I have a data frame...
example <- data.frame(obs_val= c(20,15,3,7,5), patient = c("pt1","pt2","pt3","pt4","pt5"))
... where every row or "patient" is a unique observation.
My goal is to generate a data frame that subtracts each patient's observed value (obs_val) from another patient's obs_val. This subtraction would be a permutation, where i.e. pt1 does not have their own obs_val subtracted from their self. Ideally, the final data frame should look something like the following:
pt1-pt2 pt1-pt3 pt1-pt4 pt1-pt5 pt2-pt3 pt2-pt4 ...
obs_val_diff 5 17 13 15 12 8 ...
Any suggestions on solving this problem, or reformatting the final data frame?
Another option is to use combn to get all the combinations and then map out the subtractions.
library(tidyverse)
data.frame(t(combn(example$patient, 2))) |>
mutate(obs_val_diff = map2_dbl(X1, X2, ~example[example$patient ==.x, "obs_val"] -
example[example$patient ==.y, "obs_val"])) |>
unite(test, X1, X2, sep = "-") |>
pivot_wider(names_from = test, values_from = obs_val_diff)
#> # A tibble: 1 x 10
#> `pt1-pt2` `pt1-pt3` `pt1-pt4` pt1-pt~1 pt2-p~2 pt2-p~3 pt2-p~4 pt3-p~5 pt3-p~6
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 5 17 13 15 12 8 10 -4 -2
#> # ... with 1 more variable: `pt4-pt5` <dbl>, and abbreviated variable names
#> # 1: `pt1-pt5`, 2: `pt2-pt3`, 3: `pt2-pt4`, 4: `pt2-pt5`, 5: `pt3-pt4`,
#> # 6: `pt3-pt5`
or in base R:
apply(t(combn(example$patient, 2)), 1,
\(x) -diff(example[example$patient %in% x, "obs_val"])) |>
(\(v) matrix(v, ncol = length(v)))() |>
as.data.frame() |>
`colnames<-`(apply(t(combn(example$patient, 2)), 1,
\(x) paste(x, collapse = "-")))
#> pt1-pt2 pt1-pt3 pt1-pt4 pt1-pt5 pt2-pt3 pt2-pt4 pt2-pt5 pt3-pt4 pt3-pt5
#> 1 5 17 13 15 12 8 10 -4 -2
#> pt4-pt5
#> 1 2