在组中查找重叠间隔并保留最大的非重叠周期

Question

问题我有一个间隔重叠的分组数据框（日期为 ymd）。我只想保留每个组中最大的非重叠间隔。

示例数据

# Packages
library(tidyverse)
library(lubridate)

# Example data
df <- tibble(
  group = c(1, 1, 1, 2, 2, 3, 3, 3, 3),
  start = as_date(
    c("2019-01-10", "2019-02-01", "2019-10-05", "2018-07-01", "2019-01-01", "2019-10-01", "2019-10-01", "2019-11-30","2019-11-20")),
  end = as_date(
    c("2019-02-07", "2019-05-01", "2019-11-15", "2018-07-31", "2019-05-05", "2019-11-06", "2019-10-07", "2019-12-10","2019-12-31"))) %>%
  mutate(intval = interval(start, end),
         intval_length = intval / days(1))

df
#> # A tibble: 9 x 5
#>   group start      end        intval                         intval_length
#>   <dbl> <date>     <date>     <Interval>                             <dbl>
#> 1     1 2019-01-10 2019-02-07 2019-01-10 UTC--2019-02-07 UTC            28
#> 2     1 2019-02-01 2019-05-01 2019-02-01 UTC--2019-05-01 UTC            89
#> 3     1 2019-10-05 2019-11-15 2019-10-05 UTC--2019-11-15 UTC            41
#> 4     2 2018-07-01 2018-07-31 2018-07-01 UTC--2018-07-31 UTC            30
#> 5     2 2019-01-01 2019-05-05 2019-01-01 UTC--2019-05-05 UTC           124
#> 6     3 2019-10-01 2019-11-06 2019-10-01 UTC--2019-11-06 UTC            36
#> 7     3 2019-10-01 2019-10-07 2019-10-01 UTC--2019-10-07 UTC             6
#> 8     3 2019-11-30 2019-12-10 2019-11-30 UTC--2019-12-10 UTC            10
#> 9     3 2019-11-20 2019-12-31 2019-11-20 UTC--2019-12-31 UTC            41

# Goal
# Row: 1 and 2; 6 to 9 have overlaps; Keep rows with largest intervals (in days)
df1 <- df[-c(1, 7, 8),]

df1
#> # A tibble: 6 x 5
#>   group start      end        intval                         intval_length
#>   <dbl> <date>     <date>     <Interval>                             <dbl>
#> 1     1 2019-02-01 2019-05-01 2019-02-01 UTC--2019-05-01 UTC            89
#> 2     1 2019-10-05 2019-11-15 2019-10-05 UTC--2019-11-15 UTC            41
#> 3     2 2018-07-01 2018-07-31 2018-07-01 UTC--2018-07-31 UTC            30
#> 4     2 2019-01-01 2019-05-05 2019-01-01 UTC--2019-05-05 UTC           124
#> 5     3 2019-10-01 2019-11-06 2019-10-01 UTC--2019-11-06 UTC            36
#> 6     3 2019-11-20 2019-12-31 2019-11-20 UTC--2019-12-31 UTC            41

当前方法 我在另一个线程中发现了一个相关问题（参见：）。但是，相应的解决方案按组识别所有重叠的行。这样我就无法识别出最大的非重叠区间

df$overlap <- unlist(tapply(df$intval, #loop through intervals
                            df$group,  #grouped by id
                            function(x) rowSums(outer(x,x,int_overlaps)) > 1))

例如，考虑我的示例数据中的第 3 组。这里第 6/7 行和第 8/9 行重叠。由于第 6 行和第 9 行是最大的非重叠周期，我想删除第 7 行和第 8 行。

如果有人能指出我的解决方案，我将不胜感激。

Answer 1

在Whosebug上搜索相关问题后，我发现以下方法（此处：) and (here: ）可以适用于我的问题。

# Solution adapted from:
# here 
# and here:  

# Note: df and df1 created in the initial reprex (above)

df2 <- df %>%
  group_by(group) %>%
  arrange(group, start) %>%
  mutate(indx = c(0, cumsum(as.numeric(lead(start))  >            # find overlaps
                              cummax(as.numeric(end)))[-n()])) %>%
  ungroup() %>%
  group_by(group, indx) %>%
  arrange(desc(intval_length)) %>%                                # retain largest interval
  filter(row_number() == 1) %>%
  ungroup() %>%
  select(-indx) %>%
  arrange(group, start)

# Desired output?
identical(df1, df2)
#> [1] TRUE

在组中查找重叠间隔并保留最大的非重叠周期

Find overlapping intervals in groups and retain largest non-overlapping periods

r

lubridate

dplyr