This can be useful if you are imputing specific values, however we would
generally recommend to impute using other model based approaches. See
the simputation package, for example simputation::impute_lm().
impute_fixed(x, value)
# Default S3 method
impute_fixed(x, value)vector with a fixed values replaced
vec <- rnorm(10)
vec[sample(1:10, 3)] <- NA
vec
#> [1] 2.4815984 -1.1383021 NA -0.6310333 0.5363818 -1.4013999
#> [7] 1.5176090 NA NA 0.3987200
impute_fixed(vec, -999)
#> [1] 2.4815984 -1.1383021 -999.0000000 -0.6310333 0.5363818
#> [6] -1.4013999 1.5176090 -999.0000000 -999.0000000 0.3987200
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 NA 1 A
#> 2 NA NA B
#> 3 -0.813 NA C
#> 4 -0.0584 1 NA
#> 5 -2.26 9 E
#> 6 -1.14 7 F
#> 7 -0.294 2 G
#> 8 -0.493 5 NA
#> 9 1.95 7 I
#> 10 0.349 3 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 -9999 1 A NA !NA !NA
#> 2 -9999 0 B NA NA !NA
#> 3 -0.813 0 C !NA NA !NA
#> 4 -0.0584 1 out !NA !NA NA
#> 5 -2.26 9 E !NA !NA !NA
#> 6 -1.14 7 F !NA !NA !NA
#> 7 -0.294 2 G !NA !NA !NA
#> 8 -0.493 5 out !NA !NA NA
#> 9 1.95 7 I !NA !NA !NA
#> 10 0.349 3 J !NA !NA !NA