Last updated: 2019-12-05

Checks: 7 0

Knit directory: reproducible_bioinformatics/

This reproducible R Markdown analysis was created with workflowr (version 1.5.0). 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(20191203)

The command set.seed(20191203) 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: 0b04728

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:    .Rhistory

Unstaged changes:
    Modified:   analysis/_site.yml

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
Rmd 0b04728 davetang 2019-12-05 wflow_publish(files = "analysis/*.Rmd")
  • Tools like Conda and Docker simplify the installation of tools, which can be a major headache when tools have a long list of dependencies
  • Conda is a package management tool while Docker is a platform that can deliver software in packages called containers
  • Both tools can create isolated environments that can be easily shared with others so that others have an identical copy of your working space
    • Docker does this slightly better across different operating systems
  • The workflowr package provides a framework for promoting reproducible research
    • It creates a consistent directory structure that helps you stay organised
    • It seemlessly generates a website (that can be easily uploaded online) containing time-stamped, versioned, and documented results
    • It automatically performs various checks to ensure that your analysis was run in a clean environment
  • Follow these Ten Simple Rules for Reproducible Computational Research
  • At the very least, you should be able to understand and reproduce your work

sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.6

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

locale:
[1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8

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

loaded via a namespace (and not attached):
 [1] workflowr_1.5.0 Rcpp_1.0.3      rprojroot_1.3-2 digest_0.6.22  
 [5] later_1.0.0     R6_2.4.1        backports_1.1.5 git2r_0.26.1   
 [9] magrittr_1.5    evaluate_0.14   stringi_1.4.3   rlang_0.4.1    
[13] fs_1.3.1        promises_1.1.0  whisker_0.4     rmarkdown_1.17 
[17] tools_3.6.1     stringr_1.4.0   glue_1.3.1      httpuv_1.5.2   
[21] xfun_0.11       yaml_2.2.0      compiler_3.6.1  htmltools_0.4.0
[25] knitr_1.26