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.
13.1 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 6 160 110 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
#> Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
#> ...<26 more rows>...
#> Ferrari Dino 19.7 6 145 175 3.62 2.770 15.50 0 1 5 6
#> Maserati Bora 15.0 8 301 335 3.54 3.570 14.60 0 1 5 8
#> Volvo 142E 21.4 4 121 109 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
#> ...<5 more rows>...
#> am 0.60 -0.52 -0.59 -0.24 0.71 -0.69 -0.23 0.17 1.00 0.79 0.06
#> gear 0.48 -0.49 -0.56 -0.13 0.70 -0.58 -0.21 0.21 0.79 1.00 0.27
#> carb -0.55 0.53 0.39 0.75 -0.09 0.43 -0.66 -0.57 0.06 0.27 1.00
If there are any columns that you don’t want used for correlations ...
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