intro

library(projr)

Introduction

TL;DR

Extreme version

The projr workflow:

Initialisation: Run projr::projr_init() to set up the project with metadata, projr-specific configuration and a Git repo connected to GitHub (if you so choose).
Building the project: Run projr::projr_build_output() to build the project, version the project and its components, and upload the components to GitHub, OSF and/or a local folder.

Project directory structure (e.g. that raw data are kept in the data-raw folder) and on-build actions (e.g. uploading to GitHub) are specified in the _projr.yml file.

There are only three functions to remember: projr_init(), projr_build_output() and projr_path_get(). The projr configuration file, _projr.yml, specifies project defaults that are appropriate for the majority of projects, but can be customised to your needs.

Less extreme version

projr is a package that helps you to set up and maintain a project in a way that is reproducible, shareable and versioned.

Initialisation: An interactive initialisation function, projr::projr_init().
- This sets up the project with metadata, projr-specific configuration and a Git repo connected to GitHub (if you so choose).
Project and component versioning: The project as a whole is given a version (e.g. v1.2.1), and the components of the project (e.g. code, data, figures, documents) versioned individually and linked to this version.
- This means that you can always find the exact inputs that were used to create the outputs.
Test builds: To test build the project, run projr::projr_build_dev().
- This renders the literate programming documents in your project and saves the outputs to a temporary directory (in your workspace).
Builds: To build the project, run projr::projr_build_output().
- This saves the outputs to their final destination (e.g. the _output folder), as these are regarded as the latest state of the project (rather than a development version).
- In addition, a component of the pverall project version is bumped (e.g. v1.2.1 changes to v2.0.0 if the major version is bumped), and the individual project components (e.g. code, data, figures, documents) are versioned and linked to this new version.
- Finally, the project components are uploaded to GitHub, OSF and/or a local folder.

The _projr.yml file specifies the project directory structure and on-build actions. The directories key specifies the project directory structure, in terms of where raw data, outputs (intermediate and final) and rendered documents are kept. The build key specifies on-build actions, such as whether and how the code are versioned, and which project components (raw data, outputs, docs) are uploaded to GitHub, OSF and/or a local folder.

There are only three functions to remember: projr_init(), projr_build_output() and projr_path_get() (which is described later). The projr configuration file, _projr.yml, specifies project defaults that are appropriate for the majority of projects, but is highly customisable for your needs.

Install `projr`

Install projr:

if (!requireNamespace("remotes", quietly = TRUE)) {
  install.packages("remotes")
}
remotes::install_github("SATVILab/projr")

The `projr` workflow

Initialising the project

Open R in the working directory you want to use it in.

If using RStudio, then the easiest way to do this is to open RStudio, click File, then New Project then choose whether you want to open it in an existing directory or create a new one.

Run projr::projr_init().

This will prompt you for metadata about your project, such as a description for it as well as your name and email address. You are also able to choose whether to create a Git repo and, if so, whether to use GitHub (if not already set up to use GitHub, then see the tips below on creating a GitHub account)

Overall, what projr_init does is set up the project with metadata, projr-specific configuration and a Git repo connected to GitHub (if you so choose).

Building the project

`projr_build_output()`

One can build the project using projr_build_output(). This will automatically detect whatever literate programming documents are used (Rmd’s, Qmd’s, Quarto projects, bookdown projects) and render all of them. By deafult, the rendered documents (e.g. HTML, PDF) are saved to the _docs directory.

Versioning

Project versioning

The project, as whole, is versioned. This version is specified in the DESCRIPTION file, and is updated automatically by projr at each build.

Semantic versioning is used, so the version is of the form x.y.z, where x is the major version, y is the minor version and z is the patch version. For example, a project version (before a build) might be 0.1.0.

If the patch version is updated, then the version would become 0.1.1.
If the minor version is updated, then the version would become 0.2.0.
If the major version is updated, then the version would become 1.0.0.

This helps communicate the size of the changes made in a build. In software development, the patch version is updated for small changes, the minor version for new features and the major version for breaking changes.

For analysis projects (including, e.g. data processing sub-projects), the patch version might be updated for small changes, the minor version for new analyses and the major version for changes to the data processing pipeline. It’s up to you to decide what each one means - just communicate it to your collaborators.

Component versioning

At each build, projr also versions project components (code, R packages and files). This ensures that the outputs (e.g. figures, tables and documents) are linked to versioned inputs (e.g. raw data and code). They are linked by the project version, so that you can always find the exact inputs that were used to create the outputs.

Optionally, immediately after each build projr uploads these components to GitHub, OSF and/or a local folder. For example, by default the raw data, output (e.g. figures) and rendered documents (e.g. HTMLs, PDFs)s are uploaded to GitHub as GitHub releases.

It is not important to understand exactly what a GitHub release is at this point, other than to note that it is a free, easy, secure way to share non-code files (e.g. data, documents) with others.

This is done automatically at each build, and is highly customisable. Uploads to the Open Science Foundation (OSF) and local folders are also supported.

To understand and customise the above process further, it’s important to familiarse yourself with the projr configuration file, _projr.yml. Details of all the options are available in later vignettes.

`_projr.yml`

The projr configuration file, _projr.yml, specifies two main things:

What is kept where (e.g. which folder contains the raw data). This falls under the directories key.
If and how project inputs and outputs are shared and stored (e.g. on GitHub, OSF or a local path). This falls under the build key.

`directories`

Structure

Here is the default directories key:

# default directory settings:
directories:
  data-raw:
    path: _data_raw
  cache: 
    path: _tmp
  output: 
    path: _output
  docs:
    path: docs

The purpose is to specify the paths to the key folders in your project. They are as follows:

data-raw: The folder where raw data is stored.
cache: This stores files created during project builds that you do not want to share. For example, if you use the R package targets to cache intermediate output, the cached intermediate output will go here.
output: This also stores files created during project builds - but these you want to share, e.g. figures or tables.
docs: This is where the rendered documents are stored, e.g. the HTML or PDF files.

To access the paths to these folders, use the projr_path_get function. For example:

projr_path_get("output")

will return

"_output"

It can be given further arguments, specifying sub-directories and file names. For example:

projr_path_get("output", "figure", "nobel_prize_winning_plot.png")

will return

"_output/figure/nobel_prize_winning_plot.png"

So, code saving a figure to this path might look like this:

png(filename = projr_path_get("output", "figure", "nobel_prize_winning_plot.png")
# ... code to create the plot
dev.off()

In future, if the path to the output folder changes in _projr.yml (e.g. to _output_figures instead of _output), projr_path_get will automatically update the path.

Essentially, projr_path_get should replace either hardcoded paths ("_output/figure/nobel_prize_winning_plot.png") or file.path/here::here calls.

Benefits

Using folders in the manner described (e.g. storing raw data in the data-raw folder and outputs in the output folder) has the following benefits:

A person new to the project can read one file (_projr.yml) rather than trawling through source code to understand the project structure.
Sharing parts of the project (e.g. the raw data) and setting up the project again (e.g. on a new computer or on a collaborator’s computer) is easier as like things are kept together. This is as opposed to, for example, keeping some raw data in _data_raw, others in inst/extdata and others at the root of the project, which makes bringing all the pieces together harder.
It helps projr help you, as it understands your project structure. This takes the following forms:
- projr can “protect” the output folder. The projr_build_dev() function (described more below) builds the project, but saves outputs to the cache folder rather than the output folder. This prevents you accidentally overwriting outputs from a previous successful build whilst iterating on the code in preparation for the next build.
- projr can upload the data to various places. As described in the next subsection (build), projr can send data from these folders (data-raw, output, etc.) to various places - at present, this includes GitHub releases, OSF and local folders (e.g. your OneDrive project folder).
- projr can automatically ignore large files from Git. If you don’t understand what this means, then don’t worry - in fact, it’s great that you’re here as this means projr can help you to use the incredibly useful versioning tool that is Git without having to understand its details.

There are other benefits, but they rely on understanding some more advanced projr features, which are not covered in this vignette.

`build`

The build key specifies actions that take place automatically when you build the project.

Here is the first part of the default build key (if the project is connected to GitHub):

build:
  github:
    input:
      content: [data-raw]
      description: "Project inputs"

This specifies that, after the project is built, the data-raw directory (i.e. the contents of _data_raw) is uploaded to GitHub as a release whose name is input.

By default, it is uploaded to a file called data-raw-v<project_version>.zip, where <project_version> is the version of the project (e.g. 0.1.0). Furthermore, by default this only takes place when anything changes within data-raw, i.e. file(s) are added, removed or changed.

These uploads are designed to be highly flexible, and can be customised to your needs. Please see the destination vignette for more details (evignette("destination")).

By default, the build key can also specify that the output and docs folders are uploaded to GitHub as releases, or alternatively to OSF or a local folder.

In addition, the build key handles Git settings, such as whether or not Git commits are automatically created before and after each build, and whether they are pushed to GitHub automatically. By default, they are, which means that the Git history is updated with each build, and the changes are pushed to GitHub.

Use `projr_build_` functions to build your project

projr provides two main functions to help you build your project, projr_build_output() and projr_build_dev(). In a nutshell, projr_build_dev() is for checking code as you write it, and projr_build_output() is for snapshotting and sharing your project.

Both will automatically detect the type of source documents you have (Rmd’s, Qmd’s, Quarto projects, bookdown projects) and render them accordingly.

`projr_build_output()`

projr_build_output() - This does the same as projr_build_dev(), except that the output documents are saved to their final destination (as specified in docs/output) and the outputs (GitHub/OSF) are sent to, as desired). - If you set the project up with GitHub, then by default the data-raw, docs and output folders are uploaded to GitHub as GitHub releases.

`projr_build_dev()`

projr_build_dev() renders the literate programming documents in your document.

Any results are saved to the cache directory, under <cache>/projr/v<project_version>/<directory_label>. For example, suppose we have the code

png(filename = projr_path_get("output", "figure", "nobel_prize_winning_plot.png")
# ... code to create the plot
dev.off()

in our Rmd file. When using projr_build_dev, the plot will be saved to _tmp/projr/v<project_version>/output/figure/nobel_prize_winning_plot.png. This is instead of saving it to _output/figure/nobel_prize_winning_plot.png, which is the final destination.

The same goes for the rendered documents (e.g. HTML files), which would be saved to _tmp/projr/v<project_version>/docs.

In addition, projr_build_dev allows you to render just a chosen selection of documents, e.g. projr_build_dev("analysis.Rmd") will render just the analysis.Rmd document (can do more than one).

Create a GitHub account, and save GitHub credentials

This section is not really necessary to introduct projr, but Git and GitHub are so useful for code versioning and sharing that it’s worth mentioning here.

Do I need Git and GitHub?

Strictly speaking, you do not need a GitHub account. However, it is a good idea to have one, as it is a great way to collaborate with and share with others. Don’t worry about learning Git - projr can (and will by default) handle that for you.

There are three steps to setting up Git and GitHub:

Create a GitHub account.
Generate a GitHub personal authentication token (PAT).
Save this token to your computer.

One does not need to install Git, as projr reverts to the gert R package if Git is not installed.

The steps

Create a GitHub account

If you don’t have a GitHub account, create one here. You can follow the advice here for choosing a user name.

Generate a GitHub PAT

If you have not set up your GitHub credentials, do the following:

Install the usethis package:

if (!requireNamespace("usethis", quietly = TRUE)) {
  install.packages("usethis")
}

Use usethis to set up your GitHub credentials:

Run the following in R:

usethis::create_github_token()

Your browser should open to GitHub. At the bottom of the page, click Generate token. Then, copy the token.

Save your GitHub PAT

Use usethis to save your GitHub credentials:

Run the following in R:

gitcreds::gitcreds_set()

Paste your Git credentials when prompted.

You should now be able to download and upload private repositories to GitHub (i.e. repositories that are only accessible to you and those you give access to).

Note: The above instructions are taken from here, which provides slightly more details.

Introduction

TL;DR

Extreme version

Less extreme version

Install projr

The projr workflow

Initialising the project

Building the project

projr_build_output()

Versioning

Project versioning

Component versioning

Sharing the project

_projr.yml

directories

Structure

Benefits

build

Use projr_build_ functions to build your project

projr_build_output()

projr_build_dev()

Create a GitHub account, and save GitHub credentials

Do I need Git and GitHub?

The steps

Install `projr`

The `projr` workflow

`projr_build_output()`

`_projr.yml`

`directories`

`build`

Use `projr_build_` functions to build your project

`projr_build_output()`

`projr_build_dev()`