Extract rows where value appears in any of multiple columns

user_372526 0
Viewed 106

Let' say I have two data.frames

name_df = read.table(text = "player_name
a
b
c
d
e
f
g", header = T)

game_df = read.table(text = "game_id winner_name loser_name
1 a b
2 b a
3 a c
4 a d
5 b c
6 c d
7 d e
8 e f
9 f a
10 g f
11 g a
12 f e
13 a d", header = T)

name_df contains a unique list of all the winner_name or loser_name values in game_df. I want to create a new data.frame that has, for each person in the name_df a row if a given name (e.g. a) appears in either the winner_name or loser_name column

So I essentially want to merge game_df with name_df, but the key column (name) can appear in either winner_name or loser_name.

So, for just a and b the final output would look something like:

final_df = read.table(text = "player_name game_id winner_name loser_name
a 1 a b
a 2 b a
a 3 a c
a 4 a d
a 9 f a
a 11 g a
a 13 a d
b 1 a b
b 2 b a
b 5 b c", header = T)
data.table dplyr r
5 Answers

We can loop over the elements in 'name_df' for 'player_name', filter the rows from 'game_df' for either the 'winner_name' or 'loser_name'

library(dplyr)
library(purrr)
map_dfr(setNames(name_df$player_name, name_df$player_name), 
   ~ game_df %>%
        filter(winner_name %in% .x|loser_name %in% .x), .id = 'player_name')

Or if there are many columns, use if_any

map_dfr(setNames(name_df$player_name, name_df$player_name), 
  ~ {
     nm1 <- .x
     game_df %>%
       filter(if_any(c(winner_name, loser_name), ~ . %in%  nm1))
      }, .id = 'player_name')
user_37322710

Dedicated to our teacher and mentor dear @akrun

I think we can also make use of the add_row() function you first taught me the other day. Unbelievable!!!

library(dplyr)
library(purrr)
library(tibble)

game_df %>%
  rowwise() %>%
  mutate(player_name = winner_name) %>%
  group_split(game_id) %>%
  map_dfr(~ add_row(.x, game_id = .x$game_id, winner_name = .x$winner_name, 
                    loser_name = .x$loser_name, player_name = .x$loser_name)) %>%
  arrange(player_name) %>%
  relocate(player_name)


# A tibble: 26 x 4
   player_name game_id winner_name loser_name
   <chr>         <int> <chr>       <chr>     
 1 a                 1 a           b         
 2 a                 2 b           a         
 3 a                 3 a           c         
 4 a                 4 a           d         
 5 a                 9 f           a         
 6 a                11 g           a         
 7 a                13 a           d         
 8 b                 1 a           b         
 9 b                 2 b           a         
10 b                 5 b           c         
# ... with 16 more rows
user_143145200

This can be directly expressed in SQL:

library(sqldf)

sqldf("select * 
  from name_df 
  left join game_df on winner_name = player_name or loser_name = player_name")
user_5165480

Without using purrr. I think this is appropriate use case of tidyr::unite with argument remove = F where we can first unite the winners' and losers' names and then use tidyr::separate_rows to split new column into rows.

library(tidyr)
library(dplyr)

game_df %>% unite(Player_name, winner_name, loser_name, remove = F, sep = ', ') %>%
  separate_rows(Player_name) %>%
  relocate(Player_name) %>%
  arrange(Player_name)

# A tibble: 26 x 4
   Player_name game_id winner_name loser_name
   <chr>         <int> <chr>       <chr>     
 1 a                 1 a           b         
 2 a                 2 b           a         
 3 a                 3 a           c         
 4 a                 4 a           d         
 5 a                 9 f           a         
 6 a                11 g           a         
 7 a                13 a           d         
 8 b                 1 a           b         
 9 b                 2 b           a         
10 b                 5 b           c         
# ... with 16 more rows
user_28848590

A Base R approach :

result <- do.call(rbind, lapply(name_df$player_name, function(x) 
                   cbind(plaername = x, 
                   subset(game_df, winner_name == x | loser_name == x))))

rownames(result) <- NULL

result
#   playername game_id winner_name loser_name
#1           a       1           a          b
#2           a       2           b          a
#3           a       3           a          c
#4           a       4           a          d
#5           a       9           f          a
#6           a      11           g          a
#7           a      13           a          d
#8           b       1           a          b
#...
#...
user_39629140
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