R：计算指定时间范围内不同类别的数量

这是一些虚拟数据：

  user_id       date category
       27 2016-01-01    apple
       27 2016-01-03    apple
       27 2016-01-05     pear
       27 2016-01-07     plum
       27 2016-01-10    apple
       27 2016-01-14     pear
       27 2016-01-16     plum
       11 2016-01-01    apple
       11 2016-01-03     pear
       11 2016-01-05     pear
       11 2016-01-07     pear
       11 2016-01-10    apple
       11 2016-01-14    apple
       11 2016-01-16    apple

我想计算一下对于每个user_id不同的数量categories在指定时间段内（例如过去7、14天内），包括当前订单

解决方案如下所示：

 user_id       date category distinct_7 distinct_14
      27 2016-01-01    apple          1           1
      27 2016-01-03    apple          1           1
      27 2016-01-05     pear          2           2
      27 2016-01-07     plum          3           3
      27 2016-01-10    apple          3           3
      27 2016-01-14     pear          3           3
      27 2016-01-16     plum          3           3
      11 2016-01-01    apple          1           1
      11 2016-01-03     pear          2           2
      11 2016-01-05     pear          2           2
      11 2016-01-07     pear          2           2
      11 2016-01-10    apple          2           2
      11 2016-01-14    apple          2           2
      11 2016-01-16    apple          1           2

我发过类似的问题here https://stackoverflow.com/questions/41615967/r-calculate-the-number-of-occurrences-of-a-specific-event-in-the-past-and-futur or here https://stackoverflow.com/questions/41020670/r-calculate-cumulative-sums-and-counts-since-the-last-occurrence-of-a-value，但是都没有提到计算指定时间段内的累积唯一值。非常感谢你的帮助！

我建议使用runner https://cran.r-project.org/web/packages/runner/index.html包裹。您可以在运行 Windows 时使用任何 R 函数runner功能。下面的代码获取指定输出，即过去 7 天 + 当前和过去 14 天 + 当前（当前 8 天和 15 天）：

df <- read.table(
  text = "  user_id       date category
       27 2016-01-01    apple
  27 2016-01-03    apple
  27 2016-01-05     pear
  27 2016-01-07     plum
  27 2016-01-10    apple
  27 2016-01-14     pear
  27 2016-01-16     plum
  11 2016-01-01    apple
  11 2016-01-03     pear
  11 2016-01-05     pear
  11 2016-01-07     pear
  11 2016-01-10    apple
  11 2016-01-14    apple
  11 2016-01-16    apple", header = TRUE, colClasses = c("integer", "Date", "character"))



library(dplyr)
library(runner)
df %>%
  group_by(user_id) %>%
  mutate(distinct_7  = runner(category, k = 7 + 1, idx = date, 
                              f = function(x) length(unique(x))),
         distinct_14 = runner(category, k = 14 + 1, idx = date, 
                              f = function(x) length(unique(x))))

更多信息在package https://gogonzo.github.io/runner/ and function https://gogonzo.github.io/runner/reference/runner.html文档。