README.md

xlcutter

This package allows you to parse entire folders of non-rectangular ‘xlsx’ files into a single rectangular and tidy ‘data.frame’ based on a custom template file defining the column names of the output.

Installation

You can install the latest stable version of this package from CRAN:

install.packages("xlcutter")

or the development version from GitHub with:

# install.packages("remotes")
remotes::install_github("Bisaloo/xlcutter")

Example

Non-rectangular excel files are common in many domains. For a simple demonstration here, we use the example of the “Blue timesheet” from https://templates.office.com/, where employees can log their working hours.

A typical use case of xlcutter in this example would be for a manager who want to get a single rectangular dataset with the timesheets from different employees.

Your first step to extract the data is to define the various columns you want in the output in a template file. You can mark the data cells to extract with any custom marker, with the default being {{ column_name }}.

library(xlcutter)

data_files <- list.files(
  system.file("example", "timesheet", package = "xlcutter"),
  pattern = "\\.xlsx$",
  full.names = TRUE
)

template_file <- system.file(
  "example", "timesheet_template.xlsx",
  package = "xlcutter"
)

xlsx_cutter(
  data_files,
  template_file
)
#>   employee_firstname contract_hours employee_lastname realised_hours
#> 1               Leon             35              Bedu          29.00
#> 2               Paul             35            Dupont          35.00
#> 3           Marianne             35            Lebrun          36.25
#>   manager_firstname manager_lastname period_start period_end
#> 1              <NA>           Dubois   2022-01-03 2022-01-07
#> 2             Lydia           Dubois   2022-01-03 2022-01-07
#> 3             Lydia           Dubois   2022-01-03 2022-01-07

Other example of use cases

Other typical use cases for this package could be:

• an hospital that wants to collate non-rectangular information sheets from different patients into a single rectangular dataset