R - 跨月的普通用户

Question

我有一笔交易 table 包含以下列：

TransactionId  UserId  YearMonth  Group

我想要完成的是在不同月份获得唯一身份用户。例如：

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

基本上 Month1 和 Month2 是基于记录的 YearMonth 值的增量月份。

我正在使用此结果进行留存分析。

Answer 1

我记得你昨天在寻找分析订阅队列的可能性。所以我想你可以做到

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) {
# 
  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

R - 跨月的普通用户

R - Common users across months

r

retention