Pad within grouped dates in R

Question

library(tidyverse)
library(lubridate)
library(padr)
df <- tibble(`Action Item ID` = c("ABC", "DEF", "GHI", "JKL", "MNO", "PQR"),
             `Date Created` = as.Date(c("2019-01-01", "2019-01-01", 
                                        "2019-06-01", "2019-06-01",
                                        "2019-08-01", "2019-08-01")),
             `Date Closed` = as.Date(c("2019-01-15", "2019-05-31", 
                                        "2019-06-15", "2019-07-05",
                                        "2019-08-15", NA)),
             `Current Status` = c(rep("Closed", 5), "Open")) %>% 
  pivot_longer(-c(`Action Item ID`, `Current Status`), 
               names_to = "Type",
               values_to = "Date")
#> # A tibble: 12 x 4
#>    `Action Item ID` `Current Status` Type         Date      
#>    <chr>            <chr>            <chr>        <date>    
#>  1 ABC              Closed           Date Created 2019-01-01
#>  2 ABC              Closed           Date Closed  2019-01-15
#>  3 DEF              Closed           Date Created 2019-01-01
#>  4 DEF              Closed           Date Closed  2019-05-31
#>  5 GHI              Closed           Date Created 2019-06-01
#>  6 GHI              Closed           Date Closed  2019-06-15
#>  7 JKL              Closed           Date Created 2019-06-01
#>  8 JKL              Closed           Date Closed  2019-07-05
#>  9 MNO              Closed           Date Created 2019-08-01
#> 10 MNO              Closed           Date Closed  2019-08-15
#> 11 PQR              Open             Date Created 2019-08-01
#> 12 PQR              Open             Date Closed  NA

I've got my data frame above and I'm trying to pad dates within each group with the padr R package.

df %>% group_by(`Action Item ID`) %>% pad()
#> Error: Not all grouping variables are column names of x.

The error doesn't make much sense to me. I'm looking for output that would look like the following:

#> # 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
#>  ...              ...              ...          ...

Anybody have any idea what went wrong?

I'm not familiar with `padr` but if you change your column names to syntactically valid ones, i.e. "action_item_id", then it works. So maybe a tidyeval problem under the hood? `df %>% janitor::clean_names() %>% group_by(action_item_id) %>% pad() ` has warnings about NAs but no errors — camille, Jan 14 '20 at 19:49
The error message you posted tipped me off that column names were getting lost somewhere. Perfect example of how including error messages makes it easier to help! — camille, Jan 15 '20 at 16:19

akrun · Accepted Answer · 2020-01-14T19:12:56.377

According to ?pad, there is a group argument

group - Optional character vector that specifies the grouping variable(s). Padding will take place within the different groups. When interval is not specified, it will be determined applying get_interval on the datetime variable as a whole, ignoring groups (see last example).

So, it is better to make use of that parameter

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

Pad within grouped dates in R

1 Answers1