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