CODEWITHSUNDEEP
rarules
KarC
KarC

Reputation: 101

How to convert a data frame to arules' transaction object

I'm trying to do association rules on a dataset using the library arules in R. The dataset has a transaction column and 5 items columns - I'm trying to turn the data into a list to then use arules but because there is more then one items column I'm not sure how to go about this.

my data set looks like the below:

Transaction     Item1        Item2         Item3    

12/09/2001     lipstick      Bronzer        Mascara
2/09/2001     Eyeshadow     lipstick
13/09/2002     Powder        Remover
14/09/2003     Nail varnish  Lip gloss      Eyeliner

The code I would usually use for one transaction column and one items columns is below. 

library(arules)
Transactions <- split(data$item, data$transaction)

basketanalysis <- as(Transactions, "transactions")

Any help would be hugely appreciated.

Upvotes: 2

Views: 615

Answers (1)

jazzurro
jazzurro

Reputation: 23574

Here is what I tried. I think you need to manipulate your data and create lists. First, I created transaction ID just in case. Then, I transformed the data to a long-format data frame. By this time, all products stay in one column. I removed all rows that have NA. Then, I converted products to factor. For each group (transaction id), I created list containing all products. x has a column called whatever. This is the list you want to use to create a transaction object. 

library(tidyverse)
library(arules)

mutate(mydata, transaction_id = 1:n()) %>% 
pivot_longer(cols = contains("Item"), names_to = "item", values_to = "product") %>% 
filter(complete.cases(product)) %>% 
mutate(product = factor(product)) %>% 
group_by(transaction_id) %>% 
summarize(whatever = list(product)) -> x

# Assign transaction ID as name to whatever
names(x$whatever) <- x$transaction_id

$`1`
[1] lipstick Bronzer  Mascara 
Levels: Bronzer Eyeliner Eyeshadow Lip gloss lipstick Mascara Nail varnish Powder Remover

$`2`
[1] Eyeshadow lipstick 
Levels: Bronzer Eyeliner Eyeshadow Lip gloss lipstick Mascara Nail varnish Powder Remover

$`3`
[1] Powder  Remover
Levels: Bronzer Eyeliner Eyeshadow Lip gloss lipstick Mascara Nail varnish Powder Remover

$`4`
[1] Nail varnish Lip gloss    Eyeliner    
Levels: Bronzer Eyeliner Eyeshadow Lip gloss lipstick Mascara Nail varnish Powder Remover

Finally, I created a transaction-class object.

mybasket <- as(x$whatever, "transactions")

> summary(mybasket)
transactions as itemMatrix in sparse format with
 4 rows (elements/itemsets/transactions) and
 9 columns (items) and a density of 0.2777778 

most frequent items:
 lipstick   Bronzer  Eyeliner Eyeshadow Lip gloss   (Other) 
        2         1         1         1         1         4 

element (itemset/transaction) length distribution:
sizes
2 3 
2 2 

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   2.0     2.0     2.5     2.5     3.0     3.0 

includes extended item information - examples:
     labels
1   Bronzer
2  Eyeliner
3 Eyeshadow

includes extended transaction information - examples:
  transactionID
1             1
2             2
3             3

DATA

mydata <- structure(list(Transaction = c("12/09/2001", "2/09/2001", "13/09/2002", 
"14/09/2003"), Item1 = c("lipstick", "Eyeshadow", "Powder", "Nail varnish"
), Item2 = c("Bronzer", "lipstick", "Remover", "Lip gloss"), 
Item3 = c("Mascara", NA, NA, "Eyeliner")), row.names = c(NA, 
-4L), class = c("tbl_df", "tbl", "data.frame"))

Upvotes: 2

Related Questions