r - 在 R (dplyr) 中计算重叠日期

Question

我有一个个人数据集(CSN)，每个人在入院期间都接受过从零到多次的干预(在本例中，放置了中心线)，每个干预都有开始日期和结束日期。我正在尝试生成一个新的日期范围来计算任何重叠的日期。换句话说，我正在尝试计算一个人有一条中心线时的总日期范围。

数据例如:

structure(list(CSN_id = c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L), First_day = structure(c(1514937600, 
1514937600, 1515024000, 1515024000, 1515110400, 1515974400, 1516147200, 
1516147200, 1516147200, 1516233600, 1516233600, 1517097600, 1517097600, 
1517702400, 1517356800, 1518220800, 1519257600, 1519948800, 1520812800, 
1521504000, 1522022400), tzone = "UTC", class = c("POSIXct", 
"POSIXt")), Last_day = structure(c(1515628800, 1515110400, 1515542400, 
1515542400, 1515628800, 1516579200, 1516320000, 1517184000, 1516233600, 
1517184000, 1517702400, 1517184000, 1517616000, 1517702400, 1518220800, 
1518825600, 1519689600, 1520812800, 1521763200, 1522108800, 1522108800
), tzone = "UTC", class = c("POSIXct", "POSIXt"))), row.names = c(NA, 
-21L), class = c("tbl_df", "tbl", "data.frame"))

理想情况下，输出将为所有重叠日期返回单个日期范围，但如果每个日期都错过了一段日期，则会创建一个新的间隔。因此，对于第 1 组，第 1-5 行的 start = 2018-01-03 和 end = 2018-01-11，但第 6 行的 start = 2018-01-15 和 end = 2018-01-22 .

我尝试执行以下操作:

df %>% 
  arrange(CSN_id, First_day) %>% 
  mutate(First_day = ymd(First_day),
         Last_day = ymd(Last_day),
         start = ymd("1970-01-01"),
         end = ymd("1970-01-01")) %>% 
  group_by(CSN_id) %>% 
  rowwise() %>% 
  mutate(test = if_else(row_number() == 1, interval(First_day, Last_day), interval(lag(start), lag(end))),
         start = if_else(row_number() == 1, First_day,
                         if_else(First_day <= lag(end), lag(First_day), First_day)),
         end = if_else(row_number() == 1, Last_day,
                       if_else(Last_day %within% lag(test) == TRUE, lag(end), Last_day)))

但是，我认为滞后函数没有按预期工作，并且由于某种原因它总是返回 Last_day。我尝试摆脱 rowwise，但随后间隔变得困惑(一直停留在 1970 年代)。

我得到的输出是:

structure(list(CSN_id = c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L), First_day = structure(c(17534, 
17534, 17535, 17535, 17536, 17546, 17548, 17548, 17548, 17549, 
17549, 17559, 17559, 17566, 17562, 17572, 17584, 17592, 17602, 
17610, 17616), class = "Date"), Last_day = structure(c(17542, 
17536, 17541, 17541, 17542, 17553, 17550, 17560, 17549, 17560, 
17566, 17560, 17565, 17566, 17572, 17579, 17589, 17602, 17613, 
17617, 17617), class = "Date"), start = structure(c(17534, 17534, 
17535, 17535, 17536, 17546, 17548, 17548, 17548, 17549, 17549, 
17559, 17559, 17566, 17562, 17572, 17584, 17592, 17602, 17610, 
17616), class = "Date"), end = structure(c(17542, 17536, 17541, 
17541, 17542, 17553, 17550, 17560, 17549, 17560, 17566, 17560, 
17565, 17566, 17572, 17579, 17589, 17602, 17613, 17617, 17617
), class = "Date"), test = new("Interval", .Data = c(691200, 
172800, 518400, 518400, 518400, 604800, 172800, 1036800, 86400, 
950400, 1468800, 86400, 518400, 0, 864000, 604800, 432000, 864000, 
950400, 604800, 86400), start = structure(c(1514937600, 1514937600, 
1515024000, 1515024000, 1515110400, 1515974400, 1516147200, 1516147200, 
1516147200, 1516233600, 1516233600, 1517097600, 1517097600, 1517702400, 
1517356800, 1518220800, 1519257600, 1519948800, 1520812800, 1521504000, 
1522022400), class = c("POSIXct", "POSIXt"), tzone = "UTC"), 
    tzone = "UTC")), class = c("rowwise_df", "tbl_df", "tbl", 
"data.frame"), row.names = c(NA, -21L), groups = structure(list(
    CSN_id = c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L), .rows = structure(list(
        1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 
        14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L), ptype = integer(0), class = c("vctrs_list_of", 
    "vctrs_vctr", "list"))), row.names = c(NA, -21L), class = c("tbl_df", 
"tbl", "data.frame")))

我是否遗漏了一些明显的东西？任何帮助将不胜感激!

Answer 1

我不太确定您想要的输出是什么，但您可以尝试这种方法:

dat %>% 
  arrange(CSN_id,First_day,Last_day) %>% 
  group_by(CSN_id, First_day) %>%
  summarize(Last_day=max(Last_day,na.rm=T)) %>% 
  mutate(interval=as.numeric(First_day- lag(Last_day))>0,
         interval=cumsum(if_else(is.na(interval),FALSE,interval))+1) %>% 
  group_by(CSN_id,interval) %>% 
  summarize(start = min(First_day),
         end = max(Last_day))

输出:

  CSN_id interval start               end                
   <int>    <dbl> <dttm>              <dttm>             
1      1        1 2018-01-03 00:00:00 2018-01-11 00:00:00
2      1        2 2018-01-15 00:00:00 2018-01-22 00:00:00
3      2        1 2018-01-17 00:00:00 2018-01-19 00:00:00
4      3        1 2018-01-17 00:00:00 2018-02-04 00:00:00
5      3        2 2018-02-04 00:00:00 2018-02-04 00:00:00
6      4        1 2018-01-31 00:00:00 2018-02-17 00:00:00
7      4        2 2018-02-22 00:00:00 2018-02-27 00:00:00
8      4        3 2018-03-02 00:00:00 2018-03-27 00:00:00

如果您希望保留所有原始行，并且所有日期都是日期而不是日期时间，您也可以执行以下操作:

dat %>% 
  mutate(across(First_day:Last_day, ~as.Date(.x))) %>% 
  arrange(CSN_id,First_day,Last_day) %>% 
  group_by(CSN_id) %>%
  mutate(interval=as.numeric(First_day- lag(Last_day))>0,
         interval=cumsum(if_else(is.na(interval),FALSE,interval))+1) %>% 
  group_by(CSN_id,interval) %>% 
  mutate(start = min(First_day),
            end = max(Last_day))

输出:

   CSN_id First_day  Last_day   interval start      end       
    <int> <date>     <date>        <dbl> <date>     <date>    
 1      1 2018-01-03 2018-01-05        1 2018-01-03 2018-01-11
 2      1 2018-01-03 2018-01-11        1 2018-01-03 2018-01-11
 3      1 2018-01-04 2018-01-10        1 2018-01-03 2018-01-11
 4      1 2018-01-04 2018-01-10        1 2018-01-03 2018-01-11
 5      1 2018-01-05 2018-01-11        1 2018-01-03 2018-01-11
 6      1 2018-01-15 2018-01-22        2 2018-01-15 2018-01-22
 7      2 2018-01-17 2018-01-19        1 2018-01-17 2018-01-19
 8      3 2018-01-17 2018-01-18        1 2018-01-17 2018-02-04
 9      3 2018-01-17 2018-01-29        1 2018-01-17 2018-02-04
10      3 2018-01-18 2018-01-29        1 2018-01-17 2018-02-04
# ... with 11 more rows