Creating a retention cohort by date of acquistion in R - r

Question

I'm new to R have gone through several tutorials online and paid but still struggling with the implementation of my requirement. I would like to build a retention cohort in R. I currently do this in excel, which takes me 4-5 hours every time I need to implement one. Therefore, exploring to see if R can help out. It seems so but need some direction.
Below is the output in excel. If you notice May 2011 I had 31 customers who joined and their progression monthwise till the current month.
Output in excel
This is the input variable
Initial columns are customer id, his date of joining, and his date of expiry. Two additional columns convert the dj & de to text. Columns K onwards is processing of the data to check if the customer is active in given month or has he churned? I use date of join in text from to indicate active and append it to with "C" to denote expiry. Later I just count the columns with the date to get the cohort.
So, how do I implement this in R.
Assuming this is the sample data and I need the cohort from 1 May 2015 to 1 Jan 2016
customer dj exp
abc 01/05/15 25/6/15
efg 01/05/15 25/7/15
ghd 01/05/15 25/7/15
mkd 01/06/15 25/7/15
kskm 01/06/15 05/8/15
This is what I would like to create from the above data.
Cohort M0 M1 M2 M3 M4
2015-05 3 3 2 0 0
2015-06 2 2 1 1 0
Explanation: M0 is the months from the date of joining. So 3 people joined us in 2015 May and all of them are active in May. M1 would be June and all of them are active in June. We lose 1 customer in the end of June on the 25th, so will consider him active in June but in M2 my count drops from 3 to 2. M3 for the May joined customers correspond to the month of August in which we have lost our customers.
Similar process of 2015-06 cohort. M1 would be the month of July and M3 the month of September.

Answer 1

Modified your code as below, this works thanks! Now trying to figure out a way to make M0 to M(n) dynamic.
library(readxl)
library(zoo)
library(plyr)
# Read in the data
df <- read.csv("~/Desktop/R/data.csv")
df$dj <- as.Date(df$dj,"%d/%m/%y")
df$exp <- as.Date(df$exp,"%d/%m/%y")
# The data in the file has different variable names than your example data
# so I'm changing them to match
names(df)[1:3] <- c("customer","dj","exp")
# Make a variable called Cohort that contains only the year and month of joining
# as.yearmon() comes from the 'zoo' package
df$Cohort <- as.yearmon(df$dj)
# Calculate the difference in months between date of expiry and date of joining
df$MonthDiff <- ceiling((df$exp-df$dj)/30)
#df$MonthDiff <- 12*(as.yearmon(df$exp+months(1))-df$Cohort)
# Use ddply() from the 'plyr' package to get the frequency of subjects that are
# still active after 0, 1, 2, 3, and 4 months.
df1 <- ddply(df,.(Cohort),summarize,
M0 = sum(MonthDiff > 0),
M1 = sum(MonthDiff > 1),
M2 = sum(MonthDiff > 2),
M3 = sum(MonthDiff > 3),
M4 = sum(MonthDiff > 4),
M5 = sum(MonthDiff > 5)
)
df1
df1
Cohort M0 M1 M2 M3 M4 M5
1 May 2015 3 3 2 0 0 0
2 Jun 2015 2 2 1 0 0 0
Now

Answer 2

Try this:
library(readxl)
library(zoo)
library(plyr)
# Read in the data
df <- read_excel("MyFile.xlsx")
# The data in the file has different variable names than your example data
# so I'm changing them to match
names(df)[1:3] <- c("customer","dj","exp")
# Make a variable called Cohort that contains only the year and month of joining
# as.yearmon() comes from the 'zoo' package
df$Cohort <- as.yearmon(df$dj)
# Calculate the difference in months between date of expiry and date of joining
df$MonthDiff <- 12*(as.yearmon(df$exp)-df$Cohort)
# Use ddply() from the 'plyr' package to get the frequency of subjects that are
# still active after 0, 1, 2, 3, and 4 months.
df1 <- ddply(df,.(Cohort),summarize,
M0 = sum(MonthDiff >= 0),
M1 = sum(MonthDiff >= 1),
M2 = sum(MonthDiff >= 2),
M3 = sum(MonthDiff >= 3),
M4 = sum(MonthDiff >= 4))
df1
# Cohort M0 M1 M2 M3 M4
# May 2015 3 3 2 0 0
# Jun 2015 2 1 0 0 0
This assumes that when you read the data in from Excel, the dates are formatted as dates. In case they are not you could use the following:
df$dj <- as.Date(df$dj,"%d/%m/%y")
df$exp <- as.Date(df$exp,"%d/%m/%y")