在R中的两个数据框中按组比较日期 [英] Compare date by group in two data frames in R

问题描述

我有一个包含事件ID的数据框：

I have one data frame containing event date by id:

data.frame(id = c("a", "a", "a", "d", "d"),
           date = as.Date(c("2018-01-03", "2018-02-02", "2018-02-22", "2018-02-13", "2018-05-01")))

  id       date
1  a 2018-01-03
2  a 2018-02-02
3  a 2018-02-22
4  d 2018-02-13
5  d 2018-05-01

另一个包含ID开头和结尾的时段：

And another one containing start and end of periods by id:

data.frame(id = c("a", "a", "d", "d", "d", "d"),
           start = as.Date(c("2018-01-15", "2018-01-30", "2018-03-01", "2018-02-01", "2018-04-02", "2018-03-19")),
           end = as.Date(c("2018-01-18", "2018-02-10", "2018-03-03", "2018-04-22", "2018-05-23", "2018-08-29")))

  id      start        end
1  a 2018-01-15 2018-01-18
2  a 2018-01-30 2018-02-10
3  d 2018-03-01 2018-03-03
4  d 2018-02-01 2018-04-22
5  d 2018-04-02 2018-05-23
6  d 2018-03-19 2018-08-29

对于每个id，我需要计算从第二个数据帧到每个日期所在的时间段数第一个数据框属于。

For each id, I need to count the number of periods from the second data frame to which each date in the first data frame belongs.

我希望的数据框是：

  id       date n
1  a 2018-01-03 0    # does not belong to any period
2  a 2018-02-02 1    # belongs to [2018-01-30,2018-02-10]
3  a 2018-02-22 0    # does not belong to any period
4  d 2018-02-13 1    # belongs to [2018-02-01,2018-04-22]
5  d 2018-05-01 2    # belongs to [2018-04-02,2018-05-23] and [2018-03-19,2018-08-29]

我的问题不是关于日期比较和结果求和。我的问题是关于在每个id组中执行这些分析。我想有一种方法可以使用 split 和/或 apply 系列，但是我没有找到方法。

My problem is not about date comparison and summing the results. My problem is about performing those analysis inside each id group. I guess there is a way using split and/or the apply family, but I did not find how.

我如何在基数R中做到这一点？

How can I do it in base R? I work in a restrictive environment where I only have access to base R.

推荐答案

另一种基本R方法：

dates <- data.frame(id = c("a", "a", "a", "d", "d"),
                    date = as.Date(c("2018-01-03", "2018-02-02", "2018-02-22", "2018-02-13", "2018-05-01")))
periods <- data.frame(id = c("a", "a", "d", "d", "d", "d"),
                      start = as.Date(c("2018-01-15", "2018-01-30", "2018-03-01", "2018-02-01", "2018-04-02", "2018-03-19")),
                      end = as.Date(c("2018-01-18", "2018-02-10", "2018-03-03", "2018-04-22", "2018-05-23", "2018-08-29")))

df <- transform(merge(dates, periods), belongs = date >= start & date <= end)

aggregate(belongs ~ date + id, data = df, sum)
#         date id belongs
# 1 2018-01-03  a       0
# 2 2018-02-02  a       1
# 3 2018-02-22  a       0
# 4 2018-02-13  d       1
# 5 2018-05-01  d       2

或使用 data.table ：

library(data.table)
dt <- as.data.table(merge(dates, periods))
dt[, .(n = sum(date >= start & date <= end)), by=c("id","date")]
#    id       date n
# 1:  a 2018-01-03 0
# 2:  a 2018-02-02 1
# 3:  a 2018-02-22 0
# 4:  d 2018-02-13 1
# 5:  d 2018-05-01 2

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