Last updated: 2020-10-21

Checks: 7 0

Knit directory: rr_tools/

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(20201021)

The command set.seed(20201021) 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: 06a1c66

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 06a1c66. 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:  README.html

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/file_org.rmd) and HTML (docs/file_org.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
Rmd f3d4d87 jean997 2020-10-21 add content to version_control plus some small edits
Rmd 0809fee jean997 2020-10-21 initial structure

The first step of trying to reproduce an analysis is figuring out where the old analysis is and how you did it. Towards this end it is good to have some overarching organizational structure for your digital content. This is a personal matter that everyone will sort out for themselves but you should give it some thought.

Here is one structure I have found useful for project directories:

YYYY_project_name:
    data_analysis:
        YYYY-MM-DD_analysis_name1:
        YYYY-MM-DD_analysis_name2:
    simulations:
        YYYY-MM-DD_simulations_name1:
        YYYY-MM-DD_simulations_name2:
    R_package_directory:
    reports:
    data:
  • If you pre-pend directory and file names with the date in YYYY-MM-DD format they will automatically be ordered chronologically which in some cases is exactly what you want. It will also make it easier to find things later. I think I got this tip from a tweet by Jenny Bryan and it has made my life a lot better.
  • If some of your project material lives in other places in your filesystem you can create symbolic links in your main project directory. In linux you can create symbolic links using ln -s. Read more here.

sessionInfo()
R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.5 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

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_1.0.5       rstudioapi_0.11  whisker_0.4      knitr_1.30      
 [5] magrittr_1.5     R6_2.4.1         rlang_0.4.7      stringr_1.4.0   
 [9] tools_4.0.3      xfun_0.18        git2r_0.27.1     htmltools_0.5.0 
[13] ellipsis_0.3.1   rprojroot_1.3-2  yaml_2.2.1       digest_0.6.25   
[17] tibble_3.0.3     lifecycle_0.2.0  crayon_1.3.4     later_1.1.0.1   
[21] vctrs_0.3.4      promises_1.1.1   fs_1.5.0         glue_1.4.2      
[25] evaluate_0.14    rmarkdown_2.3    stringi_1.5.3    compiler_4.0.3  
[29] pillar_1.4.6     backports_1.1.10 httpuv_1.5.4     pkgconfig_2.0.3