Migrating an existing project to use workflowr

Introduction

This vignette is for those users that already have an existing project and wish to incorporate workflowr to create a research website. Migrating an existing project to use workflowr varies from straightforward to difficult depending on the scenario and your comfort level with Git. This vignette assumes that you have the background knowledge of workflowr explained in the Getting started vignette. Even if you have no need for a new workflowr project, please run through that vignette first as an exercise to familiarize yourself with the workflowr philosophy and functions.

vignette("wflow-01-getting-started", "workflowr")

Scenario: I have a collection of R Markdown files

If you have a collection of R Markdown files, but no version control or other files, the quickest solution is to use the function wflow_quickstart(). The code below 1) starts a new workflowr project in ~/projects/new-project/, 2) copies the existing Rmd files in ~/projects/misc/ to the analysis/ subdirectory of the new project, 3) builds and commits the website, and 4) configures the project to use GitHub (which is why the GitHub username is required).

library("workflowr")
wflow_quickstart("~/projects/misc/*Rmd", username = "<github-username>",
                 directory = "~/projects/new-project/")

Alternatively, you can manually perform each step to migrate your existing analysis by starting a workflowr project in a new directory and then moving the R Markdown files to the analysis/ subdirectory. In the hypothetical example below, the original R Markdown files are located in the directory ~/projects/misc/ and the workflowr project will be created in the new directory ~/projects/new-project/.

library("workflowr")
# Create project directory and change working directory to this location
wflow_start("~/projects/new-project")
# Copy the files to the analysis subdirectory of the workflowr project
file.copy(from = Sys.glob("~/projects/misc/*Rmd"), to = "analysis")

Next run wflow_build() to see if your files run without error. Lastly, build and commit the website using wflow_publish():

wflow_publish("analysis/*Rmd", "Publish analysis files")

When you are ready to share the results online, you can run wflow_use_github() or wflow_use_gitlab().

Scenario: I have a collection of R Markdown files and other project infrastructure

If your project already has lots of infrastructure, it is most convenient to add the workflowr files directory to your already existing directory. This is controlled with the argument existing. In the hypothetical example below, the existing project is located at ~/projects/mature-project/.

library("workflowr")
wflow_start("~/projects/mature-project", existing = TRUE)

The above command will add the workflowr files to your existing project and also commit them to version control (it will initialize a Git repo if it doesn’t already exist). If you’d prefer to not use version control for your project or you’d prefer to commit the workflowr files yourself manually, you can set git = FALSE (this is also useful if you want to first test to see what would happen without committing the results).

By default wflow_start() will not overwrite your existing files (e.g. if you already have a README.md). If you’d prefer to overwrite your files with the default workflowr files, set overwrite = TRUE.

To add your R Markdown files to the research website, you can move them to the subdirectory analysis/ (note you can do this before or after running wflow_start()).

Next run wflow_build() to see if your files run without error. Lastly, build and commit the website using wflow_publish():

wflow_publish("analysis/*Rmd", "Publish analysis files")

Scenario: I have an R package

If your project is organized as an R package, you can still add a website using workflowr. In the hypothetical example below, the existing package is located at ~/projects/my-package/.

library("workflowr")
wflow_start("~/projects/my-package", existing = TRUE)

The above command will add the workflowr files to your existing project and also commit them to version control (it will initialize a Git repo if it doesn’t already exist). If you’d prefer to not use version control for your project or you’d prefer to commit the workflowr files yourself manually, you can set git = FALSE (this is also useful if you want to first test to see what would happen without committing the results).

You’ll want R to ignore the workflowr directories when building the R package. Thus add the following to the .Rbuildignore file:

^analysis$
^docs$
^data$
^code$
^output$
^_workflowr.yml$

Furthermore, to prevent R from compressing the files in data/ (which is harmless but time-consuming), you can set LazyData: false in the file DESCRIPTION. However, if you do want to distribute data files with your R package, you’ll need to instead rename the workflowr subdirectory and update the R Markdown files to search for files in the updated directory name (and also update .Rbuildignore to ignore this new directory and not data/). Then you can save the data files to distribute with the package in data/. For more details, see the relevant sections in the CRAN manual Writing R Extensions and Hadley’s R Packages.

If your primary purpose for creating a website to accompany your package is to share the package documentation, please check out the package pkgdown. It creates a website from the vignettes and function documentation files (i.e. the Rd files in man/). In contrast, if the purpose of the website is to demonstrate results you obtained using the package, use workflowr.