Fill up missing values based on other entries on R

Question

I have dataset input with a couple of missing values. and I have to create dataset output with the following logic:

• If there is a missing in any of the columns b, c, or d, then check the correspondent a column and fill up the missing with the correspondent value from that row to the specific column.

I tried to do that with _join functions from dplyr but was unsuccessful.

I can do it manually, but this option is off the table because I have a big dataset with multiple instances like that.

Input

library(dplyr)

input <- tibble( a = rep(c("A", "B", "C", "D"),2 ),
        b = c(1:3, NA, rep(NA,4)),
        c = c(21:28),
        d = c(rep(NA,4), 54, NA, 34,11)) %>% 
  arrange(a)

Input view

# A tibble: 8 × 4
#  a         b     c     d
#  <chr> <int> <int> <dbl>
#1 A         1    21    NA
#2 A        NA    25    54
#3 B         2    22    NA
#4 B        NA    26    NA
#5 C         3    23    NA
#6 C        NA    27    34
#7 D        NA    24    NA
#8 D        NA    28    11

Output - expected view

# A tibble: 8 × 4
#   a         b     c     d
# <chr> <int> <int> <dbl>
# 1 A         1    21    54
# 2 A         1    25    54
# 3 B         2    22    NA
# 4 B         2    26    NA
# 5 C         3    23    34
# 6 C         3    27    34
# 7 D        NA    24    11
# 8 D        NA    28    11

Answer 1

Use function na.locf from package zoo to carry the last observation forward or in the opposite direction.

suppressPackageStartupMessages(library(dplyr))

input <- tibble( a = rep(c("A", "B", "C", "D"),2 ),
                 b = c(1:3, NA, rep(NA,4)),
                 c = c(21:28),
                 d = c(rep(NA,4), 54, NA, 34,11)) %>% 
  arrange(a)

input %>%
  group_by(a) %>%
  mutate(across(b:d, zoo::na.locf, na.rm =  FALSE)) %>%
  mutate(across(b:d, zoo::na.locf, na.rm =  FALSE, fromLast = TRUE))
#> # A tibble: 8 × 4
#> # Groups:   a [4]
#>   a         b     c     d
#>   <chr> <int> <int> <dbl>
#> 1 A         1    21    54
#> 2 A         1    25    54
#> 3 B         2    22    NA
#> 4 B         2    26    NA
#> 5 C         3    23    34
#> 6 C         3    27    34
#> 7 D        NA    24    11
#> 8 D        NA    28    11

^{Created on 2022-05-14 by the reprex package (v2.0.1)}

Answer 2

This is hasty imputation:

library(dplyr)
input %>%
  group_by(a) %>%
  mutate(across(b:d, ~ if_else(is.na(.), na.omit(.)[1], .))) %>%
  ungroup()
# # A tibble: 8 x 4
#   a         b     c     d
#   <chr> <int> <int> <dbl>
# 1 A         1    21    54
# 2 A         1    25    54
# 3 B         2    22    NA
# 4 B         2    26    NA
# 5 C         3    23    34
# 6 C         3    27    34
# 7 D        NA    24    11
# 8 D        NA    28    11

I think the group_by(a) is fairly intuitive and makes sense. The "hasty" part of my first sentence is that we find the first non-NA value and use it. Other imputation techniques may use the average, median, previous valid data ("locf" as in Rui's answer), or random sampling. The mice package specializes in imputation.