我有一个带有句点的数据集
active <- data.table(id=c(1,1,2,3), beg=as.POSIXct(c("2018-01-01 01:10:00","2018-01-01 01:50:00","2018-01-01 01:50:00","2018-01-01 01:50:00")), end=as.POSIXct(c("2018-01-01 01:20:00","2018-01-01 02:00:00","2018-01-01 02:00:00","2018-01-01 02:00:00")))
> active
id beg end
1: 1 2018-01-01 01:10:00 2018-01-01 01:20:00
2: 1 2018-01-01 01:50:00 2018-01-01 02:00:00
3: 2 2018-01-01 01:50:00 2018-01-01 02:00:00
4: 3 2018-01-01 01:50:00 2018-01-01 02:00:00
在其ID有效期间。我想汇总ids
,并确定
time <- data.table(seq(from=min(active$beg),to=max(active$end),by="mins"))
无效的ID数以及直到激活为止的平均分钟数。也就是说,理想情况下,表格看起来像
>ans
time inactive av.time
1: 2018-01-01 01:10:00 2 30
2: 2018-01-01 01:11:00 2 29
...
50: 2018-01-01 02:00:00 0 0
我相信可以使用data.table
来完成此操作,但是我无法弄清楚语法以获得时差。
答案 0 :(得分:0)
使用dplyr
,我们可以通过虚拟变量加入以创建time
和active
的笛卡尔积。 inactive
和av.time
的定义可能与您所寻找的不完全相同,但是它可以帮助您入门。如果您的数据非常大,我同意data.table
将是处理此问题的更好方法。
library(tidyverse)
time %>%
mutate(dummy = TRUE) %>%
inner_join({
active %>%
mutate(dummy = TRUE)
#join by the dummy variable to get the Cartesian product
}, by = c("dummy" = "dummy")) %>%
select(-dummy) %>%
#define what makes an id inactive and the time until it becomes active
mutate(inactive = time < beg | time > end,
TimeUntilActive = ifelse(beg > time, difftime(beg, time, units = "mins"), NA)) %>%
#group by time and summarise
group_by(time) %>%
summarise(inactive = sum(inactive),
av.time = mean(TimeUntilActive, na.rm = TRUE))
# A tibble: 51 x 3
time inactive av.time
<dttm> <int> <dbl>
1 2018-01-01 01:10:00 3 40
2 2018-01-01 01:11:00 3 39
3 2018-01-01 01:12:00 3 38
4 2018-01-01 01:13:00 3 37
5 2018-01-01 01:14:00 3 36
6 2018-01-01 01:15:00 3 35
7 2018-01-01 01:16:00 3 34
8 2018-01-01 01:17:00 3 33
9 2018-01-01 01:18:00 3 32
10 2018-01-01 01:19:00 3 31