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Francisco Bischoff
on July 15, 2021
Last updated: 2021-07-15
Checks: 7 0
Knit directory: false.alarm/
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(20201020) 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.
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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 64efa8b. 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: renv/library/
Ignored: renv/staging/
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/index.Rmd) and HTML (docs/index.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 | 1473a05 | Francisco Bischoff | 2021-07-15 | report |
| html | 1473a05 | Francisco Bischoff | 2021-07-15 | report |
| html | bf772a5 | GitHub Actions | 2021-03-24 | Build site. |
| Rmd | b869025 | Francisco Bischoff | 2021-03-24 | targets and workflowr |
| html | 52e7f0b | GitHub Actions | 2021-03-24 | Build site. |
| Rmd | c87e8e1 | Francisco Bischoff | 2021-03-24 | targets and workflowr |
| Rmd | 7c3cc31 | Francisco Bischoff | 2021-03-23 | Targets |
| html | 7c3cc31 | Francisco Bischoff | 2021-03-23 | Targets |
| html | 01a7ace | Francisco Bischoff | 2020-07-23 | Refactor |
| html | 5f450d2 | Francisco Bischoff | 2020-05-05 | gh-pages placeholder |
Dataset hosted by:
TLDR;
This website contains all the necessary for reproducibility of this PhD. It is build joining three frameworks:
- workflowr, the main infrastructure that organizes every analysis step as of Github versions. But instead of showing all commits, it just keep the important ones.
- targets, a Make-like pipeline toolkit for Statistics and data science in R. It supersedes drake package. This allows to keep track of every step of the data science workflow and helps to understand what needs to be rebuild and what has not changed since last iteration. Every complete analysis shall be registered by one entry in the previous package (workflowr)
- thesisdown, inspired by the package bookdown, and helps to write the thesis in rmarkdown that is easily compiled in PDF, Doc, EPUB, and allows to change the theme/format for several universities/journals.
Use the big button above 
to navigate thorough the older commits.
Use the top menu for further information and the thesis itself.
Last Targets Run network:
R version 4.1.0 (2021-05-18)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.2 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
locale:
[1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
[4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
[7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices datasets utils methods base
other attached packages:
[1] visNetwork_2.0.9 tarchetypes_0.2.1 targets_0.4.2 workflowr_1.6.2
[5] here_1.0.1
loaded via a namespace (and not attached):
[1] Rcpp_1.0.7 git2r_0.28.0 jquerylib_0.1.4 bslib_0.2.5.1
[5] compiler_4.1.0 pillar_1.6.1 later_1.2.0 tools_4.1.0
[9] digest_0.6.27 jsonlite_1.7.2 evaluate_0.14 lifecycle_1.0.0
[13] tibble_3.1.2 pkgconfig_2.0.3 rlang_0.4.11 igraph_1.2.6
[17] cli_3.0.0 yaml_2.2.1 xfun_0.24 stringr_1.4.0
[21] withr_2.4.2 knitr_1.33 htmlwidgets_1.5.3 sass_0.4.0
[25] fs_1.5.0 vctrs_0.3.8 rprojroot_2.0.2 tidyselect_1.1.1
[29] glue_1.4.2 data.table_1.14.0 R6_2.5.0 processx_3.5.2
[33] fansi_0.5.0 rmarkdown_2.9.3 whisker_0.4 callr_3.7.0
[37] purrr_0.3.4 magrittr_2.0.1 promises_1.2.0.1 ps_1.6.0
[41] codetools_0.2-18 ellipsis_0.3.2 htmltools_0.5.1.1 renv_0.13.2
[45] httpuv_1.6.1 utf8_1.2.1 stringi_1.7.2 crayon_1.4.1