A data set of 1,000 simulated cases may be found using the following commands in R 
 datafilename="http://personality-project.org/R/datasets/psychometrics.prob2.txt" 
 dataset =read.table(datafilename,header=TRUE)  #read the data file
Get the data set and find the basic descriptive statistics. Then plot GREV versus GREQ against each other.

Find the correlation matrix of all of the variables in the data set.

Show the scatter plot of two variables.

Problem 1: Find the basic descriptive statistics: Use the summary() command 
 
summary(dataset)


       ID              GREV            GREQ            GREA      
 Min.   :   1.0   Min.   :138.0   Min.   :191.0   Min.   :207.0  
 1st Qu.: 250.8   1st Qu.:427.0   1st Qu.:428.0   1st Qu.:431.8  
 Median : 500.5   Median :497.5   Median :498.0   Median :495.0  
 Mean   : 500.5   Mean   :499.8   Mean   :500.5   Mean   :498.1  
 3rd Qu.: 750.2   3rd Qu.:570.2   3rd Qu.:571.0   3rd Qu.:569.0  
 Max.   :1000.0   Max.   :873.0   Max.   :914.0   Max.   :848.0  
      Ach             Anx            Prelim           GPA       
 Min.   :16.00   Min.   :14.00   Min.   : 7.00   Min.   :2.500  
 1st Qu.:43.00   1st Qu.:44.00   1st Qu.: 9.00   1st Qu.:3.640  
 Median :50.00   Median :50.00   Median :10.00   Median :4.020  
 Mean   :49.93   Mean   :50.32   Mean   :10.03   Mean   :4.003  
 3rd Qu.:56.00   3rd Qu.:57.00   3rd Qu.:11.00   3rd Qu.:4.360  
 Max.   :79.00   Max.   :78.00   Max.   :13.00   Max.   :5.380  
       MA       
 Min.   :1.400  
 1st Qu.:2.700  
 Median :3.000  
 Mean   :2.996  
 3rd Qu.:3.300  
 Max.   :4.500

Problem 2: Find the correlation matrix for these cases. Although the basic command cor(dataset) will do what we want, a more useful call is to round these results to two decimals.
round(cor(dataset),2)
produces this output. 


> round(cor(dataset),2)
          ID  GREV GREQ  GREA   Ach   Anx Prelim   GPA    MA
ID      1.00 -0.01 0.00 -0.01  0.00 -0.01   0.02  0.00 -0.01
GREV   -0.01  1.00 0.73  0.64  0.01  0.01   0.43  0.42  0.32
GREQ    0.00  0.73 1.00  0.60  0.01  0.01   0.38  0.37  0.29
GREA   -0.01  0.64 0.60  1.00  0.45 -0.39   0.57  0.52  0.45
Ach     0.00  0.01 0.01  0.45  1.00 -0.56   0.30  0.28  0.26
Anx    -0.01  0.01 0.01 -0.39 -0.56  1.00  -0.23 -0.22 -0.22
Prelim  0.02  0.43 0.38  0.57  0.30 -0.23   1.00  0.42  0.36
GPA     0.00  0.42 0.37  0.52  0.28 -0.22   0.42  1.00  0.31
MA     -0.01  0.32 0.29  0.45  0.26 -0.22   0.36  0.31  1.00
To show the scatter plot of the GREV and GREQ, use the plot() function.
In order to use the variable names directly, first attach the data file


attach(dataset) #this makes the names in the file available to be called
plot(GREV,GREQ,main="Simulated GRE V vs. GRE Q")

Which produces this plot:

Perhaps more useful is to do this for all the pairs of variables. This may be done as a SPLOM (scatter plot matrix) using the pairs() command.

pairs(dataset)

produces this output

Using a bit more code, it is possible to combine the correlation matrix with the SPLOM 

#first, we create a new function (taken from the example for pairs)

panel.cor <- function(x, y, digits=2, prefix="", cex.cor)
     {
         usr <- par("usr"); on.exit(par(usr))
         par(usr = c(0, 1, 0, 1))
         r = (cor(x, y,use="pairwise"))
         txt <- format(c(r, 0.123456789), digits=digits)[1]
         txt <- paste(prefix, txt, sep="")
         if(missing(cex.cor)) cex <- 0.8/strwidth(txt)
         text(0.5, 0.5, txt, cex = cex * abs(r))
     }
     
 # now use the function for the data. (see figure)
 #do the following one at at time
 
  pairs(dataset, lower.panel=panel.smooth, upper.panel=panel.cor)