Last updated: 2020-11-01

Checks: 7 0

Knit directory: r4ds_book/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

The command set.seed(20200814) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 3ec3460. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:

Ignored files:
    Ignored:    .Rproj.user/

Untracked files:
    Untracked:  analysis/images/
    Untracked:  code_snipp.txt

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/ch6_projects.Rmd) and HTML (docs/ch6_projects.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
html bf15f3b sciencificity 2020-11-01 Build site.
html 0aef1b0 sciencificity 2020-10-31 Build site.
html bdc0881 sciencificity 2020-10-26 Build site.
html 8224544 sciencificity 2020-10-26 Build site.
html 2f8dcc0 sciencificity 2020-10-25 Build site.
html 61e2324 sciencificity 2020-10-25 Build site.
html 570c0bb sciencificity 2020-10-22 Build site.
html cfbefe6 sciencificity 2020-10-21 Build site.
Rmd 8e445e8 sciencificity 2020-10-21 updated the workflow-projects section
html 4497db4 sciencificity 2020-10-18 Build site.
html 1a3bebe sciencificity 2020-10-18 Build site.
html ce8c214 sciencificity 2020-10-16 Build site.
Rmd 50bf4b7 sciencificity 2020-10-16 added new chapters

I almost always work in projects now.

Recently thanks to a hosting opportunity for useR!2020 I also learnt about {workflowr} created by John Blischak, Peter Carbonetto and Matthew Stephens. The R package {workflowr} facilitates reproducible research, and works great with GitHub once you setup your RStudio environment to talk to GitHub.

Tools -> Global Options -> Git/SVN

Options to enable Git access

Tools -> Global Options -> Git/SVN

{workflowr} Resources

  1. Tutorial slides:

  2. RStudio Cloud project:

  3. Video resource of an RLadies meetup:

{workflowr} Commands

  • install.packages(“workflowr”)

  • library(workflowr)

  • Configure Git:

    wflow_git_config( = “Your Name”, = “”)

    • Only need to do this once on a computer if you use the same GitHub account for all your work.
    • I use different GitHub accounts so I sometimes need to redo this command based on the Git profile I am using.

  • Start Project (Existing Project):

    wflow_start(directory = “.”, name = “Analysis XXX”, existing = TRUE)

  • Start a New {workflowr} Project from scratch:

    Create a workflowr project from the menu

    File -> New Project -> New Directory -> scroll to workflowr project

  • Build Website

    The site will be built from the .Rmd files in your analysis folder.


  • If you’re converting an existing project into a {workflowr} project move your .Rmd files into the analysis folder, move your data into the data folder, and move any images into some sub-folder (you can name as you wish I believe - I use assets) in the docs folder.

  • If you are starting a new {workflowr} project, and don’t yet have any analysis files, once you do create .Rmd files save these in the analysis folder.

  • Add the .html link(s) in index.Rmd. For example:

    [Chapter 1: Data Viz with ggplot](ch1_ggplot.html)

  • Publish website (this builds the website and does a git commit):

                    message = "Add analysis")
  • Create repo on GitHub (only need to do this once).

  • Connect the repo on GitHub to the {workflowr} project (only need to do this once).

                          user = "",
                          repo = "workflowr-analysis")
  • Push project to GitHub


{workflowr} Common Commands

After you have setup your {workflowr} project and you’re working on your analysis these are the commands that you will typically use in a solo project.

  1. wflow_build()

  2.  wflow_publish(
         message = "Some more specific message than this ;)"
  3. wflow_git_push()

R version 3.6.3 (2020-02-29)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)

Matrix products: default

[1] LC_COLLATE=English_South Africa.1252  LC_CTYPE=English_South Africa.1252   
[3] LC_MONETARY=English_South Africa.1252 LC_NUMERIC=C                         
[5] LC_TIME=English_South Africa.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.4.6    rprojroot_1.3-2 digest_0.6.25   later_1.0.0    
 [5] R6_2.4.1        backports_1.1.6 git2r_0.26.1    magrittr_1.5   
 [9] evaluate_0.14   stringi_1.4.6   rlang_0.4.7     fs_1.4.1       
[13] promises_1.1.0  whisker_0.4     rmarkdown_2.4   tools_3.6.3    
[17] stringr_1.4.0   glue_1.4.1      httpuv_1.5.2    xfun_0.13      
[21] yaml_2.2.1      compiler_3.6.3  htmltools_0.5.0 knitr_1.28