knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
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 )
But users have long wanted a way to specify a name repair strategy, as opposed to enumerating the actual column names.
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.
.name_repair argument in
read_xlsx() works exactly the same way as it does in
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.
.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" ) read_excel( readxl_example("deaths.xlsx"), range = "arts!A5:F8", .name_repair = "universal" )
If you don't want readxl to touch your column names at all, use
.name_repair = "minimal".
.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) ## 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 ) ## 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) )
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)]) )
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