Removing rows in data.frame having columns subsumed in others - r

Question

I am trying to achieve something similar to unique in a data.frame where column each element of a column in a row are vectors. What I want to do is if the elements of the vector in the column of that hat row a subset or equal to another remove the row with smaller number of elements. I can achieve this with a nested for loop but since data contains 400,000 rows the program is very inefficient.
Sample data
# Set the seed for reproducibility
set.seed(42)
# Create a random data frame
mydf <- data.frame(items = rep(letters[1:4], length.out = 20),
grps = sample(1:5, 20, replace = TRUE),
supergrp = sample(LETTERS[1:4], replace = TRUE))
# Aggregate items into a single column
temp <- aggregate(items ~ grps + supergrp, mydf, unique)
# Arrange by number of items for each grp and supergroup
indx <- order(lengths(temp$items), decreasing = T)
temp <- temp[indx, ,drop=FALSE]
Temp looks like
grps supergrp items
1 4 D a, c, d
2 3 D c, d
3 5 D a, d
4 1 A b
5 2 A b
6 3 A b
7 4 A b
8 5 A b
9 1 D d
10 2 D c
Now you can see that second combination of supergrp and items in second and third row is contained in first row. So, I want to delete the second and third rows from the result. Similarly, rows 5 to 8 are contained in row 4. Finally, rows 9 and 10 are contained in the first row, so I want to delete rows 9 and 10.
Hence, my result would look like:
grps supergrp items
1 4 D a, c, d
4 1 A b
My implementation is as follows::
# initialise the result dataframe by first row of old data frame
newdf <-temp[1, ]
# For all rows in the the original data
for(i in 1:nrow(temp))
{
# Index to check if all the items are found
indx <- TRUE
# Check if item in the original data appears in the new data
for(j in 1:nrow(newdf))
{
if(all(c(temp$supergrp[[i]], temp$items[[i]]) %in%
c(newdf$supergrp[[j]], newdf$items[[j]]))){
# set indx to false if a row with same items and supergroup
# as the old data is found in the new data
indx <- FALSE
}
}
# If none of the rows in new data contain items and supergroup in old data append that
if(indx){
newdf <- rbind(newdf, temp[i, ])
}
}
I believe there is an efficient way to implement this in R; may be using the tidy framework and dplyr chains but I am missing the trick. Apologies for a longish question. Any input would be highly appreciated.

Answer 1

I would try to get the items out of a list column and store them in a longer dataframe. Here is my somewhat hacky solution:
library(stringr)
items <- temp$items %>%
map(~str_split(., ",")) %>%
map_df(~data.frame(.))
out <- bind_cols(temp[, c("grps", "supergrp")], items)
out %>%
gather(item_name, item, -grps, -supergrp) %>%
select(-item_name, -grps) %>%
unique() %>%
filter(!is.na(item))