Changes to .name_repair make the example code on README.md not work with Version 2.1.1 of the tibble package

[jonleslie]

It seems that the demo on the README.md file does not work with Version 2.1.1 of the tibble package, as called from the readxl package. The .name_repair argument causes the example to have column names: Certification --> Certification...9 Certification1 --> Certification...10 Certification2 --> Certification...11

I have made changes to README.rmd to reflect this change, and can submit a pull request for it if you would like me to. I haven't done so yet as per your contributing guidelines, which ask that an issue be raised first.

library(pacman) # for loading packages
p_load(readxl, janitor, dplyr, here)

roster_raw <- read_excel(here("data", "dirty_data.xlsx")) # available at http://github.com/sfirke/janitor
#> New names:
#> * Certification -> Certification...9
#> * Certification -> Certification...10
#> * Certification -> Certification...11

glimpse(roster_raw)
#> Observations: 13
#> Variables: 11
#> $ `First Name`        <chr> "Jason", "Jason", "Alicia", "Ada", "Desus", …
#> $ `Last Name`         <chr> "Bourne", "Bourne", "Keys", "Lovelace", "Nic…
#> $ `Employee Status`   <chr> "Teacher", "Teacher", "Teacher", "Teacher", …
#> $ Subject             <chr> "PE", "Drafting", "Music", NA, "Dean", "Phys…
#> $ `Hire Date`         <dbl> 39690, 39690, 37118, 27515, 41431, 11037, 11…
#> $ `% Allocated`       <dbl> 0.75, 0.25, 1.00, 1.00, 1.00, 0.50, 0.50, NA…
#> $ `Full time?`        <chr> "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Y…
#> $ `do not edit! --->` <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
#> $ Certification...9   <chr> "Physical ed", "Physical ed", "Instr. music"…
#> $ Certification...10  <chr> "Theater", "Theater", "Vocal music", "Comput…
#> $ Certification...11  <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …

# Doesn't work:
roster <- roster_raw %>%
  clean_names() %>%
  remove_empty(c("rows", "cols")) %>%
  mutate(hire_date = excel_numeric_to_date(hire_date),
         cert = coalesce(certification, certification_1)) %>% # from dplyr
  select(-certification, -certification_1) # drop unwanted columns
#> Error in list2(...): object 'certification' not found

# Fixed version:
roster <- roster_raw %>%
  clean_names() %>%
  remove_empty(c("rows", "cols")) %>%
  mutate(hire_date = excel_numeric_to_date(hire_date),
         cert = coalesce(certification_9, certification_10)) %>% # from dplyr
  select(-certification_9, -certification_10) # drop unwanted columns

roster
#> # A tibble: 12 x 8
#>    first_name last_name employee_status subject hire_date  percent_allocat…
#>    <chr>      <chr>     <chr>           <chr>   <date>                <dbl>
#>  1 Jason      Bourne    Teacher         PE      2008-08-30             0.75
#>  2 Jason      Bourne    Teacher         Drafti… 2008-08-30             0.25
#>  3 Alicia     Keys      Teacher         Music   2001-08-15             1   
#>  4 Ada        Lovelace  Teacher         <NA>    1975-05-01             1   
#>  5 Desus      Nice      Administration  Dean    2013-06-06             1   
#>  6 Chien-Shi… Wu        Teacher         Physics 1930-03-20             0.5 
#>  7 Chien-Shi… Wu        Teacher         Chemis… 1930-03-20             0.5 
#>  8 James      Joyce     Teacher         English 1990-05-01             0.5 
#>  9 Hedy       Lamarr    Teacher         Science 1976-06-08             0.5 
#> 10 Carlos     Boozer    Coach           Basket… 2015-08-05            NA   
#> 11 Young      Boozer    Coach           <NA>    1995-01-01            NA   
#> 12 Micheal    Larsen    Teacher         English 2009-09-15             0.8 
#> # … with 2 more variables: full_time <chr>, cert <chr>

^{Created on 2019-04-25 by the reprex package (v0.2.1)}

[sfirke]

Thanks for flagging this. I now see I was using an old version of readxl when knitting these docs.

As to how to handle this: the new version of readxl::read_excel() has the .name_repair argument and that + the new janitor::make_clean_names() are perfect for each other. Though it means this is not as good a case for demo'ing clean_names.

I lean toward addressing this by:

• Briefly note that name cleaning comes in two flavors, the main make_clean_names() and the convenience version clean_names() for piped data.frame workflows
• Add .name_repair = make_clean_names to the read_excel call, explaining it
• Add a short example of clean_names() afterward, showing how it works on say the iris data set.

The changes would also need to be made to the Index.Rmd file that builds the pkgdown main page.

1) What do you (or anyone else) think about that resolution? 2) Once we agree on #1, if you're up for sending a pull request to implement it, that would be swell. There might still be some tinkering given that it involves a bit of open-ended writing.

[jonleslie]

Hi, Sam,

Thanks for your response to the above issue. Your solution sounds sensible to me, and I'm very happy to submit a pull request with the proposed changes. I'll aim to turn it around today.

Cheers, Jon