Replace mean or mode for missing values in R

Question

I have a large database made up of mixed data types (numeric, character, factor, ordinal factor) with missing values, and I am trying to create a for loop to substitute the

Answer 1

First, you need to write the mode function taking into consideration the missing values of the Categorical data, which are of length<1.
The mode function:

getmode <- function(v){
  v=v[nchar(as.character(v))>0]
  uniqv <- unique(v)
  uniqv[which.max(tabulate(match(v, uniqv)))]
}

Then you can iterate of columns and if the column is numeric to fill the missing values with the mean otherwise with the mode.

The loop statement below:

for (cols in colnames(df)) {
  if (cols %in% names(df[,sapply(df, is.numeric)])) {
    df<-df%>%mutate(!!cols := replace(!!rlang::sym(cols), is.na(!!rlang::sym(cols)), mean(!!rlang::sym(cols), na.rm=TRUE)))

  }
  else {

    df<-df%>%mutate(!!cols := replace(!!rlang::sym(cols), !!rlang::sym(cols)=="", getmode(!!rlang::sym(cols))))

  }
}

Let's provide an example:

library(tidyverse)

df<-tibble(id=seq(1,10), ColumnA=c(10,9,8,7,NA,NA,20,15,12,NA), 
           ColumnB=factor(c("A","B","A","A","","B","A","B","","A")),
           ColumnC=factor(c("","BB","CC","BB","BB","CC","AA","BB","","AA")),
           ColumnD=c(NA,20,18,22,18,17,19,NA,17,23)
           )

df

The initial df with the missing values:

# A tibble: 10 x 5
      id ColumnA ColumnB ColumnC ColumnD
               
 1     1      10 "A"     ""           NA
 2     2       9 "B"     "BB"         20
 3     3       8 "A"     "CC"         18
 4     4       7 "A"     "BB"         22
 5     5      NA ""      "BB"         18
 6     6      NA "B"     "CC"         17
 7     7      20 "A"     "AA"         19
 8     8      15 "B"     "BB"         NA
 9     9      12 ""      ""           17
10    10      NA "A"     "AA"         23

By running the for loop above, we get:

# A tibble: 10 x 5
      id ColumnA ColumnB ColumnC ColumnD
               
 1     1    10   A       BB         19.2
 2     2     9   B       BB         20  
 3     3     8   A       CC         18  
 4     4     7   A       BB         22  
 5     5    11.6 A       BB         18  
 6     6    11.6 B       CC         17  
 7     7    20   A       AA         19  
 8     8    15   B       BB         19.2
 9     9    12   A       BB         17  
10    10    11.6 A       AA         23 

As we can see, the missing values have been imputed. You can see an example here