在R中按日期间隔汇总结果 [英] Aggregate results by date intervals in R

问题描述

我正在使用R，并且我的数据存储在data.tables对象中.我的数据的格式为ID，Date1，Date2，Row

I'm using R and I have my data on data.tables objects. My data is of the format ID, Date1, Date2, Row

对于每个ID，我可以有多个条目，并且两个日期定义一个时间间隔.

For each ID I can have more than one entry, and the two dates define a time interval.

我希望能够按ID和重叠的时间间隔汇总所有条目.我确实知道如何使用for循环之类的方法，但是我想知道是否有更好的方法.

I want to be able to aggregate all the entries by id and overlapping time intervals. I do know how to do it with for loops and such, but I wonder if there is a better way.

示例:

data = data.table(
    id = c(1,1,1,2,2,3,3),
    Row = c(1,2,3,4,5,6,7),
    Date1 = c("2018-01-01", 
               "2018-01-05",
                "2018-01-21",
                "2018-01-01",
                "2018-01-15",
                "2018-01-01",
                "2018-01-19"),
    Date2 = c("2018-01-10", 
               "2018-01-20",
                "2018-01-22",
                "2018-01-31",
                "2018-01-19",
                "2018-01-15",
                "2018-01-23"))

所需的输出将是标识以下几组行的内容:((1,2)，(3)，(4,5)，(6)，(7))，这样我可以生成一个新的行ID，基于此分组.

The desired output would be something that identifies the following groups of rows: ((1,2),(3),(4,5),(6),(7)) , so that I can generate a new ID, based on this grouping.

推荐答案

引用输出:

   id Row      Date1      Date2 g
1:  1   1 2018-01-01 2018-01-10 0
2:  1   2 2018-01-05 2018-01-20 0
3:  1   3 2018-01-21 2018-01-22 1
4:  2   4 2018-01-01 2018-01-31 2
5:  2   5 2018-01-15 2018-01-19 2
6:  3   6 2018-01-01 2018-01-15 3
7:  3   7 2018-01-19 2018-01-23 4

数据:

library(data.table)
data = data.table(
    id = c(1,1,1,2,2,3,3),
    Row = c(1,2,3,4,5,6,7),
    Date1 = c("2018-01-01","2018-01-05","2018-01-21","2018-01-01","2018-01-15","2018-01-01","2018-01-19"),
    Date2 = c("2018-01-10","2018-01-20","2018-01-22","2018-01-31","2018-01-19","2018-01-15","2018-01-23"))
cols <- c("Date1", "Date2")
data[, (cols) := lapply(.SD, as.Date, format="%Y-%m-%d"), .SDcols=cols]

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