impute_mean: Impute the mean value into a vector with missing values

In njtierney/ggmissing: Data Structures, Summaries, and Visualisations for Missing Data

Impute the mean value into a vector with missing values

Description

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)

Arguments

x

vector

Value

vector with mean values replaced

Examples

library(dplyr)
vec <- rnorm(10)

vec[sample(1:10, 3)] <- NA

impute_mean(vec)

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

dat %>%
  nabular() %>%
  mutate(
    num = impute_mean(num),
    int = impute_mean(int),
    fct = impute_mean(fct),
  )

dat %>%
  nabular() %>%
  mutate(
    across(
      where(is.numeric),
      impute_mean
    )
  )

dat %>%
  nabular() %>%
  mutate(
    across(
      c("num", "int"),
      impute_mean
    )
  )

njtierney/ggmissing documentation built on July 4, 2025, 12:54 a.m.