Aggregated rolling average with a conditional statement in R - r

Question

I have a data frame that follows the following format.
match team1 team2 winningTeam
1 A D A
2 B E E
3 C F C
4 D C C
5 E B B
6 F A A
7 A D D
8 D A A
What I want to do is to crate variables that calculates the form of both team 1 and 2 over the last x matches. For example, I would want to create a variable called team1_form_last3_matches which for match 8 would be 0.33 (as they won 1 of their last 3 matches) and there would also be a variable called team2_form_last3_matches which would be 0.66 in match 8 (as they won 2 of their last 3 matches). Ideally I would like to be able to specify the number of previous matches to be considered when calculating the teamx_form_lasty variable and those variables to be automatically created. I have tried a bunch of approaches using dplyr, zoo rolling mean functions and a load of nested for / if statements. However, I have not quite cracked it and certainly not in an elegant way. I feel like I am missing a simple solution to this generic problem. Any help would be much appreciated!
Cheers,
Jack

Answer 1

This works for t1l3, you will need to replicate it for t2.
dat <- data.frame(match = c(1:8), team1 = c("A","B","C","D","E","F","A","D"), team2 = c("D","E","F","C","B","A","D","A"), winningTeam = c("A","E","C","C","B","A","D","A"),stringsAsFactors = FALSE)
dat$t1l3 <- c(NA,sapply(2:nrow(dat),function(i) {
df <- dat[1:(i-1),] #just previous games, i.e. excludes current game
df <- df[df$team1==dat$team1[i] | df$team2==dat$team1[i],] #just those containing T1
df <- tail(df,3) #just the last three (or fewer if there aren't three previous games)
return(sum(df$winningTeam==dat$team1[i])/nrow(df)) #total wins/total games (up to three)
}))

Answer 2

How about something like:
dat <- data.frame(match = c(1:8), team1 = c("A","B","C","D","E","F","A","D"), team2 = c("D","E","F","C","B","A","D","A"), winningTeam = c("A","E","C","C","B","A","D","A"))
match team1 team2 winningTeam
1 1 A D A
2 2 B E E
3 3 C F C
4 4 D C C
5 5 E B B
6 6 F A A
7 7 A D D
8 8 D A A
Allteams <- c("A","B","C","D","E","F")
# A vectorized function for you to use to do as you ask:
teamX_form_lastY <- function(teams, games, dat){
sapply(teams, function(x) {
games_info <- rowSums(dat[,c("team1","team2")] == x) + (dat[,"winningTeam"] == x)
lookup <- ifelse(rev(games_info[games_info != 0])==2,1,0)
games_won <- sum(lookup[1:games])
if(length(lookup) < games) warning(paste("maximum games for team",x,"should be",length(lookup)))
games_won/games
})
}
teamX_form_lastY("A", 4, dat)
A
0.75
# Has a warning for the number of games you should be using
teamX_form_lastY("A", 5, dat)
A
NA
Warning message:
In FUN(X[[i]], ...) : maximum games for team A should be 4
# vectorized input
teamX_form_lastY(teams = c("A","B"), games = 2, dat = dat)
A B
0.5 0.5
# so you ca do all teams
teamX_form_lastY(teams = Allteams, 2, dat)
A B C D E F
0.5 0.5 1.0 0.5 0.5 0.0