This is a small section of a dataset I'm working on.
dat2 <- read.table(text = "
nodepair V1 V2 V3 V4 V5 V6 V7 V8 V9 ES
1 A1_A1 0 21 0 0 0 0 0 0 78 45
2 A2_A1 0 0 0 0 0 0 0 0 99 45
3 A2_A2 0 1 0 0 0 0 0 0 98 45
4 A3_A1 0 0 0 0 0 6 1 3 89 45
5 A3_A2 0 0 0 0 0 0 0 0 99 45
6 A1_A1 0 20 0 0 0 0 0 0 65 46
7 A2_A1 0 0 0 0 0 0 0 0 85 46
8 A2_A2 0 1 0 0 0 0 0 0 84 46
9 A3_A1 0 0 0 0 2 6 3 3 71 46
10 A3_A2 0 0 0 0 0 0 0 0 85 46
11 A1_A1 0 25 0 0 0 0 0 0 45 47
12 A2_A1 0 0 0 0 0 0 0 0 70 47
13 A2_A2 0 1 0 0 0 0 0 0 69 47
14 A3_A1 0 0 0 0 0 8 0 1 61 47
15 A3_A2 0 0 0 0 0 0 0 0 70 47
16 A1_A1 0 37 0 0 0 0 0 0 77 48
17 A2_A1 0 0 0 0 0 0 0 0 114 48
18 A2_A2 0 0 0 0 0 0 0 0 114 48
19 A3_A1 0 0 0 0 2 9 0 3 100 48
20 A3_A2 0 0 0 0 0 0 0 0 114 48
", header = TRUE)
I'm trying to write a program that will do all pairwise comparisons (grouped by the nodepair) across the 'ES' groups.
I'd like to write a series of functions to specifically compare each pair of rows. For example, when V1:V9 is > 0 for both ESs, this should result in 1, indicating presence of data.
I'm imagining the output to look something like this:
dat3 <- read.table(text = "
nodepair1 nodepair2 V1 V2 V3 V4 V5 V6 V7 V8 V9
A1_A1(45) A1_A1(46) 0 0 1 0 0 0 0 0 1
", header = TRUE)
etc.
Unfortunately, I haven't gotten very far:
dat2 <- dat2 %>%
group_by(nodepair) %>%
col2 = t(combn(nodepair,2)))
I'm pretty sure I need 'combn' here, but I'm very new to this function and can't figure it out.
Now with the TO having clarified their question, I'd propose the following solution:
library(tidyverse)
ES_combs <- combn(unique(dat2$ES), 2, simplify = FALSE)
dat2 |>
group_split(nodepair) |>
map(.x = _,
.f = \(df) df |>
map(.x = 1:length(ES_combs),
.f = ~df |>
filter(ES %in% ES_combs[[.x]]) |>
summarize(nodepair = first(nodepair),
ES_1 = ES[1],
ES_2 = ES[2],
across(V1:V9, ~as.numeric(all(. >0)))))) |>
bind_rows()
which gives:
# A tibble: 30 × 12
nodepair ES_1 ES_2 V1 V2 V3 V4 V5 V6 V7 V8 V9
<chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 A1_A1 45 46 0 1 0 0 0 0 0 0 1
2 A1_A1 45 47 0 1 0 0 0 0 0 0 1
3 A1_A1 45 48 0 1 0 0 0 0 0 0 1
4 A1_A1 46 47 0 1 0 0 0 0 0 0 1
5 A1_A1 46 48 0 1 0 0 0 0 0 0 1
6 A1_A1 47 48 0 1 0 0 0 0 0 0 1
7 A2_A1 45 46 0 0 0 0 0 0 0 0 1
8 A2_A1 45 47 0 0 0 0 0 0 0 0 1
9 A2_A1 45 48 0 0 0 0 0 0 0 0 1
10 A2_A1 46 47 0 0 0 0 0 0 0 0 1
11 A2_A1 46 48 0 0 0 0 0 0 0 0 1
12 A2_A1 47 48 0 0 0 0 0 0 0 0 1
13 A2_A2 45 46 0 1 0 0 0 0 0 0 1
14 A2_A2 45 47 0 1 0 0 0 0 0 0 1
15 A2_A2 45 48 0 0 0 0 0 0 0 0 1
16 A2_A2 46 47 0 1 0 0 0 0 0 0 1
17 A2_A2 46 48 0 0 0 0 0 0 0 0 1
18 A2_A2 47 48 0 0 0 0 0 0 0 0 1
19 A3_A1 45 46 0 0 0 0 0 1 1 1 1
20 A3_A1 45 47 0 0 0 0 0 1 0 1 1
21 A3_A1 45 48 0 0 0 0 0 1 0 1 1
22 A3_A1 46 47 0 0 0 0 0 1 0 1 1
23 A3_A1 46 48 0 0 0 0 1 1 0 1 1
24 A3_A1 47 48 0 0 0 0 0 1 0 1 1
25 A3_A2 45 46 0 0 0 0 0 0 0 0 1
26 A3_A2 45 47 0 0 0 0 0 0 0 0 1
27 A3_A2 45 48 0 0 0 0 0 0 0 0 1
28 A3_A2 46 47 0 0 0 0 0 0 0 0 1
29 A3_A2 46 48 0 0 0 0 0 0 0 0 1
30 A3_A2 47 48 0 0 0 0 0 0 0 0 1
This probably needs a bit of explanation:
ES_combsmap where we go through each group's data frame. It is important here to define an anonymous function, because we have an inner map, so we can't use the .x parameter twice.map takes each combination pair from ES_combs and filters the current group's data to these two rows. We then apply the summarize part.bind_rows to merge everything into a nice tibble instead of having an annoyingly long list.