The goal of this document is to show you how to visualize correlation matrix using R heatmap function. This type of plot can help to quickly identify the most correlated variables. You can read more on correlation matrix by clicking here.
mtcars data is used in the following examples :
mydata <- mtcars[, c(1,3,4,5,6,7)] head(mydata)
mpg disp hp drat wt qsec Mazda RX4 21.0 160 110 3.90 2.620 16.46 Mazda RX4 Wag 21.0 160 110 3.90 2.875 17.02 Datsun 710 22.8 108 93 3.85 2.320 18.61 Hornet 4 Drive 21.4 258 110 3.08 3.215 19.44 Hornet Sportabout 18.7 360 175 3.15 3.440 17.02 Valiant 18.1 225 105 2.76 3.460 20.22
Computing the correlation matrix
The correlation matrix is computed as follow:
mpg disp hp drat wt qsec mpg 1.00 -0.85 -0.78 0.680 -0.87 0.420 disp -0.85 1.00 0.79 -0.710 0.89 -0.430 hp -0.78 0.79 1.00 -0.450 0.66 -0.710 drat 0.68 -0.71 -0.45 1.000 -0.71 0.091 wt -0.87 0.89 0.66 -0.710 1.00 -0.170 qsec 0.42 -0.43 -0.71 0.091 -0.17 1.000
Drawing a heatmap
# Get some colors col<- colorRampPalette(c("blue", "white", "red"))(20) heatmap(cormat, col=col, symm=TRUE)
Negative correlations are shown in blue and the positive ones in red.
This analysis has been performed with R (ver. 3.1.0).
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