Performs EDA of categorical features.

Creates a data frame containing column names and corresponding details about unique values, null values and most frequent category in every column Plots count-plots for given categorical columns

explore_categorical_columns(df, categorical_cols)

Arguments

df	input data as a data frame
categorical_cols	vector containing categorical columns

Value

A list object with first list element being a tibble with details about unique, null values and most frequent category in every column and a second list element being count plots of user provided column names

Examples

library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:plyr’:
#> 
#>     arrange, count, desc, failwith, id, mutate, rename, summarise,
#>     summarize
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union
library(MASS)
#> 
#> Attaching package: ‘MASS’
#> The following object is masked from ‘package:dplyr’:
#> 
#>     select
library(knitr)
df <- data.frame(lapply(survey[, c('Sex','Clap')], as.character),
stringsAsFactors=FALSE) %>% tibble()
results <- explore_categorical_columns(df, c('Sex','Clap'))
results[[1]] %>% knitr::kable()
#> 
#> 
#> |column_name |unique_items             | no_of_nulls| percentage_missing|
#> |:-----------|:------------------------|-----------:|------------------:|
#> |Sex         |Female, Male, NA         |           1|              0.422|
#> |Clap        |Left, Neither, Right, NA |           1|              0.422|
results[[2]][[1]]
results[[2]][[2]]