Which Variables Are Continuous Which Are Categorical Mtcars
Categorical Data
2020-12-09
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, by = c("cyl", "gear", "count") #> # A tibble: 8 x 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 | | #> ------------------------------------------------------------- Source: https://cran.r-project.org/web/packages/descriptr/vignettes/categorical-data.html
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