Darshan BI-DW - 1 year ago 76
R Question

# R - Common users across months

I have a transaction table with the following columns:

``````TransactionId  UserId  YearMonth  Group
``````

What I am trying to accomplish is to get unique users across different months.
Eg:

``````YearMonth Group UsersCountMonth1 UsersCountMonth2 UsersCountMonth3
201301    A     1000             900              800
201301    B     1200             940              700
201302    B     1300             1140             900
201303    A     12e0             970              706
``````

Basically Month1 and Month2 are the incremental months based on YearMonth value for the record.

I am using this result to perform retention analysis.

I remember you were looking for a possibility to analyze subscription cohorts, yesterday. So I guess you can do

``````library(tidyverse)
set.seed(1)
n <- 100
df <- data.frame(
user = sample(1:20, n, T),
transDate = sample(seq(as.Date("2016-01-01"), as.Date("2016-12-31"), "1 month"), n, T),
group = sample(LETTERS[1:2], n, T)
)
diffmonth <- function(d1, d2) {
# http://stackoverflow.com/questions/1995933/number-of-months-between-two-dates
monnb <- function(d) {
lt <- as.POSIXlt(as.Date(d, origin="1900-01-01"))
lt\$year*12 + lt\$mon
}
monnb(d2) - monnb(d1) + 1L
}
df %>%
group_by(user, group) %>%
mutate(cohort = min(transDate), month = diffmonth(cohort, transDate)) %>%
unite(cohort, cohort, group, remove = T) %>%
group_by(month, cohort) %>%
summarise(n=n()) %>%
spread(month, n, fill = 0, drop = F)
# # A tibble: 16 × 12
#          cohort   `1`   `2`   `3`   `4`   `5`   `6`   `7`   `8`   `9`  `10`  `11`
# *         <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1  2016-01-01_A     5     1     0     1     1     1     1     0     2     0     0
# 2  2016-02-01_A     1     0     0     0     0     0     0     0     1     0     1
# 3  2016-02-01_B     4     1     2     1     0     1     2     0     1     1     0
# 4  2016-03-01_A     5     0     3     1     2     2     2     0     1     2     0
# 5  2016-03-01_B     4     0     0     0     2     0     1     0     0     0     0
# 6  2016-04-01_A     4     0     2     1     0     1     0     2     1     0     0
# 7  2016-04-01_B     1     0     0     0     0     0     0     0     0     0     0
# 8  2016-05-01_A     2     0     2     2     0     0     2     0     0     0     0
# 9  2016-05-01_B     1     0     0     1     0     0     2     0     0     0     0
# 10 2016-06-01_A     1     0     2     0     0     1     0     0     0     0     0
# 11 2016-06-01_B     4     0     0     0     0     1     1     0     0     0     0
# 12 2016-07-01_A     1     0     1     0     0     0     0     0     0     0     0
# 13 2016-08-01_B     4     1     1     0     0     0     0     0     0     0     0
# 14 2016-09-01_A     1     0     0     0     0     0     0     0     0     0     0
# 15 2016-10-01_B     1     0     0     0     0     0     0     0     0     0     0
# 16 2016-12-01_A     3     0     0     0     0     0     0     0     0     0     0
``````
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