My goal
My goal is to find, based on the index, the best combinations of 10 players that have a score sum between 95.5-100.4.
The detail
There is an important detail. From the 10 players, there should be based on the role column 2C (two Centers), 4F (four Forwards), 4*G (four Guards).
Atm I am struggling with for loops, but I am sure there is something bright in the dplyr package that I am missing. Any help or guidance is highly appreciated.
set.seed(123)
players <- paste("player",rep(1:20))
score <- runif(20, min=4, max=16.7)
index <- runif(20, min=-1, max=9)
role <- rep(c("C","F","F","G","G"),4)
df <- data.frame(players, score, index,role)
df
#> players score index role
#> 1 player 1 7.652235 7.8953932 C
#> 2 player 2 14.011475 5.9280341 F
#> 3 player 3 9.194007 5.4050681 F
#> 4 player 4 15.214321 8.9426978 G
#> 5 player 5 15.943935 5.5570580 G
#> 6 player 6 4.578568 6.0853047 C
#> 7 player 7 10.706940 4.4406602 F
#> 8 player 8 15.333722 4.9414202 F
#> 9 player 9 11.003225 1.8915974 G
#> 10 player 10 9.799007 0.4711365 G
#> 11 player 11 16.151783 8.6302423 C
#> 12 player 12 9.757344 8.0229905 F
#> 13 player 13 12.605147 5.9070528 F
#> 14 player 14 11.272444 6.9546742 G
#> 15 player 15 5.307143 -0.7538632 G
#> 16 player 16 15.427777 3.7779597 C
#> 17 player 17 7.125314 6.5845954 F
#> 18 player 18 4.534156 1.1640794 F
#> 19 player 19 8.164593 2.1818101 G
#> 20 player 20 16.122196 1.3162579 G
Created on 2021-10-16 by the reprex package (v2.0.1)
Thank you for your time
UPDATE:
My logic so far is:
- to transpose my df
df <- as.data.frame(t(df))
- and create all possible combinations of 10 players
combn(df, 10, simplify=FALSE)
Now I need to select the list which the right roles and sum between 95.5-100.4. Damn there should be a more clever way.
CodePudding user response：
I used data.table. choose(4, 2) * choose(8, 4) * choose(8, 4) = 29400 combinations. Basically brute force. I'm sure there's a more elegant solution.
library(data.table)
library(magrittr)
set.seed(123)
players <- paste("player",rep(1:20))
score <- runif(20, min=4, max=16.7)
index <- runif(20, min=-1, max=9)
role <- rep(c("C","F","F","G","G"),4)
dt <- data.table(players, score, index,role)
centers <- dt[role == "C"]
forwards <- dt[role == "F"]
guards <- dt[role == "G"]
c_combos <- combn(nrow(centers), 2) %>% t() %>% as.data.table()
c_combos <- lapply(c_combos, function(x) centers[x])
c_combos <- data.table(c_combos[[1]]$players, c_combos[[2]]$players,
c_combos[[1]]$score c_combos[[2]]$score,
c_combos[[1]]$index c_combos[[2]]$index) %>%
setnames(c('C1', 'C2', 'score_c', 'index_c'))
f_combos <- combn(nrow(forwards), 4) %>% t() %>% as.data.table()
f_combos <- lapply(f_combos, function(x) forwards[x])
f_combos <- data.table(f_combos[[1]]$players, f_combos[[2]]$players,
f_combos[[3]]$players, f_combos[[4]]$players,
f_combos[[1]]$score f_combos[[2]]$score
f_combos[[3]]$score f_combos[[4]]$score,
f_combos[[1]]$index f_combos[[2]]$index
f_combos[[3]]$index f_combos[[4]]$index) %>%
setnames(c('F1', 'F2', 'F3', 'F4', 'score_f', 'index_f'))
g_combos <- combn(nrow(guards), 4) %>% t() %>% as.data.table()
g_combos <- lapply(g_combos, function(x) guards[x])
g_combos <- data.table(g_combos[[1]]$players, g_combos[[2]]$players,
g_combos[[3]]$players, g_combos[[4]]$players,
g_combos[[1]]$score g_combos[[2]]$score
g_combos[[3]]$score g_combos[[4]]$score,
g_combos[[1]]$index g_combos[[2]]$index
g_combos[[3]]$index g_combos[[4]]$index) %>%
setnames(c('G1', 'G2', 'G3', 'G4', 'score_g', 'index_g'))
combined <- expand.grid(1:nrow(c_combos), 1:nrow(f_combos), 1:nrow(g_combos))
ans <- rbindlist(lapply(1:nrow(combined),
function(x) data.table(c_combos[combined$Var1[x]],
f_combos[combined$Var2[x]],
g_combos[combined$Var3[x]])))
ans[, score := score_c score_f score_g]
ans[, index := index_c index_f index_g]
ans[, c('score_c', 'score_f', 'score_g', 'index_c', 'index_f', 'index_g') := NULL]
ans[score