[英]Merging contiguous date ranges in R
我想将观察结果合并为连续的(涵盖的天数没有间隔)日期范围。 每个patid在结果数据框中可能有多个范围。我知道它可以用循环来完成。但是,有没有一种有效的方法来处理这个任务? 请注意,这里的时间间隔没有重叠,并且 start_date 正在增加。
数据在这里(我使用 R:dput,您可以在 R 中复制并分配给您的对象):
structure(list(patid = c(1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 3L, 3L, 3L), start_date = structure(c(1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 8L, 9L, 6L, 7L, 10L), .Label = c("1/1/2010", "2/1/2010",
"3/1/2010", "4/1/2010", "5/1/2010", "5/6/2011", "7/1/2012", "8/1/2010",
"9/1/2010", "9/1/2012"), class = "factor"), end_date = structure(c(1L,
3L, 4L, 5L, 6L, 1L, 3L, 4L, 8L, 10L, 7L, 9L, 2L), .Label = c("1/31/2010",
"12/1/2012", "2/28/2010", "3/31/2010", "4/30/2010", "5/31/2010",
"6/15/2011", "8/31/2010", "8/31/2012", "9/30/2010"), class = "factor")), class = "data.frame", row.names = c(NA,
-13L))
data.table
方法(使用magrittr
以提高可读性)(健壮版):
library(data.table)
library(magrittr)
calc_cummax <- function(x) (setattr(cummax(unclass(x)), "class", c("Date", "IDate")))
df_merged <- setDT(df) %>%
.[, `:=` (cont_start = as.Date(as.character(start_date), "%m/%d/%Y"),
cont_end = as.Date(as.character(end_date), "%m/%d/%Y"))] %>%
.[order(patid, start_date),] %>%
.[, max_until_now := shift(calc_cummax(cont_end)), by = patid] %>%
.[, lead_max := shift(max_until_now, type = "lead"), by = patid] %>%
.[is.na(max_until_now), max_until_now := lead_max, by = patid] %>%
.[(max_until_now + 1L) >= cont_start, gap_between_contracts := 0, by = patid] %>%
.[(max_until_now + 1L) < cont_start, gap_between_contracts := 1, by = patid] %>%
.[is.na(gap_between_contracts), gap_between_contracts := 0] %>%
.[, ("fakeidx") := cumsum(gap_between_contracts), by = patid] %>%
.[, .(cont_start = min(cont_start), cont_end = max(cont_end)), by = .(patid, fakeidx)] %>%
.[, ("fakeidx") := NULL]
您的情况下的输出:
patid cont_start cont_end
1: 1 2010-01-01 2010-05-31
2: 2 2010-01-01 2010-03-31
3: 2 2010-08-01 2010-09-30
4: 3 2011-05-06 2011-06-15
5: 3 2012-07-01 2012-12-01
一种tidyverse
方法(非健壮的简单版本):
library(tidyverse)
df %>%
mutate(
cont_start = as.Date(as.character(start_date), "%m/%d/%Y"),
cont_end = as.Date(as.character(end_date), "%m/%d/%Y")
) %>%
arrange(patid, cont_start) %>%
group_by(patid) %>%
mutate(
idx = cumsum(coalesce(as.numeric(cont_start != (lag(cont_end) + 1)), 0))
) %>%
group_by(patid, idx) %>%
summarise(
cont_start = min(cont_start),
cont_end = max(cont_end)
) %>% select(-idx)
输出:
# A tibble: 5 x 3
# Groups: patid [3]
patid cont_start cont_end
<int> <date> <date>
1 1 2010-01-01 2010-05-31
2 2 2010-01-01 2010-03-31
3 2 2010-08-01 2010-09-30
4 3 2011-05-06 2011-06-15
5 3 2012-07-01 2012-12-01
您的情况下的输出是相同的,但是如果在任何时候发生,您在序列中的开始日期将比较晚的开始日期更高,那么您需要选择第一个(健壮) 方法(当然,如果您不认为这是错误的话)。
在这种情况下,健壮性与data.table
或tidyverse
没有任何tidyverse
(您也可以通过重写tidyverse
版本来使用calc_cummax
函数,但您需要加载data.table
)。
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