Create new column based on non-numerical variables from several columns in the same dataframe in R

I have a large df which I have simplified where I would love to create two new column K_status and S_status with based on variables in another column and I am struggling on how best to code for this.

A <- c("K", "K", "K", "S", "S", "S", "NA")
B <- c("NA", "AA", "AC", "NA", "AA", "AB", "LD")
C <- c("TT", "YY", "YY", "TT", "YY", "Y", "TT")
df <- data.frame(A, B, C)

To generate the K_status and S_status additional columns to df my current code is:

df <- df %>%
mutate(K_status = case_when(all("K", "AA", "YY") %in% df) ~ "Mut",
TRUE ~ "WT")) %>%
mutate(S_status = case_when(all("S", "AB", "Y") %in% df) ~ "Mut",
TRUE ~ "WT")) 

This code is not working as my intended new df should look like this

A <- c("K", "K", "K", "S", "S", "S", "NA")
B <- c("NA", "AA", "AC", "NA", "AA", "AB", "LD")
C <- c("TT", "YY", "YY", "TT", "YY", "Y", "TT")
K_status <- c("WT", "Mut", "WT", "WT", "WT", "WT", "WT")
S_status <- c("WT", "WT", "WT", "WT", "WT", "Mut", "WT")
df <- data.frame(A, B, C, K_status, S_status)

Any help in writing this code to generate K_status and S_status would be greatly appreciated. Thank you.

We may use base R - would be more efficient with rowSums to create a logical vector and then do the assignment based on it

 i1 <- rowSums(df == c("K", "AA", "YY")[col(df)]) == 3
 i2 <- rowSums(df == c("S", "AB", "Y")[col(df)]) == 3
 df$K_status <- "WT"
 df$K_status[i1] <- "Mut"
  df$S_status <- "WT"
 df$S_status[i2] <- "Mut"

-output

> df
   A  B  C K_status S_status
1  K NA TT       WT       WT
2  K AA YY      Mut       WT
3  K AC YY       WT       WT
4  S NA TT       WT       WT
5  S AA YY       WT       WT
6  S AB  Y       WT      Mut
7 NA LD TT       WT       WT

Or with tidyverse in a vectorized way for efficient execution of code - just create a key/value dataset or a named list, then loop over the columns in if_all, extract the corresponding value from keydat dataset, compare and use case_when to create new columns

library(dplyr)
keydat <- tibble(A = c("K", "S"), B = c("AA", "AB"), C = c("YY", "Y"))

df %>%
   mutate(K_status = case_when(if_all(everything(),
    ~ .x == keydat[[cur_column()]][1]) ~ "Mut", TRUE ~ "WT"), 
   S_status = case_when(if_all(A:C, ~
    .x == keydat[[cur_column()]][2]) ~ "Mut", TRUE ~ "WT"))

-output

   A  B  C K_status S_status
1  K NA TT       WT       WT
2  K AA YY      Mut       WT
3  K AC YY       WT       WT
4  S NA TT       WT       WT
5  S AA YY       WT       WT
6  S AB  Y       WT      Mut
7 NA LD TT       WT       WT

We can use similar code ofter correcting several inconsistencies:

-include rowwise as we are comparing x %in% z, in which z is used rowwise

-%in% df should be replaced with a proper reference to the columns A:C of the dataframe, with c_across.

-use all(c(...) %in% x) instead of all(...) %in% x

df %>%
    rowwise() %>%
    mutate(K_status = case_when(all(c("K", "AA", "YY") %in% c_across(A:C)) ~ "Mut",
           TRUE ~ "WT")) %>%
    mutate(S_status = case_when(all(c("S", "AB", "Y") %in% c_across(A:C)) ~ "Mut",
           TRUE ~ "WT")) %>%
    ungroup()


# A tibble: 7 × 5
  A     B     C     K_status S_status
  <chr> <chr> <chr> <chr>    <chr>   
1 K     NA    TT    WT       WT      
2 K     AA    YY    Mut      WT      
3 K     AC    YY    WT       WT      
4 S     NA    TT    WT       WT      
5 S     AA    YY    WT       WT      
6 S     AB    Y     WT       Mut     
7 NA    LD    TT    WT       WT  

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