complete.test <- tibble(col1 = c("a", "a", "b", "b"),
col2 = c(as.Date("2019-01-01"),
as.Date("2019-01-02"),
as.Date("2019-01-03"),
as.Date("2019-01-04")),
col3 = runif(4),
col4 = runif(4))
complete.test %>% complete(col1, col2)
#> # A tibble: 8 x 4
#> col1 col2 col3 col4
#> <chr> <date> <dbl> <dbl>
#> 1 a 2019-01-01 0.154 0.143
#> 2 a 2019-01-02 0.746 0.526
#> 3 a 2019-01-03 NA NA
#> 4 a 2019-01-04 NA NA
#> 5 b 2019-01-01 NA NA
#> 6 b 2019-01-02 NA NA
#> 7 b 2019-01-03 0.997 0.772
#> 8 b 2019-01-04 0.989 0.460
tidyr complete()函数的工作与上面一样。但是，如果我们使用下面显示的特定数据集，它将“停止”工作。可能是用户错误。请继续阅读。
library(tidyverse)
df <- structure(list(`Business Group` = c("ABC", "ABC", "ABC",
"ABC", "ABC", "ABC", "ABC", "ABC", "ABC",
"ABC", "DEF", "DEF", "DEF", "DEF", "DEF",
"DEF", "DEF", "DEF", "GHI", "GHI",
"GHI", "GHI", "GHI",
"GHI", "GHI", "GHI",
"GHI", "GHI", "GHI",
"GHI"), Month = structure(c(17866, 17897, 17928,
17956, 17987, 18017, 18048, 18078, 18109, 18140, 17956, 17987,
18017, 18048, 18078, 18109, 18140, 18170, 13970, 14000, 14031,
14061, 14092, 14123, 14153, 14184, 14214, 14245, 14276, 14304
), class = "Date"), SumChange = c(0, 0, 0, 1, 1, 1, 1, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, -3, 0, 0, 0, 0, 2, 0, -12, 3, 4, 3, 4,
3), `Qty Items Open 90 Days` = c(0, 0, 0, 1, 2, 3, 4, 5, 5, 5,
0, 0, 0, 0, 0, 0, 0, -3, 0, 0, 0, 0, 2, 2, -10, -7, -3, 0, 4,
7)), row.names = c(NA, -30L), groups = structure(list(`Business Group` = c("ABC",
"DEF", "GHI"), .rows = list(1:10, 11:18, 19:30)), row.names = c(NA,
-3L), class = c("tbl_df", "tbl", "data.frame"), .drop = FALSE), class = c("grouped_df",
"tbl_df", "tbl", "data.frame"))
#> # A tibble: 30 x 4
#> # Groups: Business Group [3]
#> `Business Group` Month SumChange `Qty Items Open 90 Days`
#> <chr> <date> <dbl> <dbl>
#> 1 ABC 2018-12-01 0 0
#> 2 ABC 2019-01-01 0 0
#> 3 ABC 2019-02-01 0 0
#> 4 ABC 2019-03-01 1 1
#> 5 ABC 2019-04-01 1 2
#> 6 ABC 2019-05-01 1 3
#> 7 ABC 2019-06-01 1 4
#> 8 ABC 2019-07-01 1 5
#> 9 ABC 2019-08-01 0 5
#> 10 ABC 2019-09-01 0 5
#> 11 DEF 2019-03-01 0 0
#> 12 DEF 2019-04-01 0 0
#> 13 DEF 2019-05-01 0 0
#> 14 DEF 2019-06-01 0 0
#> 15 DEF 2019-07-01 0 0
#> 16 DEF 2019-08-01 0 0
#> 17 DEF 2019-09-01 0 0
#> 18 DEF 2019-10-01 -3 -3
#> 19 GHI 2008-04-01 0 0
#> 20 GHI 2008-05-01 0 0
#> 21 GHI 2008-06-01 0 0
#> 22 GHI 2008-07-01 0 0
#> 23 GHI 2008-08-01 2 2
#> 24 GHI 2008-09-01 0 2
#> 25 GHI 2008-10-01 -12 -10
#> 26 GHI 2008-11-01 3 -7
#> 27 GHI 2008-12-01 4 -3
#> 28 GHI 2009-01-01 3 0
#> 29 GHI 2009-02-01 4 4
#> 30 GHI 2009-03-01 3 7
您可以看到上面的数据框由三组“ ABC”，“ DEF”和“ GHI”组成。我知道complete(`Business Group`, Month)函数的行为不符合我的预期，因为它无法完成缺少Business Group和Month的数据组合的数据帧。 “ GHI”业务组的历史可以追溯到2009年，但“ ABC”和“ DEF”组的工作尚未完成。另外，什么都没有完成。知道怎么了吗？
df %>% complete(`Business Group`, Month)
#> # A tibble: 30 x 4
#> # Groups: Business Group [3]
#> `Business Group` Month SumChange `Qty Items Open 90 Days`
#> <chr> <date> <dbl> <dbl>
#> 1 ABC 2018-12-01 0 0
#> 2 ABC 2019-01-01 0 0
#> 3 ABC 2019-02-01 0 0
#> 4 ABC 2019-03-01 1 1
#> 5 ABC 2019-04-01 1 2
#> 6 ABC 2019-05-01 1 3
#> 7 ABC 2019-06-01 1 4
#> 8 ABC 2019-07-01 1 5
#> 9 ABC 2019-08-01 0 5
#> 10 ABC 2019-09-01 0 5
#> 11 DEF 2019-03-01 0 0
#> 12 DEF 2019-04-01 0 0
#> 13 DEF 2019-05-01 0 0
#> 14 DEF 2019-06-01 0 0
#> 15 DEF 2019-07-01 0 0
#> 16 DEF 2019-08-01 0 0
#> 17 DEF 2019-09-01 0 0
#> 18 DEF 2019-10-01 -3 -3
#> 19 GHI 2008-04-01 0 0
#> 20 GHI 2008-05-01 0 0
#> 21 GHI 2008-06-01 0 0
#> 22 GHI 2008-07-01 0 0
#> 23 GHI 2008-08-01 2 2
#> 24 GHI 2008-09-01 0 2
#> 25 GHI 2008-10-01 -12 -10
#> 26 GHI 2008-11-01 3 -7
#> 27 GHI 2008-12-01 4 -3
#> 28 GHI 2009-01-01 3 0
#> 29 GHI 2009-02-01 4 4
#> 30 GHI 2009-03-01 3 7
答案 0 :(得分：2)
这是一组tbl_df
library(dplyr)
library(tidyr)
df %>%
group_vars()
#[1] "Business Group"
ungroup，它应该可以工作
df %>%
ungroup %>%
complete(`Business Group`, Month)
# A tibble: 69 x 4
# `Business Group` Month SumChange `Qty Items Open 90 Days`
# <chr> <date> <dbl> <dbl>
# 1 ABC 2008-04-01 NA NA
# 2 ABC 2008-05-01 NA NA
# 3 ABC 2008-06-01 NA NA
# 4 ABC 2008-07-01 NA NA
# 5 ABC 2008-08-01 NA NA
# 6 ABC 2008-09-01 NA NA
# 7 ABC 2008-10-01 NA NA
# 8 ABC 2008-11-01 NA NA
# 9 ABC 2008-12-01 NA NA
#10 ABC 2009-01-01 NA NA
# … with 59 more rows