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A template utility for R projects that provides a skeletal project.
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The ProjectTemplate package provides a function, create.project(), that automatically builds a directory for a new R project with a clean sub-directory structure and automatic data and library loading tools. The hope is that standardized data loading, automatic importing of best practice packages, integrated unit testing and useful nudges towards keeping a cleanly organized codebase will improve the quality of R coding.

The inspiration comes from the rails command from Ruby on Rails, which initializes a new Rails project with the proper skeletal structure automatically. Also, ProjectTemplate follows Rails's approach of preferring convention over configuration: the automatic data and library loading as well as the automatic testing work easily because assumptions are made about the directory structure and naming conventions used in your code. You can customize your codebase however you'd like, but you will have to edit the automation scripts to use your conventions instead of the defaults before you'll get their benefits again.


This project is now on CRAN and can be installed using a simple call to install.packages():


If you would like access to changes to this package that are not available in the current version on CRAN, please download the contents of this repository and then run,

R CMD INSTALL ProjectTemplate_*.tar.gz

For most users, running the bleeding edge version of this package is probably a mistake. It is generally much less stable than the versions that have been released on CRAN.

Example Code

To create a project called my-project, open R and type:


load.project() is essentially a mnemonic for calling source('lib/boot.R'). Similarly, run.tests() is essentially a mnemonic for calling source('lib/run_test.R').


As far as ProjectTemplate is concerned, a good project should look like the following:

  • project/
    • data/
    • diagnostics/
    • doc/
    • graphs/
    • lib/
      • boot.R
      • load_data.R
      • load_libraries.R
      • preprocess_data.R
      • run_tests.R
      • utilities.R
    • logs/
    • profiling/
      • 1.R
    • reports/
    • tests/
      • 1.R
    • README
    • TODO

To do work on such a project, enter the main directory, open R and type source('lib/boot.R'). This will then automatically perform the following actions:

  • source('lib/load_libraries.R'), which automatically load the packages required for ProjectTemplate to function. This includes:
    • testthat
    • yaml
    • foreign
  • You can edit lib/load_libraries.R to automatically load the suggested packages as well, which are:
    • reshape
    • plyr
    • stringr
    • ggplot2
    • log4r
  • source('lib/load_data.R'), which automatically imports any CSV or TSV data files inside of the data/ directory.
  • source('lib/preprocess_data.R'), which allows you to make any run-time modifications to your data sets automatically. This is blank by default.

Default Project Layout

Within your project directory, ProjectTemplate creates the following directories and files whose purpose is explained below:

  • data/: Store your raw data files here. If they are a supported file format, they will automatically be loaded when you call load.project() or source('lib/boot.R'), for which load.project() is essentially a mnemonic.
  • diagnostics/: Store any scripts you use to diagnose your data sets for corruption or problematic data points. You should also put code that globally censors any data points here.
  • doc/: Store documentation for your analysis here.
  • graphs/: Store any graphs that you produce here.
  • lib/: Store any files that provide useful functionality for your work, but do not constitute a statistical analysis per se here.
  • lib/boot.R: This script handles automatically loading the other files in lib/ automatically. Calling load.project() automatically loads this file.
  • lib/load_data.R: This script handles the automatic loading of any supported files contained in data/.
  • lib/load_libraries.R: This script handles the automatic loading of the required packages, which are testthat, yaml and foreign. In addition, you can uncomment the lines that would automatically load the suggested packages, which are reshape, plyr, stringr, ggplot2 and log4r.
  • lib/preprocess_data.R: This script handles the preprocessing of your data, if you need to add columns at run-time, merge normalized data sets or perform similar operations.
  • lib/run_tests.R: This script automatically runs any test files contained in the tests/ directory using the testthat package. Calling run.tests() automatically runs this script.
  • lib/utilities.R: This script should contain quick general purpose code that belongs in a package, but hasn't been packaged up yet.
  • profiling/: Store any scripts you use to benchmark and time your code here.
  • reports/: Store any output reports, such as HTML or LaTeX versions of tables here. Sweave or brew documents should also go here.
  • tests/: Store any test cases in this directory. Your test files should use testthat style tests.
  • README: Write notes to help orient newcomers to your project.
  • TODO: Write a list of future improvements and bug fixes you have planned.

Automatic Data Loading

One of the major goals for ProjectTemplate is providing fully automatic data loading for R. For example, if your data/ directory contains a data file called data/choices.csv, then ProjectTemplate will automatically load this file and create a global variable called choices. Using the function found in lib/utilities.R, filenames that contain underscores, dashes and whitespace are changed to use periods instead. For instance, data/image_properties.tsv creates a global variable called

A large and growing number of file formats are supported by the automatic data loading script, including CSV files and related formats, RData files, remote data sets available over HTTP, Stata and SPSS formats and MySQL tables. For further details, read the file_formats.markdown file.

As of v0.1-3, load.project() prints out the name of every data set as it is loaded.

Contributors and Thanks

Diego Valle-Jones contributed a patch that enabled the autoloading of compressed CSV data files. Inspiration for further extensions to the autoloading system came from reading the documentation for David Edgar Liebke's get-dataset function, which is part of the Clojure statistical library Incanter.

Many thanks to anyone who's made suggestions or comments about ProjectTemplate.

Finding Out More

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