Summarise and compare the levels within each categorical feature in one or two dataframes.
inspect_cat(df1, df2 = NULL, show_plot = FALSE)A dataframe
An optional second data frame for comparing categorical levels.
Defaults to NULL.
(Deprecated) Logical flag indicating whether a plot should be shown.
Superseded by the function show_plot() and will be dropped in a future version.
A tibble summarising and comparing the categorical features in one or a pair of data frames.
When only df1 is specified, a tibble is returned which
contains summaries of the categorical levels in df1.
col_name character vector containing column names of df1.
cnt integer column containing count of unique levels found in each column
(including NA).
common character column containing the name of the most common level.
common_pcnt percentage of each column occupied by the most common level shown in
common.
levels relative frequency of levels in each column.
When both df1 and df2 are specified, the relative frequencies of levels across columns
common both dataframes are compared. In particular, the population stability index, and Fisher's
exact test are performed as part of the comparison.
col_name character vector containing column names of df1 and df2.
jsd numeric column containing the Jensen-Shannon divergence. This measures the difference in distribution of a pair of categorical features. Values near to 0 indicate agreement of the distributions, while 1 indicates disagreement.
fisher_p p-value corresponding to Fisher's exact test. A small p indicates evidence that the the two sets of relative frequencies are actually different.
lvls_1, lvls_2 relative frequency of levels in each of df1 and df2.
# NOT RUN {
data("starwars", package = "dplyr")
inspect_cat(starwars)
# compare the levels in two data frames
inspect_cat(starwars, starwars[1:20, ])
# }
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