I have this dataframe.
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)
)
I have created the mean_NA_conditional_function function to replace the NAs with the mean (based on some conditions), and then I use lapply to do the same on all columns of the dataframe - this is, however, not important, I could also have simply used the regular mean.
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))
So far, this works. But, what I want to do is to replace the NA based on the group that each value belongs to (i.e., 'A', 'B').
I tried to group_by(), but it didn't work. Not sure if I made something wrong. Any ideas on how to solve this?
# This doesn't work:
df_after_imputation <- df %>% group_by(group) %>% replace(., TRUE, lapply(df, NAs_replace_function))
You can use :
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