Last updated: 2020-10-22
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.
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(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.
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 b1ec62c. 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: .Rhistory
Ignored: .Rproj.user/
Untracked files:
Untracked: README.html
Untracked: exercise.rmd
Untracked: figure/
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 repository in which changes were made to the R Markdown (analysis/version_control.rmd) and HTML (docs/version_control.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 | 971cbb5 | jean997 | 2020-10-21 | Build site. |
| Rmd | 34cf544 | jean997 | 2020-10-21 | wflow_publish(all = TRUE) |
| html | 34cf544 | jean997 | 2020-10-21 | wflow_publish(all = TRUE) |
| html | 2ce37af | jean997 | 2020-10-21 | Build site. |
| Rmd | f3d4d87 | jean997 | 2020-10-21 | add content to version_control plus some small edits |
| Rmd | 0809fee | jean997 | 2020-10-21 | initial structure |
Version control is a way to track the status of a project over time. If you are tracking a set of files (a “repository”) with Git, you can see the history of every change that’s been made since you started tracking. Importantly, you can also “rewind” your repository to a time before something went wrong.
Here I will only talk about Git but there are other options. Git + GitHub is pretty much standard for the types of projects we usually do in statistics/biostatistics/data science. GitHub is an online collection of Git repositories. If you are working on a project and want to share it with the world (or your adviser or your collaborator), you can put it on GitHub (I have also used GitLab they are very similar). Git + GitHub makes it easy for multiple people to work simultaneously on the same project without conflicts.
You can find a much better and more thorough intro to Git and GitHub in Happy Git with R by Jenny Bryan. This is a sort of R specific resource but Git can be used to track anything – track your manuscripts! track your data sets! track your homework!
Getting going with Git is a little obnoxious but once you are using it, it makes life better. Gone are worries about irretrievably breaking a complicated web of code or deleting an important file.
If a repository is being tracked by Git, each “commit” has a unique identifier. If/when the time comes that you need to reproduce something you did years ago, you will need to be able to locate the code you used. For an analysis that you know you might want to reproduce, you can record the repository version and then even if later down the road the code changes, you will still be able to recover a snapshot of what things looked like at the time of the analysis. Of course you may also need to know the versions of any other software you were using which Git can’t help you with. More on that to come!
There are so many good Git tutorials that I won’t put my own here. Check out Chapter 18 from the R Packages Book!
Git chapter from R Packages Book
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