Harry M Harry M - 5 months ago 44
R Question

Generate all possible pairs and count frequency in R

I have a data frame of products (apple, pear, banana) sold across different locations (cities) within different categories (food and edibles).

I would like to count how many times any given pair of products appeared together in any category.

This is an example dataset I'm trying to make this to work on:

category <- c('food','food','food','food','food','food','edibles','edibles','edibles','edibles', 'edibles')
location <- c('houston, TX', 'houston, TX', 'las vegas, NV', 'las vegas, NV', 'philadelphia, PA', 'philadelphia, PA', 'austin, TX', 'austin, TX', 'charlotte, NC', 'charlotte, NC', 'charlotte, NC')
item <- c('apple', 'banana', 'apple', 'pear', 'apple', 'pear', 'pear', 'apple', 'apple', 'pear', 'banana')

food_data <- data.frame(cbind(category, location, item), stringsAsFactors = FALSE)

For example, the pair "apple & banana" appeared together in the "food" category in "las vegas, NV", but also in the "edibles" category in "charlotte, NC". Therefore, the count for the "apple & banana" pair would be 2.

My desired output is count of pairs like this:

(unordered) count of apple & banana


(unordered) count of apple & pear


Anyone have an idea for how to accomplish this? Relatively new to R and have been confused for a while.

I'm trying to use this to calculate affinities between different items.

Additional clarification on output:
My full dataset consists of hundreds of different items. Would like to get a data frame where the first column is the pair and the second column is the count for each pair.

Answer Source

Here is one way using tidyverse and crossprod; By using spread, it turns all item/fruit from the same category-location combination into one row with the item as headers (this requires you have no duplicated item in each category-country, otherwise you need a pre-aggregation step), values indicating existence; crossprod essentially evaluates the inner product of pairs of items columns and gives the number of cooccurrences.

food_data %>% 
    mutate(n = 1) %>% 
    spread(item, n, fill=0) %>% 
    select(-category, -location) %>% 
    {crossprod(as.matrix(.))} %>% 

#       apple banana pear
#apple      0      2    4
#banana     2      0    1
#pear       4      1    0

To convert this to a data frame:

food_data %>% 
    mutate(n = 1) %>% 
    spread(item, n, fill=0) %>% 
    select(-category, -location) %>% 
    {crossprod(as.matrix(.))} %>% 
    replace(lower.tri(., diag=T), NA) %>%
    reshape2::melt(na.rm=T) %>%
    unite('Pair', c('Var1', 'Var2'), sep=", ")

#           Pair value
#4 apple, banana     2
#7   apple, pear     4
#8  banana, pear     1
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