Graphics Reference
In-Depth Information
Chapter13.Miscellaneous Graphs
There are many, many ways of visualizing data, and sometimes things don't fit into nice, tidy
categories. This chapter shows how to make some of these other visualizations.
Making a Correlation Matrix
Problem
You want to make a graphical correlation matrix.
Solution
We'll look at the
mtcars
data set:
mtcars
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4
21.0
6 160.0 110 3.90 2.620 16.46 0 1
4
4
Mazda RX4 Wag
21.0
6 160.0 110 3.90 2.875 17.02 0 1
4
4
Datsun
710
22.8
4 108.0 93 3.85 2.320 18.61 1 1
4
1
...
Ferrari Dino
19.7
6 145.0 175 3.62 2.770 15.50 0 1
5
6
Maserati Bora
15.0
8 301.0 335 3.54 3.570 14.60 0 1
5
8
Volvo
142
E
21.4
4 121.0 109 4.11 2.780 18.60 1 1
4
2
First, generate the numerical correlation matrix using
cor
. This will generate correlation coeffi-
cients for each pair of columns:
mcor
<-
cor(mtcars)
# Print mcor and round to 2 digits
round(mcor, digits
=
2
)
mpg cyl disp hp drat wt qsec vs am gear carb
mpg
1.00 -0.85 -0.85 -0.78 0.68 -0.87 0.42 0.66 0.60 0.48 -0.55
cyl
-0.85 1.00 0.90 0.83 -0.70 0.78 -0.59 -0.81 -0.52 -0.49 0.53
disp
-0.85 0.90 1.00 0.79 -0.71 0.89 -0.43 -0.71 -0.59 -0.56 0.39
hp
-0.78 0.83 0.79 1.00 -0.45 0.66 -0.71 -0.72 -0.24 -0.13 0.75
drat
0.68 -0.70 -0.71 -0.45 1.00 -0.71 0.09 0.44 0.71 0.70 -0.09
wt
-0.87 0.78 0.89 0.66 -0.71 1.00 -0.17 -0.55 -0.69 -0.58 0.43
qsec
0.42 -0.59 -0.43 -0.71 0.09 -0.17 1.00 0.74 -0.23 -0.21 -0.66
vs
0.66 -0.81 -0.71 -0.72 0.44 -0.55 0.74 1.00 0.17 0.21 -0.57
