R rowwise replace the first instance of the minimum

Question

How can I do the following:

1. replace all values < 6 with NA,
2. if there is only one NA in the row, replace the first instance of the minimum value with -99?

Some data that includes an ID variable and a total column:

library(tidyverse)
df <- data.frame(id = c(1,2,3,4,5), a = c(10,12,4,17,3), b = c(9,12,3,20,6), c = c(2,2,10,10,10), d = c(12,16,12,10,12))
df$total <- apply(df[,c(2:5)], 1, sum)

Giving

  id  a   b   c   d   total
  1   10  9   2   12   33
  2   12  12  2   16   42
  3   4   3   10  12   29
  4   17  20  10  10   57
  5   3   6   10  12   31

My desired output is

 id    a    b     c     d   total
 1    10   -99    NA    12    33
 2   -99    12    NA    16    42
 3    NA    NA    10    12    29
 4    17    20    10    10    57
 5    NA   -99    10    12    31

My attempt

df_mod <- df %>%

  # Make <6 NA
  mutate(
    across(
      .cols = 'a':'total',
      ~case_when(
        .x < 6 ~ as.numeric(NA),
        TRUE ~ .x
      )
    )
  ) %>%
  # Add a count of NAs
  rowwise() %>%
  mutate(Count_NA = sum(is.na(cur_data()))) %>%
  ungroup() 

# Transpose and get row minimum
df_mod2 <- t(df_mod[,-c(1,ncol(df_mod))]) %>%
  apply(., 2, function(a){
      min <- min(a, na.rm = TRUE)
    }
  ) %>% 
  cbind(df_mod, .) %>%
  rename(., min = .) %>%
  tibble(.)
  
# If count_NA  = 1 replace the first instance of min
df_mod2 %>%
  rowwise() %>%
  mutate(
    across(
      .cols = 'a':'total',
      ~case_when(
        Count_NA == 1 & .x == min ~ replace(.x, first(match(min, .x)), -99),
        TRUE ~ .x)
    )
  ) %>%
  select(-'Count_NA', -'min')

Which gives the following

     id    a    b     c     d   total
     1    10   -99    NA    12    33
     2   -99   -99    NA    16    42
     3    NA    NA    10    12    29
     4    17    20    10    10    57
     5    NA   -99    10    12    31

Thanks

Answer 1

If you're willing to pivot rather than work rowwise, then this solution will work.

library(dplyr)
df %>% 
  pivot_longer(names_to = 'col',
               values_to = 'val',
               -c(id, total)) %>%
  group_by(id) %>%
  mutate(val2 = rank(val, ties.method = 'first'),
         val = ifelse(val < 6, NA , val),
         val = ifelse(sum(is.na(val)) == 1 & val2 == 2, -99, val)) %>% 
  select(-val2) %>%
  pivot_wider(names_from = col, 
              values_from = val) %>% 
  relocate(total, .after = "d")

Here's the result:

# A tibble: 5 × 6
# Groups:   id [5]
     id     a     b     c     d total
  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1     1    10   -99    NA    12    33
2     2   -99    12    NA    16    42
3     3    NA    NA    10    12    29
4     4    17    20    10    10    57
5     5    NA   -99    10    12    31