Chapter 6: Workflow

Last updated: 2020-11-10

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.

R Markdown file: up-to-date

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.

Environment: empty

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.

Seed: set.seed(20200814)

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.

Session information: recorded

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

Cache: none

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

File paths: relative

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

Repository version: cf689c8

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 cf689c8. 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	4879249	sciencificity	2020-11-09	Build site.
html	e423967	sciencificity	2020-11-08	Build site.
html	0d223fb	sciencificity	2020-11-08	Build site.
html	ecd1d8e	sciencificity	2020-11-07	Build site.
html	274005c	sciencificity	2020-11-06	Build site.
html	60e7ce2	sciencificity	2020-11-02	Build site.
html	db5a796	sciencificity	2020-11-01	Build site.
html	d8813e9	sciencificity	2020-11-01	Build site.
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

Tutorial slides: https://bit.ly/useR-workflowr-slides
RStudio Cloud project: https://bit.ly/useR-workflowr-rstudio
Video resource of an RLadies meetup: http://bit.ly/workflowr-video

{workflowr} Commands

install.packages(“workflowr”)
library(workflowr)
Configure Git:

wflow_git_config(user.name = “Your Name”, user.email = “email@domain”)
- 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:

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

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

wflow_build()
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):

  wflow_publish(c("analysis/*.Rmd", 
                  "data/*.csv",
                  "docs/assets/*.*"),
                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).

    wflow_git_remote("origin",
                      user = "",
                      repo = "workflowr-analysis")

Push project to GitHub

wflow_git_push()

{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.

wflow_build()

 wflow_publish(
     c("analysis/*.Rmd", 
       "data/*.csv",
       "docs/assets/*.*"),
     message = "Some more specific message than this ;)"
 )

wflow_git_push()

sessionInfo()

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

locale:
[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    rstudioapi_0.11 whisker_0.4     knitr_1.28     
 [5] magrittr_1.5    R6_2.4.1        rlang_0.4.8     stringr_1.4.0  
 [9] tools_3.6.3     xfun_0.13       git2r_0.26.1    htmltools_0.5.0
[13] ellipsis_0.3.1  rprojroot_1.3-2 yaml_2.2.1      digest_0.6.27  
[17] tibble_3.0.3    lifecycle_0.2.0 crayon_1.3.4    later_1.0.0    
[21] vctrs_0.3.2     promises_1.1.0  fs_1.5.0        glue_1.4.2     
[25] evaluate_0.14   rmarkdown_2.4   stringi_1.5.3   compiler_3.6.3 
[29] pillar_1.4.6    backports_1.1.6 httpuv_1.5.2    pkgconfig_2.0.3

Chapter 6: Workflow - Projects

Vebash Naidoo

16/10/2020

Tools -> Global Options -> Git/SVN

{workflowr} Resources

{workflowr} Commands

{workflowr} Common Commands