根据日期是否在给定日期范围内对行进行分类答案

【问题标题】：Classifying rows based on if a date falls within a given date range根据日期是否在给定日期范围内对行进行分类
【发布时间】：2021-08-13 23:55:12
【问题描述】：

希望根据现有事件日期是否在另一列中的 2 个日期内对现有事件日期进行分类

我的原始数据如下所示：

study_id	Event	Event_Date	Event_Result	Pre1	CheckUp	Post1	Pre2	CheckUp2	Post2	Pre3	CheckUp3	Post3	Pre4	CheckUp4	Post4	Pre5	CheckUp5	Post5
1	event1	5/4/2012	1	7/23/2012	10/23/2012	1/23/2013	11/25/2013	2/25/2014	5/25/2014	8/1/2014	11/1/2014	2/1/2015	7/4/2015	10/4/2015	1/4/2016	7/4/2016	10/4/2016	1/4/2017
1	event1	5/15/2012	1	7/23/2012	10/23/2012	1/23/2013	11/25/2013	2/25/2014	5/25/2014	8/1/2014	11/1/2014	2/1/2015	7/4/2015	10/4/2015	1/4/2016	7/4/2016	10/4/2016	1/4/2017
1	event1	6/5/2012	1	7/23/2012	10/23/2012	1/23/2013	11/25/2013	2/25/2014	5/25/2014	8/1/2014	11/1/2014	2/1/2015	7/4/2015	10/4/2015	1/4/2016	7/4/2016	10/4/2016	1/4/2017
1	event1	7/3/2012	0.8	7/23/2012	10/23/2012	1/23/2013	11/25/2013	2/25/2014	5/25/2014	8/1/2014	11/1/2014	2/1/2015	7/4/2015	10/4/2015	1/4/2016	7/4/2016	10/4/2016	1/4/2017
2	event2	8/14/2012	1	7/23/2012	10/23/2012	1/23/2013	11/25/2013	2/25/2014	5/25/2014	8/1/2014	11/1/2014	2/1/2015	7/4/2015	10/4/2015	1/4/2016	7/4/2016	10/4/2016	1/4/2017
2	event2	9/11/2012	1.2	7/23/2012	10/23/2012	1/23/2013	11/25/2013	2/25/2014	5/25/2014	8/1/2014	11/1/2014	2/1/2015	7/4/2015	10/4/2015	1/4/2016	7/4/2016	10/4/2016	1/4/2017
2	event1	9/21/2012	1	7/23/2012	10/23/2012	1/23/2013	11/25/2013	2/25/2014	5/25/2014	8/1/2014	11/1/2014	2/1/2015	7/4/2015	10/4/2015	1/4/2016	7/4/2016	10/4/2016	1/4/2017
3	event1	10/9/2012	1.1	7/23/2012	10/23/2012	1/23/2013	11/25/2013	2/25/2014	5/25/2014	8/1/2014	11/1/2014	2/1/2015	7/4/2015	10/4/2015	1/4/2016	7/4/2016	10/4/2016	1/4/2017
3	event1	10/23/2012	1.1	7/23/2012	10/23/2012	1/23/2013	11/25/2013	2/25/2014	5/25/2014	8/1/2014	11/1/2014	2/1/2015	7/4/2015	10/4/2015	1/4/2016	7/4/2016	10/4/2016	1/4/2017
3	event2	10/25/2012	1	7/23/2012	10/23/2012	1/23/2013	11/25/2013	2/25/2014	5/25/2014	8/1/2014	11/1/2014	2/1/2015	7/4/2015	10/4/2015	1/4/2016	7/4/2016	10/4/2016	1/4/2017
4	event2	11/2/2012	1	7/23/2012	10/23/2012	1/23/2013	11/25/2013	2/25/2014	5/25/2014	8/1/2014	11/1/2014	2/1/2015	7/4/2015	10/4/2015	1/4/2016	7/4/2016	10/4/2016	1/4/2017
4	event1	11/13/2012	1	7/23/2012	10/23/2012	1/23/2013	11/25/2013	2/25/2014	5/25/2014	8/1/2014	11/1/2014	2/1/2015	7/4/2015	10/4/2015	1/4/2016	7/4/2016	10/4/2016	1/4/2017

预期结果我希望每个间隔规则都有一个列（有关间隔详细信息，请参阅下面我尝试过的部分，但基本上总共有 10 个额外的列），如果 event_Date 下降在区间规则内，标记为真，否则为假。我可以在 Excel 中做到这一点，但在 R 中寻找解决方案。

请参阅下面的示例。

personID	Event_Date	Event_Result	Pre1	CheckUp	Post1	Pre2	CheckUp2	Post2	Pre3	CheckUp3	Post3	Pre4	CheckUp4	Post4	Pre5	CheckUp5	Post5	Interval1	Interval2	Interval3	Interval4	Interval5	Interval6	Interval7	Interval8	Interval9	Interval10
1	5/4/2012	1	7/23/2012	10/23/2012	1/23/2013	11/25/2013	2/25/2014	5/25/2014	8/1/2014	11/1/2014	2/1/2015	7/4/2015	10/4/2015	1/4/2016	7/4/2016	10/4/2016	1/4/2017	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
1	5/15/2012	1	7/23/2012	10/23/2012	1/23/2013	11/25/2013	2/25/2014	5/25/2014	8/1/2014	11/1/2014	2/1/2015	7/4/2015	10/4/2015	1/4/2016	7/4/2016	10/4/2016	1/4/2017	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
1	6/5/2012	1	7/23/2012	10/23/2012	1/23/2013	11/25/2013	2/25/2014	5/25/2014	8/1/2014	11/1/2014	2/1/2015	7/4/2015	10/4/2015	1/4/2016	7/4/2016	10/4/2016	1/4/2017	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
2	7/3/2012	0.8	7/23/2012	10/23/2012	1/23/2013	11/25/2013	2/25/2014	5/25/2014	8/1/2014	11/1/2014	2/1/2015	7/4/2015	10/4/2015	1/4/2016	7/4/2016	10/4/2016	1/4/2017	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
2	8/14/2012	1	7/23/2012	10/23/2012	1/23/2013	11/25/2013	2/25/2014	5/25/2014	8/1/2014	11/1/2014	2/1/2015	7/4/2015	10/4/2015	1/4/2016	7/4/2016	10/4/2016	1/4/2017	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
3	9/11/2012	1.2	7/23/2012	10/23/2012	1/23/2013	11/25/2013	2/25/2014	5/25/2014	8/1/2014	11/1/2014	2/1/2015	7/4/2015	10/4/2015	1/4/2016	7/4/2016	10/4/2016	1/4/2017	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
3	9/21/2012	1	7/23/2012	10/23/2012	1/23/2013	11/25/2013	2/25/2014	5/25/2014	8/1/2014	11/1/2014	2/1/2015	7/4/2015	10/4/2015	1/4/2016	7/4/2016	10/4/2016	1/4/2017	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
3	10/9/2012	1.1	7/23/2012	10/23/2012	1/23/2013	11/25/2013	2/25/2014	5/25/2014	8/1/2014	11/1/2014	2/1/2015	7/4/2015	10/4/2015	1/4/2016	7/4/2016	10/4/2016	1/4/2017	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4	10/23/2012	1.1	7/23/2012	10/23/2012	1/23/2013	11/25/2013	2/25/2014	5/25/2014	8/1/2014	11/1/2014	2/1/2015	7/4/2015	10/4/2015	1/4/2016	7/4/2016	10/4/2016	1/4/2017	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4	10/25/2012	1	7/23/2012	10/23/2012	1/23/2013	11/25/2013	2/25/2014	5/25/2014	8/1/2014	11/1/2014	2/1/2015	7/4/2015	10/4/2015	1/4/2016	7/4/2016	10/4/2016	1/4/2017	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
4	11/2/2012	1	7/23/2012	10/23/2012	1/23/2013	11/25/2013	2/25/2014	5/25/2014	8/1/2014	11/1/2014	2/1/2015	7/4/2015	10/4/2015	1/4/2016	7/4/2016	10/4/2016	1/4/2017	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
5	11/13/2012	1	7/23/2012	10/23/2012	1/23/2013	11/25/2013	2/25/2014	5/25/2014	8/1/2014	11/1/2014	2/1/2015	7/4/2015	10/4/2015	1/4/2016	7/4/2016	10/4/2016	1/4/2017	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE

我的尝试 我试图创建间隔来查看 Event_Result 并查看它是否在基于 Post、Pre 和 CheckUp 日期的范围内。我关心的区间规则如下10条：

Interval1 = 如果 EventDate >= Pre1 AND EvenDate

Interval2 = 如果 EventDate > CheckUp AND EvenDate

Interval3 = 如果 EventDate >= Pre2 AND EvenDate

Interval4 = 如果 EventDate > CheckUp2 AND EvenDate

Interval9 = 如果 EventDate >= Pre5 AND EventDate

Interval10 = 如果 EventDate > CheckUp5 AND EventDate

我尝试使用 LUBRIDATE，但没有成功。正如我提到的，我能够在 Excel 中执行此操作，但只是在 R 中寻找一种方法（对 LUBRIDATE、DPLYR 或其他库开放）

INTERVAL_1

【问题讨论】：

标签： r date dplyr

【解决方案1】：

这是另一种尝试的方法。

首先，确保您的日期采用Date 格式。将CheckUp 重命名为CheckUp1，以便与其他列保持一致。

然后，您可以尝试使用pivot_longer 将数据转换为长格式，并使用group_by 查看事件日期。在这种情况下，创建了一个列来评估事件日期是否在 pre 和 checkup 之间的时间间隔内，并创建第二列用于 post 的检查。

之后，您可以再次将结果转换为宽格式，并与原始数据连接。

library(tidyverse)

prep_data <- raw_data %>%
  mutate(across(starts_with(c("Pre", "CheckUp", "Post")), as.Date, format = "%m/%d/%Y"),
         Event_Date = as.Date(Event_Date, format = "%m/%d/%Y")) %>%
  rename(CheckUp1 = CheckUp)

prep_data %>%
  pivot_longer(cols = starts_with(c("Pre", "CheckUp", "Post")), names_to = c("Event_Type", "Number"), names_pattern = "(\\w+)(\\d+)") %>%
  group_by(study_id, Event, Event_Date, Event_Result, Number) %>%
  mutate(interval_pre = Event_Date >= value[Event_Type == "Pre"] & Event_Date <= value[Event_Type == "CheckUp"],
         interval_post = Event_Date > value[Event_Type == "CheckUp"] & Event_Date <= value[Event_Type == "Post"]) %>%
  pivot_wider(id_cols = c(study_id, Event, Event_Date, Event_Result), names_from = Number, values_from = c("interval_pre", "interval_post"), values_fn = first) %>%
  right_join(prep_data %>% select(study_id, Event, Event_Date, Event_Result))

输出

   study_id Event  Event_Date Event_Result interval_pre_1 interval_pre_2 interval_pre_3 interval_pre_4 interval_pre_5 interval_post_1 interval_post_2 interval_post_3 interval_post_4 interval_post_5
      <int> <chr>  <date>            <dbl> <lgl>          <lgl>          <lgl>          <lgl>          <lgl>          <lgl>           <lgl>           <lgl>           <lgl>           <lgl>          
 1        1 event1 2012-05-04          1   FALSE          FALSE          FALSE          FALSE          FALSE          FALSE           FALSE           FALSE           FALSE           FALSE          
 2        1 event1 2012-05-15          1   FALSE          FALSE          FALSE          FALSE          FALSE          FALSE           FALSE           FALSE           FALSE           FALSE          
 3        1 event1 2012-06-05          1   FALSE          FALSE          FALSE          FALSE          FALSE          FALSE           FALSE           FALSE           FALSE           FALSE          
 4        1 event1 2012-07-03          0.8 FALSE          FALSE          FALSE          FALSE          FALSE          FALSE           FALSE           FALSE           FALSE           FALSE          
 5        2 event2 2012-08-14          1   TRUE           FALSE          FALSE          FALSE          FALSE          FALSE           FALSE           FALSE           FALSE           FALSE          
 6        2 event2 2012-09-11          1.2 TRUE           FALSE          FALSE          FALSE          FALSE          FALSE           FALSE           FALSE           FALSE           FALSE          
 7        2 event1 2012-09-21          1   TRUE           FALSE          FALSE          FALSE          FALSE          FALSE           FALSE           FALSE           FALSE           FALSE          
 8        3 event1 2012-10-09          1.1 TRUE           FALSE          FALSE          FALSE          FALSE          FALSE           FALSE           FALSE           FALSE           FALSE          
 9        3 event1 2012-10-23          1.1 TRUE           FALSE          FALSE          FALSE          FALSE          FALSE           FALSE           FALSE           FALSE           FALSE          
10        3 event2 2012-10-25          1   FALSE          FALSE          FALSE          FALSE          FALSE          TRUE            FALSE           FALSE           FALSE           FALSE          
11        4 event2 2012-11-02          1   FALSE          FALSE          FALSE          FALSE          FALSE          TRUE            FALSE           FALSE           FALSE           FALSE          
12        4 event1 2012-11-13          1   FALSE          FALSE          FALSE          FALSE          FALSE          TRUE            FALSE           FALSE           FALSE           FALSE

【讨论】：

【解决方案2】：

library(lubridate)
library(dplyr)
df <- data.frame(
  person_id = c(1, 1, 1, 2, 2, 3, 3, 3),
  event_date = ymd(c("20120504",
                     "20120515",
                     "20120605",
                     "20120703",
                     "20120814",
                     "20120911",
                     "20120921",
                     "20121009"))
)

pre1 <- ymd("20120723")
checkup <- ymd("20121023")
post1 <- ymd("20130123")

interval1 <- interval(pre1, checkup)
interval2 <- interval(checkup, post1)

df %>%
  mutate(
    int1 = event_date %within% interval1,
    int2 = event_date %within% interval2
  )

  person_id event_date  int1  int2
1         1 2012-05-04 FALSE FALSE
2         1 2012-05-15 FALSE FALSE
3         1 2012-06-05 FALSE FALSE
4         2 2012-07-03 FALSE FALSE
5         2 2012-08-14  TRUE FALSE
6         3 2012-09-11  TRUE FALSE
7         3 2012-09-21  TRUE FALSE
8         3 2012-10-09  TRUE FALSE

另一种选择

library(lubridate)
library(dplyr)
df <- data.frame(
  person_id = c(1, 1, 1, 2, 2, 3, 3, 3),
  event_date = ymd(c("20120504",
                     "20120515",
                     "20120605",
                     "20120703",
                     "20120814",
                     "20120911",
                     "20120921",
                     "20121009")),
  pre1 = ymd("20120723"),
  checkup = ymd("20121023"),
  post1 = ymd("20130123")
)

df %>%
  mutate(
    int1 = event_date %within% interval(pre1, checkup),
    int2 = event_date %within% interval(checkup, post1)
  )

  person_id event_date       pre1    checkup      post1  int1  int2
1         1 2012-05-04 2012-07-23 2012-10-23 2013-01-23 FALSE FALSE
2         1 2012-05-15 2012-07-23 2012-10-23 2013-01-23 FALSE FALSE
3         1 2012-06-05 2012-07-23 2012-10-23 2013-01-23 FALSE FALSE
4         2 2012-07-03 2012-07-23 2012-10-23 2013-01-23 FALSE FALSE
5         2 2012-08-14 2012-07-23 2012-10-23 2013-01-23  TRUE FALSE
6         3 2012-09-11 2012-07-23 2012-10-23 2013-01-23  TRUE FALSE
7         3 2012-09-21 2012-07-23 2012-10-23 2013-01-23  TRUE FALSE
8         3 2012-10-09 2012-07-23 2012-10-23 2013-01-23  TRUE FALSE

【讨论】：

谢谢！这是有道理的。我确实有超过 1000 行和至少 15 列。有没有办法自动创建 pre1、checkup、post1 而不是手动声明它们？
也许，您可以像您一样在数据框中进行分配。我已经编辑了我的答案。