template packageThe template package automates creation of new projects with all the
necessary scaffolding: different folders for data, scripts, and
functions, plus (optionally) additional files required for an R package
structure. It can simultaneously create and synchronise a new repository
on GitHub so one can start working immediately.
template can create both projects with or without R package structure.
Structuring data analysis projects as R packages (a.k.a. “research
compendia”) can bring some advantages (e.g. see this
blogpost, this
repo,
these and these
slides or read
Marwick et al.). But
there are also good
reasons why an R package
structure may not always be needed or convenient.
# install.packages("remotes")
# remotes::install_github("Pakillo/template")
install.packages("template", repos = c('https://pakillo.r-universe.dev', 'https://cloud.r-project.org'))
First, load the package:
library("template")
Now run the function new_project to create a directory with all the
scaffolding (slightly modified from R package structure). For example,
to start a new project about tree growth, just use:
new_project("treegrowth")
This will create a new Rstudio project with this structure:
You can create a GitHub repository for the project at the same time:
new_project("treegrowth", github = TRUE, private.repo = FALSE)
You could choose either public or private repository. Note that to create a GitHub repo you will need to have configured your system as explained in https://usethis.r-lib.org/articles/articles/usethis-setup.html.
There are other options you could choose, like setting up testthat or
continuous integration (Travis-CI, GitHub Actions…). Or skip R package
structure altogether. See ?new_project for all options.
Now edit README.Rmd and the DESCRIPTION file with some basic
information about your project: title, brief description, licence,
package dependencies, etc.
Place original (raw) data in data-raw folder. Save all R scripts
(or Rmarkdown documents) used for data preparation in the same
folder.
Save final (clean, tidy) datasets in the data folder. You may
write
documentation
for these data.
R scripts or Rmarkdown documents used for data analyses may be
placed at the analyses folder. The final manuscript/report may be
placed at the manuscript folder. You could use one of the many
Rmarkdown templates available out there
(e.g. rticles,
rrtools or
rmdTemplates).
If you write custom functions, place them in the R folder.
Document all your functions with
Roxygen. Write tests for
your functions and place them in the tests folder.
If your analysis uses functions from other CRAN packages, include
these as dependencies (Imports) in the DESCRIPTION file
(e.g. using usethis::use_package() or
rrtools::add_dependencies_to_description(). Also, use Roxygen
@import or @importFrom in your function definitions, or
alternatively package::function(), to import these dependencies in
the namespace.
I recommend using an advanced tool like
targets to manage your
project workflow. A simpler alternative might be writing a
makefile or master script to organise and execute all parts of the
analysis. A template makefile is included with this package (use
makefile = TRUE when calling new_project).
Render Rmarkdown reports using rmarkdown::render, and use Rstudio
Build menu to create/update documentation, run tests, build
package, etc.
Record the exact dependencies of your project. One option is simply
running sessionInfo() but many more sophisticated alternatives
exist. For example, automagic::make_deps_file() or
renv::snapshot() will create a file recording the exact versions
of all packages used, which can be used to recreate such environment
in the future or in another computer. If you want to use Docker, you
could use e.g. containerit::dockerfile() or
rrtools::use_dockerfile().
Archive your repository (e.g. in Zenodo), get a DOI, and include citation information in your README.