I have a dataframe with test results (rows being Players; and columns Q1... Q6 being the different questions). Now I want to find out which pair of players scored the highest sum in total:
# Generating sample data.
n = 6
set.seed(1986)
results_df = data.frame(Player = c("A", "B", "C", "D", "E", "F"),
Q1 = sample(0:1, size = n, replace = TRUE),
Q2 = sample(0:1, size = n, replace = TRUE),
Q3 = sample(0:1, size = n, replace = TRUE),
Q4 = sample(0:1, size = n, replace = TRUE),
Q5 = sample(0:1, size = n, replace = TRUE),
Q6 = sample(0:1, size = n , replace = TRUE))
head(results_df)
Player Q1 Q2 Q3 Q4 Q5 Q6
1 A 1 0 1 0 0 0
2 B 1 1 0 0 0 0
3 C 0 1 0 1 0 1
4 D 0 1 1 0 1 1
5 E 1 1 1 1 1 1
6 F 1 0 0 1 0 1
The 1's and 0's are dummies for whether each player got their question right (1) or wrong (0). Now I would like to combine each pair of players to see how well they would have done it as a pair.
Does anyone know how I can transform the dataframe above to something looking like this below?
(Here I have just summed each combination of pairs by hand: A had 3 right, combined with B who had 3 questions right that A had wrong, would make a combination of 6, and so on...)
Player A B C D E F
1 A 2 3 5 5 6 4
2 B 3 2 4 5 6 4
3 C 5 4 3 5 6 4
4 D 5 5 5 4 6 6
5 E 6 6 6 6 6 6
6 F 4 4 4 6 6 3
in base R you could do:
a <- data.frame(t(as.matrix(results_df[-1])))
b <- combn(a, 2, function(x)sum(x[1] | x[2]))
attributes(b) <- list(Size = ncol(a), Labels = results_df$Player)
d <- as.matrix(structure(b, class = 'dist'))
diag(d) <- colSums(a)
d
A B C D E F
A 2 3 5 5 6 4
B 3 2 4 5 6 4
C 5 4 3 5 6 4
D 5 5 5 4 6 6
E 6 6 6 6 6 6
F 4 4 4 6 6 3
You can use this to get the sums
n <- 6
# get the combinations
ee <- expand.grid(1:n, 1:n)
res <- setNames(data.frame(matrix(
rowSums(results_df[,-1][ee[,1],] | results_df[,-1][ee[,2],]), n)),
results_df[,1])
rownames(res) <- results_df[,1]
res
A B C D E F
A 2 3 5 5 6 4
B 3 2 4 5 6 4
C 5 4 3 5 6 4
D 5 5 5 4 6 6
E 6 6 6 6 6 6
F 4 4 4 6 6 3
Another option, based on dplyr::coalesce
:
library(dplyr)
df <- na_if(results_df, 0)
mat <- lapply(seq(nrow(df)), function(x) sapply(seq(nrow(df)), function(y) dplyr::coalesce(df[x,-1], df[y,-1]))) |>
lapply(function(x) colSums(matrix(as.numeric(x),nrow=6), na.rm=T))
mat <- do.call(rbind, mat)
colnames(mat) <- df$Player
rownames(mat) <- df$Player
mat
# A B C D E F
# A 2 3 5 5 6 4
# B 3 2 4 5 6 4
# C 5 4 3 5 6 4
# D 5 5 5 4 6 6
# E 6 6 6 6 6 6
# F 4 4 4 6 6 3
With this input data:
# Player Q1 Q2 Q3 Q4 Q5 Q6
#1 A 0 1 1 0 1 1
#2 B 0 0 1 0 1 1
#3 C 1 1 0 0 0 0
#4 D 0 1 0 1 1 1
#5 E 1 1 0 1 0 1
#6 F 0 0 0 1 0 1
There is simple base
solution:
scr <- rowSums(dat[, -1]) # 1)
res <- data.frame(outer(scr, scr, '+') - diag(scr)) # 2)
dimnames(res) <- dat[, c(1, 1)] # 3)
Player
);A
- A
score original number obtained in (1), not a double of it;Which gives you this result:
# A B C D E F
# A 4 7 6 8 8 6
# B 7 3 5 7 7 5
# C 6 5 2 6 6 4
# D 8 7 6 4 8 6
# E 8 7 6 8 4 6
# F 6 5 4 6 6 2
Data:
dat <- structure(
list(
Player = c("A", "B", "C", "D", "E", "F"),
Q1 = c(0, 0, 1, 0, 1, 0),
Q2 = c(1, 0, 1, 1, 1, 0),
Q3 = c(1, 1, 0, 0, 0, 0),
Q4 = c(0, 0, 0, 1, 1, 1),
Q5 = c(1, 1, 0, 1, 0, 0),
Q6 = c(1, 1, 0, 1, 1, 1)
),
row.names = c(NA,-6L),
class = "data.frame"
)
A base R option with outer
> lst <- asplit(`row.names<-`(as.matrix(results_df[-1]), results_df$Player), 1)
> outer(lst, lst, FUN = Vectorize(function(x, y) sum(x + y > 0)))
A B C D E F
A 2 3 5 5 6 4
B 3 2 4 5 6 4
C 5 4 3 5 6 4
D 5 5 5 4 6 6
E 6 6 6 6 6 6
F 4 4 4 6 6 3
So here is the code to compute all player's combined score. I dont know, why you need them in a matrix form but using this you should be able to create the matrix. The solution is to use tidyr::pivot_longer()
and afterwards dplyr.
# Generating sample data.
set.seed(1986)
n <- 6
results_df <- data.frame(
Player = c("A", "B", "C", "D", "E", "F"),
Q1 = sample(0:1, size = n, replace = TRUE),
Q2 = sample(0:1, size = n, replace = TRUE),
Q3 = sample(0:1, size = n, replace = TRUE),
Q4 = sample(0:1, size = n, replace = TRUE),
Q5 = sample(0:1, size = n, replace = TRUE),
Q6 = sample(0:1, size = n, replace = TRUE)
)
results_df
#> Player Q1 Q2 Q3 Q4 Q5 Q6
#> 1 A 1 0 1 0 0 0
#> 2 B 1 1 0 0 0 0
#> 3 C 0 1 0 1 0 1
#> 4 D 0 1 1 0 1 1
#> 5 E 1 1 1 1 1 1
#> 6 F 1 0 0 1 0 1
results_df |>
tidyr::pivot_longer(cols = tidyselect::starts_with("Q"), names_to = "question", values_to = "score") |>
dplyr::group_by(Player) |>
dplyr::summarise(total = sum(score))
#> # A tibble: 6 x 2
#> Player total
#> <chr> <int>
#> 1 A 2
#> 2 B 2
#> 3 C 3
#> 4 D 4
#> 5 E 6
#> 6 F 3
Created on 2022-02-04 by the reprex package (v2.0.1)
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