We wish to look at the variation in one categorical variable against another categorical variable. This is often referred to as cross tabulation.
We wish to know what are the distinct combinations that the values of two categorical columns take in a data frame.
In this example, we wish to get the unique combinations of the values of col_1 and col_2.
df %>% distinct(col_1, col_2)
Here is how this works:
df to the function distinct().distinct() the names of the columns whose value combinations we are interested in. In this case, those column names are col_1 and col_2.distinct() is a data frame that has the input columns (in this case col_1 and col_2) and one row for each unique combination of the values of the input columns.distinct() as per the needs of the situation.Cross Table
We wish to know the number of times each combination of values of categorical columns occurs in a data frame.
In this example, we wish to get the number of times each unique combination of the values of col_1 and col_2 occurs in a data frame.
library(janitor)
df %>% tabyl(col_1, col_2)
Here is how this works:
df to the function tabyl().tabyl() the names of the columns whose combinations we wish to count. In this case, those column names are col_1 and col_2.tabyl() is a kind of data frame (a tabyl object) where the values of the first column (in this example col_1) are represented by the rows and the values of the second column (in this example col_2) are represented by the columns. The cells hold the number of rows of the original data frame (in this example df) where the value of col_1 designated by the current row occurs with the value of col_2 designated by the current column.tabyl() from the janitor package instead of base R’s table() because it returns a clean data frame, automatically returns the percent and has enhanced tabulation functionality which we will make use of in the remainder of this page.Add Totals
Adding to the previous section, we wish to include the totals for each row and each column of the cross table (often referred to as marginal totals).
library(janitor)
df %>%
tabyl(col_1, col_2) %>%
adorn_totals(c("row", "col"))
Here is how this works:
tabyl() to generate the cross table for col_1 and col_2 as described in the previous section.tabyl() to adorn_totals() to add the totals to the output summary data frame.c("row", "col") to adorn_totals() to instruct it to add totals for both the rows and columns. Alternative arguments are “row“ and “col”.tabyl() to one of many adorn_ functions is how the tabyl set of functions is to be used.We wish to know the proportion (percentage or density) of the total number of rows (observations) that take each possible combination of values of two columns (variables).
In order to compute a proportion we need to designate what is it that we are comparing i.e. what the numerator and denominator are. In this situation, the numerator is the frequency of each combination of values of the two categorical variables. The denominator, however, can take one of three forms:
col_1 == a, what proportion (percent) of those rows have col_2 == b (essentially the conditional probability of col_2 == b given that col_1 == a).col_2 == b, what proportion (percent) of those rows have col_1 == a (essentially the conditional probability of col_1 == a given that col_2 == b).col_1 == a and col_2==b.on Rows
We wish to get the proportion of each combination of values of two columns relative to the first column (represented by the rows of the cross-table).
In this example, we compute a cross table between col_1 and col_2 and obtain the proportions of combinations relative to col_1.
df %>%
tabyl(col_1, col_2) %>%
adorn_percentages('row')
Here is how this works:
tabyl() to obtain the cross table between col_1 and col_2 of the data frame df as described above.tabyl() to the function adorn_percentages() and pass the denominator argument (unstated) as ‘row’ to compute proportions over rows.on Columns
We wish to get the proportion of each combination of values of two columns relative to the second column (represented by the columns of the cross-table).
In this example, we compute a cross table between col_1 and col_2 and obtain the proportions of combinations relative to col_2.
df %>%
tabyl(col_1, col_2) %>%
adorn_percentages('col')
Here is how this works:
tabyl() to obtain the cross table between col_1 and col_2 of the data frame df as described above.tabyl() to the function adorn_percentages() and pass the denominator argument (unstated) as ‘col’ to compute proportions over columns.on Table
We wish to get the proportion of each combination of values of two columns relative to the total number of rows in the data frame.
In this example, we compute a cross table between col_1 and col_2 and obtain the proportions of combinations relative to the number of rows in the data frame df.
df %>%
tabyl(col_1_b, col_7) %>%
adorn_percentages('all')
Here is how this works:
tabyl() to obtain the cross table between col_1 and col_2 of the data frame df as described above.tabyl() to the function adorn_percentages() and pass the denominator argument (unstated) as ‘all’ to compute proportions over the table (total number of rows in the original data frame df).Rounding
We wish to set a level of precision for the percentages computed.
In this example, we set the level of precision to 2 decimal places i.e. 0.xx .
df %>% tabyl(col_1, col_2) %>%
adorn_percentages('all') %>%
adorn_rounding(2)
Here is how this works:
tabyl() and adorn_percentages() as described above.adorn_percentages() to adorn_rounding() while setting the digits argument (unstated) to 2 to obtain a precision of 2 decimal places.