get_categorical_bins: get_categorical_bins

In cjodice10/eda: Exploratory Data Analysis - Variable Groupings and Transformations

get_categorical_bins

Description

Categorical grouping

Usage

get_categorical_bins(
  run_id,
  df,
  dv,
  dv.type,
  dv.denominator = NULL,
  var.list,
  max.levels = 200,
  min.Pct = 0.02,
  bin_random_together = 0.005,
  tracking = TRUE,
  path_2_save = getwd()
)

Arguments

run_id	An identifier that will be used when naming output tables to the specified path (path_2_save parameter). Example: 'MyRun1'
df	A dataframe you are wanting to analyze
dv	The name of the dependent variable (dv). Example: 'target'
dv.type	Can take on 1 of two inpunts - c('Binary','Frequency'). Both should be numeric. If 'Frequency' is the input, it should be the numerator (if it is a rate). The denominator will be specified as a separate parameter
dv.denominator	The denominator of your dependent variable. In many cases, this can be considered the exposure
var.list	A list of non-numeric variables to analyze and create bins for
max.levels	If a variable initially has more unique levels than max.levels, it will be skipped
min.Pct	This is the minimun percent of records a final bin should have. The input should be between (0,1). Generally applies to only bins that are not NA. Default is 0.02 (or 2 percent)
bin_random_together	This is the threshold to identify if a level belongs in a random bin. The input should be between (0,1). Generally applies to only bins that are not NA. Default is 0.005 (or 0.5 percent)
tracking	Logical TRUE/FALSE inputs. If set to TRUE, the user will be able to see what variable the function is analyzing. Default is TRUE
path_2_save	A path to a folder to save a log file

Value

A list of dataframes. First in the list will be 'CategoricalEDA' - this is an aggregated dataframe showing the groups created along with other key information. The second is 'categorical_iv' - This is a dataframe with each variable processed and their information value. The last is 'categorical_logics' - This is a dataframe with the information needed to apply to your dataframe and transform your variables. This table will be the input to apply_categorical_logic(logic_df=categorical_logics)

cjodice10/eda documentation built on Feb. 7, 2023, 3:26 p.m.