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Categorical Data
Aravind Hebbali
2024-11-08
Introduction
In this document, we will introduce you to functions for exploring
and visualizing categorical data.
Data
We have modified the mtcars data to create a new data
set mtcarz. The only difference between the two data sets
is related to the variable types.
str(mtcarz)
#> 'data.frame': 32 obs. of 11 variables:
#> $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
#> $ cyl : Factor w/ 3 levels "4","6","8": 2 2 1 2 3 2 3 1 1 2 ...
#> $ disp: num 160 160 108 258 360 ...
#> $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
#> $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
#> $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
#> $ qsec: num 16.5 17 18.6 19.4 17 ...
#> $ vs : Factor w/ 2 levels "0","1": 1 1 2 2 1 2 1 2 2 2 ...
#> $ am : Factor w/ 2 levels "0","1": 2 2 2 1 1 1 1 1 1 1 ...
#> $ gear: Factor w/ 3 levels "3","4","5": 2 2 2 1 1 1 1 2 2 2 ...
#> $ carb: Factor w/ 6 levels "1","2","3","4",..: 4 4 1 1 2 1 4 2 2 4 ...
Cross Tabulation
The ds_cross_table() function creates two way tables of
categorical variables.
ds_cross_table(mtcarz, cyl, gear)
#> Cell Contents
#> |---------------|
#> | Frequency |
#> | Percent |
#> | Row Pct |
#> | Col Pct |
#> |---------------|
#>
#> Total Observations: 32
#>
#> ----------------------------------------------------------------------------
#> | | gear |
#> ----------------------------------------------------------------------------
#> | cyl | 3 | 4 | 5 | Row Total |
#> ----------------------------------------------------------------------------
#> | 4 | 1 | 8 | 2 | 11 |
#> | | 0.031 | 0.25 | 0.062 | |
#> | | 0.09 | 0.73 | 0.18 | 0.34 |
#> | | 0.07 | 0.67 | 0.4 | |
#> ----------------------------------------------------------------------------
#> | 6 | 2 | 4 | 1 | 7 |
#> | | 0.062 | 0.125 | 0.031 | |
#> | | 0.29 | 0.57 | 0.14 | 0.22 |
#> | | 0.13 | 0.33 | 0.2 | |
#> ----------------------------------------------------------------------------
#> | 8 | 12 | 0 | 2 | 14 |
#> | | 0.375 | 0 | 0.062 | |
#> | | 0.86 | 0 | 0.14 | 0.44 |
#> | | 0.8 | 0 | 0.4 | |
#> ----------------------------------------------------------------------------
#> | Column Total | 15 | 12 | 5 | 32 |
#> | | 0.468 | 0.375 | 0.155 | |
#> ----------------------------------------------------------------------------
If you want the above result as a tibble, use
ds_twoway_table().
ds_twoway_table(mtcarz, cyl, gear)
#> Joining with `by = join_by(cyl, gear, count)`
#> # A tibble: 8 × 6
#> cyl gear count percent row_percent col_percent
#> <fct> <fct> <int> <dbl> <dbl> <dbl>
#> 1 4 3 1 0.0312 0.0909 0.0667
#> 2 4 4 8 0.25 0.727 0.667
#> 3 4 5 2 0.0625 0.182 0.4
#> 4 6 3 2 0.0625 0.286 0.133
#> 5 6 4 4 0.125 0.571 0.333
#> 6 6 5 1 0.0312 0.143 0.2
#> 7 8 3 12 0.375 0.857 0.8
#> 8 8 5 2 0.0625 0.143 0.4
A plot() method has been defined which will
generate:
Grouped Bar Plots
k <- ds_cross_table(mtcarz, cyl, gear)
plot(k)
Stacked Bar Plots
k <- ds_cross_table(mtcarz, cyl, gear)
plot(k, stacked = TRUE)
Proportional Bar Plots
k <- ds_cross_table(mtcarz, cyl, gear)
plot(k, proportional = TRUE)
Frequency Table
The ds_freq_table() function creates frequency
tables.
ds_freq_table(mtcarz, cyl)
#> Variable: cyl
#> -----------------------------------------------------------------------
#> Levels Frequency Cum Frequency Percent Cum Percent
#> -----------------------------------------------------------------------
#> 4 11 11 34.38 34.38
#> -----------------------------------------------------------------------
#> 6 7 18 21.88 56.25
#> -----------------------------------------------------------------------
#> 8 14 32 43.75 100
#> -----------------------------------------------------------------------
#> Total 32 - 100.00 -
#> -----------------------------------------------------------------------
A plot() method has been defined which will create a bar
plot.
k <- ds_freq_table(mtcarz, cyl)
plot(k)
Multiple One Way Tables
The ds_auto_freq_table() function creates multiple one
way tables by creating a frequency table for each categorical variable
in a data set. You can also specify a subset of variables if you do not
want all the variables in the data set to be used.
ds_auto_freq_table(mtcarz)
#> Variable: cyl
#> -----------------------------------------------------------------------
#> Levels Frequency Cum Frequency Percent Cum Percent
#> -----------------------------------------------------------------------
#> 4 11 11 34.38 34.38
#> -----------------------------------------------------------------------
#> 6 7 18 21.88 56.25
#> -----------------------------------------------------------------------
#> 8 14 32 43.75 100
#> -----------------------------------------------------------------------
#> Total 32 - 100.00 -
#> -----------------------------------------------------------------------
#>
#> Variable: vs
#> -----------------------------------------------------------------------
#> Levels Frequency Cum Frequency Percent Cum Percent
#> -----------------------------------------------------------------------
#> 0 18 18 56.25 56.25
#> -----------------------------------------------------------------------
#> 1 14 32 43.75 100
#> -----------------------------------------------------------------------
#> Total 32 - 100.00 -
#> -----------------------------------------------------------------------
#>
#> Variable: am
#> -----------------------------------------------------------------------
#> Levels Frequency Cum Frequency Percent Cum Percent
#> -----------------------------------------------------------------------
#> 0 19 19 59.38 59.38
#> -----------------------------------------------------------------------
#> 1 13 32 40.62 100
#> -----------------------------------------------------------------------
#> Total 32 - 100.00 -
#> -----------------------------------------------------------------------
#>
#> Variable: gear
#> -----------------------------------------------------------------------
#> Levels Frequency Cum Frequency Percent Cum Percent
#> -----------------------------------------------------------------------
#> 3 15 15 46.88 46.88
#> -----------------------------------------------------------------------
#> 4 12 27 37.5 84.38
#> -----------------------------------------------------------------------
#> 5 5 32 15.62 100
#> -----------------------------------------------------------------------
#> Total 32 - 100.00 -
#> -----------------------------------------------------------------------
#>
#> Variable: carb
#> -----------------------------------------------------------------------
#> Levels Frequency Cum Frequency Percent Cum Percent
#> -----------------------------------------------------------------------
#> 1 7 7 21.88 21.88
#> -----------------------------------------------------------------------
#> 2 10 17 31.25 53.12
#> -----------------------------------------------------------------------
#> 3 3 20 9.38 62.5
#> -----------------------------------------------------------------------
#> 4 10 30 31.25 93.75
#> -----------------------------------------------------------------------
#> 6 1 31 3.12 96.88
#> -----------------------------------------------------------------------
#> 8 1 32 3.12 100
#> -----------------------------------------------------------------------
#> Total 32 - 100.00 -
#> -----------------------------------------------------------------------
Multiple Two Way Tables
The ds_auto_cross_table() function creates multiple two
way tables by creating a cross table for each unique pair of categorical
variables in a data set. You can also specify a subset of variables if
you do not want all the variables in the data set to be used.
ds_auto_cross_table(mtcarz, cyl, gear, am)
#> Cell Contents
#> |---------------|
#> | Frequency |
#> | Percent |
#> | Row Pct |
#> | Col Pct |
#> |---------------|
#>
#> Total Observations: 32
#>
#> cyl vs gear
#> ----------------------------------------------------------------------------
#> | | gear |
#> ----------------------------------------------------------------------------
#> | cyl | 3 | 4 | 5 | Row Total |
#> ----------------------------------------------------------------------------
#> | 4 | 1 | 8 | 2 | 11 |
#> | | 0.031 | 0.25 | 0.062 | |
#> | | 0.09 | 0.73 | 0.18 | 0.34 |
#> | | 0.07 | 0.67 | 0.4 | |
#> ----------------------------------------------------------------------------
#> | 6 | 2 | 4 | 1 | 7 |
#> | | 0.062 | 0.125 | 0.031 | |
#> | | 0.29 | 0.57 | 0.14 | 0.22 |
#> | | 0.13 | 0.33 | 0.2 | |
#> ----------------------------------------------------------------------------
#> | 8 | 12 | 0 | 2 | 14 |
#> | | 0.375 | 0 | 0.062 | |
#> | | 0.86 | 0 | 0.14 | 0.44 |
#> | | 0.8 | 0 | 0.4 | |
#> ----------------------------------------------------------------------------
#> | Column Total | 15 | 12 | 5 | 32 |
#> | | 0.468 | 0.375 | 0.155 | |
#> ----------------------------------------------------------------------------
#>
#>
#> cyl vs am
#> -------------------------------------------------------------
#> | | am |
#> -------------------------------------------------------------
#> | cyl | 0 | 1 | Row Total |
#> -------------------------------------------------------------
#> | 4 | 3 | 8 | 11 |
#> | | 0.094 | 0.25 | |
#> | | 0.27 | 0.73 | 0.34 |
#> | | 0.16 | 0.62 | |
#> -------------------------------------------------------------
#> | 6 | 4 | 3 | 7 |
#> | | 0.125 | 0.094 | |
#> | | 0.57 | 0.43 | 0.22 |
#> | | 0.21 | 0.23 | |
#> -------------------------------------------------------------
#> | 8 | 12 | 2 | 14 |
#> | | 0.375 | 0.062 | |
#> | | 0.86 | 0.14 | 0.44 |
#> | | 0.63 | 0.15 | |
#> -------------------------------------------------------------
#> | Column Total | 19 | 13 | 32 |
#> | | 0.594 | 0.406 | |
#> -------------------------------------------------------------
#>
#>
#> gear vs am
#> -------------------------------------------------------------
#> | | am |
#> -------------------------------------------------------------
#> | gear | 0 | 1 | Row Total |
#> -------------------------------------------------------------
#> | 3 | 15 | 0 | 15 |
#> | | 0.469 | 0 | |
#> | | 1 | 0 | 0.47 |
#> | | 0.79 | 0 | |
#> -------------------------------------------------------------
#> | 4 | 4 | 8 | 12 |
#> | | 0.125 | 0.25 | |
#> | | 0.33 | 0.67 | 0.38 |
#> | | 0.21 | 0.62 | |
#> -------------------------------------------------------------
#> | 5 | 0 | 5 | 5 |
#> | | 0 | 0.156 | |
#> | | 0 | 1 | 0.16 |
#> | | 0 | 0.38 | |
#> -------------------------------------------------------------
#> | Column Total | 19 | 13 | 32 |
#> | | 0.594 | 0.406 | |
#> -------------------------------------------------------------
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They may not be fully stable and should be used with caution. We make no claims about them.