Chapter 13. 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 coefficients 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
drat0.68
-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
qsec0.42
-0.59
-0.43
-0.71
0.09
-0.17
1.00
0.74
-0.23
-0.21
-0.66
vs0.66
-0.81
-0.71
-0.72
0.44
-0.55
0.74
1.00
0.17
0.21
-0.57
am0.60
-0.52
-0.59
-0.24 ...
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