I am trying to create similar_player_selected column. I have first 4 columns. For row 1, player_id =1 and the most similar player to player 1 is player 3. But player 3 (row 3) isn't selected for campaign 1(player_selected=0) so I assign a value of 0 to similar_player_selected for row 1. For row 2, player_id=2 and the most similar player to player 2 is player 4. Player 4 is selected for the campaign 1(row 4) so I assign a value of 1 to similar_player_selected for row 2. Please note there are more than 1000 campaigns overall.

CodePudding user response：

Using match we can subset player selected at matched locations

library(dplyr)

df |> 
  group_by(campaign_id) |> 
  mutate(
    similar_player_selected = player_selected[match(most_similar_player, player_id)]
    ) |> 
  ungroup()

Faster base R alternative

df$similar_player_selected <- lapply(split(df, df$campaign_id), \(x) 
       with(x, player_selected[match(most_similar_player, player_id)])) |> 
  unlist()

  campaign_id player_id most_similar_player player_selected similar_player_selected
        <dbl>     <dbl>               <dbl>           <dbl>                   <dbl>
1           1         1                   3               1                       0
2           1         2                   4               0                       1
3           1         3                   4               0                       1
4           1         4                   1               1                       1
5           2         1                   3               1                       0
6           2         2                   4               1                       0
7           2         3                   4               0                       0
8           2         4                   1               0                       1