我想在我的长格式数据集中添加4个变量并对其进行过滤。 基本上我会从5年（2016-2020）开始。 2016年相对于其他年份有更多行，所以我想总结2016年到2020年作为新列 - 变量将其值复制到2016年的每一行。在excel中，我将通过SUMIFS执行此操作。我有点在dplyr管理，但我得到0。
newdt <- dt %>%
group_by(time, country, age5, sex1, geo) %>%
summarise(T.age.2017 = sum(value[time==2017]),
T.age.2018 = sum(value[time==2018]),
T.age.2019 = sum(value[time==2019]),
T.age.2020 = sum(value[time==2020])) %>%
ungroup() %>%
filter(time==2016)
以下是我想要的内容，过滤时间== 2016并将其他年份仅保留为汇总列：
time country geo age5 sex1 value T.age.2017 T.age.2018
2016 AT AT11 0 1 6137 420814 427950
2016 AT AT11 5 1 6582 411300 416616
2016 AT AT11 10 1 6922 419810 418522
2016 AT AT11 15 1 7461 444286 439986
2016 AT AT11 0 2 5839 420814 427950
2016 AT AT11 5 2 6354 411300 416616
2016 AT AT11 10 2 6552 419810 418522
2016 AT AT11 15 2 6769 444286 439986
2016 AT AT12 0 1 39017 420814 427950
dput：
dt = structure(list(time = c(2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2017L,
2017L, 2017L, 2017L, 2018L, 2018L, 2018L, 2018L, 2019L, 2019L,
2019L, 2019L, 2020L, 2020L, 2020L, 2020L), country = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "AT", class = "factor"),
geo = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("AT",
"AT1", "AT11", "AT12", "AT13", "AT2", "AT21", "AT22", "AT3",
"AT31", "AT32", "AT33", "AT34"), class = "factor"), age5 = c(0L,
5L, 10L, 15L, 0L, 5L, 10L, 15L, 0L, 5L, 10L, 15L, 0L, 5L,
10L, 15L, 0L, 5L, 10L, 15L, 0L, 5L, 10L, 15L, 0L, 5L, 10L,
15L, 0L, 5L, 10L, 15L, 0L, 5L, 10L, 15L, 0L, 5L, 10L, 15L,
0L, 5L, 10L, 15L, 0L, 5L, 10L, 15L, 0L, 5L, 10L, 15L, 0L,
5L, 10L, 15L, 0L, 5L, 10L, 15L, 0L, 5L, 10L, 15L, 0L, 5L,
10L, 15L, 0L, 5L, 10L, 15L, 0L, 5L, 10L, 15L, 0L, 5L, 10L,
15L, 0L, 5L, 10L, 15L, 0L, 5L, 10L, 15L, 0L, 5L, 10L, 15L,
0L, 5L, 10L, 15L, 0L, 5L, 10L, 15L, 0L, 5L, 10L, 15L, 0L,
5L, 10L, 15L, 0L, 5L, 10L, 15L, 0L, 5L, 10L, 15L, 0L, 5L,
10L, 15L), sex1 = c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA), value = c(214538L, 210372L, 215218L,
239274L, 200991L, 200273L, 203787L, 221272L, 94210L, 91724L,
92439L, 100055L, 88171L, 87172L, 86792L, 93008L, 6137L, 6582L,
6922L, 7461L, 5839L, 6354L, 6552L, 6769L, 39017L, 40381L,
43136L, 47241L, 36855L, 38487L, 40292L, 42981L, 49056L, 44761L,
42381L, 45353L, 45477L, 42331L, 39948L, 43258L, 40212L, 40464L,
41996L, 47804L, 37323L, 38646L, 39861L, 44218L, 12266L, 12928L,
13451L, 15108L, 11554L, 12204L, 12827L, 13938L, 27946L, 27536L,
28545L, 32696L, 25769L, 26442L, 27034L, 30280L, 80116L, 78184L,
80783L, 91415L, 75497L, 74455L, 77134L, 84046L, 37488L, 36674L,
37456L, 42495L, 35219L, 34859L, 35959L, 38955L, 13774L, 13185L,
14012L, 16206L, 12907L, 12629L, 13265L, 14534L, 18414L, 17944L,
18544L, 20879L, 17482L, 17045L, 17609L, 19752L, 10440L, 10381L,
10771L, 11835L, 9889L, 9922L, 10301L, 10805L, 420814L, 411300L,
419810L, 444286L, 427950L, 416616L, 418522L, 439986L, 435796L,
420548L, 420646L, 436501L, 444137L, 425721L, 420044L, 437446L
)), .Names = c("time", "country", "geo", "age5", "sex1",
"value"), class = "data.frame", row.names = c(NA, -120L))
答案 0 :(得分：1)
你在找这样的东西吗？
dt %>%
group_by(time, country, age5, sex1, geo) %>%
summarise(T.age = sum(value)) %>%
filter(time==2016) %>%
left_join(.,dt %>%
group_by(time, country, age5, geo) %>%
summarise(T.age = sum(value)) %>%
mutate(time2 = 2016) %>%
filter(time != 2016) %>%
spread(time, T.age),
by = c('time' = 'time2', 'country', 'age5')) %>%
select(-geo.y) %>%
arrange(time, country, geo.x, sex1, age5)
结果：
# A tibble: 104 x 10
# Groups: time, country, age5, sex1 [8]
time country age5 sex1 geo.x T.age `2017` `2018` `2019` `2020`
<dbl> <fctr> <int> <int> <fctr> <int> <int> <int> <int> <int>
1 2016 AT 0 1 AT 214538 420814 427950 435796 444137
2 2016 AT 5 1 AT 210372 411300 416616 420548 425721
3 2016 AT 10 1 AT 215218 419810 418522 420646 420044
4 2016 AT 15 1 AT 239274 444286 439986 436501 437446
5 2016 AT 0 2 AT 200991 420814 427950 435796 444137
6 2016 AT 5 2 AT 200273 411300 416616 420548 425721
7 2016 AT 10 2 AT 203787 419810 418522 420646 420044
8 2016 AT 15 2 AT 221272 444286 439986 436501 437446
9 2016 AT 0 1 AT1 94210 420814 427950 435796 444137
10 2016 AT 5 1 AT1 91724 411300 416616 420548 425721
# ... with 94 more rows