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

Value

A data.frame with the chosen modifications applied.

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
)

# Example 1
# 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

# Example 2
# Here we purposefully convert all `M` values to `NA` in the `SEX` variable.
# After running `df_explicit_na` the `NA` values are encoded as `<Missing>` but they are not
# included when generating `rtables`.
adsl <- tern_ex_adsl
adsl$SEX[adsl$SEX == "M"] <- NA
adsl <- df_explicit_na(adsl)

# If you want the `Na` values to be displayed in the table use the `na_level` argument.
adsl <- tern_ex_adsl
adsl$SEX[adsl$SEX == "M"] <- NA
adsl <- df_explicit_na(adsl, na_level = "Missing Values")

# Example 3
# Numeric variables that have missing values are not altered. This means that any `NA` value in
# a numeric variable will not be included in the summary statistics, nor will they be included
# in the denominator value for calculating the percent values.
adsl <- tern_ex_adsl
adsl$AGE[adsl$AGE < 30] <- NA
adsl <- df_explicit_na(adsl)