我有一个看起来像这样的df（dput()在问题末尾）：
id leg_activity
1044 home, pt, work, adpt, home, adpt, work, adpt, home
1215 home, adpt, work, adpt, home, car, outside, car, work, car, work, car, home
1238 home, work, leisure, adpt, home
1458 home, outside, pt, home, adpt, leisure, adpt, home
1569 home, car_passenger, leisure, walk, work, walk, leisure, walk, work, adpt, home, walk, home
1615 home, adpt, work, walk, work, walk, work, pt, home
我想将的单元格leg_activity分成多个列。字符串的分离应尽快, home发生。想法是查看两个home条目之间会发生什么。对于第一行，所需结果将如下所示：
id 1 2
1044 home, pt, work, adpt, home home, adpt, work, adpt, home
如您所见，我想保留home两个新单元格。
到目前为止，我的方法无法做到这一点，它只是删除了，home但我需要它出现在剪切的两侧：
library(dplyr)
df <- agents %>%
mutate(n = row_number()) %>%
separate_rows(leg_activity, sep = 'home, ') %>%
group_by(n, id) %>%
mutate(n2 = row_number()) %>%
spread(n2, leg_activity) %>%
select(-n)
非常感谢您的提前帮助！
数据尝试代码：
structure(list(id = c(9L, 10L, 11L, 96L, 97L, 101L, 103L, 248L,
499L, 1044L, 1215L, 1238L, 1458L, 1569L, 1615L, 1626L, 1734L,
1735L, 1790L, 1912L, 9040L, 14858L, 14859L, 14967L, 15011L, 15012L,
15015L, 15045L, 15050L, 15058L, 15060L, 15086L, 15088L, 15094L,
15109L, 15113L, 15152L, 15157L, 15192L, 15193L, 15222L, 15230L,
15231L, 15234L, 15235L, 15237L, 15256L, 15257L, 15258L, 15269L
), leg_activity = c("home, adpt, shop, car_passenger, home, adpt, work, adpt, home, work, outside, pt, home",
"home, pt, outside, pt, home, car, leisure, car, other, car, leisure, car, leisure, car, other, car, leisure, car, other, car, leisure, car, home, adpt, leisure, adpt, home",
"home, work, adpt, home", "home, car, work, car, home, work, adpt, home",
"home, adpt, work, car_passenger, leisure, car_passenger, work, adpt, home, car_passenger, outside, car_passenger, outside, car_passenger, home",
"home, bike, outside, car_passenger, outside, car_passenger, outside, bike, home, adpt, leisure, adpt, home, bike, leisure, bike, home",
"home, adpt, work, adpt, home, walk, other, pt, home", "home, adpt, work, walk, home, adpt, work, walk, home",
"home, adpt, leisure, adpt, home, bike, outside, bike, home",
"home, pt, work, adpt, home, adpt, work, adpt, home", "home, adpt, work, adpt, home, car, outside, car, work, car, work, car, home",
"home, work, leisure, adpt, home", "home, outside, pt, home, adpt, leisure, adpt, home",
"home, car_passenger, leisure, walk, work, walk, leisure, walk, work, adpt, home, walk, home",
"home, adpt, work, walk, work, walk, work, pt, home", "home, car, work, pt, leisure, adpt, work, car, home, car, home",
"home, adpt, other, adpt, home, car, home", "home, adpt, other, adpt, home",
"home, education, walk, shop, walk, education, pt, outside, home, adpt, leisure, adpt, home",
"home, adpt, work, adpt, home, walk, home", "home, adpt, work, pt, leisure, adpt, work, adpt, work, adpt, home, adpt, other, walk, home",
"home, adpt, work, adpt, home, adpt, work, adpt, home, walk, leisure, walk, home",
"home, adpt, work, adpt, home, work, adpt, home, walk, leisure, walk, home",
"home, adpt, work, adpt, home, car_passenger, outside, car_passenger, leisure, car_passenger, home, car_passenger, home",
"home, adpt, other, adpt, home, car, work, car, home", "home, adpt, education, adpt, leisure, adpt, home, walk, leisure, walk, home",
"home, car_passenger, other, pt, home, walk, other, walk, home, car_passenger, other, walk, home, adpt, other, adpt, home",
"home, work, pt, work, adpt, work, adpt, home", "home, adpt, leisure, adpt, home, car, shop, car, other, car, home",
"home, adpt, work, adpt, home, walk, other, adpt, home", "home, adpt, work, adpt, home, car_passenger, leisure, car_passenger, home",
"home, car, other, car, home, adpt, shop, adpt, home", "home, pt, work, adpt, home",
"home, adpt, work, adpt, home", "home, adpt, work, adpt, home",
"home, walk, education, adpt, home, walk, education, walk, home, bike, leisure, bike, home",
"home, adpt, shop, adpt, home, car, home", "home, adpt, leisure, walk, leisure, walk, leisure, adpt, home",
"home, adpt, shop, pt, home, adpt, other, adpt, home", "home, adpt, other, adpt, home, car_passenger, leisure, walk, home",
"home, adpt, work, adpt, home, car_passenger, shop, car_passenger, home",
"home, adpt, other, adpt, work, adpt, home", "home, adpt, work, adpt, home, adpt, other, walk, shop, walk, home, car, outside, car, outside, car, outside, car, home",
"home, adpt, other, adpt, home", "home, adpt, education, adpt, home, adpt, education, adpt, home",
"home, pt, work, adpt, work, adpt, work, adpt, work, adpt, home, adpt, work, adpt, home",
"home, walk, other, car_passenger, education, walk, home, car_passenger, education, adpt, home",
"home, walk, shop, walk, home, walk, leisure, adpt, leisure, adpt, home",
"home, adpt, work, adpt, home, walk, shop, walk, home, walk, leisure, walk, home, walk, home",
"home, adpt, leisure, adpt, home")), row.names = c(NA, 50L), class = "data.frame")
下面是使用的溶液dplyr，stringr和tidyr（或更好tidyverse）：
df %>%
separate_rows(leg_activity, sep = "home, ") %>%
mutate(leg_activity = str_replace(leg_activity, "(, (home)*)$", "")) %>%
filter(leg_activity != "") %>%
mutate(leg_activity = str_c("home, ", leg_activity, ", home")) %>%
group_by(id) %>%
mutate(n = row_number()) %>%
pivot_wider(names_from = n, values_from = leg_activity)
我想有更好的解决方案，但这应该可以为您提供所需的输出。
> df2
# A tibble: 50 x 5
# Groups: id [50]
id `1` `2` `3` `4`
<int> <chr> <chr> <chr> <chr>
1 9 home, adpt, shop, car_passen~ home, adpt, work, adpt, home home, work, ~ NA
2 10 home, pt, outside, pt, home home, car, leisure, car, other~ home, adpt, ~ NA
3 11 home, work, adpt, home NA NA NA
4 96 home, car, work, car, home home, work, adpt, home NA NA
5 97 home, adpt, work, car_passen~ home, car_passenger, outside, ~ NA NA
6 101 home, bike, outside, car_pas~ home, adpt, leisure, adpt, home home, bike, ~ NA
7 103 home, adpt, work, adpt, home home, walk, other, pt, home NA NA
8 248 home, adpt, work, walk, home home, adpt, work, walk, home NA NA
9 499 home, adpt, leisure, adpt, h~ home, bike, outside, bike, home NA NA
10 1044 home, pt, work, adpt, home home, adpt, work, adpt, home NA NA
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