Calculate Pair-Wise Combinations of Factor Vector with Summed Values - r

Question

I'm trying to generate network graph data from raw occurrence data. In the raw data, I have the occurrence rate of features in a variety of contexts. Let's say it's actors in different movies. Each row is [context, feature, weight], where weight might be amount of screen time. Here's a toy data set:
df <- data.frame(context = sample(LETTERS[1:10], 500, replace=TRUE),
feature = sample(LETTERS, 500, replace=TRUE),
weight = sample(1:100, 500, replace=TRUE)
)
So for Movie A, we might have 20 rows, where each row is an actor's name and their screen time in that movie.
What I'd like to generate is the pairwise combination of all actors for each movie, with the sum of their respective weights. So for example, if we start with:
[A, A, 5]
[A, B, 2]
I'd like output in the format of [context, feature1, feature2, sum.weight]. So:
[A, A, B, 7]
I know how to run through this with a combination of for loops, but I'd like to know if there is a more "classic R" way of approaching this, particularly with something like data.table.

Answer 1

Here's a possible solution using the data.table package:
library(data.table)
# keep a record of feature's levels
feature.levels <- levels(df$feature)
# for each context, create a data table for all pair combinations of features,
# & sum of said pair's weights
df <- df[,
as.data.table(
cbind(t(combn(feature, 2)),
rowSums(t(combn(weight, 2))))
),
by = context]
# map features (converted into integers in the previous step) back to factors
df[,
c('V1', 'V2') := lapply(.SD,
function(x){factor(x, labels = feature.levels)}),
.SDcols = c('V1', 'V2')]
# rename features / sum weights
setnames(df,
old = c("V1", "V2", "V3"),
new = c("feature1", "feature2", "sum.weights"))
> head(df)
context feature1 feature2 sum.weights
1: C j l 373
2: C j z 282
3: C j v 382
4: C j h 488
5: C j c 280
6: C j u 360
Data (I used lower case for "feature" so that it's visually distinct from upper case "context"):
set.seed(123)
df <- data.frame(context = sample(LETTERS[1:10], 500, replace=TRUE),
feature = sample(letters, 500, replace=TRUE),
weight = sample(1:100, 500, replace=TRUE))
# convert to data table & summarize to unique combinations by context + feature
setDT(df)
df <- df[,
list(weight = sum(weight)),
by = list(context, feature)]