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Encode Categorical Missing Values in a Data Frame

Source: R/df_explicit_na.R
df_explicit_na.Rd

[Stable]

This is a helper function to encode missing entries across groups of categorical variables in a data frame.

Usage 

df_explicit_na(
  data,
  omit_columns = NULL,
  char_as_factor = TRUE,
  logical_as_factor = FALSE,
  na_level = "<Missing>"
)

Arguments

data

(data.frame)
data set.

omit_columns

(character)
names of variables from data that should not be modified by this function.

char_as_factor

(flag)
whether to convert character variables in data to factors.

logical_as_factor

(flag)
whether to convert logical variables in data to factors.

na_level

(string)
used to replace all NA or empty values inside non-omit_columns columns.

Value

The data frame with the desired changes made.

Details

Missing entries are those with NA or empty strings and will be replaced with a specified value. If factor variables include missing values, the missing value will be inserted as the last level. Similarly, in case character or logical variables should be converted to factors with the char_as_factor or logical_as_factor options, the missing values will be set as the last level.

See also

sas_na() and explicit_na() for other missing data helper functions.

Examples 


my_data <- data.frame(
  u = c(TRUE, FALSE, NA, TRUE),
  v = factor(c("A", NA, NA, NA), levels = c("Z", "A")),
  w = c("A", "B", NA, "C"),
  x = c("D", "E", "F", NA),
  y = c("G", "H", "I", ""),
  z = c(1, 2, 3, 4),
  stringsAsFactors = FALSE
)

# Encode missing values in all character or factor columns.
df_explicit_na(my_data)
#>       u         v         w         x         y z
#> 1  TRUE         A         A         D         G 1
#> 2 FALSE <Missing>         B         E         H 2
#> 3    NA <Missing> <Missing>         F         I 3
#> 4  TRUE <Missing>         C <Missing> <Missing> 4
# Also convert logical columns to factor columns.
df_explicit_na(my_data, logical_as_factor = TRUE)
#>           u         v         w         x         y z
#> 1      TRUE         A         A         D         G 1
#> 2     FALSE <Missing>         B         E         H 2
#> 3 <Missing> <Missing> <Missing>         F         I 3
#> 4      TRUE <Missing>         C <Missing> <Missing> 4
# Encode missing values in a subset of columns.
df_explicit_na(my_data, omit_columns = c("x", "y"))
#>       u         v         w    x y z
#> 1  TRUE         A         A    D G 1
#> 2 FALSE <Missing>         B    E H 2
#> 3    NA <Missing> <Missing>    F I 3
#> 4  TRUE <Missing>         C <NA>   4