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().
Usage
impute_mean(x)
# Default S3 method
impute_mean(x)
# S3 method for class 'factor'
impute_mean(x)Examples
library(dplyr)
vec <- rnorm(10)
vec[sample(1:10, 3)] <- NA
impute_mean(vec)
#> [1] 0.5301633 0.7462801 1.3446716 0.5301633 -0.4860343 0.8088018
#> [7] 0.3218633 0.0581052 0.5301633 0.9174552
dat <- tibble(
num = rnorm(10),
int = as.integer(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 7 A
#> 2 1.35 NA B
#> 3 NA 4 C
#> 4 0.590 4 NA
#> 5 1.23 5 E
#> 6 -1.42 NA F
#> 7 -1.04 9 NA
#> 8 1.28 3 H
#> 9 -1.31 7 I
#> 10 1.60 6 J
dat %>%
nabular() %>%
mutate(
num = impute_mean(num),
int = impute_mean(int),
fct = impute_mean(fct),
)
#> # A tibble: 10 × 6
#> num int fct num_NA int_NA fct_NA
#> <dbl> <dbl> <fct> <fct> <fct> <fct>
#> 1 0.285 7 A NA !NA !NA
#> 2 1.35 5.62 B !NA NA !NA
#> 3 0.285 4 C NA !NA !NA
#> 4 0.590 4 J !NA !NA NA
#> 5 1.23 5 E !NA !NA !NA
#> 6 -1.42 5.62 F !NA NA !NA
#> 7 -1.04 9 J !NA !NA NA
#> 8 1.28 3 H !NA !NA !NA
#> 9 -1.31 7 I !NA !NA !NA
#> 10 1.60 6 J !NA !NA !NA
dat %>%
nabular() %>%
mutate(
across(
where(is.numeric),
impute_mean
)
)
#> # A tibble: 10 × 6
#> num int fct num_NA int_NA fct_NA
#> <dbl> <dbl> <fct> <fct> <fct> <fct>
#> 1 0.285 7 A NA !NA !NA
#> 2 1.35 5.62 B !NA NA !NA
#> 3 0.285 4 C NA !NA !NA
#> 4 0.590 4 NA !NA !NA NA
#> 5 1.23 5 E !NA !NA !NA
#> 6 -1.42 5.62 F !NA NA !NA
#> 7 -1.04 9 NA !NA !NA NA
#> 8 1.28 3 H !NA !NA !NA
#> 9 -1.31 7 I !NA !NA !NA
#> 10 1.60 6 J !NA !NA !NA
dat %>%
nabular() %>%
mutate(
across(
c("num", "int"),
impute_mean
)
)
#> # A tibble: 10 × 6
#> num int fct num_NA int_NA fct_NA
#> <dbl> <dbl> <fct> <fct> <fct> <fct>
#> 1 0.285 7 A NA !NA !NA
#> 2 1.35 5.62 B !NA NA !NA
#> 3 0.285 4 C NA !NA !NA
#> 4 0.590 4 NA !NA !NA NA
#> 5 1.23 5 E !NA !NA !NA
#> 6 -1.42 5.62 F !NA NA !NA
#> 7 -1.04 9 NA !NA !NA NA
#> 8 1.28 3 H !NA !NA !NA
#> 9 -1.31 7 I !NA !NA !NA
#> 10 1.60 6 J !NA !NA !NA