Arrange dataframe for pairwise correlations - r

Question

I am working with data in the following form:
Country Player Goals
"USA" "Tim" 0
"USA" "Tim" 0
"USA" "Dempsey" 3
"USA" "Dempsey" 5
"Brasil" "Neymar" 6
"Brasil" "Neymar" 2
"Brasil" "Hulk" 5
"Brasil" "Luiz" 2
"England" "Rooney" 4
"England" "Stewart" 2
Each row represents the number of goals that a player scored per game, and also contains that player's country. I would like to have the data in the form such that I can run pairwise correlations to see whether being from the same country has some association with the number of goals that a player scores. The data would look like this:
Player_1 Player_2
0 8 # Tim Dempsey
8 5 # Neymar Hulk
8 2 # Neymar Luiz
5 2 # Hulk Luiz
4 2 # Rooney Stewart
(You can ignore the comments, they are there simply to clarify what each row contains).
How would I do this?
table(df$player)
gets me the number of goals per player, but then how to I generate these pairwise combinations?

Answer 1

This is a pretty classic self-join problem. I'm gonna start by summarizing your data to get the total goals for each player. I like dplyr for this, but aggregate or data.table work just fine too.
library(dplyr)
df <- df %>% group_by(Player, Country) %>% dplyr::summarize(Goals = sum(Goals))
> df
Source: local data frame [7 x 3]
Groups: Player
Player Country Goals
1 Dempsey USA 8
2 Hulk Brasil 5
3 Luiz Brasil 2
4 Neymar Brasil 8
5 Rooney England 4
6 Stewart England 2
7 Tim USA 0
Then, using good old merge, we join it to itself based on country, and then so we don't get each row twice (Dempsey, Tim and Tim, Dempsey---not to mention Dempsey, Dempsey), we'll subset it so that Player.x is alphabetically before Player.y. Since I already loaded dplyr I'll use filter, but subset would do the same thing.
df2 <- merge(df, df, by.x = "Country", by.y = "Country")
df2 <- filter(df2, as.character(Player.x) < as.character(Player.y))
> df2
Country Player.x Goals.x Player.y Goals.y
2 Brasil Hulk 5 Luiz 2
3 Brasil Hulk 5 Neymar 8
6 Brasil Luiz 2 Neymar 8
11 England Rooney 4 Stewart 2
15 USA Dempsey 8 Tim 0
The self-join could be done in dplyr if we made a little copy of the data and renamed the Player and Goals columns so they wouldn't be joined on. Since merge is pretty smart about the renaming, it's easier in this case.

Answer 2

There is probably a smarter way to get from the aggregated data to the pairs, but assuming your data is not too big (national soccer data), you can always do something like:
A<-aggregate(df$Goals~df$Player+df$Country,data=df,sum)
players_in_c<-table(A[,2])
dat<-NULL
for(i in levels(df$Country)) {
count<-players_in_c[i]
pair<-combn(count,m=2)
B<-A[A[,2]==i,]
dat<-rbind(dat, cbind(B[pair[1,],],B[pair[2,],]) )
}
dat
> dat
df$Player df$Country df$Goals df$Player df$Country df$Goals
1 Hulk Brasil 5 Luiz Brasil 2
1.1 Hulk Brasil 5 Neymar Brasil 8
2 Luiz Brasil 2 Neymar Brasil 8
4 Rooney England 4 Stewart England 2
6 Dempsey USA 8 Tim USA 0