library(readxl)

Column names via col_names

readxl has always let you specify col_names explicitly at the time of import:

read_excel(
  readxl_example("datasets.xlsx"), sheet = "chickwts",
  col_names = c("chick_weight", "chick_ate_this"), skip = 1
)
#> # A tibble: 71 x 2
#>    chick_weight chick_ate_this
#>           <dbl> <chr>         
#>  1          179 horsebean     
#>  2          160 horsebean     
#>  3          136 horsebean     
#>  4          227 horsebean     
#>  5          217 horsebean     
#>  6          168 horsebean     
#>  7          108 horsebean     
#>  8          124 horsebean     
#>  9          143 horsebean     
#> 10          140 horsebean     
#> # … with 61 more rows

But users have long wanted a way to specify a name repair strategy, as opposed to enumerating the actual column names.

Built-in levels of .name_repair

As of v1.2.0, readxl provides the .name_repair argument, which affords control over how column names are checked or repaired. This requires v2.0.0 or higher of the tibble package, which powers this feature under the hood.

The .name_repair argument in read_excel(), read_xls(), and read_xlsx() works exactly the same way as it does in tibble::tibble() and tibble::as_tibble(). Full documentation is in the ?name-repair topic of tibble. The reasoning behind the name repair strategy is laid out in principles.tidyverse.org.

readxl’s default is .name_repair = "unique", which ensures each column has a unique name. If that is already true of the column names, readxl won’t touch them.

The value .name_repair = "universal" goes further and makes column names syntactic, i.e. makes sure they don’t contain any forbidden characters or reserved words. This makes life easier if you use packages like ggplot2 and dplyr downstream, because the column names will “just work” everywhere and won’t require protection via backtick quotes.

Compare the column names in these two calls. This shows the difference between "unique" (names can contain spaces) and "universal" (spaces replaced by .).

read_excel(
  readxl_example("deaths.xlsx"),  range = "arts!A5:F8"
)
#> # A tibble: 3 x 6
#>   Name       Profession   Age `Has kids` `Date of birth`     `Date of death`    
#>   <chr>      <chr>      <dbl> <lgl>      <dttm>              <dttm>             
#> 1 David Bow… musician      69 TRUE       1947-01-08 00:00:00 2016-01-10 00:00:00
#> 2 Carrie Fi… actor         60 TRUE       1956-10-21 00:00:00 2016-12-27 00:00:00
#> 3 Chuck Ber… musician      90 TRUE       1926-10-18 00:00:00 2017-03-18 00:00:00

read_excel(
  readxl_example("deaths.xlsx"), range = "arts!A5:F8",
  .name_repair = "universal"
)
#> New names:
#> * `Has kids` -> Has.kids
#> * `Date of birth` -> Date.of.birth
#> * `Date of death` -> Date.of.death
#> # A tibble: 3 x 6
#>   Name         Profession   Age Has.kids Date.of.birth       Date.of.death      
#>   <chr>        <chr>      <dbl> <lgl>    <dttm>              <dttm>             
#> 1 David Bowie  musician      69 TRUE     1947-01-08 00:00:00 2016-01-10 00:00:00
#> 2 Carrie Fish… actor         60 TRUE     1956-10-21 00:00:00 2016-12-27 00:00:00
#> 3 Chuck Berry  musician      90 TRUE     1926-10-18 00:00:00 2017-03-18 00:00:00

If you don’t want readxl to touch your column names at all, use .name_repair = "minimal".

Pass a function to .name_repair

The .name_repair argument also accepts a function – pre-existing or written by you – or an anonymous formula. This function must operate on a “names in, names out” basis.

## ALL CAPS! via built-in toupper()
read_excel(readxl_example("clippy.xlsx"), .name_repair = toupper)
#> # A tibble: 4 x 2
#>   NAME                 VALUE    
#>   <chr>                <chr>    
#> 1 Name                 Clippy   
#> 2 Species              paperclip
#> 3 Approx date of death 39083    
#> 4 Weight in grams      0.9

## lower_snake_case via a custom function
my_custom_name_repair <- function(nms) tolower(gsub("[.]", "_", nms))
read_excel(
  readxl_example("datasets.xlsx"), n_max = 3,
  .name_repair = my_custom_name_repair
)
#> # A tibble: 3 x 5
#>   sepal_length sepal_width petal_length petal_width species
#>          <dbl>       <dbl>        <dbl>       <dbl> <chr>  
#> 1          5.1         3.5          1.4         0.2 setosa 
#> 2          4.9         3            1.4         0.2 setosa 
#> 3          4.7         3.2          1.3         0.2 setosa

## take first 3 characters via anonymous function
read_excel(
  readxl_example("datasets.xlsx"),
  sheet = "chickwts", n_max = 3,
  .name_repair = ~ substr(.x, start = 1, stop = 3)
)
#> # A tibble: 3 x 2
#>     wei fee      
#>   <dbl> <chr>    
#> 1   179 horsebean
#> 2   160 horsebean
#> 3   136 horsebean

This means you can also perform name repair in the style of base R or another package, such as janitor::make_clean_names() (requires janitor > v1.1.1).

read_excel(
  SOME_SPREADSHEET,
  .name_repair = ~ make.names(.x, unique = TRUE)
)

read_excel(
  SOME_SPREADSHEET,
  .name_repair = ~ janitor::make_clean_names
)

What if you have a spreadsheet with lots of missing column names? Here’s how you could fall back to letter-based column names, for easier troubleshooting.

read_excel(
  SOME_SPREADSHEET,
  .name_repair = ~ ifelse(nzchar(.x), .x, LETTERS[seq_along(.x)])
)