Mutate value of a range of columns if columns name meets another column value

I have a wide df with columns representing the months of many given years and the changes of colour in each month:

df <- data.frame(id = as.integer(c(123,124,125,126)),
                 no_change = as.character(c("May.2010", NA, NA, "Sep.2010")),
                 `Jan.2010` = as.character(c("green", "black", "pink", "grey")),
                 `Feb.2010` = as.character(c("green", "black", "pink", "grey")),
                 `Mar.2010` = as.character(c("green", "red", "pink", "grey")),
                 `Apr.2010` = as.character(c("green", "red", "pink", "grey")),
                 `May.2010` = as.character(c("green", "red", "pink", "grey")),
                 `Jun.2010` = as.character(c("green", "red", "pink", "grey")),
                 `Jul.2010` = as.character(c("green", "white", "pink", "grey")),
                 `Ago.2010` = as.character(c("red", "white", "pink", "grey")),
                 `Sep.2010` = as.character(c("red", "white", "pink", "grey")),
                 `Oct.2010` = as.character(c("red", "white", "pink", "grey")),
                 `Nov.2010` = as.character(c("red", "white", "pink", "grey")),
                 `Dez.2010` = as.character(c("red", "white", "grey", "blue"))
                 )
df     
   id no_change Jan.2010 Feb.2010 Mar.2010 Apr.2010 May.2010 Jun.2010 Jul.2010 Ago.2010 Sep.2010 Oct.2010 Nov.2010 Dez.2010
1 123  May.2010    green    green    green    green    green    green    green      red      red      red      red      red
2 124      <NA>    black    black      red      red      red      red    white    white    white    white    white    white
3 125      <NA>     pink     pink     pink     pink     pink     pink     pink     pink     pink     pink     pink     grey
4 126  Sep.2010     grey     grey     grey     grey     grey     grey     grey     grey     grey     grey     grey     blue     

I want to apply NA to each column that contains a month equal to and above that specified in column 'no_change'. This is the desired output:

   id no_change Jan.2010 Feb.2010 Mar.2010 Apr.2010 May.2010 Jun.2010 Jul.2010 Ago.2010 Sep.2010 Oct.2010 Nov.2010 Dez.2010
1 123  May.2010    green    green    green    green       NA       NA       NA       NA       NA       NA       NA       NA
2 124      <NA>    black    black      red      red      red      red    white    white    white    white    white    white
3 125      <NA>     pink     pink     pink     pink     pink     pink     pink     pink     pink     pink     pink     grey
4 126  Sep.2010     grey     grey     grey     grey     grey     grey     grey     grey       NA       NA       NA       NA

Here is one way to do it using dplyr::across() and a custom function:

library(dplyr)

set_na <- function(x, dat) {
  
  col_nm <- cur_column()
  col_dat <- lubridate::dmy(paste0("01.", col_nm))
  
  if_else(col_dat <= dat | is.na(dat), x, NA)
  
}


df |> 
  mutate(
    no_change_dat = lubridate::dmy(paste0("01.", no_change)),
    across(-c(id, no_change, no_change_dat), \(x) set_na(x, no_change_dat))
    )

#> Warning: There were 2 warnings in `mutate()`.
#> The first warning was:
#> ℹ In argument: `no_change_dat = lubridate::dmy(paste0("01.", no_change))`.
#> Caused by warning:
#> !  2 failed to parse.
#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.
#>    id no_change Jan.2010 Feb.2010 Mar.2010 Apr.2010 May.2010 Jun.2010 Jul.2010
#> 1 123  May.2010    green    green    green    green    green     <NA>     <NA>
#> 2 124      <NA>    black    black      red      red      red      red    white
#> 3 125      <NA>     pink     pink     pink     pink     pink     pink     pink
#> 4 126  Sep.2010     grey     grey     grey     grey     grey     grey     grey
#>   Ago.2010 Sep.2010 Oct.2010 Nov.2010 Dez.2010 no_change_dat
#> 1     <NA>     <NA>     <NA>     <NA>     <NA>    2010-05-01
#> 2    white    white    white    white    white          <NA>
#> 3     pink     pink     pink     pink     grey          <NA>
#> 4     <NA>     grey     <NA>     <NA>     <NA>    2010-09-01

Data from OP

df <- data.frame(id = as.integer(c(123,124,125,126)),
                 no_change = as.character(c("May.2010", NA, NA, "Sep.2010")),
                 `Jan.2010` = as.character(c("green", "black", "pink", "grey")),
                 `Feb.2010` = as.character(c("green", "black", "pink", "grey")),
                 `Mar.2010` = as.character(c("green", "red", "pink", "grey")),
                 `Apr.2010` = as.character(c("green", "red", "pink", "grey")),
                 `May.2010` = as.character(c("green", "red", "pink", "grey")),
                 `Jun.2010` = as.character(c("green", "red", "pink", "grey")),
                 `Jul.2010` = as.character(c("green", "white", "pink", "grey")),
                 `Ago.2010` = as.character(c("red", "white", "pink", "grey")),
                 `Sep.2010` = as.character(c("red", "white", "pink", "grey")),
                 `Oct.2010` = as.character(c("red", "white", "pink", "grey")),
                 `Nov.2010` = as.character(c("red", "white", "pink", "grey")),
                 `Dez.2010` = as.character(c("red", "white", "grey", "blue"))
)

^{Created on 2023-06-19 with reprex v2.0.2}

You can pivot the format into a "long" format, and find out which rows should be turned into NA.

library(tidyverse)

df %>% 
  pivot_longer(ends_with("2010")) %>% 
  group_by(id) %>% 
  mutate(value = ifelse(cumsum(name == no_change & !is.na(no_change)), NA, value)) %>% 
  pivot_wider() %>% 
  ungroup()

# A tibble: 4 × 14
     id no_change Jan.2010 Feb.2010 Mar.2010 Apr.2010 May.2010 Jun.2010 Jul.2010 Ago.2010 Sep.2010 Oct.2010 Nov.2010 Dez.2010
  <int> <chr>     <chr>    <chr>    <chr>    <chr>    <chr>    <chr>    <chr>    <chr>    <chr>    <chr>    <chr>    <chr>   
1   123 May.2010  green    green    green    green    NA       NA       NA       NA       NA       NA       NA       NA      
2   124 NA        black    black    red      red      red      red      white    white    white    white    white    white   
3   125 NA        pink     pink     pink     pink     pink     pink     pink     pink     pink     pink     pink     grey    
4   126 Sep.2010  grey     grey     grey     grey     grey     grey     grey     grey     NA       NA       NA       NA      

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