Replace missing values if previous and next values are consistent

I am currently working with time series data which looks something like the following:

df <- data.frame("ID" = c(1, 2, 3, 4, 5),
             "Var1" = c("A", "B", "A", "A", "C"),
             "Var2" = c(NA, "C", "A", "B", NA),
             "Var3" = c("A", NA, NA, NA, "B"),
             "Var4" = c(NA, NA, NA, NA, NA),
             "Var5" = c("A", "B", "A", "B", "B"))

I wish to fill in the "NA" values if the first non-missing previous and first non-missing next value are consistent. That is, the desired result would be

Where the data for ID = 2 is not replaced, since Var2 and Var5 do not match. Moreover, the missing value for ID = 2 at Var2 is not replaced, since Var1 and Var3 are not consistent. I am struggling with how to accomplish this, and any help would be appreciated.

>Solution :

1. Pivot longer to make use of tidyr::fill().
2. Use fill() to create fill_down and fill_up columns, which will include the previous and next non-missing values, respectively.
3. If previous non-missing == next non-missing, use that value; otherwise keep value as is. (This will also keep non-missing values as is, because in this case previous non-missing will always == next non-missing.)
4. Pivot back to original format.

library(tidyverse)

df_filled <- df %>% 
  pivot_longer(!ID) %>% 
  mutate(
    fill_down = value,
    fill_up = value
  ) %>% 
  group_by(ID) %>% 
  fill(fill_down) %>% 
  fill(fill_up, .direction = "up") %>% 
  mutate(value = if_else(fill_down == fill_up, fill_down, value)) %>% 
  ungroup() %>% 
  pivot_wider(id_cols = ID)
  
df_filled
# # A tibble: 5 x 6
#      ID Var1  Var2  Var3  Var4  Var5 
#   <dbl> <chr> <chr> <chr> <chr> <chr>
# 1     1 A     A     A     A     A    
# 2     2 B     C     NA    NA    B    
# 3     3 A     A     A     A     A    
# 4     4 A     B     B     B     B    
# 5     5 C     NA    B     B     B