For imputing fixed factor levels. It adds the new imputed value to the end
of the levels of the vector. We generally recommend to impute using other
model based approaches. See the simputation package, for example
simputation::impute_lm().
Usage
impute_factor(x, value)
# S3 method for default
impute_factor(x, value)
# S3 method for factor
impute_factor(x, value)
# S3 method for character
impute_factor(x, value)
# S3 method for shade
impute_factor(x, value)Examples
vec <- factor(LETTERS[1:10])
vec[sample(1:10, 3)] <- NA
vec
#> [1] A B C D E <NA> G <NA> I <NA>
#> Levels: A B C D E F G H I J
impute_factor(vec, "wat")
#> [1] A B C D E wat G wat I wat
#> Levels: A B C D E F G H I J wat
library(dplyr)
dat <- tibble(
num = rnorm(10),
int = rpois(10, 5),
fct = factor(LETTERS[1:10])
) %>%
mutate(
across(
everything(),
\(x) set_prop_miss(x, prop = 0.25)
)
)
dat
#> # A tibble: 10 × 3
#> num int fct
#> <dbl> <int> <fct>
#> 1 0.0742 4 A
#> 2 -0.201 NA B
#> 3 1.51 NA C
#> 4 NA 4 D
#> 5 NA 3 E
#> 6 1.37 6 NA
#> 7 1.06 3 G
#> 8 -1.00 3 NA
#> 9 0.880 4 I
#> 10 0.987 7 J
dat %>%
nabular() %>%
mutate(
num = impute_fixed(num, -9999),
int = impute_zero(int),
fct = impute_factor(fct, "out")
)
#> # A tibble: 10 × 6
#> num int fct num_NA int_NA fct_NA
#> <dbl> <dbl> <fct> <fct> <fct> <fct>
#> 1 0.0742 4 A !NA !NA !NA
#> 2 -0.201 0 B !NA NA !NA
#> 3 1.51 0 C !NA NA !NA
#> 4 -9999 4 D NA !NA !NA
#> 5 -9999 3 E NA !NA !NA
#> 6 1.37 6 out !NA !NA NA
#> 7 1.06 3 G !NA !NA !NA
#> 8 -1.00 3 out !NA !NA NA
#> 9 0.880 4 I !NA !NA !NA
#> 10 0.987 7 J !NA !NA !NA