 3 3 3 2 5 160 5 3 2 2 3 2 1 2 5 6 4 3 3 5 1 161 6 2 2 1 2 2 3 2 6 6 3 5 3 3 3 162 6 1 1 5 2 2 4 5 4 3 5 5 4 2 2 163 5 1 3 2 3 5 4 5 3 2 5 4 3 2 3 164 6 2 5 4 3 5 2 1 2 5 2 5 6 4 3 165 6 1 1 1 1 2 2 2 5 5 5 5 2 1 3 166 5 3 2 2 2 6 6 4 3 2 6 5 2 2 5 167 5 3 2 1 2 3 1 3 6 5 6 6 1 1 2 168 5 1 2 3 3 3 5 3 2 2 5 NA 3 4 2 169 6 2 2 2 2 2 1 3 5 5 3 6 2 3 1 170 6 1 1 6 4 1 1 2 6 3 2 1 6 3 6 171 4 3 3 4 3 4 4 2 4 1 5 2 2 4 3 172 3 3 2 1 3 2 1 1 3 5 5 5 2 2 2 173 6 2 2 2 2 3 3 3 3 2 5 5 2 2 2 174 5 1 2 2 3 2 3 2 4 3 3 1 2 1 1 175 5 1 1 1 2 2 2 2 5 6 5 6 2 2 1 176 6 5 5 3 4 1 3 1 2 1 3 2 6 1 4 177 6 1 2 1 1 3 2 4 4 3 1 4 3 2 2 178 1 2 1 1 2 1 1 1 6 4 1 1 3 3 2 179 3 4 3 4 3 3 4 2 3 3 5 3 3 4 3 180 4 3 5 5 4 4 5 5 3 6 5 5 2 3 2 181 3 4 3 4 4 2 5 3 3 2 4 2 3 6 3 182 6 1 2 1 1 2 3 2 5 3 4 3 2 1 3 183 4 2 3 3 3 4 4 3 3 3 2 3 4 5 3 184 3 5 5 4 4 3 4 4 3 2 3 2 5 5 2 185 4 3 3 4 4 2 2 3 4 5 4 3 3 5 4 186 6 1 2 1 4 4 2 6 6 1 6 4 4 4 1 187 6 2 2 3 2 3 3 3 3 3 3 6 2 2 3 188 5 5 6 1 4 1 1 1 6 6 5 5 2 1 1 189 5 3 3 2 2 2 2 2 6 5 1 2 5 2 2 190 6 1 1 5 2 2 2 2 6 5 5 5 1 1 1 191 5 4 3 3 3 4 6 6 4 5 4 4 4 4 3 192 6 1 3 4 1 3 3 2 5 2 5 2 3 2 1 193 5 4 3 2 4 3 2 3 5 2 2 4 4 3 3 194 2 3 4 2 3 3 3 3 3 2 6 2 2 1 3 195 6 2 3 3 3 2 2 2 5 3 4 3 5 3 3 196 6 1 1 2 4 6 4 5 5 2 1 1 2 6 5 197 5 2 1 4 2 2 4 4 4 3 6 5 1 2 1 198 5 3 2 1 2 5 3 3 4 5 3 3 4 2 5 199 5 1 2 2 2 5 3 4 3 1 6 4 2 1 1 200 6 2 1 3 2 4 5 2 5 6 6 6 5 2 3 201 5 2 2 2 2 2 1 2 6 5 5 4 4 2 3 202 6 2 5 5 5 5 5 1 5 4 2 1 3 6 2 203 4 4 2 2 2 3 3 3 4 3 4 5 4 2 3 204 5 3 2 1 2 2 1 3 6 5 5 6 3 1 2 205 6 1 2 2 2 2 3 3 4 3 4 5 3 2 3 206 5 1 3 1 5 3 2 2 6 6 1 3 2 3 1 207 5 2 2 1 3 5 3 5 5 3 2 3 4 4 2 208 6 3 NA 1 1 1 3 3 6 6 1 1 1 6 1 209 6 2 1 2 2 4 4 4 3 3 6 6 2 2 2 210 5 4 5 6 3 6 5 5 6 2 6 2 5 3 2 211 5 2 2 2 2 1 5 2 5 5 5 5 3 1 2 212 6 1 2 1 1 3 2 3 2 1 4 3 1 2 2 213 2 3 3 3 2 2 3 NA 3 3 3 3 3 2 4 214 4 3 4 4 4 4 4 4 3 1 2 2 5 5 3 215 6 1 1 3 1 2 2 2 6 4 4 6 1 5 NA 216 5 3 3 6 3 2 3 3 5 5 3 2 4 4 2 217 6 1 1 1 1 1 2 3 6 5 6 6 2 1 1 218 5 3 3 3 4 2 6 4 5 6 6 6 4 1 2 219 2 2 3 2 3 1 1 1 6 1 5 5 3 2 2 220 2 4 6 5 4 1 5 5 3 2 2 3 4 3 5 221 5 2 2 2 3 2 2 2 3 4 5 3 2 2 3 222 6 2 NA 2 1 1 1 6 6 6 1 6 6 2 6 223 6 2 2 1 1 2 2 2 4 4 6 6 2 2 2 224 6 2 1 1 3 2 4 3 4 2 5 5 4 1 3 225 6 2 1 2 1 5 2 2 5 5 6 4 3 1 1 226 6 1 1 1 3 6 2 1 4 5 3 5 1 3 5 227 4 2 2 4 3 3 4 3 4 4 4 3 4 3 4 228 5 2 3 4 5 1 2 1 5 3 5 3 5 5 2 229 6 2 1 2 2 2 4 3 6 2 5 5 3 2 2 230 5 1 1 2 2 2 2 3 5 6 5 4 2 3 1 231 2 3 2 1 3 6 3 1 5 5 4 4 2 3 2 232 5 1 3 4 2 2 4 4 5 3 3 3 4 4 4 233 2 3 2 1 5 2 1 3 6 6 6 6 4 1 4 234 4 2 5 3 2 2 3 1 4 4 4 2 5 4 5 235 4 1 2 1 1 1 2 2 6 6 6 5 3 1 1 236 5 2 3 3 3 4 4 3 2 3 5 5 3 3 3 237 6 NA 3 1 2 2 2 5 4 4 5 5 3 5 3 238 6 1 1 1 2 3 4 3 2 1 5 5 3 2 4 239 4 3 3 1 4 2 2 2 5 3 1 3 4 4 3 240 4 2 1 2 1 2 1 2 5 6 4 6 3 1 2 241 6 2 NA 1 2 2 2 1 6 6 6 5 3 1 1 242 5 3 3 1 4 3 2 3 5 3 1 3 5 4 3 243 3 2 2 2 2 2 3 2 5 3 5 5 3 2 2 244 6 1 3 1 1 2 3 2 6 5 2 6 2 3 1 245 3 4 3 5 2 2 2 2 5 5 5 5 4 4 1 246 4 2 2 2 2 3 4 4 4 2 4 3 4 2 2 247 2 5 6 6 5 5 6 5 2 1 5 2 5 3 5 248 4 4 4 3 3 2 5 2 5 4 3 5 4 2 3 249 3 5 3 6 3 2 3 3 6 2 5 5 3 4 2 250 4 2 3 2 2 3 5 3 3 3 4 3 1 1 1 251 4 2 1 1 2 3 3 1 4 4 4 3 3 2 3 252 3 2 2 1 3 1 2 2 5 5 2 2 3 3 3 253 1 6 6 3 1 2 1 2 6 6 6 6 2 1 1 254 4 2 2 1 1 1 1 1 6 6 6 5 3 3 1 255 2 2 2 1 1 2 3 2 5 5 3 6 2 1 2 256 4 3 3 2 4 3 3 3 5 5 3 4 4 4 2 257 2 1 2 2 1 2 3 1 6 5 6 4 2 1 2 258 6 1 2 1 2 4 4 2 3 3 3 2 3 4 2 259 6 1 1 1 2 1 3 3 6 2 6 6 1 4 4 260 3 3 1 2 3 3 2 4 2 3 5 3 3 2 4 261 2 2 2 6 2 1 2 5 1 1 3 3 1 4 4 262 4 3 3 4 5 1 2 2 6 4 5 2 2 5 2 263 1 1 1 1 1 2 2 1 1 6 6 6 1 1 1 264 5 1 2 1 2 1 2 2 6 6 2 6 2 2 1 265 3 6 3 1 1 1 3 3 5 1 6 6 1 1 2 266 4 2 3 2 2 2 1 2 5 6 4 4 3 1 2 267 5 3 2 2 2 3 3 3 5 4 4 3 4 2 4 268 6 2 2 2 2 2 3 2 6 6 5 5 4 2 2 269 6 1 1 2 2 2 1 2 5 2 3 3 4 1 2 270 3 2 3 4 3 3 3 2 3 2 3 3 3 3 2 271 4 2 3 2 2 2 2 4 3 1 6 1 2 1 1 272 3 2 1 3 1 3 2 2 4 4 6 6 2 2 1 273 6 1 1 1 1 1 2 1 6 4 6 6 1 1 1 274 6 2 1 1 1 2 1 1 2 6 3 6 1 1 2 275 5 3 3 3 2 3 2 3 5 3 6 6 2 1 1 276 4 2 1 1 1 3 2 1 4 6 5 6 2 5 1 277 6 2 1 2 1 1 2 2 6 5 5 6 2 2 1 278 6 3 3 5 2 2 3 4 5 3 2 2 3 5 4 279 3 1 1 1 2 3 3 2 5 2 6 6 3 2 2 280 5 1 2 1 4 4 4 2 4 2 6 3 4 2 5 281 4 3 2 3 3 3 3 4 3 2 2 3 4 4 3 282 5 1 1 1 1 2 3 2 6 6 6 6 2 2 2 283 5 5 5 3 4 4 5 3 3 1 3 3 4 4 4 284 5 2 3 1 3 3 2 3 4 3 6 4 4 3 3 285 4 3 1 1 3 3 3 2 2 3 5 3 3 4 4 286 6 2 2 1 2 2 3 2 5 6 6 4 2 1 2 287 5 2 1 2 3 4 2 2 5 6 3 2 3 6 1 288 5 3 5 3 3 2 4 2 5 4 1 3 3 2 2 289 6 1 1 1 1 1 3 3 4 3 6 6 1 1 1 290 6 1 1 1 2 2 3 1 5 5 5 3 3 1 4 291 6 2 2 1 2 5 3 2 2 2 4 2 2 2 5 292 5 2 2 2 2 2 2 2 5 6 5 5 3 2 2 293 4 3 2 2 2 3 3 2 3 3 2 3 4 NA 3 294 4 1 2 2 2 3 3 2 3 3 6 5 2 1 1 295 6 2 1 3 1 2 4 4 3 2 6 6 2 1 2 296 2 1 1 1 1 3 2 3 4 2 6 5 3 1 1 297 6 1 1 6 2 2 1 1 2 1 6 4 1 2 2 298 4 3 3 3 4 4 5 5 3 1 2 4 3 3 2 299 3 2 2 1 1 4 2 1 5 6 1 1 6 2 1 300 4 3 3 4 3 3 3 3 3 4 4 3 4 3 4 301 6 2 2 1 2 2 1 5 6 2 4 4 2 1 3 302 6 2 2 2 3 3 3 3 5 5 3 3 3 5 2 303 5 1 1 2 2 2 5 1 2 2 1 2 4 3 5 304 4 2 1 3 2 3 4 2 4 3 6 5 3 2 3 305 3 2 3 2 3 2 2 3 6 6 5 5 3 2 2 306 5 1 3 5 2 2 2 3 5 3 5 3 4 5 1 307 2 5 4 5 4 2 3 4 5 4 3 2 3 3 6 308 6 1 2 1 2 2 1 1 5 6 2 5 2 6 1 309 4 3 3 3 3 1 1 2 6 5 5 5 2 2 1 310 6 1 1 3 1 3 2 2 5 4 6 6 1 1 2 311 4 2 1 1 1 4 2 4 5 5 5 3 3 3 3 312 6 1 2 3 2 1 2 2 6 5 6 3 6 2 3 313 5 2 2 1 2 3 3 3 6 5 4 5 3 2 2 314 6 1 1 1 1 2 3 1 6 5 6 5 1 1 1 315 4 2 2 1 6 1 2 2 3 1 6 6 1 4 1 316 5 3 3 1 5 2 2 2 5 6 4 1 2 3 2 317 4 2 1 3 1 5 6 2 1 1 6 6 2 2 6 318 4 2 3 5 5 3 3 3 5 5 3 3 5 3 3 319 5 2 4 5 5 2 4 3 3 3 5 5 3 3 2 320 5 1 1 1 1 3 2 2 6 5 5 5 4 2 1 321 5 5 1 5 5 1 1 1 6 5 1 5 1 6 1 322 5 1 1 1 1 3 2 3 5 5 6 6 2 1 2 323 2 5 5 5 2 2 2 1 5 2 3 2 4 4 4 324 3 3 3 3 3 2 2 2 2 2 4 4 4 4 4 325 4 4 3 1 4 3 3 4 3 3 2 2 5 4 2 326 6 1 2 1 2 2 2 2 5 3 5 5 2 2 2 327 5 NA 2 2 5 2 2 1 6 5 1 2 5 3 2 328 3 2 2 1 3 3 3 4 4 3 1 1 4 5 4 329 6 2 3 2 1 1 1 5 1 1 6 6 1 1 1 330 5 5 2 3 3 2 2 3 4 2 2 2 3 2 3 331 6 1 1 1 3 2 2 4 3 2 3 3 3 2 4 332 5 1 2 2 1 3 1 2 6 5 4 5 2 2 2 333 3 3 4 5 4 3 3 2 5 4 5 3 3 6 2 334 6 3 1 1 1 2 1 3 6 6 4 3 1 1 1 335 5 1 1 1 2 3 1 1 5 2 1 1 5 4 3 336 4 5 1 6 1 4 6 2 5 2 4 2 5 3 2 337 4 2 2 2 2 4 2 2 5 6 6 6 2 1 2 338 6 1 1 1 1 1 1 1 6 6 2 6 1 1 1 339 5 2 2 1 3 4 4 3 3 5 2 2 3 4 4 340 6 2 3 4 5 1 2 5 6 2 5 5 6 5 5 341 4 2 4 3 3 4 5 2 3 2 5 2 4 4 3 342 6 2 2 2 2 3 6 5 1 4 5 6 2 4 1 343 5 3 5 1 2 2 3 3 1 2 2 1 4 4 2 344 6 1 2 1 2 2 1 3 5 3 6 6 2 1 1 345 5 3 3 1 2 3 3 3 4 3 6 6 2 2 1 346 6 2 2 2 2 2 3 2 4 4 4 5 2 2 2 347 5 3 2 1 2 3 4 4 2 2 2 3 2 4 6 348 2 2 2 1 2 3 2 1 3 4 2 3 3 3 1 349 5 2 2 1 2 1 1 2 6 4 6 6 3 3 1 350 5 2 6 1 3 3 3 1 3 5 3 4 3 2 3 351 3 1 1 3 3 1 3 1 6 3 6 2 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6 2464 5 4 5 2 5 1 4 3 1 4 2465 3 3 3 1 2 1 2 2 1 5 2466 6 6 6 5 5 3 3 3 2 2 2467 4 3 3 3 3 2 3 3 1 3 2468 4 5 4 4 6 2 5 1 1 2 2469 4 4 4 5 3 2 4 1 2 5 2470 5 3 3 2 4 3 6 2 1 6 2471 5 5 6 4 3 1 5 2 2 5 2472 1 1 2 1 2 2 5 2 5 3 2473 1 1 3 3 6 1 6 1 1 6 2474 6 5 4 1 5 2 6 1 1 4 2475 6 6 6 6 6 2 4 1 4 5 2476 1 1 1 2 1 2 6 1 1 1 2477 5 5 5 5 6 2 6 1 1 6 2478 3 3 3 2 3 3 5 3 1 5 2479 5 NA 6 6 6 1 6 1 1 5 2480 3 2 1 3 2 2 4 3 2 5 2481 2 2 2 1 2 5 2 4 1 3 2482 1 2 2 1 6 2 1 5 1 3 2483 2 2 2 3 2 1 3 3 3 3 2484 3 3 4 3 2 3 1 4 2 3 2485 3 3 3 3 2 3 3 3 3 3 2486 6 4 6 5 6 3 5 3 3 5 2487 6 5 5 3 4 5 3 3 3 3 2488 6 5 3 2 NA 3 6 1 3 5 2489 6 5 3 2 5 1 6 2 1 6 2490 1 1 1 1 2 1 6 2 1 6 2491 3 2 5 3 3 5 5 4 2 5 2492 5 5 5 5 5 1 5 2 1 5 2493 3 3 3 3 4 3 4 5 3 3 2494 6 5 4 5 5 NA 6 1 1 6 2495 3 2 3 4 2 1 3 2 1 4 2496 5 5 6 6 6 3 5 3 1 5 2497 6 6 3 3 5 2 5 2 1 5 2498 5 5 5 5 NA 2 6 2 3 4 2499 5 3 5 6 4 6 5 6 3 5 2500 5 3 5 5 6 1 5 1 1 6 2501 1 1 5 5 5 1 6 3 1 5 2502 6 4 6 3 3 1 5 1 1 6 2503 2 4 4 4 3 3 3 3 4 3 2504 4 3 3 3 2 4 2 3 3 2 2505 2 3 3 NA 4 3 5 4 3 5 2506 4 4 4 4 5 1 6 1 1 6 2507 3 2 5 4 6 1 6 1 1 6 2508 2 3 1 3 3 2 4 2 1 2 2509 5 4 4 3 4 3 4 3 2 5 2510 3 3 5 4 2 3 2 4 2 2 2511 6 6 6 5 5 2 6 2 3 5 2512 2 2 2 3 2 3 4 1 2 4 2513 3 4 3 6 5 2 5 3 2 4 2514 5 5 5 5 3 1 6 1 2 6 2515 6 6 6 6 6 3 5 3 4 2 2516 6 5 6 4 6 2 3 2 2 5 2517 4 5 4 2 4 3 5 4 3 5 2518 4 6 3 1 1 5 3 5 2 5 2519 4 3 5 3 5 2 5 1 1 5 2520 2 2 1 1 2 2 6 2 1 6 2521 2 2 3 3 2 1 6 2 1 5 2522 4 5 6 2 5 5 2 2 3 5 2523 5 3 3 6 6 2 5 3 4 6 2524 2 2 1 2 1 4 5 3 1 3 2525 5 5 5 1 4 3 6 3 1 6 2526 3 3 2 2 3 2 3 2 2 4 2527 6 6 6 6 6 2 2 3 2 3 2528 5 5 5 6 6 3 4 2 1 3 2529 6 3 2 1 1 2 5 3 1 5 2530 6 6 6 6 6 1 6 1 1 6 2531 6 5 3 4 3 2 6 3 2 5 2532 4 3 2 3 2 2 3 2 3 4 2533 4 2 5 6 6 2 6 2 3 5 2534 2 6 5 5 6 2 6 1 1 6 2535 4 4 2 5 2 2 4 3 2 3 2536 2 1 3 5 5 1 4 2 4 2 2537 3 4 3 3 3 2 3 2 2 5 2538 2 2 3 3 3 1 3 3 2 2 2539 4 4 3 3 3 2 5 2 2 6 2540 4 4 3 3 3 3 6 3 2 4 2541 2 2 1 3 2 3 3 3 3 3 2542 4 3 4 2 3 2 3 2 4 3 2543 3 NA 2 3 6 4 2 5 5 5 2544 6 3 4 1 6 1 6 5 1 6 2545 4 3 6 6 6 1 3 1 2 6 2546 5 5 4 3 4 1 5 3 2 5 2547 4 3 2 5 2 2 4 2 2 5 2548 6 5 6 5 1 3 6 2 1 5 2549 6 5 6 2 3 2 6 3 1 3 2550 5 3 5 5 5 2 5 4 2 6 2551 6 5 6 6 6 2 6 3 3 2 2552 5 2 3 5 5 1 6 2 1 6 2553 2 2 1 NA 3 2 6 1 1 6 2554 5 3 1 3 1 4 2 5 1 6 2555 5 5 5 2 3 2 2 2 5 3 2556 6 3 5 6 6 2 4 2 4 3 2557 3 2 3 2 6 2 4 1 1 6 2558 6 5 5 2 5 5 2 5 4 5 2559 3 3 4 5 3 3 2 3 2 3 2560 1 1 1 1 1 1 6 1 1 6 2561 5 5 6 6 5 2 5 2 5 5 2562 5 5 4 4 4 1 6 1 2 6 2563 6 6 6 6 5 2 6 2 3 5 2564 5 3 2 2 6 1 6 2 3 6 2565 1 3 2 4 1 3 2 2 2 2 2566 3 3 3 5 6 1 6 1 1 5 2567 4 4 3 5 3 4 4 4 3 4 2568 6 6 3 3 6 2 6 5 2 4 2569 5 5 5 6 4 1 3 2 1 4 2570 3 6 4 6 1 2 1 1 1 5 2571 5 5 5 4 6 3 5 1 1 6 2572 6 6 6 6 6 2 3 6 3 6 2573 3 4 5 5 4 1 6 2 4 6 2574 4 5 5 3 3 1 3 3 1 6 2575 1 1 3 5 6 1 2 3 1 5 2576 6 3 4 5 6 2 6 2 1 6 2577 3 4 3 3 4 2 3 2 3 3 2578 1 1 2 2 6 2 3 2 3 1 2579 6 3 2 3 4 2 1 2 1 1 2580 3 3 3 5 6 2 6 1 2 1 2581 6 3 5 6 6 2 1 6 3 4 2582 6 5 6 6 5 4 6 2 2 2 2583 6 6 6 6 6 1 6 1 1 6 2584 6 5 6 6 5 3 4 2 4 1 2585 6 5 5 4 5 5 1 5 3 4 2586 1 3 2 4 1 5 1 2 2 4 2587 6 3 6 6 3 1 6 1 1 4 2588 5 5 2 2 6 2 3 3 2 5 2589 6 5 6 NA 6 1 5 2 4 2 2590 2 2 4 3 2 1 6 1 1 5 2591 3 5 4 4 1 1 2 2 3 2 2592 1 1 1 1 5 1 5 1 1 6 2593 1 3 4 3 3 2 6 4 6 5 2594 4 4 4 5 4 3 3 3 3 3 2595 6 6 3 5 6 2 5 2 6 4 2596 6 6 5 5 4 1 3 2 1 5 2597 5 5 3 5 2 2 6 2 2 5 2598 1 1 1 6 3 2 2 1 2 5 2599 3 4 3 2 6 3 5 3 6 3 2600 2 4 4 5 3 3 5 4 3 4 2601 6 4 3 6 4 3 3 2 2 6 2602 1 1 1 2 3 1 4 1 2 5 2603 5 5 5 4 4 5 2 4 4 2 2604 1 1 1 4 6 1 2 2 2 6 2605 5 4 4 5 5 3 5 2 4 4 2606 1 1 3 1 6 1 6 2 1 5 2607 1 1 2 5 1 1 5 2 2 6 2608 3 3 4 3 5 2 5 3 5 5 2609 4 5 4 1 2 2 3 3 6 5 2610 2 1 1 2 2 2 6 4 3 5 2611 3 2 3 1 3 4 6 3 1 4 2612 1 1 1 1 3 4 1 6 1 1 2613 4 5 3 5 3 4 6 5 4 2 2614 3 1 1 NA 2 1 1 1 1 5 2615 4 2 1 3 4 4 5 3 2 4 2616 3 3 2 4 3 3 3 2 3 3 2617 2 2 5 6 5 2 1 2 3 5 2618 3 4 2 4 3 2 2 1 3 4 2619 1 1 6 6 5 1 3 5 3 2 2620 2 3 4 NA 3 4 3 3 4 4 2621 5 5 5 6 5 2 1 6 5 4 2622 3 5 3 2 4 3 5 4 3 3 2623 5 3 2 4 3 3 5 3 2 5 2624 6 3 6 6 6 1 6 1 2 2 2625 5 3 5 2 4 3 4 3 1 3 2626 1 1 2 1 5 1 5 2 1 6 2627 6 6 3 4 3 5 5 2 1 5 2628 1 2 3 2 4 3 4 2 1 6 2629 4 3 5 6 6 2 5 3 3 5 2630 6 6 5 6 5 2 5 3 1 5 2631 6 3 2 4 4 1 5 5 1 4 2632 2 2 5 3 3 3 2 3 2 2 2633 3 2 6 6 5 2 5 2 3 5 2634 6 6 6 2 6 2 5 4 1 6 2635 6 3 4 5 3 3 3 5 1 6 2636 4 4 5 5 6 3 4 4 3 3 2637 3 5 4 3 4 2 3 3 3 5 2638 3 4 4 6 5 2 3 4 4 5 2639 5 1 2 4 5 2 1 3 1 3 2640 4 4 4 6 1 2 5 1 1 6 2641 6 6 5 6 6 1 6 4 6 6 2642 3 3 1 3 2 4 3 2 2 4 2643 6 3 1 3 3 1 5 3 1 6 2644 NA 2 1 3 NA 3 2 NA 1 NA 2645 5 5 6 5 6 4 6 5 6 6 2646 2 2 3 5 6 2 6 3 6 6 2647 4 3 3 3 4 3 3 4 3 4 2648 2 2 6 3 6 1 6 4 3 3 2649 5 3 6 5 5 4 5 3 4 5 2650 2 3 3 2 6 3 3 3 4 3 2651 6 4 4 6 6 6 4 5 4 4 2652 2 1 2 2 2 1 5 1 1 2 2653 6 6 6 6 1 2 6 3 4 4 2654 6 6 5 6 5 2 6 2 4 3 2655 3 2 6 3 5 2 6 1 3 5 2656 5 4 5 5 5 4 5 4 3 4 2657 6 6 6 6 NA 1 5 5 1 4 2658 3 2 2 3 5 1 5 2 1 6 2659 2 1 1 2 4 1 5 3 5 5 2660 6 6 6 6 6 1 6 1 1 1 2661 6 6 6 6 1 1 2 1 1 6 2662 4 3 3 5 4 2 5 1 2 6 2663 2 4 3 3 5 2 3 3 2 4 2664 5 5 4 5 6 1 6 2 3 6 2665 4 4 3 5 3 2 5 1 2 3 2666 3 2 3 3 1 1 5 2 2 3 2667 5 5 5 6 5 3 2 3 4 4 2668 4 4 5 4 6 2 4 2 1 4 2669 3 2 1 2 1 2 3 2 1 4 2670 3 3 2 2 4 2 4 3 2 4 2671 3 3 2 3 3 2 3 4 2 3 2672 1 1 4 4 1 3 6 2 4 6 2673 5 4 3 4 3 3 4 3 4 4 2674 5 3 4 3 5 3 2 3 5 3 2675 5 3 4 4 6 1 6 3 3 5 2676 5 5 4 4 4 3 4 4 4 4 2677 6 6 4 5 6 1 6 1 1 6 2678 3 4 5 4 4 2 6 3 1 5 2679 4 3 4 1 3 1 6 2 1 1 2680 3 3 4 6 4 1 4 3 2 4 2681 1 3 2 3 5 2 6 1 2 5 2682 4 3 3 4 3 4 4 4 4 5 2683 3 3 5 5 6 NA 6 2 2 5 2684 6 5 5 3 5 2 3 3 2 5 2685 5 5 4 5 5 2 6 2 2 5 2686 5 5 5 6 6 1 5 2 2 2 2687 6 6 6 6 5 3 5 4 3 3 2688 4 3 5 1 2 4 6 1 1 3 2689 4 4 6 4 5 1 5 3 1 6 2690 6 5 4 4 2 4 5 3 1 5 2691 4 3 5 3 6 1 6 1 6 6 2692 2 1 1 1 6 1 6 1 1 6 2693 6 6 6 6 6 1 6 2 1 6 2694 3 2 6 3 6 6 4 4 1 3 2695 3 3 2 1 5 1 6 2 1 5 2696 4 2 2 2 2 1 5 3 1 3 2697 5 3 4 6 4 1 5 1 1 6 2698 3 2 5 2 3 2 5 2 1 6 2699 6 6 6 6 6 1 4 1 1 6 2700 5 6 4 2 6 1 5 5 1 6 2701 5 5 2 5 2 2 5 2 3 5 2702 NA 3 4 1 NA 1 5 NA 4 NA 2703 3 3 3 1 3 1 5 NA 2 3 2704 3 3 3 1 3 1 5 2 2 3 2705 2 2 3 2 1 3 2 4 1 2 2706 4 3 NA 3 4 2 5 2 2 5 2707 5 4 3 3 5 2 4 2 1 5 2708 3 NA 2 3 4 3 2 2 2 5 2709 5 5 4 4 4 3 4 3 2 4 2710 6 6 6 6 5 2 5 2 4 4 2711 3 3 4 4 4 2 6 3 1 5 2712 6 6 6 6 6 2 4 2 5 4 2713 6 6 5 2 5 3 4 3 2 2 2714 4 1 2 3 3 4 4 3 1 3 2715 2 2 2 4 5 1 6 2 1 5 2716 3 2 2 1 1 1 3 2 1 2 2717 4 3 NA 6 5 3 2 6 6 2 2718 2 4 3 5 2 3 4 3 5 3 2719 2 3 5 2 2 4 3 3 3 5 2720 5 3 4 3 5 2 6 2 3 5 2721 4 4 4 4 4 4 6 3 3 6 2722 1 1 1 2 1 3 5 2 2 5 2723 3 2 4 1 5 1 6 2 3 6 2724 4 1 6 NA 6 6 6 6 6 NA 2725 4 3 4 5 2 1 6 2 3 6 2726 3 3 3 3 5 1 2 1 1 6 2727 3 2 2 5 3 2 5 3 1 6 2728 5 5 4 3 5 2 4 2 1 5 2729 5 4 5 4 4 1 5 1 1 5 2730 6 5 5 4 5 3 2 3 1 3 2731 2 2 1 1 2 2 1 2 1 6 2732 2 2 2 2 5 5 5 5 1 3 2733 2 2 2 1 1 1 6 2 1 5 2734 5 5 5 NA 5 3 5 4 3 4 2735 4 4 3 2 2 1 5 3 4 4 2736 1 2 2 1 6 2 6 4 3 3 2737 5 5 1 2 3 1 4 2 1 6 2738 5 4 4 3 5 4 5 4 2 4 2739 5 5 5 6 4 1 6 3 2 6 2740 4 5 6 6 6 2 4 2 4 5 2741 5 3 6 5 5 2 5 5 2 5 2742 3 2 2 5 1 2 4 4 2 4 2743 6 6 6 2 6 1 6 2 3 6 2744 4 3 3 3 3 5 4 NA 4 2 2745 3 1 2 2 6 1 6 1 1 6 2746 6 6 6 3 5 1 6 1 1 6 2747 5 1 3 2 2 1 6 1 1 6 2748 6 3 3 1 5 2 5 2 2 5 2749 4 4 6 NA 4 4 2 6 1 5 2750 1 1 6 6 6 6 5 6 2 6 2751 1 1 1 1 1 2 1 2 1 1 2752 4 3 4 3 3 3 5 3 3 5 2753 3 3 5 3 5 2 5 2 3 5 2754 5 4 6 5 5 1 6 1 1 5 2755 5 5 5 4 6 4 2 1 2 5 2756 5 5 3 2 4 3 3 3 3 4 2757 6 6 6 6 6 4 5 3 2 2 2758 3 2 4 5 6 2 5 6 3 5 2759 3 2 3 4 3 3 6 NA 6 4 2760 2 2 3 4 3 3 4 4 2 4 2761 4 3 4 2 1 3 6 3 2 6 2762 3 3 3 3 5 2 3 3 NA 4 2763 6 3 5 6 6 3 6 2 3 6 2764 5 3 3 2 3 4 6 2 1 5 2765 3 2 1 1 1 2 3 6 2 3 2766 5 1 3 2 5 1 6 3 1 5 2767 2 2 2 4 5 5 2 3 3 5 2768 3 3 2 3 2 2 1 5 2 3 2769 3 3 3 1 1 3 4 3 2 4 2770 5 4 5 5 5 2 5 2 2 6 2771 6 6 4 6 6 1 6 2 2 6 2772 1 6 4 4 3 6 6 3 1 1 2773 6 6 6 6 6 2 6 5 1 5 2774 4 4 2 1 3 4 6 2 2 4 2775 1 2 3 2 1 2 4 5 2 3 2776 5 5 5 5 4 2 4 3 2 3 2777 6 6 6 6 6 1 6 1 2 6 2778 6 6 6 6 4 1 6 1 2 5 2779 1 1 1 1 6 1 6 2 1 4 2780 6 5 5 3 5 2 1 3 2 6 2781 5 5 2 3 5 2 6 1 1 6 2782 3 2 6 3 5 2 5 2 2 6 2783 4 3 2 3 3 2 6 2 1 4 2784 4 3 2 3 3 2 6 2 1 4 2785 6 6 4 3 6 2 6 3 1 6 2786 3 3 2 5 2 1 5 1 1 5 2787 3 3 3 4 6 1 6 2 2 5 2788 4 3 5 5 6 1 6 2 3 5 2789 NA 6 3 5 3 2 3 2 2 6 2790 3 3 4 5 5 2 3 3 4 3 2791 6 3 2 2 5 3 5 3 2 4 2792 3 3 1 1 3 2 3 2 5 3 2793 2 3 2 4 3 3 2 2 3 3 2794 2 1 2 2 1 1 6 3 2 5 2795 3 2 4 2 5 1 6 2 1 5 2796 6 6 6 NA 6 1 6 1 1 6 2797 NA 4 5 4 4 1 4 2 3 5 2798 4 3 4 4 6 2 6 1 3 4 2799 2 2 1 3 6 2 5 2 2 6 2800 6 5 5 6 6 4 6 4 2 6 > > > > cleanEx() > nameEx("rowMeans_if") > ### * rowMeans_if > > flush(stderr()); flush(stdout()) > > ### Name: rowMeans_if > ### Title: Row Means Conditional on Frequency of Observed Values > ### Aliases: rowMeans_if > > ### ** Examples > > rowMeans_if(airquality) 1 2 3 4 5 6 7 8 51.90000 40.16667 42.60000 68.91667 NA NA 67.93333 33.96667 9 10 11 12 13 14 15 16 20.35000 NA NA 61.28333 65.70000 64.31667 29.03333 74.08333 17 18 19 20 21 22 23 24 73.50000 30.40000 75.91667 25.28333 17.28333 74.60000 21.28333 37.66667 25 26 27 28 29 30 31 32 NA NA NA 24.66667 71.15000 76.28333 72.56667 NA 33 34 35 36 37 38 39 40 NA NA NA NA NA 43.45000 NA 80.13333 41 42 43 44 45 46 47 48 79.41667 NA NA 46.66667 NA NA 54.31667 72.78333 49 50 51 52 53 54 55 56 25.86667 40.25000 43.71667 NA NA NA NA NA 57 58 59 60 61 62 63 64 NA NA NA NA NA 83.35000 66.70000 61.36667 65 66 67 68 69 70 71 72 NA 56.43333 76.81667 76.68333 79.55000 80.45000 62.23333 NA 73 74 75 76 77 78 79 80 63.38333 52.98333 NA 28.55000 69.81667 70.88333 76.88333 64.01667 81 82 83 84 85 86 87 88 67.75000 21.98333 NA NA 83.26667 76.00000 37.43333 44.33333 89 90 91 92 93 94 95 96 70.90000 75.73333 74.06667 73.53333 36.48333 22.96667 32.23333 NA 97 98 99 100 101 102 103 104 NA NA 80.83333 72.38333 72.00000 NA NA 58.91667 105 106 107 108 109 110 111 112 69.25000 55.61667 NA 34.05000 36.71667 41.23333 65.15000 58.38333 113 114 115 116 117 118 119 120 66.91667 26.88333 NA 62.95000 87.23333 69.33333 NA 70.28333 121 122 123 124 125 126 127 128 79.38333 76.88333 68.71667 61.81667 63.85000 60.63333 65.10000 41.73333 129 130 131 132 133 134 135 136 39.75000 63.15000 58.05000 59.15000 64.11667 65.98333 65.41667 61.88333 137 138 139 140 141 142 143 144 22.98333 38.58333 65.48333 58.13333 25.55000 61.38333 56.00000 59.60000 145 146 147 148 149 150 151 152 24.70000 49.71667 28.05000 24.60000 55.81667 NA 55.21667 45.16667 153 60.25000 > rowMeans_if(x = airquality, ov.min = 5, prop = FALSE) 1 2 3 4 5 6 7 8 51.90000 40.16667 42.60000 68.91667 NA 23.98000 67.93333 33.96667 9 10 11 12 13 14 15 16 20.35000 57.32000 20.78000 61.28333 65.70000 64.31667 29.03333 74.08333 17 18 19 20 21 22 23 24 73.50000 30.40000 75.91667 25.28333 17.28333 74.60000 21.28333 37.66667 25 26 27 28 29 30 31 32 33.92000 73.98000 NA 24.66667 71.15000 76.28333 72.56667 75.92000 33 34 35 36 37 38 39 40 75.74000 66.82000 57.84000 64.92000 73.86000 43.45000 76.18000 80.13333 41 42 43 44 45 46 47 48 79.41667 75.98000 73.84000 46.66667 89.16000 86.70000 54.31667 72.78333 49 50 51 52 53 54 55 56 25.86667 40.25000 43.71667 52.06000 32.94000 40.12000 72.46000 49.80000 57 58 59 60 61 62 63 64 49.00000 32.66000 44.70000 31.58000 53.00000 83.35000 66.70000 61.36667 65 66 67 68 69 70 71 72 41.38000 56.43333 76.81667 76.68333 79.55000 80.45000 62.23333 49.52000 73 74 75 76 77 78 79 80 63.38333 52.98333 83.58000 28.55000 69.81667 70.88333 76.88333 64.01667 81 82 83 84 85 86 87 88 67.75000 21.98333 75.54000 83.70000 83.26667 76.00000 37.43333 44.33333 89 90 91 92 93 94 95 96 70.90000 75.73333 74.06667 73.53333 36.48333 22.96667 32.23333 36.58000 97 98 99 100 101 102 103 104 28.08000 34.32000 80.83333 72.38333 72.00000 68.12000 50.70000 58.91667 105 106 107 108 109 110 111 112 69.25000 55.61667 35.50000 34.05000 36.71667 41.23333 65.15000 58.38333 113 114 115 116 117 118 119 120 66.91667 26.88333 74.72000 62.95000 87.23333 69.33333 56.34000 70.28333 121 122 123 124 125 126 127 128 79.38333 76.88333 68.71667 61.81667 63.85000 60.63333 65.10000 41.73333 129 130 131 132 133 134 135 136 39.75000 63.15000 58.05000 59.15000 64.11667 65.98333 65.41667 61.88333 137 138 139 140 141 142 143 144 22.98333 38.58333 65.48333 58.13333 25.55000 61.38333 56.00000 59.60000 145 146 147 148 149 150 151 152 24.70000 49.71667 28.05000 24.60000 55.81667 54.24000 55.21667 45.16667 153 60.25000 > > > > cleanEx() > nameEx("rowNA") > ### * rowNA > > flush(stderr()); flush(stdout()) > > ### Name: rowNA > ### Title: Frequency of Missing Values by Row > ### Aliases: rowNA > > ### ** Examples > > rowNA(as.matrix(airquality)) # count of missing values [1] 0 0 0 0 2 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 2 0 0 0 0 1 1 1 1 1 1 [38] 0 1 0 0 1 1 0 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0 1 0 0 0 0 0 0 1 0 0 [75] 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 1 0 0 0 1 0 0 0 0 [112] 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [149] 0 1 0 0 0 > rowNA(as.data.frame(airquality)) # with rownames 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 0 0 0 0 2 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 0 0 0 0 1 1 2 0 0 0 0 1 1 1 1 1 1 0 1 0 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 0 1 1 0 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 1 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 1 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 0 1 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 141 142 143 144 145 146 147 148 149 150 151 152 153 0 0 0 0 0 0 0 0 0 1 0 0 0 > rowNA(as.matrix(airquality), prop = TRUE) # proportion of missing values [1] 0.0000000 0.0000000 0.0000000 0.0000000 0.3333333 0.1666667 0.0000000 [8] 0.0000000 0.0000000 0.1666667 0.1666667 0.0000000 0.0000000 0.0000000 [15] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [22] 0.0000000 0.0000000 0.0000000 0.1666667 0.1666667 0.3333333 0.0000000 [29] 0.0000000 0.0000000 0.0000000 0.1666667 0.1666667 0.1666667 0.1666667 [36] 0.1666667 0.1666667 0.0000000 0.1666667 0.0000000 0.0000000 0.1666667 [43] 0.1666667 0.0000000 0.1666667 0.1666667 0.0000000 0.0000000 0.0000000 [50] 0.0000000 0.0000000 0.1666667 0.1666667 0.1666667 0.1666667 0.1666667 [57] 0.1666667 0.1666667 0.1666667 0.1666667 0.1666667 0.0000000 0.0000000 [64] 0.0000000 0.1666667 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [71] 0.0000000 0.1666667 0.0000000 0.0000000 0.1666667 0.0000000 0.0000000 [78] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.1666667 0.1666667 [85] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [92] 0.0000000 0.0000000 0.0000000 0.0000000 0.1666667 0.1666667 0.1666667 [99] 0.0000000 0.0000000 0.0000000 0.1666667 0.1666667 0.0000000 0.0000000 [106] 0.0000000 0.1666667 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [113] 0.0000000 0.0000000 0.1666667 0.0000000 0.0000000 0.0000000 0.1666667 [120] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [127] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [134] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [141] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [148] 0.0000000 0.0000000 0.1666667 0.0000000 0.0000000 0.0000000 > rowNA(as.matrix(airquality), ov = TRUE) # count of observed values [1] 6 6 6 6 4 5 6 6 6 5 5 6 6 6 6 6 6 6 6 6 6 6 6 6 5 5 4 6 6 6 6 5 5 5 5 5 5 [38] 6 5 6 6 5 5 6 5 5 6 6 6 6 6 5 5 5 5 5 5 5 5 5 5 6 6 6 5 6 6 6 6 6 6 5 6 6 [75] 5 6 6 6 6 6 6 6 5 5 6 6 6 6 6 6 6 6 6 6 6 5 5 5 6 6 6 5 5 6 6 6 5 6 6 6 6 [112] 6 6 6 5 6 6 6 5 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 [149] 6 5 6 6 6 > rowNA(as.data.frame(airquality), prop = TRUE, ov = TRUE) # proportion of observed values 1 2 3 4 5 6 7 8 1.0000000 1.0000000 1.0000000 1.0000000 0.6666667 0.8333333 1.0000000 1.0000000 9 10 11 12 13 14 15 16 1.0000000 0.8333333 0.8333333 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 17 18 19 20 21 22 23 24 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 25 26 27 28 29 30 31 32 0.8333333 0.8333333 0.6666667 1.0000000 1.0000000 1.0000000 1.0000000 0.8333333 33 34 35 36 37 38 39 40 0.8333333 0.8333333 0.8333333 0.8333333 0.8333333 1.0000000 0.8333333 1.0000000 41 42 43 44 45 46 47 48 1.0000000 0.8333333 0.8333333 1.0000000 0.8333333 0.8333333 1.0000000 1.0000000 49 50 51 52 53 54 55 56 1.0000000 1.0000000 1.0000000 0.8333333 0.8333333 0.8333333 0.8333333 0.8333333 57 58 59 60 61 62 63 64 0.8333333 0.8333333 0.8333333 0.8333333 0.8333333 1.0000000 1.0000000 1.0000000 65 66 67 68 69 70 71 72 0.8333333 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.8333333 73 74 75 76 77 78 79 80 1.0000000 1.0000000 0.8333333 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 81 82 83 84 85 86 87 88 1.0000000 1.0000000 0.8333333 0.8333333 1.0000000 1.0000000 1.0000000 1.0000000 89 90 91 92 93 94 95 96 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.8333333 97 98 99 100 101 102 103 104 0.8333333 0.8333333 1.0000000 1.0000000 1.0000000 0.8333333 0.8333333 1.0000000 105 106 107 108 109 110 111 112 1.0000000 1.0000000 0.8333333 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 113 114 115 116 117 118 119 120 1.0000000 1.0000000 0.8333333 1.0000000 1.0000000 1.0000000 0.8333333 1.0000000 121 122 123 124 125 126 127 128 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 129 130 131 132 133 134 135 136 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 137 138 139 140 141 142 143 144 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 145 146 147 148 149 150 151 152 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.8333333 1.0000000 1.0000000 153 1.0000000 > > > > cleanEx() > nameEx("rowSums_if") > ### * rowSums_if > > flush(stderr()); flush(stdout()) > > ### Name: rowSums_if > ### Title: Row Sums Conditional on Frequency of Observed Values > ### Aliases: rowSums_if > > ### ** Examples > > rowSums_if(airquality) 1 2 3 4 5 6 7 8 9 10 11 12 13 311.4 241.0 255.6 413.5 NA NA 407.6 203.8 122.1 NA NA 367.7 394.2 14 15 16 17 18 19 20 21 22 23 24 25 26 385.9 174.2 444.5 441.0 182.4 455.5 151.7 103.7 447.6 127.7 226.0 NA NA 27 28 29 30 31 32 33 34 35 36 37 38 39 NA 148.0 426.9 457.7 435.4 NA NA NA NA NA NA 260.7 NA 40 41 42 43 44 45 46 47 48 49 50 51 52 480.8 476.5 NA NA 280.0 NA NA 325.9 436.7 155.2 241.5 262.3 NA 53 54 55 56 57 58 59 60 61 62 63 64 65 NA NA NA NA NA NA NA NA NA 500.1 400.2 368.2 NA 66 67 68 69 70 71 72 73 74 75 76 77 78 338.6 460.9 460.1 477.3 482.7 373.4 NA 380.3 317.9 NA 171.3 418.9 425.3 79 80 81 82 83 84 85 86 87 88 89 90 91 461.3 384.1 406.5 131.9 NA NA 499.6 456.0 224.6 266.0 425.4 454.4 444.4 92 93 94 95 96 97 98 99 100 101 102 103 104 441.2 218.9 137.8 193.4 NA NA NA 485.0 434.3 432.0 NA NA 353.5 105 106 107 108 109 110 111 112 113 114 115 116 117 415.5 333.7 NA 204.3 220.3 247.4 390.9 350.3 401.5 161.3 NA 377.7 523.4 118 119 120 121 122 123 124 125 126 127 128 129 130 416.0 NA 421.7 476.3 461.3 412.3 370.9 383.1 363.8 390.6 250.4 238.5 378.9 131 132 133 134 135 136 137 138 139 140 141 142 143 348.3 354.9 384.7 395.9 392.5 371.3 137.9 231.5 392.9 348.8 153.3 368.3 336.0 144 145 146 147 148 149 150 151 152 153 357.6 148.2 298.3 168.3 147.6 334.9 NA 331.3 271.0 361.5 > rowSums_if(x = airquality, ov.min = 5, prop = FALSE) 1 2 3 4 5 6 7 8 9 10 11 311.40 241.00 255.60 413.50 NA 143.88 407.60 203.80 122.10 343.92 124.68 12 13 14 15 16 17 18 19 20 21 22 367.70 394.20 385.90 174.20 444.50 441.00 182.40 455.50 151.70 103.70 447.60 23 24 25 26 27 28 29 30 31 32 33 127.70 226.00 203.52 443.88 NA 148.00 426.90 457.70 435.40 455.52 454.44 34 35 36 37 38 39 40 41 42 43 44 400.92 347.04 389.52 443.16 260.70 457.08 480.80 476.50 455.88 443.04 280.00 45 46 47 48 49 50 51 52 53 54 55 534.96 520.20 325.90 436.70 155.20 241.50 262.30 312.36 197.64 240.72 434.76 56 57 58 59 60 61 62 63 64 65 66 298.80 294.00 195.96 268.20 189.48 318.00 500.10 400.20 368.20 248.28 338.60 67 68 69 70 71 72 73 74 75 76 77 460.90 460.10 477.30 482.70 373.40 297.12 380.30 317.90 501.48 171.30 418.90 78 79 80 81 82 83 84 85 86 87 88 425.30 461.30 384.10 406.50 131.90 453.24 502.20 499.60 456.00 224.60 266.00 89 90 91 92 93 94 95 96 97 98 99 425.40 454.40 444.40 441.20 218.90 137.80 193.40 219.48 168.48 205.92 485.00 100 101 102 103 104 105 106 107 108 109 110 434.30 432.00 408.72 304.20 353.50 415.50 333.70 213.00 204.30 220.30 247.40 111 112 113 114 115 116 117 118 119 120 121 390.90 350.30 401.50 161.30 448.32 377.70 523.40 416.00 338.04 421.70 476.30 122 123 124 125 126 127 128 129 130 131 132 461.30 412.30 370.90 383.10 363.80 390.60 250.40 238.50 378.90 348.30 354.90 133 134 135 136 137 138 139 140 141 142 143 384.70 395.90 392.50 371.30 137.90 231.50 392.90 348.80 153.30 368.30 336.00 144 145 146 147 148 149 150 151 152 153 357.60 148.20 298.30 168.30 147.60 334.90 325.44 331.30 271.00 361.50 > x <- data.frame("x" = c(1, 1, NA), "y" = c(2, NA, NA), "z" = c(NA, NA, NA)) > rowSums_if(x) 1 2 3 NA NA NA > rowSums_if(x, ov.min = 0) 1 2 3 4.5 3.0 NA > rowSums_if(x, ov.min = 0, allNA = 0) 1 2 3 4.5 3.0 0.0 > identical(x = rowSums(x, na.rm = TRUE), + y = unname(rowSums_if(x, impute = FALSE, ov.min = 0, allNA = 0))) # identical to [1] TRUE > # rowSums(x, na.rm = TRUE) > > > > cleanEx() > nameEx("rowsNA") > ### * rowsNA > > flush(stderr()); flush(stdout()) > > ### Name: rowsNA > ### Title: Frequency of Multiple Sets of Missing Values by Row > ### Aliases: rowsNA > > ### ** Examples > > vrb_list <- lapply(X = c("O","C","E","A","N"), FUN = function(chr) { + tmp <- grepl(pattern = chr, x = names(psych::bfi)) + names(psych::bfi)[tmp] + }) > rowsNA(data = psych::bfi, + vrb.nm.list = vrb_list) # names set to first elements in `vrb.nm.list`[[i]] O1 C1 E1 A1 N1 61617 0 0 0 0 0 61618 0 0 0 0 0 61620 0 0 0 0 0 61621 0 0 0 0 0 61622 0 0 0 0 0 61623 0 0 0 0 0 61624 0 0 0 0 0 61629 0 0 0 0 0 61630 0 0 1 0 0 61633 0 0 0 0 0 61634 0 0 0 0 0 61636 0 0 0 0 1 61637 0 0 0 0 0 61639 0 0 0 0 0 61640 0 0 0 0 0 61643 0 0 0 0 0 61650 0 0 0 0 0 61651 0 0 0 0 0 61653 0 0 0 0 0 61654 0 0 0 0 0 61656 0 0 0 0 0 61659 0 0 0 0 0 61661 0 0 0 0 0 61664 0 0 0 0 0 61667 0 0 0 0 0 61668 0 0 0 0 0 61669 0 0 0 0 0 61670 0 0 0 0 0 61672 0 0 0 0 0 61673 0 0 0 0 0 61678 0 0 0 0 0 61679 0 0 0 0 0 61682 0 0 0 0 0 61683 0 0 0 0 0 61684 0 0 0 0 1 61685 0 0 0 0 0 61686 0 0 0 0 0 61687 0 0 0 0 0 61688 0 0 0 0 0 61691 0 0 0 0 0 61692 0 0 0 0 0 61693 0 0 0 0 1 61696 0 0 0 0 0 61698 0 0 0 0 0 61700 0 0 0 0 0 61701 0 0 0 0 0 61702 0 0 0 0 0 61703 0 0 0 0 0 61713 0 0 0 0 0 61715 0 0 0 0 0 61716 0 0 0 0 0 61723 0 0 0 0 0 61724 0 0 0 0 0 61725 0 0 0 0 0 61728 0 0 0 0 0 61730 0 0 0 0 0 61731 0 0 0 0 0 61732 0 0 0 0 0 61740 0 0 0 0 0 61742 0 0 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0 0 0 0 67344 0 0 0 0 0 67345 0 0 0 0 0 67346 0 0 0 0 0 67347 0 1 0 1 0 67352 0 0 0 0 0 67355 0 0 0 0 0 67356 0 0 0 0 0 67357 0 0 0 0 0 67360 0 0 0 0 0 67363 0 0 0 0 0 67364 0 0 0 0 0 67365 0 0 0 0 0 67366 0 0 0 0 0 67367 0 0 0 0 0 67368 2 2 1 1 2 67370 1 0 0 0 0 67371 0 0 0 0 0 67374 0 0 0 0 0 67376 0 0 0 0 1 67377 0 0 0 0 0 67378 0 0 0 0 1 67379 0 0 0 0 0 67380 0 0 0 0 0 67382 0 0 0 0 0 67388 0 0 0 0 0 67390 0 0 0 0 0 67392 0 0 0 0 0 67393 0 0 0 0 0 67395 0 0 0 0 0 67396 0 0 0 0 1 67397 0 0 0 0 0 67399 0 0 0 0 0 67401 0 0 0 0 0 67403 0 0 0 0 0 67406 0 0 0 0 0 67408 0 0 0 0 0 67413 1 0 0 0 1 67415 0 0 0 0 0 67417 0 0 0 0 0 67418 0 0 0 0 0 67420 0 0 0 0 0 67421 0 0 0 0 0 67426 0 0 0 0 0 67427 0 0 0 0 0 67429 0 0 0 0 0 67431 0 0 0 0 0 67436 0 0 0 0 1 67438 0 0 0 0 0 67439 0 0 0 0 0 67440 0 0 0 0 0 67441 0 0 0 0 0 67442 0 0 0 0 0 67445 0 0 0 0 0 67446 0 0 0 0 0 67447 0 0 0 0 0 67449 0 0 0 0 0 67451 1 0 0 0 0 67453 0 0 0 0 0 67454 0 0 0 0 0 67455 0 0 0 0 0 67458 0 0 0 0 0 67460 0 0 0 0 1 67462 0 0 0 0 0 67465 0 0 0 0 0 67471 0 0 0 0 0 67473 0 0 0 0 0 67479 0 0 0 0 0 67480 0 0 0 0 0 67483 0 0 0 0 0 67485 0 0 0 0 0 67487 0 0 0 0 0 67488 1 0 0 0 0 67489 0 0 0 0 0 67490 0 0 0 0 0 67495 1 0 0 1 0 67497 0 0 0 0 0 67502 0 0 0 0 0 67503 0 0 0 0 0 67504 0 0 0 0 0 67505 0 0 0 0 0 67506 0 0 0 0 0 67507 0 0 0 0 0 67508 0 0 0 0 0 67509 0 0 0 0 0 67510 0 0 0 0 0 67512 0 0 0 0 0 67516 0 0 0 0 0 67518 0 0 0 0 0 67521 0 0 0 0 0 67522 0 0 0 0 0 67523 0 0 0 0 0 67524 0 1 0 0 0 67525 0 0 0 0 0 67526 0 0 0 0 0 67528 0 0 0 0 0 67529 0 0 1 2 0 67530 0 0 0 0 0 67531 0 0 0 0 0 67533 0 0 0 0 0 67534 0 0 0 0 0 67535 0 0 0 0 0 67537 0 0 0 0 1 67539 0 0 0 0 0 67540 0 0 0 0 0 67541 0 0 0 0 0 67544 0 0 0 0 0 67547 0 0 0 0 0 67549 0 0 0 0 0 67551 0 0 0 0 1 67552 0 0 0 0 1 67556 0 0 0 0 0 67559 0 0 0 0 0 67560 0 0 0 0 0 > > > > cleanEx() > nameEx("score") > ### * score > > flush(stderr()); flush(stdout()) > > ### Name: score > ### Title: Observed Unweighted Scoring of a Set of Variables/Items > ### Aliases: score > > ### ** Examples > > score(data = attitude, vrb.nm = c("complaints","privileges","learning","raises")) 1 2 3 4 5 6 7 8 9 10 11 12 13 45.25 58.00 70.75 52.25 67.75 50.50 57.75 62.50 73.00 53.75 55.50 51.25 54.00 14 15 16 17 18 19 20 21 22 23 24 25 26 67.50 70.50 68.00 74.25 63.75 62.00 61.00 37.50 60.25 57.75 46.75 52.50 73.50 27 28 29 30 71.75 49.25 76.00 61.00 > score(data = attitude, vrb.nm = c("complaints","privileges","learning","raises"), + std = TRUE) # standardized scoring 1 2 3 4 5 6 -1.48795044 -0.22156538 1.10727804 -0.83832398 0.76766728 -0.99940170 7 8 9 10 11 12 -0.23717527 0.23560434 1.28276161 -0.64995760 -0.46576994 -0.91819404 13 14 15 16 17 18 -0.66315860 0.65781104 1.08466245 0.72632862 1.43830079 0.34532693 19 20 21 22 23 24 0.22004654 0.09326523 -2.32089880 0.02987063 -0.25468110 -1.33885498 25 26 27 28 29 30 -0.74152169 1.41306311 1.21950978 -1.13987277 1.60565462 0.05017526 > score(data = airquality, vrb.nm = c("Ozone","Solar.R","Temp"), + ov.min = 0.75) # conditional on observed values 1 2 3 4 5 6 7 8 99.33333 75.33333 78.33333 131.00000 NA NA 129.00000 59.00000 9 10 11 12 13 14 15 16 29.33333 NA NA 113.66667 122.33333 118.66667 47.00000 137.33333 17 18 19 20 21 22 23 24 135.66667 47.00000 140.00000 39.00000 22.66667 134.66667 30.00000 61.66667 25 26 27 28 29 30 31 32 NA NA NA 34.33333 126.00000 139.00000 130.66667 NA 33 34 35 36 37 38 39 40 NA NA NA NA NA 79.33333 NA 150.66667 41 42 43 44 45 46 47 48 149.66667 NA NA 84.33333 NA NA 96.33333 131.00000 49 50 51 52 53 54 55 56 40.66667 68.33333 75.33333 NA NA NA NA NA 57 58 59 60 61 62 63 64 NA NA NA NA NA 162.66667 127.33333 116.33333 65 66 67 68 69 70 71 72 NA 107.33333 145.66667 147.00000 152.00000 153.66667 116.33333 NA 73 74 75 76 77 78 79 80 115.66667 94.33333 NA 45.00000 129.66667 130.33333 143.33333 117.66667 81 82 83 84 85 86 87 88 122.66667 32.33333 NA NA 153.33333 138.66667 61.00000 73.33333 89 90 91 92 93 94 95 96 127.66667 137.00000 133.33333 131.33333 67.66667 38.00000 58.33333 NA 97 98 99 100 101 102 103 104 NA NA 155.33333 136.00000 135.66667 NA NA 107.33333 105 106 107 108 109 110 111 112 127.66667 100.66667 NA 56.66667 63.00000 71.33333 117.66667 104.00000 113 114 115 116 117 118 119 120 119.00000 39.00000 NA 112.00000 162.33333 124.66667 NA 125.33333 121 122 123 124 125 126 127 128 145.66667 139.00000 122.33333 118.00000 122.33333 116.33333 124.33333 76.33333 129 130 131 132 133 134 135 136 69.33333 117.33333 107.00000 108.66667 118.66667 120.33333 118.66667 114.33333 137 138 139 140 141 142 143 144 34.66667 65.33333 120.33333 103.00000 38.66667 110.00000 99.66667 105.00000 145 146 147 148 149 150 151 152 36.00000 85.33333 41.66667 32.33333 97.66667 NA 93.33333 75.00000 153 103.66667 > > > > cleanEx() > nameEx("scores") > ### * scores > > flush(stderr()); flush(stdout()) > > ### Name: scores > ### Title: Observed Unweighted Scoring of Multiple Sets of Variables/Items > ### Aliases: scores > > ### ** Examples > > list_colnames <- list("first" = c("rating","complaints","privileges"), + "second" = c("learning","raises","critical")) > scores(data = attitude, vrb.nm.list = list_colnames) first second 1 41.33333 64.00000 2 59.33333 63.33333 3 69.66667 77.00000 4 56.33333 61.66667 5 71.66667 73.33333 6 49.00000 49.00000 7 55.66667 63.33333 8 65.33333 63.66667 9 75.33333 73.66667 10 57.66667 63.00000 11 56.66667 61.00000 12 58.00000 57.33333 13 62.66667 53.33333 14 78.00000 60.33333 15 69.33333 76.00000 16 73.66667 62.00000 17 74.33333 75.66667 18 63.33333 70.00000 19 60.33333 72.33333 20 58.66667 65.33333 21 41.00000 47.00000 22 59.00000 69.33333 23 57.00000 64.33333 24 39.66667 55.00000 25 53.00000 63.00000 26 69.66667 75.66667 27 70.33333 77.33333 28 49.66667 59.66667 29 80.33333 74.00000 30 67.66667 67.00000 > list_colnames <- list("first" = c("Ozone","Wind"), + "second" = c("Solar.R","Temp")) > scores(data = airquality, vrb.nm.list = list_colnames, ov.min = .50, + inclusive = FALSE) # scoring conditional on observed values first second 1 24.20 128.5 2 22.00 95.0 3 12.30 111.5 4 14.75 187.5 5 NA NA 6 21.45 NA 7 15.80 182.0 8 16.40 79.0 9 14.05 40.0 10 NA 131.5 11 6.95 NA 12 12.85 162.5 13 10.10 178.0 14 12.45 171.0 15 15.60 61.5 16 12.75 199.0 17 23.00 186.5 18 12.20 67.5 19 20.75 195.0 20 10.35 53.0 21 5.35 33.5 22 13.80 196.5 23 6.85 43.0 24 22.00 76.5 25 NA 61.5 26 NA 162.0 27 NA NA 28 17.50 40.0 29 29.95 166.5 30 60.35 151.0 31 22.20 177.5 32 NA 182.0 33 NA 180.5 34 NA 154.5 35 NA 135.0 36 NA 152.5 37 NA 171.5 38 19.35 104.5 39 NA 180.0 40 42.40 190.5 41 25.25 205.0 42 NA 176.0 43 NA 171.0 44 15.50 115.0 45 NA 206.0 46 NA 200.5 47 17.95 134.0 48 28.85 178.0 49 14.60 51.0 50 11.75 96.5 51 11.65 106.5 52 NA 113.5 53 NA 67.5 54 NA 83.5 55 NA 163.0 56 NA 105.0 57 NA 102.5 58 NA 60.0 59 NA 89.0 60 NA 54.0 61 NA 110.5 62 69.55 176.5 63 29.10 166.5 64 20.60 158.5 65 NA 92.5 66 34.30 129.0 67 25.45 198.5 68 41.05 182.0 69 51.65 179.5 70 51.35 182.0 71 46.20 132.0 72 NA 110.5 73 12.15 168.5 74 20.95 128.0 75 NA 191.0 76 10.65 64.0 77 27.45 170.5 78 22.65 178.0 79 33.65 184.5 80 42.05 137.0 81 37.25 152.5 82 11.45 40.5 83 NA 169.5 84 NA 188.5 85 44.30 190.0 86 58.00 154.0 87 14.30 81.5 88 32.00 84.0 89 44.70 150.5 90 28.70 180.5 91 35.70 168.0 92 34.10 167.5 93 22.95 82.0 94 11.40 52.5 95 11.70 79.5 96 42.45 NA 97 21.20 NA 98 35.30 NA 99 63.00 172.0 100 49.65 159.5 101 59.00 148.5 102 NA 157.0 103 NA 111.5 104 27.75 139.0 105 19.75 177.5 106 37.35 118.5 107 NA 71.5 108 16.15 74.0 109 32.65 65.0 110 15.20 95.5 111 20.95 161.0 112 27.15 134.0 113 18.25 168.0 114 11.65 54.0 115 NA 165.0 116 27.35 145.5 117 85.70 159.5 118 40.50 150.5 119 NA 120.5 120 42.85 150.0 121 60.15 159.5 122 45.15 166.5 123 45.65 141.0 124 51.45 129.0 125 41.55 144.5 126 37.90 138.0 127 47.80 141.0 128 27.20 91.0 129 23.75 88.0 130 15.45 166.0 131 16.65 149.0 132 15.95 152.5 133 16.85 166.0 134 29.45 158.5 135 18.25 167.5 136 17.15 157.5 137 9.95 47.5 138 12.25 91.5 139 26.45 157.5 140 15.90 145.5 141 11.65 51.5 142 17.15 153.0 143 12.00 141.5 144 12.80 151.0 145 16.10 42.5 146 23.15 110.0 147 8.65 59.0 148 15.30 41.5 149 18.45 131.5 150 NA 111.0 151 14.15 133.0 152 13.00 103.5 153 15.75 145.5 > > > > cleanEx() > nameEx("shift") > ### * shift > > flush(stderr()); flush(stdout()) > > ### Name: shift > ### Title: Shift a Vector (i.e., lag/lead) > ### Aliases: shift > > ### ** Examples > > shift(x = attitude[[1]], n = -1L) # use L to prevent problems with floating point numbers [1] NA 43 63 71 61 81 43 58 71 72 67 64 67 69 68 77 81 74 65 65 50 50 64 53 40 [26] 63 66 78 48 85 > shift(x = attitude[[1]], n = -2L) # can specify any integer up to the length of `x` [1] NA NA 43 63 71 61 81 43 58 71 72 67 64 67 69 68 77 81 74 65 65 50 50 64 53 [26] 40 63 66 78 48 > shift(x = attitude[[1]], n = +1L) # can specify negative or positive integers [1] 63 71 61 81 43 58 71 72 67 64 67 69 68 77 81 74 65 65 50 50 64 53 40 63 66 [26] 78 48 85 82 NA > shift(x = attitude[[1]], n = +2L, undefined = -999) # user-specified indefined value [1] 71 61 81 43 58 71 72 67 64 67 69 68 77 81 74 [16] 65 65 50 50 64 53 40 63 66 78 48 85 82 -999 -999 > shift(x = setNames(object = letters, nm = LETTERS), n = 3L) # names are kept A B C D E F G H I J K L M N O P Q R S T "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s" "t" "u" "v" "w" U V W X Y Z "x" "y" "z" NA NA NA > > > > cleanEx() > nameEx("shift_by") > ### * shift_by > > flush(stderr()); flush(stdout()) > > ### Name: shift_by > ### Title: Shift a Vector (i.e., lag/lead) by Group > ### Aliases: shift_by > > ### ** Examples > > shift_by(x = ChickWeight[["Time"]], grp = ChickWeight[["Chick"]], n = -1L) [1] NA 0 2 4 6 8 10 12 14 16 18 20 NA 0 2 4 6 8 10 12 14 16 18 20 NA [26] 0 2 4 6 8 10 12 14 16 18 20 NA 0 2 4 6 8 10 12 14 16 18 20 NA 0 [51] 2 4 6 8 10 12 14 16 18 20 NA 0 2 4 6 8 10 12 14 16 18 20 NA 0 2 [76] 4 6 8 10 12 14 16 18 20 NA 0 2 4 6 8 10 12 14 16 18 NA 0 2 4 6 [101] 8 10 12 14 16 18 20 NA 0 2 4 6 8 10 12 14 16 18 20 NA 0 2 4 6 8 [126] 10 12 14 16 18 20 NA 0 2 4 6 8 10 12 14 16 18 20 NA 0 2 4 6 8 10 [151] 12 14 16 18 20 NA 0 2 4 6 8 10 12 14 16 18 20 NA 0 2 4 6 8 10 12 [176] NA 0 2 4 6 8 10 NA 0 2 4 6 8 10 12 14 16 18 20 NA 0 NA 0 2 4 [201] 6 8 10 12 14 16 18 20 NA 0 2 4 6 8 10 12 14 16 18 20 NA 0 2 4 6 [226] 8 10 12 14 16 18 20 NA 0 2 4 6 8 10 12 14 16 18 20 NA 0 2 4 6 8 [251] 10 12 14 16 18 20 NA 0 2 4 6 8 10 12 14 16 18 20 NA 0 2 4 6 8 10 [276] 12 14 16 18 20 NA 0 2 4 6 8 10 12 14 16 18 20 NA 0 2 4 6 8 10 12 [301] 14 16 18 20 NA 0 2 4 6 8 10 12 14 16 18 20 NA 0 2 4 6 8 10 12 14 [326] 16 18 20 NA 0 2 4 6 8 10 12 14 16 18 20 NA 0 2 4 6 8 10 12 14 16 [351] 18 20 NA 0 2 4 6 8 10 12 14 16 18 20 NA 0 2 4 6 8 10 12 14 16 18 [376] 20 NA 0 2 4 6 8 10 12 14 16 18 20 NA 0 2 4 6 8 10 12 14 16 18 20 [401] NA 0 2 4 6 8 10 12 14 16 18 20 NA 0 2 4 6 8 10 12 14 16 18 20 NA [426] 0 2 4 6 8 10 12 14 16 18 20 NA 0 2 4 6 8 10 12 14 16 18 20 NA 0 [451] 2 4 6 8 10 12 14 16 18 20 NA 0 2 4 6 8 10 12 14 16 18 20 NA 0 2 [476] 4 6 8 10 12 14 16 18 20 NA 0 2 4 6 8 10 12 14 16 18 20 NA 0 2 4 [501] 6 8 10 12 14 16 NA 0 2 4 6 8 10 12 14 16 18 20 NA 0 2 4 6 8 10 [526] 12 14 16 18 20 NA 0 2 4 6 8 10 12 14 16 18 20 NA 0 2 4 6 8 10 12 [551] 14 16 18 20 NA 0 2 4 6 8 10 12 14 16 18 20 NA 0 2 4 6 8 10 12 14 [576] 16 18 20 > tmp_nm <- c("vs","am") # b/c Roxygen2 doesn't like c() in a [] > shift_by(x = mtcars[["disp"]], grp = mtcars[tmp_nm], n = 1L) [1] 160.0 120.3 78.7 225.0 360.0 146.7 275.8 140.8 167.6 167.6 120.1 275.8 [13] 275.8 472.0 460.0 440.0 318.0 75.7 71.1 79.0 NA 304.0 350.0 400.0 [25] NA 95.1 351.0 121.0 145.0 301.0 NA NA > tmp_nm <- c("Type","Treatment") # b/c Roxygen2 doesn't like c() in a [] > shift_by(x = as.data.frame(CO2)[["uptake"]], grp = as.data.frame(CO2)[tmp_nm], + n = 2L) # multiple grouping vectors [1] 34.8 37.2 35.3 39.2 39.7 13.6 27.3 37.1 41.8 40.6 41.4 44.3 16.2 32.4 40.3 [16] 42.1 42.9 43.9 45.5 NA NA 30.3 34.6 32.5 35.4 38.7 9.3 27.3 35.0 38.8 [31] 38.6 37.5 42.4 15.1 21.0 38.1 34.0 38.9 39.6 41.4 NA NA 26.2 30.0 30.9 [46] 32.4 35.5 12.0 22.0 30.6 31.8 32.4 31.1 31.5 11.3 19.4 25.8 27.9 28.5 28.1 [61] 27.8 NA NA 18.1 18.9 19.5 22.2 21.9 7.7 11.4 12.3 13.0 12.5 13.7 14.4 [76] 10.6 18.0 17.9 17.9 17.9 18.9 19.9 NA NA > > > > cleanEx() > nameEx("shifts") > ### * shifts > > flush(stderr()); flush(stdout()) > > ### Name: shifts > ### Title: Shift Data (i.e., lag/lead) > ### Aliases: shifts > > ### ** Examples > > shifts(data = attitude, vrb.nm = colnames(attitude), n = -1L) rating_g1 complaints_g1 privileges_g1 learning_g1 raises_g1 critical_g1 1 NA NA NA NA NA NA 2 43 51 30 39 61 92 3 63 64 51 54 63 73 4 71 70 68 69 76 86 5 61 63 45 47 54 84 6 81 78 56 66 71 83 7 43 55 49 44 54 49 8 58 67 42 56 66 68 9 71 75 50 55 70 66 10 72 82 72 67 71 83 11 67 61 45 47 62 80 12 64 53 53 58 58 67 13 67 60 47 39 59 74 14 69 62 57 42 55 63 15 68 83 83 45 59 77 16 77 77 54 72 79 77 17 81 90 50 72 60 54 18 74 85 64 69 79 79 19 65 60 65 75 55 80 20 65 70 46 57 75 85 21 50 58 68 54 64 78 22 50 40 33 34 43 64 23 64 61 52 62 66 80 24 53 66 52 50 63 80 25 40 37 42 58 50 57 26 63 54 42 48 66 75 27 66 77 66 63 88 76 28 78 75 58 74 80 78 29 48 57 44 45 51 83 30 85 85 71 71 77 74 advance_g1 1 NA 2 45 3 47 4 48 5 35 6 47 7 34 8 35 9 41 10 31 11 41 12 34 13 41 14 25 15 35 16 46 17 36 18 63 19 60 20 46 21 52 22 33 23 41 24 37 25 49 26 33 27 72 28 49 29 38 30 55 > shifts(data = mtcars, vrb.nm = colnames(mtcars), n = 2L) mpg_d2 cyl_d2 disp_d2 hp_d2 drat_d2 wt_d2 qsec_d2 vs_d2 Mazda RX4 22.8 4 108.0 93 3.85 2.320 18.61 1 Mazda RX4 Wag 21.4 6 258.0 110 3.08 3.215 19.44 1 Datsun 710 18.7 8 360.0 175 3.15 3.440 17.02 0 Hornet 4 Drive 18.1 6 225.0 105 2.76 3.460 20.22 1 Hornet Sportabout 14.3 8 360.0 245 3.21 3.570 15.84 0 Valiant 24.4 4 146.7 62 3.69 3.190 20.00 1 Duster 360 22.8 4 140.8 95 3.92 3.150 22.90 1 Merc 240D 19.2 6 167.6 123 3.92 3.440 18.30 1 Merc 230 17.8 6 167.6 123 3.92 3.440 18.90 1 Merc 280 16.4 8 275.8 180 3.07 4.070 17.40 0 Merc 280C 17.3 8 275.8 180 3.07 3.730 17.60 0 Merc 450SE 15.2 8 275.8 180 3.07 3.780 18.00 0 Merc 450SL 10.4 8 472.0 205 2.93 5.250 17.98 0 Merc 450SLC 10.4 8 460.0 215 3.00 5.424 17.82 0 Cadillac Fleetwood 14.7 8 440.0 230 3.23 5.345 17.42 0 Lincoln Continental 32.4 4 78.7 66 4.08 2.200 19.47 1 Chrysler Imperial 30.4 4 75.7 52 4.93 1.615 18.52 1 Fiat 128 33.9 4 71.1 65 4.22 1.835 19.90 1 Honda Civic 21.5 4 120.1 97 3.70 2.465 20.01 1 Toyota Corolla 15.5 8 318.0 150 2.76 3.520 16.87 0 Toyota Corona 15.2 8 304.0 150 3.15 3.435 17.30 0 Dodge Challenger 13.3 8 350.0 245 3.73 3.840 15.41 0 AMC Javelin 19.2 8 400.0 175 3.08 3.845 17.05 0 Camaro Z28 27.3 4 79.0 66 4.08 1.935 18.90 1 Pontiac Firebird 26.0 4 120.3 91 4.43 2.140 16.70 0 Fiat X1-9 30.4 4 95.1 113 3.77 1.513 16.90 1 Porsche 914-2 15.8 8 351.0 264 4.22 3.170 14.50 0 Lotus Europa 19.7 6 145.0 175 3.62 2.770 15.50 0 Ford Pantera L 15.0 8 301.0 335 3.54 3.570 14.60 0 Ferrari Dino 21.4 4 121.0 109 4.11 2.780 18.60 1 Maserati Bora NA NA NA NA NA NA NA NA Volvo 142E NA NA NA NA NA NA NA NA am_d2 gear_d2 carb_d2 Mazda RX4 1 4 1 Mazda RX4 Wag 0 3 1 Datsun 710 0 3 2 Hornet 4 Drive 0 3 1 Hornet Sportabout 0 3 4 Valiant 0 4 2 Duster 360 0 4 2 Merc 240D 0 4 4 Merc 230 0 4 4 Merc 280 0 3 3 Merc 280C 0 3 3 Merc 450SE 0 3 3 Merc 450SL 0 3 4 Merc 450SLC 0 3 4 Cadillac Fleetwood 0 3 4 Lincoln Continental 1 4 1 Chrysler Imperial 1 4 2 Fiat 128 1 4 1 Honda Civic 0 3 1 Toyota Corolla 0 3 2 Toyota Corona 0 3 2 Dodge Challenger 0 3 4 AMC Javelin 0 3 2 Camaro Z28 1 4 1 Pontiac Firebird 1 5 2 Fiat X1-9 1 5 2 Porsche 914-2 1 5 4 Lotus Europa 1 5 6 Ford Pantera L 1 5 8 Ferrari Dino 1 4 2 Maserati Bora NA NA NA Volvo 142E NA NA NA > > > > cleanEx() > nameEx("shifts_by") > ### * shifts_by > > flush(stderr()); flush(stdout()) > > ### Name: shifts_by > ### Title: Shift Data (i.e., lag/lead) by Group > ### Aliases: shifts_by > > ### ** Examples > > shifts_by(data = ChickWeight, vrb.nm = c("weight","Time"), grp.nm = "Chick", n = -1L) weight_gw1 Time_gw1 1 NA NA 2 42 0 3 51 2 4 59 4 5 64 6 6 76 8 7 93 10 8 106 12 9 125 14 10 149 16 11 171 18 12 199 20 13 NA NA 14 40 0 15 49 2 16 58 4 17 72 6 18 84 8 19 103 10 20 122 12 21 138 14 22 162 16 23 187 18 24 209 20 25 NA NA 26 43 0 27 39 2 28 55 4 29 67 6 30 84 8 31 99 10 32 115 12 33 138 14 34 163 16 35 187 18 36 198 20 37 NA NA 38 42 0 39 49 2 40 56 4 41 67 6 42 74 8 43 87 10 44 102 12 45 108 14 46 136 16 47 154 18 48 160 20 49 NA NA 50 41 0 51 42 2 52 48 4 53 60 6 54 79 8 55 106 10 56 141 12 57 164 14 58 197 16 59 199 18 60 220 20 61 NA NA 62 41 0 63 49 2 64 59 4 65 74 6 66 97 8 67 124 10 68 141 12 69 148 14 70 155 16 71 160 18 72 160 20 73 NA NA 74 41 0 75 49 2 76 57 4 77 71 6 78 89 8 79 112 10 80 146 12 81 174 14 82 218 16 83 250 18 84 288 20 85 NA NA 86 42 0 87 50 2 88 61 4 89 71 6 90 84 8 91 93 10 92 110 12 93 116 14 94 126 16 95 134 18 96 NA NA 97 42 0 98 51 2 99 59 4 100 68 6 101 85 8 102 96 10 103 90 12 104 92 14 105 93 16 106 100 18 107 100 20 108 NA NA 109 41 0 110 44 2 111 52 4 112 63 6 113 74 8 114 81 10 115 89 12 116 96 14 117 101 16 118 112 18 119 120 20 120 NA NA 121 43 0 122 51 2 123 63 4 124 84 6 125 112 8 126 139 10 127 168 12 128 177 14 129 182 16 130 184 18 131 181 20 132 NA NA 133 41 0 134 49 2 135 56 4 136 62 6 137 72 8 138 88 10 139 119 12 140 135 14 141 162 16 142 185 18 143 195 20 144 NA NA 145 41 0 146 48 2 147 53 4 148 60 6 149 65 8 150 67 10 151 71 12 152 70 14 153 71 16 154 81 18 155 91 20 156 NA NA 157 41 0 158 49 2 159 62 4 160 79 6 161 101 8 162 128 10 163 164 12 164 192 14 165 227 16 166 248 18 167 259 20 168 NA NA 169 41 0 170 49 2 171 56 4 172 64 6 173 68 8 174 68 10 175 67 12 176 NA NA 177 41 0 178 45 2 179 49 4 180 51 6 181 57 8 182 51 10 183 NA NA 184 42 0 185 51 2 186 61 4 187 72 6 188 83 8 189 89 10 190 98 12 191 103 14 192 113 16 193 123 18 194 133 20 195 NA NA 196 39 0 197 NA NA 198 43 0 199 48 2 200 55 4 201 62 6 202 65 8 203 71 10 204 82 12 205 88 14 206 106 16 207 120 18 208 144 20 209 NA NA 210 41 0 211 47 2 212 54 4 213 58 6 214 65 8 215 73 10 216 77 12 217 89 14 218 98 16 219 107 18 220 115 20 221 NA NA 222 40 0 223 50 2 224 62 4 225 86 6 226 125 8 227 163 10 228 217 12 229 240 14 230 275 16 231 307 18 232 318 20 233 NA NA 234 41 0 235 55 2 236 64 4 237 77 6 238 90 8 239 95 10 240 108 12 241 111 14 242 131 16 243 148 18 244 164 20 245 NA NA 246 43 0 247 52 2 248 61 4 249 73 6 250 90 8 251 103 10 252 127 12 253 135 14 254 145 16 255 163 18 256 170 20 257 NA NA 258 42 0 259 52 2 260 58 4 261 74 6 262 66 8 263 68 10 264 70 12 265 71 14 266 72 16 267 72 18 268 76 20 269 NA NA 270 40 0 271 49 2 272 62 4 273 78 6 274 102 8 275 124 10 276 146 12 277 164 14 278 197 16 279 231 18 280 259 20 281 NA NA 282 42 0 283 48 2 284 57 4 285 74 6 286 93 8 287 114 10 288 136 12 289 147 14 290 169 16 291 205 18 292 236 20 293 NA NA 294 39 0 295 46 2 296 58 4 297 73 6 298 87 8 299 100 10 300 115 12 301 123 14 302 144 16 303 163 18 304 185 20 305 NA NA 306 39 0 307 46 2 308 58 4 309 73 6 310 92 8 311 114 10 312 145 12 313 156 14 314 184 16 315 207 18 316 212 20 317 NA NA 318 39 0 319 48 2 320 59 4 321 74 6 322 87 8 323 106 10 324 134 12 325 150 14 326 187 16 327 230 18 328 279 20 329 NA NA 330 42 0 331 48 2 332 59 4 333 72 6 334 85 8 335 98 10 336 115 12 337 122 14 338 143 16 339 151 18 340 157 20 341 NA NA 342 42 0 343 53 2 344 62 4 345 73 6 346 85 8 347 102 10 348 123 12 349 138 14 350 170 16 351 204 18 352 235 20 353 NA NA 354 41 0 355 49 2 356 65 4 357 82 6 358 107 8 359 129 10 360 159 12 361 179 14 362 221 16 363 263 18 364 291 20 365 NA NA 366 39 0 367 50 2 368 63 4 369 77 6 370 96 8 371 111 10 372 137 12 373 144 14 374 151 16 375 146 18 376 156 20 377 NA NA 378 41 0 379 49 2 380 63 4 381 85 6 382 107 8 383 134 10 384 164 12 385 186 14 386 235 16 387 294 18 388 327 20 389 NA NA 390 41 0 391 53 2 392 64 4 393 87 6 394 123 8 395 158 10 396 201 12 397 238 14 398 287 16 399 332 18 400 361 20 401 NA NA 402 39 0 403 48 2 404 61 4 405 76 6 406 98 8 407 116 10 408 145 12 409 166 14 410 198 16 411 227 18 412 225 20 413 NA NA 414 41 0 415 48 2 416 56 4 417 68 6 418 80 8 419 83 10 420 103 12 421 112 14 422 135 16 423 157 18 424 169 20 425 NA NA 426 41 0 427 49 2 428 61 4 429 74 6 430 98 8 431 109 10 432 128 12 433 154 14 434 192 16 435 232 18 436 280 20 437 NA NA 438 42 0 439 50 2 440 61 4 441 78 6 442 89 8 443 109 10 444 130 12 445 146 14 446 170 16 447 214 18 448 250 20 449 NA NA 450 41 0 451 55 2 452 66 4 453 79 6 454 101 8 455 120 10 456 154 12 457 182 14 458 215 16 459 262 18 460 295 20 461 NA NA 462 42 0 463 51 2 464 66 4 465 85 6 466 103 8 467 124 10 468 155 12 469 153 14 470 175 16 471 184 18 472 199 20 473 NA NA 474 42 0 475 49 2 476 63 4 477 84 6 478 103 8 479 126 10 480 160 12 481 174 14 482 204 16 483 234 18 484 269 20 485 NA NA 486 42 0 487 55 2 488 69 4 489 96 6 490 131 8 491 157 10 492 184 12 493 188 14 494 197 16 495 198 18 496 199 20 497 NA NA 498 42 0 499 51 2 500 65 4 501 86 6 502 103 8 503 118 10 504 127 12 505 138 14 506 145 16 507 NA NA 508 41 0 509 50 2 510 61 4 511 78 6 512 98 8 513 117 10 514 135 12 515 141 14 516 147 16 517 174 18 518 197 20 519 NA NA 520 40 0 521 52 2 522 62 4 523 82 6 524 101 8 525 120 10 526 144 12 527 156 14 528 173 16 529 210 18 530 231 20 531 NA NA 532 41 0 533 53 2 534 66 4 535 79 6 536 100 8 537 123 10 538 148 12 539 157 14 540 168 16 541 185 18 542 210 20 543 NA NA 544 39 0 545 50 2 546 62 4 547 80 6 548 104 8 549 125 10 550 154 12 551 170 14 552 222 16 553 261 18 554 303 20 555 NA NA 556 40 0 557 53 2 558 64 4 559 85 6 560 108 8 561 128 10 562 152 12 563 166 14 564 184 16 565 203 18 566 233 20 567 NA NA 568 41 0 569 54 2 570 67 4 571 84 6 572 105 8 573 122 10 574 155 12 575 175 14 576 205 16 577 234 18 578 264 20 > shifts_by(data = mtcars, vrb.nm = c("disp","mpg"), grp.nm = c("vs","am"), n = 1L) disp_dw1 mpg_dw1 Mazda RX4 160.0 21.0 Mazda RX4 Wag 120.3 26.0 Datsun 710 78.7 32.4 Hornet 4 Drive 225.0 18.1 Hornet Sportabout 360.0 14.3 Valiant 146.7 24.4 Duster 360 275.8 16.4 Merc 240D 140.8 22.8 Merc 230 167.6 19.2 Merc 280 167.6 17.8 Merc 280C 120.1 21.5 Merc 450SE 275.8 17.3 Merc 450SL 275.8 15.2 Merc 450SLC 472.0 10.4 Cadillac Fleetwood 460.0 10.4 Lincoln Continental 440.0 14.7 Chrysler Imperial 318.0 15.5 Fiat 128 75.7 30.4 Honda Civic 71.1 33.9 Toyota Corolla 79.0 27.3 Toyota Corona NA NA Dodge Challenger 304.0 15.2 AMC Javelin 350.0 13.3 Camaro Z28 400.0 19.2 Pontiac Firebird NA NA Fiat X1-9 95.1 30.4 Porsche 914-2 351.0 15.8 Lotus Europa 121.0 21.4 Ford Pantera L 145.0 19.7 Ferrari Dino 301.0 15.0 Maserati Bora NA NA Volvo 142E NA NA > shifts_by(data = as.data.frame(CO2), vrb.nm = c("conc","uptake"), + grp.nm = c("Type","Treatment"), n = 2L) # multiple grouping columns conc_dw2 uptake_dw2 1 250 34.8 2 350 37.2 3 500 35.3 4 675 39.2 5 1000 39.7 6 95 13.6 7 175 27.3 8 250 37.1 9 350 41.8 10 500 40.6 11 675 41.4 12 1000 44.3 13 95 16.2 14 175 32.4 15 250 40.3 16 350 42.1 17 500 42.9 18 675 43.9 19 1000 45.5 20 NA NA 21 NA NA 22 250 30.3 23 350 34.6 24 500 32.5 25 675 35.4 26 1000 38.7 27 95 9.3 28 175 27.3 29 250 35.0 30 350 38.8 31 500 38.6 32 675 37.5 33 1000 42.4 34 95 15.1 35 175 21.0 36 250 38.1 37 350 34.0 38 500 38.9 39 675 39.6 40 1000 41.4 41 NA NA 42 NA NA 43 250 26.2 44 350 30.0 45 500 30.9 46 675 32.4 47 1000 35.5 48 95 12.0 49 175 22.0 50 250 30.6 51 350 31.8 52 500 32.4 53 675 31.1 54 1000 31.5 55 95 11.3 56 175 19.4 57 250 25.8 58 350 27.9 59 500 28.5 60 675 28.1 61 1000 27.8 62 NA NA 63 NA NA 64 250 18.1 65 350 18.9 66 500 19.5 67 675 22.2 68 1000 21.9 69 95 7.7 70 175 11.4 71 250 12.3 72 350 13.0 73 500 12.5 74 675 13.7 75 1000 14.4 76 95 10.6 77 175 18.0 78 250 17.9 79 350 17.9 80 500 17.9 81 675 18.9 82 1000 19.9 83 NA NA 84 NA NA > > > > cleanEx() > nameEx("sum_if") > ### * sum_if > > flush(stderr()); flush(stdout()) > > ### Name: sum_if > ### Title: Sum Conditional on Minimum Frequency of Observed Values > ### Aliases: sum_if > > ### ** Examples > > sum_if(x = airquality[[1]], ov.min = .75) # proportion of observed values [1] 6445.784 > sum_if(x = airquality[[1]], ov.min = 116, + prop = FALSE) # count of observe values [1] 6445.784 > sum_if(x = airquality[[1]], ov.min = 116, prop = FALSE, + inclusive = FALSE) # not include ov.min value itself [1] NA > sum_if(x = c(TRUE, NA, FALSE, NA), + ov.min = .50) # works with logical vectors as well as numeric [1] 2 > > > > cleanEx() > nameEx("tapply2") > ### * tapply2 > > flush(stderr()); flush(stdout()) > > ### Name: tapply2 > ### Title: Apply a Function to a (Atomic) Vector by Group > ### Aliases: tapply2 > > ### ** Examples > > > # one grouping variable > tapply2(mtcars$"cyl", .grp = mtcars$"vs", .fun = median, na.rm = TRUE) $`0` [1] 8 $`1` [1] 4 > > # two grouping variables > grp_nm <- c("vs","am") # Roxygen runs the whole script if I put a c() in a [] > x <- tapply2(mtcars$"cyl", .grp = mtcars[grp_nm], .fun = median, na.rm = TRUE) > print(x) $`0.0` [1] 8 $`1.0` [1] 6 $`0.1` [1] 6 $`1.1` [1] 4 > str(x) List of 4 $ 0.0: num 8 $ 1.0: num 6 $ 0.1: num 6 $ 1.1: num 4 > > # compare to tapply > grp_nm <- c("vs","am") # Roxygen runs the whole script if I put a c() in a [] > y <- tapply(mtcars$"cyl", INDEX = mtcars[grp_nm], + FUN = median, na.rm = TRUE, simplify = FALSE) > print(y) am vs 0 1 0 8 6 1 6 4 > str(y) # has dimnames rather than names List of 4 $ : num 8 $ : num 6 $ : num 6 $ : num 4 - attr(*, "dim")= int [1:2] 2 2 - attr(*, "dimnames")=List of 2 ..$ vs: chr [1:2] "0" "1" ..$ am: chr [1:2] "0" "1" > > > > cleanEx() > nameEx("valid_test") > ### * valid_test > > flush(stderr()); flush(stdout()) > > ### Name: valid_test > ### Title: Test for Invalid Elements in a Vector > ### Aliases: valid_test > > ### ** Examples > > valid_test(x = psych::bfi[[1]], valid = 1:6) # return TRUE [1] TRUE > valid_test(x = psych::bfi[[1]], valid = 0:5) # 6 is not present in `valid` [1] FALSE > valid_test(x = psych::bfi[[1]], valid = 1:6, + na.rm = FALSE) # NA is not present in `valid` [1] FALSE > > > > cleanEx() > nameEx("valids_test") > ### * valids_test > > flush(stderr()); flush(stdout()) > > ### Name: valids_test > ### Title: Test for Invalid Elements in Data > ### Aliases: valids_test > > ### ** Examples > > valids_test(data = psych::bfi, vrb.nm = names(psych::bfi)[1:25], + valid = 1:6) # return TRUE A1 A2 A3 A4 A5 C1 C2 C3 C4 C5 E1 E2 E3 E4 E5 N1 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE N2 N3 N4 N5 O1 O2 O3 O4 O5 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE > valids_test(data = psych::bfi, vrb.nm = names(psych::bfi)[1:25], + valid = 0:5) # 6 is not present in `valid` A1 A2 A3 A4 A5 C1 C2 C3 C4 C5 E1 E2 E3 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE E4 E5 N1 N2 N3 N4 N5 O1 O2 O3 O4 O5 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE > valids_test(data = psych::bfi, vrb.nm = names(psych::bfi)[1:25], + valid = 1:6, na.rm = FALSE) # NA is not present in `valid` A1 A2 A3 A4 A5 C1 C2 C3 C4 C5 E1 E2 E3 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE E4 E5 N1 N2 N3 N4 N5 O1 O2 O3 O4 O5 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE > valids_test(data = ToothGrowth, vrb.nm = c("supp","dose"), + valid = list("VC", "OJ", 0.5, 1.0, 2.0)) # list vector as `valid` to allow for supp dose TRUE TRUE > # elements of different typeof > > > > cleanEx() > nameEx("vecNA") > ### * vecNA > > flush(stderr()); flush(stdout()) > > ### Name: vecNA > ### Title: Frequency of Missing Values in a Vector > ### Aliases: vecNA > > ### ** Examples > > vecNA(airquality[[1]]) # count of missing values [1] 37 > vecNA(airquality[[1]], prop = TRUE) # proportion of missing values [1] 0.2418301 > vecNA(airquality[[1]], ov = TRUE) # count of observed values [1] 116 > vecNA(airquality[[1]], prop = TRUE, ov = TRUE) # proportion of observed values [1] 0.7581699 > > > > cleanEx() > nameEx("wide2long") > ### * wide2long > > flush(stderr()); flush(stdout()) > > ### Name: wide2long > ### Title: Reshape Multiple Sets of Variables From Wide to Long > ### Aliases: wide2long > > ### ** Examples > > > # SINGLE GROUPING VARIABLE > dat_wide <- data.frame( + x_1.1 = runif(5L), + x_2.1 = runif(5L), + x_3.1 = runif(5L), + x_4.1 = runif(5L), + x_1.2 = runif(5L), + x_2.2 = runif(5L), + x_3.2 = runif(5L), + x_4.2 = runif(5L), + x_1.3 = runif(5L), + x_2.3 = runif(5L), + x_3.3 = runif(5L), + x_4.3 = runif(5L), + y_1.1 = runif(5L), + y_2.1 = runif(5L), + y_1.2 = runif(5L), + y_2.2 = runif(5L), + y_1.3 = runif(5L), + y_2.3 = runif(5L)) > row.names(dat_wide) <- letters[1:5] > print(dat_wide) x_1.1 x_2.1 x_3.1 x_4.1 x_1.2 x_2.2 x_3.2 a 0.2655087 0.89838968 0.2059746 0.4976992 0.9347052 0.38611409 0.4820801 b 0.3721239 0.94467527 0.1765568 0.7176185 0.2121425 0.01339033 0.5995658 c 0.5728534 0.66079779 0.6870228 0.9919061 0.6516738 0.38238796 0.4935413 d 0.9082078 0.62911404 0.3841037 0.3800352 0.1255551 0.86969085 0.1862176 e 0.2016819 0.06178627 0.7698414 0.7774452 0.2672207 0.34034900 0.8273733 x_4.2 x_1.3 x_2.3 x_3.3 x_4.3 y_1.1 y_2.1 a 0.6684667 0.8209463 0.7893562 0.47761962 0.09946616 0.9128759 0.25801678 b 0.7942399 0.6470602 0.0233312 0.86120948 0.31627171 0.2936034 0.47854525 c 0.1079436 0.7829328 0.4772301 0.43809711 0.51863426 0.4590657 0.76631067 d 0.7237109 0.5530363 0.7323137 0.24479728 0.66200508 0.3323947 0.08424691 e 0.4112744 0.5297196 0.6927316 0.07067905 0.40683019 0.6508705 0.87532133 y_1.2 y_2.2 y_1.3 y_2.3 a 0.3390729 0.8921983 0.4346595 0.2026923 b 0.8394404 0.8643395 0.7125147 0.7111212 c 0.3466835 0.3899895 0.3999944 0.1216919 d 0.3337749 0.7773207 0.3253522 0.2454885 e 0.4763512 0.9606180 0.7570871 0.1433044 > > # vrb.nm.list = list of character vectors (conventional use) > vrb_pat <- c("x_1","x_2","x_3","x_4","y_1","y_2") > vrb_nm_list <- lapply(X = setNames(vrb_pat, nm = vrb_pat), FUN = function(pat) { + str2str::pick(x = names(dat_wide), val = pat, pat = TRUE)}) > # without `grp.nm` > z1 <- wide2long(dat_wide, vrb.nm = vrb_nm_list) > # with `grp.nm` > dat_wide$"ID" <- letters[1:5] > z2 <- wide2long(dat_wide, vrb.nm = vrb_nm_list, grp.nm = "ID") > dat_wide$"ID" <- NULL > > # vrb.nm.list = character vector + guessing (advanced use) > vrb_nm <- str2str::pick(x = names(dat_wide), val = "ID", not = TRUE) > # without `grp.nm` > z3 <- wide2long(dat_wide, vrb.nm.list = vrb_nm) > # with `grp.nm` > dat_wide$"ID" <- letters[1:5] > z4 <- wide2long(dat_wide, vrb.nm = vrb_nm, grp.nm = "ID") > dat_wide$"ID" <- NULL > > # comparisons > head(z1); head(z3); head(z2); head(z4) Row.names obs x_1 x_2 x_3 x_4 y_1 y_2 1 a 1 0.2655087 0.89838968 0.2059746 0.49769924 0.9128759 0.2580168 2 a 2 0.9347052 0.38611409 0.4820801 0.66846674 0.3390729 0.8921983 3 a 3 0.8209463 0.78935623 0.4776196 0.09946616 0.4346595 0.2026923 4 b 1 0.3721239 0.94467527 0.1765568 0.71761851 0.2936034 0.4785452 5 b 2 0.2121425 0.01339033 0.5995658 0.79423986 0.8394404 0.8643395 6 b 3 0.6470602 0.02333120 0.8612095 0.31627171 0.7125147 0.7111212 Row.names obs x_1 x_2 x_3 x_4 y_1 y_2 1 a 1 0.2655087 0.89838968 0.2059746 0.49769924 0.9128759 0.2580168 2 a 2 0.9347052 0.38611409 0.4820801 0.66846674 0.3390729 0.8921983 3 a 3 0.8209463 0.78935623 0.4776196 0.09946616 0.4346595 0.2026923 4 b 1 0.3721239 0.94467527 0.1765568 0.71761851 0.2936034 0.4785452 5 b 2 0.2121425 0.01339033 0.5995658 0.79423986 0.8394404 0.8643395 6 b 3 0.6470602 0.02333120 0.8612095 0.31627171 0.7125147 0.7111212 ID obs x_1 x_2 x_3 x_4 y_1 y_2 1 a 1 0.2655087 0.89838968 0.2059746 0.49769924 0.9128759 0.2580168 2 a 2 0.9347052 0.38611409 0.4820801 0.66846674 0.3390729 0.8921983 3 a 3 0.8209463 0.78935623 0.4776196 0.09946616 0.4346595 0.2026923 4 b 1 0.3721239 0.94467527 0.1765568 0.71761851 0.2936034 0.4785452 5 b 2 0.2121425 0.01339033 0.5995658 0.79423986 0.8394404 0.8643395 6 b 3 0.6470602 0.02333120 0.8612095 0.31627171 0.7125147 0.7111212 ID obs x_1 x_2 x_3 x_4 y_1 y_2 1 a 1 0.2655087 0.89838968 0.2059746 0.49769924 0.9128759 0.2580168 2 a 2 0.9347052 0.38611409 0.4820801 0.66846674 0.3390729 0.8921983 3 a 3 0.8209463 0.78935623 0.4776196 0.09946616 0.4346595 0.2026923 4 b 1 0.3721239 0.94467527 0.1765568 0.71761851 0.2936034 0.4785452 5 b 2 0.2121425 0.01339033 0.5995658 0.79423986 0.8394404 0.8643395 6 b 3 0.6470602 0.02333120 0.8612095 0.31627171 0.7125147 0.7111212 > all.equal(z1, z3) [1] TRUE > all.equal(z2, z4) [1] TRUE > # keeping the reshapeLong attributes > z7 <- wide2long(dat_wide, vrb.nm = vrb_nm_list, keep.attr = TRUE) > attributes(z7) $names [1] "Row.names" "obs" "x_1" "x_2" "x_3" "x_4" [7] "y_1" "y_2" $row.names [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 $class [1] "data.frame" $reshapeLong $reshapeLong$varying $reshapeLong$varying$x_1 [1] "x_1.1" "x_1.2" "x_1.3" $reshapeLong$varying$x_2 [1] "x_2.1" "x_2.2" "x_2.3" $reshapeLong$varying$x_3 [1] "x_3.1" "x_3.2" "x_3.3" $reshapeLong$varying$x_4 [1] "x_4.1" "x_4.2" "x_4.3" $reshapeLong$varying$y_1 [1] "y_1.1" "y_1.2" "y_1.3" $reshapeLong$varying$y_2 [1] "y_2.1" "y_2.2" "y_2.3" $reshapeLong$v.names [1] "x_1" "x_2" "x_3" "x_4" "y_1" "y_2" $reshapeLong$idvar [1] "Row.names" $reshapeLong$timevar [1] "obs" > > # MULTIPLE GROUPING VARIABLES > bfi2 <- psych::bfi > bfi2$"person" <- unlist(lapply(X = 1:400, FUN = rep.int, times = 7)) > bfi2$"day" <- rep.int(1:7, times = 400L) > head(bfi2, n = 15) A1 A2 A3 A4 A5 C1 C2 C3 C4 C5 E1 E2 E3 E4 E5 N1 N2 N3 N4 N5 O1 O2 O3 O4 61617 2 4 3 4 4 2 3 3 4 4 3 3 3 4 4 3 4 2 2 3 3 6 3 4 61618 2 4 5 2 5 5 4 4 3 4 1 1 6 4 3 3 3 3 5 5 4 2 4 3 61620 5 4 5 4 4 4 5 4 2 5 2 4 4 4 5 4 5 4 2 3 4 2 5 5 61621 4 4 6 5 5 4 4 3 5 5 5 3 4 4 4 2 5 2 4 1 3 3 4 3 61622 2 3 3 4 5 4 4 5 3 2 2 2 5 4 5 2 3 4 4 3 3 3 4 3 61623 6 6 5 6 5 6 6 6 1 3 2 1 6 5 6 3 5 2 2 3 4 3 5 6 61624 2 5 5 3 5 5 4 4 2 3 4 3 4 5 5 1 2 2 1 1 5 2 5 6 61629 4 3 1 5 1 3 2 4 2 4 3 6 4 2 1 6 3 2 6 4 3 2 4 5 61630 4 3 6 3 3 6 6 3 4 5 5 3 NA 4 3 5 5 2 3 3 6 6 6 6 61633 2 5 6 6 5 6 5 6 2 1 2 2 4 5 5 5 5 5 2 4 5 1 5 5 61634 4 4 5 6 5 4 3 5 3 2 1 3 2 5 4 3 3 4 2 3 5 3 5 6 61636 2 5 5 5 5 5 4 5 4 5 3 3 4 5 4 4 5 3 2 NA 4 6 4 5 61637 5 5 5 6 4 5 4 3 2 2 3 3 3 2 4 1 2 2 2 2 4 2 4 5 61639 5 5 5 6 6 4 4 4 2 1 2 2 4 6 5 1 1 1 2 1 5 3 4 4 61640 4 5 2 2 1 5 5 5 2 2 3 4 3 6 5 2 4 2 2 3 5 2 5 5 O5 gender education age person day 61617 3 1 NA 16 1 1 61618 3 2 NA 18 1 2 61620 2 2 NA 17 1 3 61621 5 2 NA 17 1 4 61622 3 1 NA 17 1 5 61623 1 2 3 21 1 6 61624 1 1 NA 18 1 7 61629 3 1 2 19 2 1 61630 1 1 1 19 2 2 61633 2 2 NA 17 2 3 61634 3 1 1 21 2 4 61636 4 1 NA 16 2 5 61637 2 2 NA 16 2 6 61639 4 1 NA 16 2 7 61640 5 1 1 17 3 1 > > # vrb.nm.list = list of character vectors (conventional use) > vrb_pat <- c("A","C","E","N","O") > vrb_nm_list <- lapply(X = setNames(vrb_pat, nm = vrb_pat), FUN = function(pat) { + str2str::pick(x = names(bfi2), val = pat, pat = TRUE)}) > z5 <- wide2long(bfi2, vrb.nm.list = vrb_nm_list, grp = c("person","day"), + rtn.obs.nm = "item") > > # vrb.nm.list = character vector + guessing (advanced use) > vrb_nm <- str2str::pick(x = names(bfi2), + val = c("person","day","gender","education","age"), not = TRUE) > z6 <- wide2long(bfi2, vrb.nm.list = vrb_nm, grp = c("person","day"), + sep = "", rtn.obs.nm = "item") # need sep = "" because no character separating > # scale name and item number > all.equal(z5, z6) [1] TRUE > > > > > cleanEx() > nameEx("winsor") > ### * winsor > > flush(stderr()); flush(stdout()) > > ### Name: winsor > ### Title: Winsorize a Numeric Vector > ### Aliases: winsor > > ### ** Examples > > > # winsorize > table(quakes$"stations") 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 20 28 25 21 39 34 35 38 33 29 28 37 35 35 14 22 18 33 23 19 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 28 15 19 13 18 20 21 9 6 10 11 12 10 14 8 6 3 8 6 14 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 10 8 6 1 8 5 5 8 2 3 3 7 1 4 4 7 1 7 7 5 70 71 72 73 74 75 76 77 78 79 80 81 82 83 85 86 87 88 89 90 3 6 3 3 4 2 1 1 4 4 2 3 3 1 1 3 4 2 2 1 91 92 93 94 95 98 99 100 104 105 106 109 110 112 115 118 119 121 122 123 3 1 1 2 1 1 1 1 2 2 1 1 1 1 1 1 2 1 1 1 129 132 1 1 > new <- winsor(quakes$"stations") > table(new) new 10 11 12 13 20 28 25 21 14 15 16 17 39 34 35 38 18 19 20 21 33 29 28 37 22 23 24 25 35 35 14 22 26 27 28 29 18 33 23 19 30 31 32 33 28 15 19 13 34 35 36 37 18 20 21 9 38 39 40 41 6 10 11 12 42 43 44 45 10 14 8 6 46 47 48 49 3 8 6 14 50 51 52 53 10 8 6 1 54 55 56 57 8 5 5 8 58 59 60 61 2 3 3 7 62 63 64 65 1 4 4 7 66 67 68 69 1 7 7 5 70 71 72 73 3 6 3 3 74 75 76 77 4 2 1 1 78 79 80 81 4 4 2 3 82 83 85 86 3 1 1 3 87 88 89 90 4 2 2 1 91 92 93 94 3 1 1 2 95 98 99 99.1191577228752 1 1 1 18 > > # recode as NA > vecNA(quakes$"stations") [1] 0 > new <- winsor(quakes$"stations", to.na = TRUE) > vecNA(new) [1] 18 > > # rtn.int = TRUE > winsor(x = cars[[1]], z.min = -2, z.max = 2, rtn.int = FALSE) [1] 4.824711 4.824711 7.000000 7.000000 8.000000 9.000000 10.000000 [8] 10.000000 10.000000 11.000000 11.000000 12.000000 12.000000 12.000000 [15] 12.000000 13.000000 13.000000 13.000000 13.000000 14.000000 14.000000 [22] 14.000000 14.000000 15.000000 15.000000 15.000000 16.000000 16.000000 [29] 17.000000 17.000000 17.000000 18.000000 18.000000 18.000000 18.000000 [36] 19.000000 19.000000 19.000000 20.000000 20.000000 20.000000 20.000000 [43] 20.000000 22.000000 23.000000 24.000000 24.000000 24.000000 24.000000 [50] 25.000000 > winsor(x = cars[[1]], z.min = -2, z.max = 2, rtn.int = TRUE) [1] 5 5 7 7 8 9 10 10 10 11 11 12 12 12 12 13 13 13 13 14 14 14 14 15 15 [26] 15 16 16 17 17 17 18 18 18 18 19 19 19 20 20 20 20 20 22 23 24 24 24 24 25 > > > > cleanEx() > nameEx("winsors") > ### * winsors > > flush(stderr()); flush(stdout()) > > ### Name: winsors > ### Title: Winsorize Numeric Data > ### Aliases: winsors > > ### ** Examples > > > # winsorize > lapply(X = quakes[c("mag","stations")], FUN = table) $mag 4 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 5 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.9 6 46 55 90 85 101 107 101 98 65 54 47 43 29 21 20 14 9 8 2 3 6.1 6.4 1 1 $stations 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 20 28 25 21 39 34 35 38 33 29 28 37 35 35 14 22 18 33 23 19 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 28 15 19 13 18 20 21 9 6 10 11 12 10 14 8 6 3 8 6 14 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 10 8 6 1 8 5 5 8 2 3 3 7 1 4 4 7 1 7 7 5 70 71 72 73 74 75 76 77 78 79 80 81 82 83 85 86 87 88 89 90 3 6 3 3 4 2 1 1 4 4 2 3 3 1 1 3 4 2 2 1 91 92 93 94 95 98 99 100 104 105 106 109 110 112 115 118 119 121 122 123 3 1 1 2 1 1 1 1 2 2 1 1 1 1 1 1 2 1 1 1 129 132 1 1 > new <- winsors(quakes, vrb.nm = names(quakes)) > lapply(X = new, FUN = table) $lat_win -35.7291226281892 -35.56 -35.48 -34.89 8 1 1 1 -34.68 -34.63 -34.4 -34.2 1 1 1 1 -34.12 -34.1 -34.02 -33.57 1 1 1 1 -33.29 -33.2 -33.09 -33.03 1 1 1 1 -33 -32.9 -32.82 -32.7 2 1 1 1 -32.62 -32.6 -32.45 -32.42 1 1 1 1 -32.22 -32.2 -32.14 -31.94 1 1 2 1 -31.8 -31.24 -31.03 -30.8 1 1 1 1 -30.72 -30.69 -30.66 -30.64 1 1 1 1 -30.63 -30.51 -30.4 -30.39 1 1 1 1 -30.3 -30.28 -30.24 -30.2 1 1 1 1 -30.17 -30.1 -30.09 -30.04 1 1 1 1 -30.01 -29.91 -29.9 -29.5 2 1 1 1 -29.33 -29.1 -29.09 -28.98 1 1 1 1 -28.96 -28.83 -28.74 -28.56 1 1 1 2 -28.25 -28.23 -28.22 -28.15 1 1 1 1 -28.11 -28.1 -28.05 -28 1 2 1 1 -27.99 -27.98 -27.89 -27.87 1 1 1 1 -27.84 -27.75 -27.72 -27.71 1 1 1 1 -27.64 -27.63 -27.6 -27.54 1 1 2 1 -27.38 -27.33 -27.28 -27.27 2 1 1 3 -27.24 -27.23 -27.22 -27.21 1 1 1 1 -27.2 -27.19 -27.18 -27.17 1 1 2 1 -27.1 -27.08 -27 -26.78 1 1 1 1 -26.72 -26.67 -26.6 -26.54 2 1 1 1 -26.53 -26.5 -26.46 -26.4 2 1 1 1 -26.2 -26.18 -26.17 -26.16 2 2 1 2 -26.13 -26.11 -26.1 -26.06 1 1 2 1 -26.02 -26 -25.93 -25.82 1 3 1 1 -25.81 -25.8 -25.79 -25.68 1 1 1 1 -25.63 -25.6 -25.59 -25.5 1 1 1 1 -25.48 -25.46 -25.42 -25.31 1 1 1 2 -25.28 -25.25 -25.23 -25.2 1 1 1 1 -25.14 -25.06 -25.04 -25 1 1 3 1 -24.97 -24.96 -24.89 -24.81 3 2 1 1 -24.78 -24.71 -24.68 -24.64 1 1 1 1 -24.6 -24.57 -24.5 -24.41 1 2 1 1 -24.4 -24.39 -24.36 -24.34 1 1 1 1 -24.33 -24.27 -24.2 -24.19 1 1 1 1 -24.18 -24.12 -24.1 -24.09 1 1 1 1 -24.08 -24.04 -24.03 -24 1 1 1 1 -23.97 -23.95 -23.93 -23.92 1 2 1 2 -23.91 -23.9 -23.89 -23.87 1 2 1 1 -23.85 -23.84 -23.83 -23.82 2 2 1 1 -23.81 -23.8 -23.79 -23.78 1 2 1 1 -23.77 -23.75 -23.74 -23.73 1 1 1 5 -23.71 -23.7 -23.65 -23.64 1 2 1 1 -23.61 -23.6 -23.58 -23.56 2 1 2 1 -23.55 -23.54 -23.53 -23.5 2 2 1 4 -23.49 -23.47 -23.46 -23.45 2 3 3 1 -23.44 -23.43 -23.42 -23.39 2 1 1 1 -23.36 -23.34 -23.33 -23.31 1 1 2 1 -23.3 -23.29 -23.28 -23.2 3 1 2 1 -23.19 -23.12 -23.11 -23.1 1 1 1 1 -23.08 -23.07 -23 -22.95 1 1 1 1 -22.91 -22.9 -22.87 -22.85 1 1 2 1 -22.82 -22.75 -22.7 -22.64 1 2 3 1 -22.63 -22.58 -22.55 -22.54 1 1 3 1 -22.5 -22.43 -22.42 -22.41 1 1 1 1 -22.37 -22.36 -22.34 -22.33 1 1 1 3 -22.32 -22.3 -22.28 -22.26 1 1 1 1 -22.24 -22.23 -22.2 -22.19 2 1 1 1 -22.16 -22.14 -22.13 -22.12 1 2 1 1 -22.1 -22.09 -22.06 -22.05 3 2 3 1 -22.04 -22.03 -22 -21.98 2 1 3 1 -21.97 -21.96 -21.92 -21.91 1 2 1 1 -21.88 -21.85 -21.81 -21.8 1 1 1 2 -21.79 -21.78 -21.77 -21.75 2 1 1 1 -21.71 -21.68 -21.63 -21.62 1 1 1 1 -21.6 -21.59 -21.58 -21.57 2 1 1 2 -21.56 -21.55 -21.54 -21.53 2 2 1 1 -21.52 -21.5 -21.48 -21.47 1 2 2 1 -21.46 -21.44 -21.4 -21.39 1 2 2 1 -21.38 -21.37 -21.35 -21.34 1 1 1 1 -21.33 -21.31 -21.3 -21.29 1 1 2 3 -21.27 -21.26 -21.24 -21.23 1 1 2 1 -21.22 -21.2 -21.19 -21.18 1 1 1 1 -21.16 -21.14 -21.13 -21.11 2 2 2 1 -21.09 -21.08 -21.07 -21.06 1 2 2 1 -21.05 -21.04 -21.03 -21 2 2 1 2 -20.99 -20.97 -20.95 -20.94 1 3 2 2 -20.93 -20.92 -20.91 -20.9 1 1 1 5 -20.89 -20.88 -20.87 -20.85 2 2 1 1 -20.84 -20.83 -20.82 -20.81 1 2 1 2 -20.77 -20.76 -20.75 -20.74 2 1 1 2 -20.73 -20.72 -20.7 -20.69 1 1 3 1 -20.68 -20.66 -20.65 -20.64 1 1 3 2 -20.63 -20.62 -20.61 -20.6 1 1 2 1 -20.58 -20.57 -20.56 -20.54 1 1 2 1 -20.51 -20.49 -20.48 -20.47 1 1 2 1 -20.45 -20.43 -20.42 -20.41 2 1 3 2 -20.4 -20.37 -20.36 -20.33 1 1 2 1 -20.32 -20.31 -20.3 -20.27 2 1 3 1 -20.25 -20.24 -20.21 -20.2 1 2 3 2 -20.18 -20.16 -20.14 -20.13 1 2 1 1 -20.12 -20.1 -20.09 -20.08 1 3 1 2 -20.07 -20.06 -20.05 -20.04 2 1 1 2 -20.02 -19.94 -19.92 -19.9 1 1 1 1 -19.89 -19.88 -19.86 -19.85 3 1 1 3 -19.84 -19.83 -19.77 -19.76 1 2 1 1 -19.73 -19.72 -19.7 -19.69 1 1 2 1 -19.68 -19.67 -19.66 -19.64 2 1 2 1 -19.62 -19.61 -19.6 -19.57 1 1 4 2 -19.52 -19.51 -19.5 -19.47 1 1 1 1 -19.45 -19.44 -19.41 -19.4 1 1 2 2 -19.36 -19.34 -19.33 -19.3 1 2 1 4 -19.28 -19.26 -19.23 -19.22 1 1 1 2 -19.21 -19.2 -19.19 -19.18 1 1 2 1 -19.17 -19.15 -19.14 -19.13 1 1 1 2 -19.1 -19.06 -19.02 -19 3 2 2 1 -18.98 -18.97 -18.96 -18.94 1 2 1 1 -18.92 -18.91 -18.89 -18.85 1 1 4 2 -18.84 -18.83 -18.82 -18.8 1 1 2 2 -18.78 -18.77 -18.76 -18.75 1 1 1 1 -18.73 -18.69 -18.68 -18.64 2 1 1 1 -18.6 -18.56 -18.55 -18.54 2 2 1 2 -18.51 -18.5 -18.49 -18.48 1 1 1 2 -18.44 -18.4 -18.35 -18.31 1 3 1 1 -18.3 -18.26 -18.21 -18.2 1 1 1 2 -18.19 -18.18 -18.17 -18.16 1 2 1 1 -18.14 -18.13 -18.12 -18.11 2 1 2 2 -18.1 -18.08 -18.07 -18.06 2 1 4 1 -18.05 -18.04 -18 -17.99 1 3 2 4 -17.98 -17.97 -17.96 -17.95 4 3 1 5 -17.94 -17.93 -17.91 -17.9 3 3 1 4 -17.88 -17.87 -17.86 -17.85 2 1 1 1 -17.84 -17.83 -17.82 -17.81 2 1 3 1 -17.8 -17.79 -17.78 -17.74 5 1 2 3 -17.72 -17.71 -17.7 -17.68 2 1 6 3 -17.67 -17.66 -17.64 -17.63 1 1 2 1 -17.61 -17.6 -17.59 -17.56 2 1 2 2 -17.47 -17.46 -17.44 -17.43 2 3 2 1 -17.42 -17.4 -17.38 -17.32 1 3 1 1 -17.2 -17.14 -17.1 -17.08 1 1 4 1 -17.05 -17.04 -17.03 -17.02 1 1 1 2 -16.99 -16.98 -16.96 -16.95 1 1 1 1 -16.9 -16.85 -16.65 -16.62 1 1 1 1 -16.52 -16.51 -16.49 -16.46 1 1 1 1 -16.45 -16.44 -16.43 -16.4 2 1 1 2 -16.32 -16.31 -16.3 -16.28 1 1 1 1 -16.24 -16.23 -16.21 -16.2 3 2 1 1 -16.17 -16.14 -16.11 -16.1 1 1 1 2 -16.09 -16.04 -16.03 -16 1 1 1 2 -15.99 -15.97 -15.96 -15.95 1 1 1 1 -15.94 -15.93 -15.9 -15.87 1 1 3 1 -15.86 -15.85 -15.83 -15.8 2 3 2 1 -15.79 -15.78 -15.77 -15.75 1 1 1 1 -15.72 -15.71 -15.7 -15.67 1 1 2 1 -15.66 -15.65 -15.61 -15.56 1 2 2 1 -15.55 -15.54 -15.5 -15.49 1 2 2 1 -15.48 -15.46 -15.45 -15.44 2 1 2 2 -15.43 -15.41 -15.4 -15.39 3 1 1 1 -15.36 -15.35 -15.34 -15.33 3 1 1 1 -15.31 -15.29 -15.28 -15.26 2 2 1 1 -15.24 -15.2 -15.18 -15.17 2 1 2 1 -15.08 -15.03 -15.02 -15 1 2 1 1 -14.99 -14.95 -14.86 -14.85 1 1 1 2 -14.82 -14.72 -14.7 -14.65 2 1 1 1 -14.6 -14.57 -14.46 -14.43 1 1 1 1 -14.32 -14.31 -14.3 -14.28 1 1 1 2 -14.12 -14.1 -13.9 -13.86 1 1 1 1 -13.84 -13.82 -13.8 -13.79 1 1 1 1 -13.7 -13.66 -13.65 -13.64 1 2 1 1 -13.62 -13.56 -13.47 -13.45 1 1 2 1 -13.44 -13.4 -13.36 -13.34 1 1 2 1 -13.26 -13.23 -13.16 -13.09 1 1 1 1 -13.07 -13.05 -12.95 -12.93 1 1 1 2 -12.85 -12.84 -12.72 -12.66 1 1 1 2 -12.59 -12.57 -12.49 -12.37 1 2 1 1 -12.34 -12.28 -12.27 -12.26 1 1 1 1 -12.25 -12.23 -12.16 -12.11 1 1 1 1 -12.08 -12.05 -12.01 -12 1 1 2 1 -11.81 -11.8 -11.77 -11.76 1 1 1 1 -11.75 -11.7 -11.67 -11.64 1 2 1 1 -11.63 -11.55 -11.54 -11.49 1 1 1 1 -11.41 -11.4 -11.38 -11.37 1 1 1 1 -11.34 -11.25 -11.02 -10.98 1 1 1 1 -10.97 -10.96 -10.8 -10.79 1 1 1 1 -10.78 -10.72 2 1 $long_win 165.67 165.76 165.77 165.8 165.96 165.97 165.98 165.99 166 166.01 166.02 1 1 1 2 2 1 1 1 1 1 1 166.06 166.07 166.1 166.14 166.18 166.2 166.22 166.24 166.26 166.28 166.29 1 2 2 1 1 3 1 2 1 1 1 166.3 166.32 166.36 166.37 166.47 166.49 166.53 166.54 166.55 166.56 166.6 1 2 2 1 1 1 2 1 1 1 1 166.62 166.64 166.66 166.69 166.72 166.74 166.75 166.78 166.8 166.83 166.85 1 1 2 1 1 1 1 1 1 1 1 166.87 166.9 166.91 166.93 166.94 166.97 166.98 167 167.01 167.02 167.03 1 2 1 1 1 1 1 1 3 1 1 167.05 167.06 167.1 167.11 167.14 167.15 167.16 167.18 167.23 167.24 167.25 1 3 4 1 1 1 2 2 1 4 1 167.26 167.32 167.33 167.34 167.38 167.39 167.4 167.41 167.42 167.43 167.44 3 4 1 1 1 1 1 1 1 1 1 167.5 167.51 167.53 167.54 167.62 167.68 167.7 167.89 167.91 167.95 168.02 1 2 1 1 1 1 1 1 2 2 1 168.08 168.52 168.63 168.69 168.71 168.75 168.8 168.93 168.98 169.01 169.04 1 1 1 1 1 1 1 1 2 1 1 169.05 169.09 169.1 169.14 169.15 169.21 169.23 169.24 169.28 169.31 169.32 1 1 1 1 1 1 1 1 1 1 1 169.33 169.37 169.41 169.42 169.44 169.46 169.48 169.49 169.5 169.52 169.53 2 1 1 1 1 2 2 1 2 1 1 169.58 169.63 169.66 169.71 169.75 169.76 169.84 169.9 169.92 170.04 170.3 1 2 1 2 1 1 1 1 1 1 2 170.34 170.4 170.45 170.5 170.52 170.56 170.62 170.7 170.99 171.17 171.39 1 1 1 1 1 2 1 1 1 1 1 171.4 171.44 171.46 171.5 171.51 171.52 171.65 171.66 171.72 172.23 172.29 2 1 1 1 1 1 1 1 1 1 1 172.38 172.65 172.76 172.91 173.2 173.49 173.5 174.21 174.46 175.7 176.03 1 1 1 1 1 1 1 1 1 1 1 176.78 177.01 177.1 177.47 177.52 177.77 177.81 178.29 178.3 178.31 178.35 1 1 1 1 1 1 1 1 2 1 1 178.4 178.41 178.42 178.43 178.47 178.52 178.57 178.59 178.9 178.98 179.02 1 1 1 1 1 1 1 1 1 1 1 179.07 179.1 179.15 179.2 179.22 179.24 179.27 179.33 179.36 179.43 179.5 1 1 1 1 1 1 1 1 1 1 2 179.52 179.54 179.59 179.6 179.61 179.62 179.67 179.68 179.69 179.7 179.71 1 2 1 2 1 2 1 1 1 1 1 179.77 179.78 179.79 179.8 179.82 179.84 179.85 179.86 179.87 179.88 179.89 1 1 1 1 2 1 2 2 1 1 1 179.9 179.91 179.92 179.93 179.95 179.96 179.97 179.98 179.99 180 180.01 4 1 1 1 1 1 2 2 3 6 1 180.02 180.03 180.04 180.05 180.06 180.08 180.09 180.1 180.11 180.12 180.13 1 1 1 1 1 1 2 2 1 1 3 180.15 180.16 180.17 180.18 180.2 180.21 180.22 180.23 180.24 180.26 180.27 2 2 2 2 3 4 2 3 1 3 2 180.28 180.3 180.31 180.34 180.38 180.39 180.4 180.47 180.48 180.49 180.5 2 3 2 1 3 1 2 1 1 2 3 180.52 180.53 180.54 180.57 180.58 180.6 180.62 180.63 180.64 180.67 180.68 1 1 2 1 3 6 2 2 2 2 1 180.69 180.7 180.74 180.77 180.78 180.79 180.8 180.81 180.82 180.84 180.85 2 2 1 1 2 1 4 2 1 1 2 180.86 180.87 180.88 180.89 180.9 180.92 180.94 180.96 180.97 180.98 180.99 2 2 1 1 4 3 3 1 1 3 1 181 181.01 181.02 181.03 181.04 181.06 181.09 181.11 181.13 181.15 181.16 2 1 3 2 1 2 2 3 1 2 3 181.17 181.18 181.19 181.2 181.21 181.22 181.23 181.24 181.25 181.26 181.27 1 1 1 8 1 1 2 2 2 1 1 181.28 181.3 181.31 181.32 181.33 181.35 181.36 181.37 181.38 181.39 181.4 2 6 2 4 2 1 1 2 3 2 8 181.41 181.42 181.43 181.44 181.47 181.48 181.49 181.5 181.51 181.52 181.53 6 5 1 1 2 3 3 8 5 1 2 181.54 181.55 181.57 181.58 181.59 181.6 181.61 181.62 181.63 181.65 181.66 2 1 4 4 5 5 2 4 3 2 4 181.67 181.69 181.7 181.71 181.72 181.73 181.74 181.75 181.77 181.8 181.82 2 3 6 4 2 1 3 3 1 1 1 181.83 181.84 181.85 181.86 181.87 181.88 181.89 181.9 181.91 181.93 181.96 1 1 2 1 2 1 1 5 2 1 2 181.98 181.99 182 182.01 182.02 182.04 182.1 182.11 182.12 182.13 182.16 1 1 3 1 3 3 7 1 1 1 2 182.18 182.2 182.21 182.22 182.23 182.25 182.26 182.28 182.29 182.3 182.31 4 2 1 2 1 1 2 2 2 7 2 182.32 182.35 182.36 182.37 182.38 182.39 182.4 182.41 182.43 182.44 182.45 2 1 1 4 3 6 8 2 3 3 1 182.47 182.5 182.51 182.53 182.54 182.56 182.6 182.61 182.62 182.64 182.65 1 4 2 3 3 1 3 1 1 1 1 182.68 182.69 182.72 182.73 182.74 182.75 182.77 182.78 182.8 182.82 182.84 2 1 1 1 1 1 1 1 5 2 1 182.85 182.9 182.92 182.93 183 183.05 183.11 183.13 183.2 183.22 183.23 1 2 1 3 3 1 1 1 4 1 1 183.3 183.32 183.33 183.34 183.35 183.4 183.41 183.44 183.45 183.47 183.48 1 1 1 1 1 6 1 1 1 1 1 183.5 183.51 183.52 183.54 183.58 183.59 183.6 183.61 183.63 183.68 183.71 4 1 1 1 1 2 2 1 1 2 1 183.78 183.8 183.81 183.83 183.84 183.86 183.87 183.88 183.91 183.95 183.97 2 1 2 1 3 2 1 1 1 3 1 183.99 184 184.03 184.06 184.08 184.09 184.1 184.13 184.14 184.16 184.2 2 1 1 1 1 1 2 1 1 1 2 184.23 184.24 184.27 184.28 184.3 184.31 184.35 184.36 184.37 184.4 184.41 2 1 1 2 2 2 1 1 1 1 1 184.42 184.46 184.47 184.48 184.49 184.5 184.51 184.52 184.53 184.56 184.6 2 2 2 2 1 5 1 3 2 1 3 184.61 184.62 184.64 184.68 184.7 184.75 184.8 184.83 184.84 184.85 184.87 1 1 1 4 3 1 1 1 1 1 1 184.89 184.9 184.91 184.93 184.95 184.97 185 185.01 185.05 185.1 185.11 1 1 1 1 2 1 2 1 1 3 1 185.13 185.16 185.17 185.18 185.19 185.2 185.23 185.25 185.26 185.27 185.3 2 1 2 1 1 2 2 2 2 1 3 185.31 185.32 185.33 185.35 185.4 185.43 185.48 185.5 185.51 185.6 185.61 1 2 1 1 1 2 2 2 1 2 1 185.62 185.64 185.68 185.7 185.72 185.74 185.75 185.77 185.8 185.86 185.9 1 1 1 1 1 2 1 3 2 3 4 185.93 185.94 185.96 185.98 186 186.04 186.08 186.1 186.16 186.2 186.21 1 1 1 1 1 1 1 2 2 1 1 186.26 186.3 186.36 186.4 186.42 186.44 186.51 186.52 186.54 186.59 186.66 1 3 1 1 1 1 1 1 1 1 1 186.71 186.72 186.73 186.74 186.75 186.78 186.8 186.83 186.87 186.9 187 1 1 3 1 1 1 3 1 1 2 1 187.09 187.1 187.15 187.2 187.32 187.48 187.55 187.8 187.81 188.1 188.13 1 1 2 1 1 1 1 2 1 1 1 $depth_win 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 12 4 11 5 5 8 4 7 6 6 9 7 2 3 5 3 8 7 3 2 60 61 62 63 64 65 66 67 68 69 70 71 72 74 75 76 77 78 79 80 5 5 3 8 4 5 5 3 7 9 8 4 1 4 6 2 1 1 1 4 81 82 83 84 85 86 87 89 90 93 94 95 96 97 98 99 100 102 103 104 1 8 4 1 3 2 2 3 3 6 4 1 2 3 2 3 5 4 3 3 105 106 107 108 109 111 112 116 117 118 119 121 123 124 125 126 127 128 129 130 1 2 3 2 2 1 4 1 2 4 2 1 4 1 4 1 2 3 3 5 132 133 134 135 136 137 138 139 140 142 143 144 146 148 149 150 151 152 153 154 1 3 2 2 2 4 4 3 2 1 2 1 2 3 2 4 1 1 3 2 155 156 158 161 162 164 165 166 168 169 170 172 174 175 176 178 179 180 182 183 1 1 3 1 6 1 2 1 1 1 1 1 2 3 2 3 1 4 2 1 184 186 188 189 190 193 194 195 197 199 200 201 202 203 204 205 206 207 208 209 2 3 2 1 2 1 1 4 1 3 1 4 2 2 4 2 2 1 1 2 210 211 213 215 216 217 218 219 220 221 223 224 225 226 228 229 230 231 232 234 5 5 2 1 1 1 3 3 2 4 5 1 1 2 1 1 2 1 1 3 236 237 239 242 243 244 246 248 249 251 254 255 257 259 260 261 262 263 264 265 1 2 1 5 3 2 3 4 3 1 1 1 1 1 3 1 1 1 2 1 266 268 269 270 271 273 275 278 280 284 286 287 290 291 292 293 294 296 297 298 1 1 1 1 2 1 1 2 2 1 2 1 1 1 1 1 2 1 1 1 299 300 302 304 306 307 309 315 323 325 326 328 329 331 332 334 338 339 342 343 1 1 1 2 2 1 2 1 1 2 1 2 1 1 1 1 1 1 1 1 348 349 350 356 360 361 364 365 367 375 376 377 383 384 385 388 390 391 393 397 1 1 2 1 1 1 1 1 3 2 1 1 2 1 1 1 3 1 2 4 399 401 402 403 405 406 409 411 413 417 420 422 423 431 432 434 440 442 445 450 1 1 1 1 1 2 2 1 1 2 1 1 1 2 1 1 1 2 2 1 452 460 462 464 467 470 474 475 476 477 479 480 481 482 483 484 485 487 488 489 1 1 1 2 1 2 2 1 2 2 2 1 1 1 1 2 1 2 2 1 490 491 492 493 497 498 499 500 501 502 504 505 506 507 508 510 511 512 513 515 1 1 3 1 3 4 2 3 2 1 2 2 2 1 2 3 3 2 1 1 517 518 520 521 522 523 524 525 526 527 528 529 530 532 533 534 535 536 537 538 1 3 1 1 1 1 6 2 1 2 1 2 6 1 2 2 3 1 4 6 539 541 542 543 544 545 546 548 549 550 553 554 555 556 557 558 559 560 561 562 2 2 1 4 4 2 5 5 1 2 5 3 2 3 1 1 4 2 2 5 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 1 3 2 2 1 3 1 2 1 1 5 5 4 3 4 1 5 2 1 4 583 584 585 586 587 589 590 591 592 593 594 595 597 598 599 600 601 602 603 604 5 2 1 4 7 4 4 2 2 5 3 5 3 4 2 4 2 1 3 1 605 606 607 608 609 611 613 614 615 616 617 618 619 622 624 625 626 627 628 629 6 2 1 2 4 2 3 2 2 2 4 3 2 3 3 1 3 4 1 1 630 631 632 636 637 638 639 640 641 642 644 646 649 650 651 654 655 658 663 664 1 2 1 2 2 2 1 2 3 2 2 1 2 1 1 1 2 2 1 1 671 680 1 1 $mag_win 4 4.1 4.2 4.3 46 55 90 85 4.4 4.5 4.6 4.7 101 107 101 98 4.8 4.9 5 5.1 65 54 47 43 5.2 5.3 5.4 5.5 29 21 20 14 5.6 5.7 5.82871891261976 9 8 7 $stations_win 10 11 12 13 20 28 25 21 14 15 16 17 39 34 35 38 18 19 20 21 33 29 28 37 22 23 24 25 35 35 14 22 26 27 28 29 18 33 23 19 30 31 32 33 28 15 19 13 34 35 36 37 18 20 21 9 38 39 40 41 6 10 11 12 42 43 44 45 10 14 8 6 46 47 48 49 3 8 6 14 50 51 52 53 10 8 6 1 54 55 56 57 8 5 5 8 58 59 60 61 2 3 3 7 62 63 64 65 1 4 4 7 66 67 68 69 1 7 7 5 70 71 72 73 3 6 3 3 74 75 76 77 4 2 1 1 78 79 80 81 4 4 2 3 82 83 85 86 3 1 1 3 87 88 89 90 4 2 2 1 91 92 93 94 3 1 1 2 95 98 99 99.1191577228752 1 1 1 18 > > # recode as NA > vecNA(quakes) [1] 0 > new <- winsors(quakes, vrb.nm = names(quakes), to.na = TRUE) > vecNA(new) [1] 0 > > # rtn.int = TRUE > winsors(data = cars, vrb.nm = names(cars), z.min = -2, z.max = 2, rtn.int = FALSE) speed_win dist_win 1 4.824711 2.00000 2 4.824711 10.00000 3 7.000000 4.00000 4 7.000000 22.00000 5 8.000000 16.00000 6 9.000000 10.00000 7 10.000000 18.00000 8 10.000000 26.00000 9 10.000000 34.00000 10 11.000000 17.00000 11 11.000000 28.00000 12 12.000000 14.00000 13 12.000000 20.00000 14 12.000000 24.00000 15 12.000000 28.00000 16 13.000000 26.00000 17 13.000000 34.00000 18 13.000000 34.00000 19 13.000000 46.00000 20 14.000000 26.00000 21 14.000000 36.00000 22 14.000000 60.00000 23 14.000000 80.00000 24 15.000000 20.00000 25 15.000000 26.00000 26 15.000000 54.00000 27 16.000000 32.00000 28 16.000000 40.00000 29 17.000000 32.00000 30 17.000000 40.00000 31 17.000000 50.00000 32 18.000000 42.00000 33 18.000000 56.00000 34 18.000000 76.00000 35 18.000000 84.00000 36 19.000000 36.00000 37 19.000000 46.00000 38 19.000000 68.00000 39 20.000000 32.00000 40 20.000000 48.00000 41 20.000000 52.00000 42 20.000000 56.00000 43 20.000000 64.00000 44 22.000000 66.00000 45 23.000000 54.00000 46 24.000000 70.00000 47 24.000000 92.00000 48 24.000000 93.00000 49 24.000000 94.51875 50 25.000000 85.00000 > winsors(data = cars, vrb.nm = names(cars), z.min = -2, z.max = 2, rtn.int = TRUE) speed_win dist_win 1 5 2 2 5 10 3 7 4 4 7 22 5 8 16 6 9 10 7 10 18 8 10 26 9 10 34 10 11 17 11 11 28 12 12 14 13 12 20 14 12 24 15 12 28 16 13 26 17 13 34 18 13 34 19 13 46 20 14 26 21 14 36 22 14 60 23 14 80 24 15 20 25 15 26 26 15 54 27 16 32 28 16 40 29 17 32 30 17 40 31 17 50 32 18 42 33 18 56 34 18 76 35 18 84 36 19 36 37 19 46 38 19 68 39 20 32 40 20 48 41 20 52 42 20 56 43 20 64 44 22 66 45 23 54 46 24 70 47 24 92 48 24 93 49 24 95 50 25 85 > > > > ### *