mv_treatment_charvar: Performs missing value treatment for character variables by...

In saushe/RED: Performs exploratory data analysis

Description

Imputes missing values with unknown or mode. The function also deletes the columns with missing values more than the specified cutoff

Usage

mv_treatment_charvar(df, default = T, col.del_cutoff = 0.5,
  char_var_list1 = "EMPTY_99", char_var_list2 = "EMPTY_99")

Arguments

df	The data frame on which the missing value treatment needs to be performed
default	T/F. For default = T, the function first drops the variables with missing value percentage more than col.del_cutoff and then imputes all the missing values with unknown
col.del_cutoff	Variables with missing value percentage greater than col.del_cutoff will be dropped. Default value is 50 percent
char_var_list1	If default = F, then user need to specify the list of variables on which the imputation needs to be performed. In char_var_list1 user can speciy the variables where NA needs to be imputed with unknown
char_var_list2	Specify the variables for which missing values needs to imputed with mode

Value

Output is the data set with missing values imputed

Warning

Enter only categorical variables in char_var_list1 and char_var_list1

saushe/RED documentation built on May 29, 2019, 3:20 p.m.