In 2DegreesInvesting/ds-incubator: Share best practices in coding

2020-02-11: R packages: Data

From https://r-pkgs.org/data.html

Exported data: data/

If you want to store binary data and make it available to the user, put it in data/. This is the best place to put example datasets.

use_data_raw()

# data-raw/mini_mtcars.R
# Prepare
mini_mtcars <- head(mtcars)

usethis::use_data(mini_mtcars)

Document exported data

# R/mini_mtcars.R

#' First six rows of [datasets::mtcars]
#' 
#' A mini dataset for examples and tests.
#'
#' @source [datasets::mtcars]
"mini_mtcars"

Internal data: R/sysdata.rda

If you want to store parsed data, but not make it available to the user, put it in R/sysdata.rda. This is the best place to put data that your functions need.

# data-raw/sysdata.R
mini_letters <- head(letters)
mini_month <- head(month.abb)

usethis::use_data(mini_letters, mini_month, internal = TRUE)

Raw data for users: inst/extdata

If you want to store raw data, put it in inst/extdata.

use_directory("inst/extdata")

system.file("extdata", "iris.csv", package = "readr", mustWork = TRUE)

`dput()`, `tibble::tribble()`, `datapasta::tribble_paste()`

A simple alternative to these three options is to include it in the source of your package, either creating by hand, or using dput() to serialise an existing data set into R code.