用基于组的平均值替换 R 数据框中的 NA，并将其应用于多列

Question

我有这个数据框。

library(tidyverse)
df <- tibble(
  "group" = c("A", "A", "B", "B"),
  "WC" = c(NA, 2.3, 3.5, 4),
  "Sixltr" = c(3.3, NA, NA, 2.7),
  "Dic" = c(NA, NA, NA, 2.4),
  "I" = c(3.1, 3, 2.7, 1.9),
  "We" = c(4.6, NA, 2.2, NA)
)

我创建了 mean_NA_conditional_function 函数来将 NAs 替换为平均值（基于某些条件），然后我使用 lapply 对所有列执行相同的操作数据框 - 然而，这并不重要，我也可以简单地使用常规平均值。

mean_NA_conditional_function <- function(vector) {
  # when NA <= 1 in vector, return the mean of available data in vector
  if (sum(is.na(vector)) <= 1) {return(mean(vector, na.rm = TRUE))}
  # when NA >= 2 in vector, return the sum of available data in vector divided by vector length - 1
  if (sum(is.na(vector)) >= 2) {return((sum(vector, na.rm = TRUE)) / (length(vector) - 1))}
}

#Create the 'NAs_replace_function' function that replaces NAs applying the 'mean_NA_conditional_function'. 
NAs_replace_function <- function(vector) replace(vector, is.na(vector), mean_NA_conditional_function(vector))

#Apply the function 'NAs_replace_function' to selected columns and replace NAs with appropriate mean.
df_after_imputation <- replace(df, TRUE, lapply(df, NAs_replace_function))

到目前为止，这有效。但是，我想要做的是根据每个值所属的组（即 'A'、'B'）替换 NA。我试过 group_by()，但没用。不确定我是否做错了什么。关于如何解决这个问题的任何想法？

# This doesn't work:
df_after_imputation <- df %>% group_by(group) %>% replace(., TRUE, lapply(df, NAs_replace_function))

Answer 1

您可以使用：

library(dplyr)

df %>%
  group_by(group) %>%
  mutate(across(WC:We, NAs_replace_function)) %>%
  ungroup -> df_after_imputation

df_after_imputation

#  group    WC Sixltr   Dic     I    We
#  <chr> <dbl>  <dbl> <dbl> <dbl> <dbl>
#1 A       2.3    3.3   0     3.1   4.6
#2 A       2.3    3.3   0     3     4.6
#3 B       3.5    2.7   2.4   2.7   2.2
#4 B       4      2.7   2.4   1.9   2.2

用基于组的平均值替换 R 数据框中的 NA，并将其应用于多列

Replacing NAs in R dataframe with mean based on group and apply the same to multiple columns

replace

r

nas