根据 dplyr 中的开始和停止日期生成新变量

在你的问题中有一些不一致之处(一些变量确实重叠，但在你的预期输出中它们被认为是独立的)，下面是一种通过重叠值进行分组的方法，并从某种程度上获得你的预期输出.此解决方案使用ivs、tidyverse和lubridate库:

library(ivs)
library(tidyverse)
library(lubridate)

df %>% 
  mutate(Stop = ifelse(Stop == "<NA>", Start, Stop),
         across(c(Start, Stop), ymd),
         Stop = if_else(Stop == Start, Stop + days(1), Stop),
         ivs = iv(Start, Stop)) %>% 
  group_by(Machine, gp = iv_identify_group(ivs)) %>% 
  summarise(ServiceType = toString(unique(ServiceType)),) %>% 
  ungroup() %>% 
  mutate(gp = iv_start(gp),
         ServiceType = ifelse(ServiceType %in% c("BB, AA", "AA, BB"), "CC", ServiceType))

# A tibble: 6 × 3
  gp         Machine ServiceType
  <date>     <chr>   <chr>      
1 2013-09-05 XX      BB         
2 2014-12-04 XX      AA         
3 2020-08-06 XX      CC         
4 2015-11-04 YY      CC         
5 2019-09-27 YY      BB         
6 2020-01-17 YY      BB         

数据

df <- read.table(header = T, text = "   Machine   Start      Stop           ServiceType 
1       XX 2014-12-04       NA          AA
2       XX 2013-09-05 2013-11-05          BB
3       XX 2013-11-21 2014-09-25          BB
4       XX 2013-10-11 2014-11-18          BB
5       XX 2021-12-03       <NA>          AA
6       XX 2020-08-06 2022-09-15          AA
7       XX 2021-06-10       <NA>          BB
8       YY 2020-01-17       <NA>          BB
9       YY 2015-11-04 2018-04-30          BB
10      YY 2016-05-28 2019-03-21          BB
11      YY 2019-09-27       <NA>          BB
12      YY 2018-01-05       <NA>          AA
")