categ_reducer: Reduce categorical values

In lares: Lean Analytics and Robust Exploration Sidekick

Reduce categorical values

Description

This function lets the user reduce categorical values in a vector. It is tidyverse friendly for use on pipelines

Usage

categ_reducer(
  df,
  var,
  nmin = 0,
  pmin = 0,
  pcummax = 100,
  top = NA,
  pvalue_max = 1,
  cor_var = "tag",
  limit = 20,
  other_label = "other",
  ...
)

Arguments

df	Categorical Vector
var	Variable. Which variable do you wish to reduce?
nmin	Integer. Number of minimum times a value is repeated
pmin	Numerical. Percentage of minimum times a value is repeated
pcummax	Numerical. Top cumulative percentage of most repeated values
top	Integer. Keep the n most frequently repeated values
pvalue_max	Numeric (0-1]. Max pvalue categories
cor_var	Character. If pvalue_max < 1, you must define which column name will be compared with (numerical or binary).
limit	Integer. Limit one hot encoding to the n most frequent values of each column. Set to NA to ignore argument.
other_label	Character. With which text do you wish to replace the filtered values with?
...	Additional parameters.

Value

data.frame df on which var has been transformed

See Also

Other Data Wrangling: balance_data(), cleanText(), date_cuts(), date_feats(), file_name(), formatHTML(), holidays(), impute(), left(), normalize(), num_abbr(), ohe_commas(), ohse(), quants(), removenacols(), replaceall(), replacefactor(), textFeats(), textTokenizer(), vector2text(), year_month(), zerovar()

Examples

data(dft) # Titanic dataset
categ_reducer(dft, Embarked, top = 2) %>% freqs(Embarked)
categ_reducer(dft, Ticket, nmin = 7, other_label = "Other Ticket") %>% freqs(Ticket)
categ_reducer(dft, Ticket, pvalue_max = 0.05, cor_var = "Survived") %>% freqs(Ticket)

lares documentation built on Aug. 18, 2025, 5:12 p.m.