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template package

Generic template for research projects structured as R packages (adapted from Pakillo/template)

Travis-CI Build Status

Rmarkdown documents are great to keep reproducible scientific workflows, tightly integrating code, results and text. I keep a collection of Rmarkdown templates (including some for writing scientific articles, or manuscript reviews) here.

Once we are dealing with more complicated data analysis, and writing custom code and functions for a research project, structuring our project as an R package can bring many advantages (e.g. see here and here).

Hence this package works as a template for new research projects, with the idea of having everything (data, R scripts, functions and manuscripts reporting results) self-contained in the same package (a "research compendium") to facilitate collaboration and promote reproducibility.

A short presentation introducing this approach on 'Structuring data analysis projects as R packages' is available here: https://github.com/Pakillo/template/blob/master/slides/Projects_as_Packages.pdf

Installation

library("devtools")
install_github("sirusb/template")

Usage

  1. First, load the package
library("template")
  1. 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")

If you want to create a GitHub repository for the project at the same time, use instead:

new_project("treegrowth", github = TRUE, private.repo = FALSE, travis = TRUE)

This will create a new folder with this structure:

Note that to create a GitHub repo you will need to have configured your system as explained in http://www.rdocumentation.org/packages/devtools/functions/use_github. And for Travis to run you will need to activate it at https://travis-ci.org/profile.

Developing the project

  1. Now edit README.Rmd and the DESCRIPTION file with some basic information about your project: title, brief description, licence, package dependencies, etc. You may also check that project options in Rstudio are fine for you.

  2. Place original (raw) data in data-raw folder. Save all R scripts used for data preparation in the same folder.

  3. Save final (clean, tidy) datasets in the data folder. You may save them as plain text (txt, csv) or rda format (using save, saveRDS or devtools::use_data). You may write documentation for these data (see http://r-pkgs.had.co.nz/data.html#documenting-data).

  4. 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 may want to use an Rmarkdown template from e.g. rmdTemplates or rticles.

  5. If your analyses use functions from other CRAN packages, include them as dependencies (Imports) in the DESCRIPTION file. Also use Roxygen @import or @importFrom in function definitions to import these dependencies in the namespace.

  6. If you write custom functions, place them in the R folder. Document all your functions with Roxygen (see http://r-pkgs.had.co.nz/man.html). Write tests for your functions (see http://r-pkgs.had.co.nz/tests.html) and place them in the tests folder.

  7. Write a makefile or master script to organise and execute all parts of the analysis. Render Rmarkdown reports using rmarkdown::render, and use Rstudio Build menu to create/update documentation, run tests, build package, etc.

  8. Save all the figures to the figures folder. You can create sub-directories inside to keep it organized.

  9. Save the ppt reports to the reports folder.

  10. Share.

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