在R中的分组日期内填充
这个错误对我来说没有多大意义。我正在寻找如下所示的输出:在R中的分组日期内填充,r,date,time-series,lubridate,padr,R,Date,Time Series,Lubridate,Padr,这个错误对我来说没有多大意义。我正在寻找如下所示的输出: df %>% group_by(`Action Item ID`) %>% pad() #> Error: Not all grouping variables are column names of x. #>#一个tible:?x4 #>`Action Item ID``当前状态`Type日期 #> #>ABC截止日期:2019-01-01
df %>% group_by(`Action Item ID`) %>% pad()
#> Error: Not all grouping variables are column names of x.
#>#一个tible:?x4
#>`Action Item ID``当前状态`Type日期
#>
#>ABC截止日期:2019-01-01
#>ABC NA 2019-01-02
#>ABC NA 2019-01-03
#> ... ... ... ...
#>ABC截止日期2019-01-15
#>DEF关闭日期创建于2019-01-01
#>DEF NA 2019-01-02
#> ... ... ... ...
#>DEF NA 2019-05-30
#>DEF关闭日期2019-05-31
#>GHI关闭日期:2019-06-01
#> ... ... ... ...
有人知道出了什么问题吗?根据
?pad
,有一个组
参数
group—指定分组变量的可选字符向量。填充将在不同的组中进行。未指定interval时,将确定对datetime变量整体应用get_interval,忽略组(请参见最后一个示例)
因此,最好利用该参数
#> # A tibble: ? x 4
#> `Action Item ID` `Current Status` Type Date
#> <chr> <chr> <chr> <date>
#> ABC Closed Date Created 2019-01-01
#> ABC NA NA 2019-01-02
#> ABC NA NA 2019-01-03
#> ... ... ... ...
#> ABC Closed Date Closed 2019-01-15
#> DEF Closed Date Created 2019-01-01
#> DEF NA NA 2019-01-02
#> ... ... ... ...
#> DEF NA NA 2019-05-30
#> DEF Closed Date Closed 2019-05-31
#> GHI Closed Date Created 2019-06-01
#> ... ... ... ...
库(dplyr)
图书馆(padr)
df%>%
pad(组=“操作项ID”)
#A tibble:233x4
#`Action Item ID``当前状态`Type日期
#
#1 ABC截止日期创建于2019-01-01
#2 ABC 2019-01-02
#3 ABC 2019-01-03
#4 ABC 2019-01-04
#5 ABC 2019-01-05
#6 ABC 2019-01-06
#7 ABC 2019-01-07
#8 ABC 2019-01-08
#9 ABC 2019-01-09
#10 ABC 2019-01-10
#…还有223行
我不熟悉padr
,但如果您将列名更改为语法上有效的列名,即“action\u item\u id”,则它会起作用。那可能是引擎盖下的小问题df%>%gatitor::clean_names()%%>%group_by(action_item_id)%%>%pad()有关于NAs的警告,但没有errors@camille这就解开了谜团。很好的侦探工作。你发布的错误消息告诉我,某个地方的列名丢失了。包含错误消息使帮助更容易的完美示例!
#> # A tibble: ? x 4
#> `Action Item ID` `Current Status` Type Date
#> <chr> <chr> <chr> <date>
#> ABC Closed Date Created 2019-01-01
#> ABC NA NA 2019-01-02
#> ABC NA NA 2019-01-03
#> ... ... ... ...
#> ABC Closed Date Closed 2019-01-15
#> DEF Closed Date Created 2019-01-01
#> DEF NA NA 2019-01-02
#> ... ... ... ...
#> DEF NA NA 2019-05-30
#> DEF Closed Date Closed 2019-05-31
#> GHI Closed Date Created 2019-06-01
#> ... ... ... ...
library(dplyr)
library(padr)
df %>%
pad(group = "Action Item ID")
# A tibble: 233 x 4
# `Action Item ID` `Current Status` Type Date
# <chr> <chr> <chr> <date>
# 1 ABC Closed Date Created 2019-01-01
# 2 ABC <NA> <NA> 2019-01-02
# 3 ABC <NA> <NA> 2019-01-03
# 4 ABC <NA> <NA> 2019-01-04
# 5 ABC <NA> <NA> 2019-01-05
# 6 ABC <NA> <NA> 2019-01-06
# 7 ABC <NA> <NA> 2019-01-07
# 8 ABC <NA> <NA> 2019-01-08
# 9 ABC <NA> <NA> 2019-01-09
#10 ABC <NA> <NA> 2019-01-10
# … with 223 more rows