A short list of the most useful R commands

A summary of the most important commands with minimal examples. See the relevant part of the guide for better examples. For all of these commands, using the help(function) or ? function is the most useful source of information. Unfortunately, knowing what to ask for help about is the hardest problem.

See the R-reference card by Tom Short for a much more complete list.

Input and display

#read files with labels in first row
read.table(filename,header=TRUE)           #read a tab or space delimited file
read.table(filename,header=TRUE,sep=',')   #read csv files

x=c(1,2,4,8,16 )                           #create a data vector with specified elements
y=c(1:10)                                  #create a data vector with elements 1-10
n=10
x1=c(rnorm(n))                             #create a n item vector of random normal deviates
y1=c(runif(n))+n                           #create another n item vector that has n added to each random uniform distribution
z=rbinom(n,size,prob)                      #create n samples of size "size" with probability prob from the binomial
vect=c(x,y)                                #combine them into one vector of length 2n
mat=cbind(x,y)                             #combine them into a n x 2 matrix
mat[4,2]                                   #display the 4th row and the 2nd column
mat[3,]                                    #display the 3rd row
mat[,2]                                    #display the 2nd column
subset(dataset,logical)                    #those objects meeting a logical criterion
subset(data.df,select=variables,logical)   #get those objects from a data frame that meet a criterion
data.df[data.df=logical]                   #yet another way to get a subset
x[order(x$B),]                             #sort a dataframe by the order of the elements in B
x[rev(order(x$B)),]                        #sort the dataframe in reverse order 

browse.workspace                           #a menu command that creates a window with information about all variables in the workspace

Moving around

ls()                                      #list the variables in the workspace
rm(x)                                     #remove x from the workspace
rm(list=ls())                             #remove all the variables from the workspace
attach(mat)                               #make the names of the variables in the matrix or data frame available in the workspace
detach(mat)                               #releases the names
new=old[,-n]                              #drop the nth column
new=old[n,]                               #drop the nth row
new=subset(old,logical)                   #select those cases that meet the logical condition
complete = subset(data.df,complete.cases(data.df)) #find those cases with no missing values
new=old[n1:n2,n3:n4]                      #select the n1 through n2 rows of variables n3 through n4)

Distributions

beta(a, b)
gamma(x)
choose(n, k)
factorial(x)

dnorm(x, mean=0, sd=1, log = FALSE)      #normal distribution
pnorm(q, mean=0, sd=1, lower.tail = TRUE, log.p = FALSE)
qnorm(p, mean=0, sd=1, lower.tail = TRUE, log.p = FALSE)
rnorm(n, mean=0, sd=1)


dunif(x, min=0, max=1, log = FALSE)      #uniform distribution
punif(q, min=0, max=1, lower.tail = TRUE, log.p = FALSE)
qunif(p, min=0, max=1, lower.tail = TRUE, log.p = FALSE)
runif(n, min=0, max=1)

Data manipulation

replace(x, list, values)                 #remember to assign this to some object i.e., x <- replace(x,x==-9,NA) 
                                         #similar to the operation x[x==-9] <- NA


cut(x, breaks, labels = NULL,
    include.lowest = FALSE, right = TRUE, dig.lab = 3, ...)

x.df=data.frame(x1,x2,x3 ...)             #combine different kinds of data into a data frame
	as.data.frame()
	is.data.frame()
x=as.matrix()
scale()                                   #converts a data frame to standardized scores

round(x,n)                                #rounds the values of x to n decimal places
ceiling(x)                                #vector x of smallest integers > x
floor(x)                                  #vector x of largest interger < x
as.integer(x)                             #truncates real x to integers (compare to round(x,0)
as.integer(x < cutpoint)                  #vector x of 0 if less than cutpoint, 1 if greater than cutpoint)
factor(ifelse(a < cutpoint, "Neg", "Pos"))  #is another way to dichotomize and to make a factor for analysis 
transform(data.df,variable names = some operation) #can be part of a set up for a data set 

x%in%y                     #tests each element of x for membership in y
y%in%x                     #tests each element of y for membership in x
all(x%in%y)                #true if x is a proper subset of y
all(x)                     # for a vector of logical values, are they all true?
any(x)                     #for a vector of logical values, is at least one true?

Statistics and transformations

max()
min()
mean()
median()
sum()
var()     #produces the variance covariance matrix
sd()      #standard deviation
mad()    #(median absolute deviation)
fivenum() #Tukey fivenumbers min, lowerhinge, median, upper hinge, max
table()    #frequency counts of entries, ideally the entries are factors(although it works with integers or even reals)
scale(data,scale=T)   #centers around the mean and scales by the sd)
cumsum(x)     #cumulative sum, etc.
cumprod(x)
cummax(x)
cummin(x)
rev(x)      #reverse the order of values in x
 
cor(x,y,use="pair")   #correlation matrix for pairwise complete data, use="complete" for complete cases
 
aov(x~y,data=datafile)  #where x and y can be matrices
aov.ex1 = aov(DV~IV,data=data.ex1)  #do the analysis of variance or
aov.ex2 = aov(DV~IV1*IV21,data=data.ex2)         #do a two way analysis of variance
summary(aov.ex1)                                    #show the summary table
print(model.tables(aov.ex1,"means"),digits=3)       #report the means and the number of subjects/cell
boxplot(DV~IV,data=data.ex1)        #graphical summary appears in graphics window

lm(x~y,data=dataset)                      #basic linear model where x and y can be matrices  (see plot.lm for plotting options)
t.test(x,g)
pairwise.t.test(x,g)
power.anova.test(groups = NULL, n = NULL, between.var = NULL,
                 within.var = NULL, sig.level = 0.05, power = NULL)
power.t.test(n = NULL, delta = NULL, sd = 1, sig.level = 0.05,
             power = NULL, type = c("two.sample", "one.sample", "paired"),
             alternative = c("two.sided", "one.sided"),strict = FALSE)

More statistics: Regression, the linear model, factor analysis and principal components analysis (PCA)

matrices
lm(Y~X1+X2)
lm(Y~X|W)                              
solve(A,B)                               #inverse of A * B   - used for linear regression
solve(A)                                 #inverse of A
factanal()    (see also fa in the psych package)
princomp()     (see principal in the psych package)

Useful additional commands

colSums (x, na.rm = FALSE, dims = 1)
     rowSums (x, na.rm = FALSE, dims = 1)
     colMeans(x, na.rm = FALSE, dims = 1)
     rowMeans(x, na.rm = FALSE, dims = 1)
     rowsum(x, group, reorder = TRUE, ...)         #finds row sums for each level of a grouping variable
     apply(X, MARGIN, FUN, ...)                    #applies the function (FUN) to either rows (1) or columns (2) on object X
     	apply(x,1,min)                             #finds the minimum for each row
    	apply(x,2,max)                            #finds the maximum for each column
    col.max(x)                                   #another way to find which column has the maximum value for each row 
    which.min(x)
    which.max(x)
    	z=apply(big5r,1,which.min)               #tells the row with the minimum value for every column

Graphics

par(mfrow=c(nrow,mcol))                        #number of rows and columns to graph
par(ask=TRUE)                             #ask for user input before drawing a new graph
par(omi=c(0,0,1,0) )                      #set the size of the outer margins 
mtext("some global title",3,outer=TRUE,line=1,cex=1.5)    #note that we seem to need to add the global title last
                     #cex = character expansion factor 

boxplot(x,main="title")                  #boxplot (box and whiskers)


title( "some title")                          #add a title to the first graph
	

hist()                                   #histogram
plot()
	plot(x,y,xlim=range(-1,1),ylim=range(-1,1),main=title)
	par(mfrow=c(1,1))     #change the graph window back to one figure
	symb=c(19,25,3,23)
	colors=c("black","red","green","blue")
	charact=c("S","T","N","H")
	plot(PA,NAF,pch=symb[group],col=colors[group],bg=colors[condit],cex=1.5,main="Postive vs. Negative Affect by Film condition")
	points(mPA,mNA,pch=symb[condit],cex=4.5,col=colors[condit],bg=colors[condit])
	
curve()
abline(a,b)
	 abline(a, b, untf = FALSE, ...)
     abline(h=, untf = FALSE, ...)
     abline(v=, untf = FALSE, ...)
     abline(coef=, untf = FALSE, ...)
     abline(reg=, untf = FALSE, ...)

identify()
	plot(eatar,eanta,xlim=range(-1,1),ylim=range(-1,1),main=title)
	identify(eatar,eanta,labels=labels(energysR[,1])  )       #dynamically puts names on the plots
locate()

legend()
pairs()                                  #SPLOM (scatter plot Matrix)
pairs.panels ()    #SPLOM on lower off diagonal, histograms on diagonal, correlations on diagonal
                   #not standard R, but uses a function found in useful.r 
matplot ()
biplot ())
plot(table(x))                           #plot the frequencies of levels in x

x= recordPlot()                           #save the current plot device output in the object x
replayPlot(x)                            #replot object x
dev.control                              #various control functions for printing/saving graphic files
pdf(height=6, width=6)              #create a pdf file for output
dev.of()                            #close the pdf file created with pdf 
layout(mat)                         #specify where multiple graphs go on the page
                                    #experiment with the magic code from Paul Murrell to do fancy graphic location
layout(rbind(c(1, 1, 2, 2, 3, 3),
             c(0, 4, 4, 5, 5, 0)))   
for (i in 1:5) {
  plot(i, type="n")
  text(1, i, paste("Plot", i), cex=4)
}

Distributions

To generate random samples from a variety of distributions
rnorm(n,mean,sd)
rbinom(n,size,p)
sample(x, size, replace = FALSE, prob = NULL)      #samples with or without replacement

Working with Dates

date <-strptime(as.character(date), "%m/%d/%y")   #change the date field to a internal form for time  
                                                  #see ?formats and ?POSIXlt  
 as.Date
 month= months(date)                #see also weekdays, Julian

And more...

The psych package includes about 300 additional functions that I have created in last 6 year. These were created because I needed some specific operation. Follow the instructions for installing the psych package.


These functions include:

#alpha.scale     #find coefficient alpha for a scale and a dataframe of items
#describe        give means, sd, skew, n, and se 
#summ.stats      #basic summary statistics by a grouping variable
#error.crosses   #(error bars in two space)
#skew            find skew
#panel.cor       taken from the examples for pairs
#pairs.panels    adapted from panel.cor  --   gives a splom, histogram, and correlation matrix
#multi.hist  #plot multiple histograms
#correct.cor    #given a correlation matrix and a vector of reliabilities, correct for reliability
#fisherz        #convert pearson r to fisher z
#paired.r       #test for difference of dependent correlations
#count.pairwise  #count the number of good cases when doing pairwise analysis
#eigen.loadings  #convert eigen vector vectors to factor loadings by unnormalizing them
#principal       #yet another way to do a principal components analysis -- brute force eignvalue decomp 
#factor.congruence #find the factor congruence coeffiecints
#factor.model    #given a factor model, find the correlation matrix
#factor.residuals #how well does it fit?
#factor.rotate    # rotate two columns of a factor matrix by theta (in degrees)
#phi2poly       #convert a matrix of phi coefficients to polychoric correlations