Feels like this should be straightforward but I have been through stack overflow and combn help, and cannot see solution.
The below players need to be in teams of 3 versus 2. I need to find all possible combinations of teams. For example two possible teams are "Ross", "Bobby" and "Casper" in one team, and "Max" and "Jake" in the other team. How can I code this?
players <- c("Ross", "Bobby", "Max", "Casper", "Jake")
On
Just to build off @ThomasIsCoding great answer. Further speed could be gained by using the great RcppAlgos library and the comboGeneral function:
players <- c("Ross", "Bobby", "Max", "Casper", "Jake")
microbenchmark(
f1 = combn(players,
3,
function(x) list(team1 = x, team2 = players[!players %in% x]),
simplify = FALSE
),
f2 = combn(players,
3,
function(x) list(team1 = x, team2 = setdiff(players, x)),
simplify = FALSE
),
f3_rcpp = RcppAlgos::comboGeneral(
v = players,
m = 3,
repetition = FALSE,
FUN = function(x) list(team1 = x, team2 = players[!players %in% x])
)
)
BENCHMARK
Unit: microseconds
expr min lq mean median uq max neval
f1 34.3 37.95 63.693 39.00 41.45 2016.7 100
f2 143.5 152.40 184.351 155.30 158.95 1961.7 100
f3_rcpp 29.3 32.40 61.820 33.95 42.40 2205.0 100
There is a function in RcppAlgos (I am the author) called comboGroups built precisely for this task. As of version 2.8.0, we can now handle groups of varying sizes.
library(RcppAlgos)
packageVersion("RcppAlgos")
#> [1] '2.8.0'
We specify the size of each team using the grpSizes parameter:
players <- c("Ross", "Bobby", "Max", "Casper", "Jake")
comboGroups(players, grpSizes = c(2, 3))
#> Grp1 Grp1 Grp2 Grp2 Grp2
#> [1,] "Ross" "Bobby" "Max" "Casper" "Jake"
#> [2,] "Ross" "Max" "Bobby" "Casper" "Jake"
#> [3,] "Ross" "Casper" "Bobby" "Max" "Jake"
#> [4,] "Ross" "Jake" "Bobby" "Max" "Casper"
#> [5,] "Bobby" "Max" "Ross" "Casper" "Jake"
#> [6,] "Bobby" "Casper" "Ross" "Max" "Jake"
#> [7,] "Bobby" "Jake" "Ross" "Max" "Casper"
#> [8,] "Max" "Casper" "Ross" "Bobby" "Jake"
#> [9,] "Max" "Jake" "Ross" "Bobby" "Casper"
#> [10,] "Casper" "Jake" "Ross" "Bobby" "Max"
It is very efficient and quite flexible. Let’s test on a larger set of players.
library(microbenchmark)
more_players <- c(players, "Kai", "Eliana", "Jayden", "Luca",
"Rowan", "Nova", "Amara", "Finn", "Zion", "Mia")
microbenchmark(
f1 = combn(more_players,
7,
function(x) list(team1 = x, team2 = more_players[!more_players %in% x]),
simplify = FALSE
),
f2 = combn(more_players,
7,
function(x) list(team1 = x, team2 = setdiff(more_players, x)),
simplify = FALSE
),
f3_rcpp = comboGeneral(
v = more_players,
m = 7,
repetition = FALSE,
FUN = function(x) list(team1 = x, team2 = more_players[!more_players %in% x])
),
f4 = comboGroups(more_players, grpSizes = c(7, 8)),
unit = "relative"
)
#> Unit: relative
#> expr min lq mean median uq max neval cld
#> f1 16.22184 19.03265 23.68141 20.78979 26.59770 92.08256 100 a
#> f2 41.34947 45.55672 57.30847 54.70320 59.88822 123.09864 100 b
#> f3_rcpp 11.83424 14.48575 17.65936 16.24856 21.43574 36.27697 100 c
#> f4 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 100 d
What about more exotic groupings? With most other approaches, efficiency and code maintenance will be an issue.
Say for example, given more_players (15 total players) what if we wanted to find all possible groupings with 2 teams of size 3, one team of size 4, and one team of size 5?
With comboGroups, it is no problem:
system.time(t <- comboGroups(more_players, grpSizes = c(3, 3, 4, 5)))
#> user system elapsed
#> 0.508 0.040 0.548
head(t, n = 2)
#> Grp1 Grp1 Grp1 Grp2 Grp2 Grp2 Grp3 Grp3 Grp3 Grp3
#> [1,] "Ross" "Bobby" "Max" "Casper" "Jake" "Kai" "Eliana" "Jayden" "Luca" "Rowan"
#> [2,] "Ross" "Bobby" "Max" "Casper" "Jake" "Kai" "Eliana" "Jayden" "Luca" "Nova"
#> Grp4 Grp4 Grp4 Grp4 Grp4
#> [1,] "Nova" "Amara" "Finn" "Zion" "Mia"
#> [2,] "Rowan" "Amara" "Finn" "Zion" "Mia"
tail(t, n = 2)
#> Grp1 Grp1 Grp1 Grp2 Grp2 Grp2 Grp3 Grp3 Grp3
#> [6306299,] "Rowan" "Zion" "Mia" "Nova" "Amara" "Finn" "Jake" "Eliana" "Jayden"
#> [6306300,] "Rowan" "Zion" "Mia" "Nova" "Amara" "Finn" "Kai" "Eliana" "Jayden"
#> Grp3 Grp4 Grp4 Grp4 Grp4 Grp4
#> [6306299,] "Luca" "Ross" "Bobby" "Max" "Casper" "Kai"
#> [6306300,] "Luca" "Ross" "Bobby" "Max" "Casper" "Jake"
If you just need a sample of possible teams, try comboGroupsSample:
comboGroupsSample(
more_players, grpSizes = c(3, 3, 4, 5), n = 2,
seed = 42, namedSample = TRUE
)
#> Grp1 Grp1 Grp1 Grp2 Grp2 Grp2 Grp3 Grp3 Grp3
#> 3207141 "Bobby" "Jake" "Mia" "Luca" "Amara" "Zion" "Max" "Casper" "Eliana"
#> 4248729 "Max" "Casper" "Nova" "Rowan" "Amara" "Finn" "Ross" "Bobby" "Kai"
#> Grp3 Grp4 Grp4 Grp4 Grp4 Grp4
#> 3207141 "Rowan" "Ross" "Kai" "Jayden" "Nova" "Finn"
#> 4248729 "Luca" "Jake" "Eliana" "Jayden" "Zion" "Mia"
I think the key step is to randomly choose 3 (or 2) out of 5 players to generate the first team, and the remaining are naturally assigned to the second team.
Probably you can try it like this
or if you don't mind the time efficiency, you can use
setdiffinsteadand either of which gives
Benchmarking
and we will see