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r - 如果它们在指定时间段内具有连续的时间间隔，则合并数据行

问题描述

我有一个包含 Start.Date 和 Stop.Date 的患者药物数据集。每个都以一行表示。我想合并代表相同药物的行，但前提是后面间隔的 Start.Date 在较早间隔的停止日期起 30 天内（或我选择指定的任意天数）。假设您有下面的数据框

 ID = c(2, 2, 2, 2, 3, 5) 
    Medication = c("aspirin", "aspirin", "aspirin", "tylenol", "lipitor", "advil") 
    Start.Date = c("05/01/2017", "05/30/2017", "07/15/2017", "05/01/2017", "05/06/2017", "05/28/2017")
Stop.Date = c("05/04/2017", "06/10/2017", "07/27/2017", "05/15/2017", "05/12/2017", "06/13/2017")
    df = data.frame(ID, Medication, Start.Date, Stop.Date) 


  ID Medication Start.Date  Stop.Date
   2    aspirin 05/01/2017 05/04/2017
   2    aspirin 05/30/2017 06/10/2017
   2    aspirin 07/15/2017 07/27/2017
   2    tylenol 05/01/2017 05/15/2017
   3    lipitor 05/06/2017 05/12/2017
   5      advil 05/28/2017 06/13/2017

如果一个人的 Stop.Date 在下一个 Start.Date 之后的指定 30 天内，我想按 ID 和药物减少行。新的 Start.Date 和 Stop.Date 将包含两种药物的时间间隔以及它们之间的 30 天或更短的间隔。它应该如下所示：

ID Medication Start.Date  Stop.Date
   2    aspirin 05/01/2017 06/10/2017
   2    aspirin 07/15/2017 07/27/2017
   2    tylenol 05/01/2017 05/15/2017
   3    lipitor 05/06/2017 05/12/2017
   5      advil 05/28/2017 06/13/2017

标签： rmergeintervalsgaps-and-islands

解决方案

如果一个时期结束和下一个时期开始之间的间隔不超过 30 天，则 OP 已要求取消服药期。

下面的解决方案要求同一个人和同一药物的用药期永远不会重叠，这是一个合理的假设（并已检查）。

1.dplyr

library(dplyr)
library(magrittr)
min_gap <- 30
df %>%
  # convert date strings to class Date
  mutate_at(c("Start.Date", "Stop.Date"), lubridate::mdy) %>%
  arrange(ID, Medication, Start.Date) %>% 
  group_by(ID, Medication) %T>%
  # medication periods must not overlap for ID and Medication
  {summarize(., tmp = all(Start.Date >= lag(Stop.Date, default = Start.Date[1] - 1))) %$% 
      stopifnot(all(tmp))} %>% 
  # count non-subsequent medication periods, i.e., with gaps of at least min_gap days
  mutate(Medic.Period = cumsum(Start.Date > lag(Stop.Date, default = Start.Date[1]) + min_gap)) %>%
  # determine start and stop dates for each collapsed period
  group_by(ID, Medication, Medic.Period) %>%
  summarise(Start.Date = first(Start.Date), Stop.Date = last(Stop.Date))

# A tibble: 5 x 5
# Groups:   ID, Medication [?]
     ID Medication Medic.Period Start.Date Stop.Date 
  <dbl> <fct>             <int> <date>     <date>    
1     2 aspirin               0 2017-05-01 2017-06-10
2     2 aspirin               1 2017-07-15 2017-07-27
3     2 tylenol               0 2017-05-01 2017-05-15
4     3 lipitor               0 2017-05-06 2017-05-12
5     5 advil                 0 2017-05-28 2017-06-13

该cumsum()功能用于在遇到新的经期时增加服药期计数器，即在前一期停止和实际经期开始之间的间隔超过 30 天。

2.data.table

library(data.table)
min_gap <- 30
# coerce date strings to class Date
cols <- stringr::str_subset(names(df), "Date$")
setDT(df)[, (cols) := lapply(.SD, lubridate::mdy), .SDcols = cols][
  # create medication counters for each ID and Medication
  order(Start.Date), 
   Medic.Period := {
     tmp <- shift(Stop.Date, fill = Start.Date[1] - 1)
     stopifnot(all(Start.Date > tmp))
     cumsum(Start.Date > tmp + min_gap)
   }, 
   by = .(ID, Medication)][
     # aggregate to find the overal start and stop dates for each medication period
     , .(Start.Date = min(Start.Date), Stop.Date = max(Stop.Date)), 
     by = .(ID, Medication, Medic.Period)]

   ID Medication Medic.Period Start.Date  Stop.Date
1:  2    aspirin            0 2017-05-01 2017-06-10
2:  2    aspirin            1 2017-07-15 2017-07-27
3:  2    tylenol            0 2017-05-01 2017-05-15
4:  3    lipitor            0 2017-05-06 2017-05-12
5:  5      advil            0 2017-05-28 2017-06-13

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