Last updated: 2019-05-03

Checks: 6 0

Knit directory: rrtools-repro-research/

This reproducible R Markdown analysis was created with workflowr (version 1.3.0). 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(20181015) 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! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.

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:    .DS_Store
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    analysis/.DS_Store
    Ignored:    analysis/data/
    Ignored:    analysis/package.Rmd
    Ignored:    assets/
    Ignored:    docs/.DS_Store
    Ignored:    docs/assets/Boettiger-2018-Ecology_Letters.pdf
    Ignored:    docs/assets/Packaging-Data-Analytical Work-Reproducibly-Using-R-and-Friends.pdf
    Ignored:    docs/css/
    Ignored:    libs/

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 R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File Version Author Date Message
html 6fa5089 Anna Krystalli 2019-03-15 update navbar
html c3a8879 annakrystalli 2018-11-10 Build site.
Rmd a6720f8 annakrystalli 2018-11-10 add devtools and licensing details
html c1a4359 annakrystalli 2018-11-10 Build site.
html b3641c1 annakrystalli 2018-10-31 Build site.
Rmd 4d9cddb annakrystalli 2018-10-31 edit description
Rmd 8615159 annakrystalli 2018-10-30 commit straglers
html 8615159 annakrystalli 2018-10-30 commit straglers
html 921a7f8 annakrystalli 2018-10-30 commit docs
Rmd 99529cf annakrystalli 2018-10-15 update readme with NW R day details, add workshop index
Rmd 572d00b annakrystalli 2018-10-15 Start workflowr project.

Reproducible Research in R with rrtools

31st October, Northwest Universities R Day

Workshop Outline




Familiarity with Version Control through RStudio and rmarkdown.

System Requirements:

Pandoc (>= 1.17.2)


If you don’t have LaTeX installed, consider installing TinyTeX, a custom LaTeX distribution based on TeX Live that is small in size but functions well in most cases, especially for R users.

Check docs before before installing.

devtools requirements

You might also need a set of development tools to install and run devtools. On Windows, download and install Rtools, and devtools takes care of the rest. On Mac, install the Xcode command line tools. On Linux, install the R development package, usually called r-devel or r-base-dev.

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Work based on:

  • Research compendium cboettig/noise-phenomena: Supplement to: “From noise to knowledge: how randomness generates novel phenomena and reveals information” by Carl Boettiger licensed under CC BY 4.0. DOI

  • Marwick, B., Boettiger, C. & L. Mullen (2017). Packaging data analytical work reproducibly using R (and friends). PeerJ Preprints 5:e3192v1

R version 3.6.0 (2019-04-26)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.3

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib

[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8

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

loaded via a namespace (and not attached):
 [1] workflowr_1.3.0 Rcpp_1.0.1      digest_0.6.18   rprojroot_1.3-2
 [5] backports_1.1.4 git2r_0.25.2    magrittr_1.5    evaluate_0.13  
 [9] stringi_1.4.3   fs_1.2.7        whisker_0.3-2   rmarkdown_1.12 
[13] tools_3.6.0     stringr_1.4.0   glue_1.3.1      xfun_0.6       
[17] yaml_2.2.0      compiler_3.6.0  htmltools_0.3.6 knitr_1.22