Return list of column names with missing (NA) data for each row of a data frame in R

Question

I am trying to create a list of data with NA values for each ID# in a dataframe so I can keep track of missing data. I have a dataframe where each row is an ID# and each column is a variable name. Each ID# may have different missing data so I want to condense this information down into a two column table. Currently, my table looks something like this:

ID  Var1 Var2 Var3 Var4 Var5
1   10   T    NA   2    NA
2   15   F    50   2    NA
3   12   NA   41   2    NA
4   NA   NA   NA   1    NA
5   NA   F    NA   NA   NA
...

I am hoping to get output that looks something like this:

ID   Missing Variables
1    Var3, Var5
2    Var5
3    Var2, Var5
4    Var1, Var2, Var3, Var5
5    Var1, Var3, Var4, Var5
...

I'm confused how I would return to column name for each missing data entry. I know you can probably do something like this with a for loop but beyond that I get a little lost. Any help is much appreciated!

Answer 1

Here is a tidyverse solution.

df <- read_table("
ID  Var1 Var2 Var3 Var4 Var5
1   10   T    NA   2    NA
2   15   F    50   2    NA
3   12   NA   41   2    NA
4   NA   NA   NA   1    NA
5   NA   F    NA   NA   NA", col_names = TRUE)

library(dplyr)
library(tidyr)
df %>%
  mutate(across(starts_with("var"), is.na)) %>%  # replace all NA with TRUE and else FALSE
  pivot_longer(-ID, names_to = "var") %>%  # pivot longer
  filter(value) %>%   # remove the FALSE rows
  group_by(ID) %>%    # group by the ID
  summarise(`Missing Variables` = toString(var)) # convert the variable names to a string column

`summarise()` ungrouping output (override with `.groups` argument)
# A tibble: 5 x 2
     ID `Missing Variables`   
  <dbl> <chr>                 
1     1 Var3, Var5            
2     2 Var5                  
3     3 Var2, Var5            
4     4 Var1, Var2, Var3, Var5
5     5 Var1, Var3, Var4, Var5