## Problem

You want to visualize the strength of correlations among many variables.

## Solution

``````set.seed(955)
vvar <- 1:20 + rnorm(20,sd=3)
wvar <- 1:20 + rnorm(20,sd=5)
xvar <- 20:1 + rnorm(20,sd=3)
yvar <- (1:20)/2 + rnorm(20, sd=10)
zvar <- rnorm(20, sd=6)

# A data frame with multiple variables
data <- data.frame(vvar, wvar, xvar, yvar, zvar)
#>        vvar       wvar     xvar       yvar      zvar
#> 1 -4.252354  5.1219288 16.02193 -15.156368 -4.086904
#> 2  1.702318 -1.3234340 15.83817 -24.063902  3.468423
#> 3  4.323054 -2.1570874 19.85517   2.306770 -3.044931
#> 4  1.780628  0.7880138 17.65079   2.564663  1.449081
#> 5 11.537348 -1.3075994 10.93386   9.600835  2.761963
#> 6  6.672130  2.0135190 15.24350  -3.465695  5.749642
``````

To make the graph:

``````library(ellipse)

# Make the correlation table
ctab <- cor(data)
round(ctab, 2)
#>       vvar  wvar  xvar  yvar  zvar
#> vvar  1.00  0.61 -0.85  0.75 -0.21
#> wvar  0.61  1.00 -0.81  0.54 -0.31
#> xvar -0.85 -0.81  1.00 -0.63  0.24
#> yvar  0.75  0.54 -0.63  1.00 -0.30
#> zvar -0.21 -0.31  0.24 -0.30  1.00

# Make the graph, with reduced margins
plotcorr(ctab, mar = c(0.1, 0.1, 0.1, 0.1))

# Do the same, but with colors corresponding to value
colorfun <- colorRamp(c("#CC0000","white","#3366CC"), space="Lab")
plotcorr(ctab, col=rgb(colorfun((ctab+1)/2), maxColorValue=255),
mar = c(0.1, 0.1, 0.1, 0.1))
``````

### Notes

For more information on generating the correlation table (with numbers), see: ../../Statistical analysis/Regression and correlation