R将数据重新计算为每周使用量

Question

我有以下问题要解决：我从 Google Analytics 下载数据，包括三列：A 列因素变量（用户 ID）、B 列、会话日期和 C 列会话数或其他指标。

我想创建一个表，在第一列中我有因子，随后的列描述了第 1-8 周。 R 是根据每周的第一个会话日期计算每个元素（因子）的会话数，因为不同的用户在不同的日期开始使用网页。这应该可以显示用户在为期 8 周的项目中的参与度。

示例如下：

df <- data.frame(
        fac=c("a","a","a","a","a","b","b","b","b","b","c","c","c","c","c","d","d","d","d","d","d"), 
        date=c("2017-01-01","2017-01-05","2017-01-13","2017-01-25","2017-02-10","2017-01-06","2017-01-16","2017-01-28","2017-02-02","2017-02-07","2017-01-11","2017-01-19","2017-01-24","2017-01-31","2017-02-09","2017-01-12","2017-01-24","2017-01-29","2017-02-04","2017-02-19","2017-03-08"), 
        sessions=c(1,2,3,6,5,1,3,2,3,3,1,5,3,2,4,1,3,5,2,6,6)
        )

应该看起来像这样： end result

示例：

可以使用任何 R 包，但如果可能的话，我宁愿避免循环。

Answer 1

我们可以使用dcast 将“长”格式转换为“宽”格式。将'data.frame'转换为'data.table'（setDT(df)），按'fac'分组，'date'转换为week，得到data.table子集的第一行（.SD[1L]）和然后dcast 从“长”到“宽”

library(data.table)
dcast(setDT(df)[, .SD[1L], .(fac, Week = factor(paste0("Week", week(as.Date(date))), 
              levels = paste0("Week", 1:10)))], fac ~ Week, drop = FALSE)

---

或者tidyverse，我们可以使用类似的方法

library(dplyr)
library(tidyr)
df %>% 
      group_by(fac, Week = factor(paste0("Week", 
               week(as.Date(date))), levels = paste0("Week", 1:10))) %>% 
      slice(1) %>% 
      select(-date) %>% 
      spread(Week, sessions)