Libraries

Here are the libraries we will use

library(data.table)

## Warning: package 'data.table' was built under R version 3.4.3

library(lubridate)

## Warning: package 'lubridate' was built under R version 3.4.3

## 
## Attaching package: 'lubridate'

## The following objects are masked from 'package:data.table':
## 
##     hour, isoweek, mday, minute, month, quarter, second, wday,
##     week, yday, year

## The following object is masked from 'package:base':
## 
##     date

library(Hmisc)

## Warning: package 'Hmisc' was built under R version 3.4.3

## Loading required package: lattice

## Loading required package: survival

## Loading required package: Formula

## Loading required package: ggplot2

## Warning: package 'ggplot2' was built under R version 3.4.3

## 
## Attaching package: 'Hmisc'

## The following objects are masked from 'package:base':
## 
##     format.pval, units

Data Preview

Here is a preview of the dataset with the dates formatted correctly

transactions <- fread('transactions.csv')
transactions[, TransDate:=dmy(TransDate, tz="UTC")]
head(transactions, 5)

Calculate Max Transaction Date

We first need to calculate the max transaction date to calculate the recency score

(max.Date <- max(transactions[,TransDate]))

## [1] "2012-12-09 UTC"

Calculate RFM scores

Now we can calculate the recency, frequency, and monetary scores like so

rfm <- transactions[,list(
    recency = as.numeric(max.Date - max(TransDate)),
    frequency = .N,
    value = mean(PurchAmount)
  ), by=Customer]

Cut2() Function

The cut2() function will allow us to create the rfm scores based on the data

rfm.scores <- rfm[,list(
  Customer,
  recency = as.numeric(cut2(-recency, g=3)),
  frequency = as.numeric(cut2(frequency, g=3)),
  value = as.numeric(cut2(value, g=3))
)]

Final Output

Finally we can calculate the unweighted and weighted RFM scores. Weights can be specified in a vector, in this case we used \((.2, .2, .6)\)