Last updated: 2023-01-24

Checks: 2 0

Knit directory: multiclass_AUC/

This reproducible R Markdown analysis was created with workflowr (version 1.7.0). 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! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

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:  .Rprofile
    Untracked:  .gitattributes
    Untracked:  .gitignore
    Untracked:  LICENSE.CC-BY
    Untracked:  LICENSE.MIT
    Untracked:  README.html
    Untracked:  README.md
    Untracked:  _workflowr.yml
    Untracked:  analysis/
    Untracked:  code/
    Untracked:  data/
    Untracked:  multiclass_AUC.Rproj
    Untracked:  output/
    Untracked:  renv.lock
    Untracked:  renv/

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.


There are no past versions. Publish this analysis with wflow_publish() to start tracking its development.


This is the website for the research project “Multiclass AUC”.

If you have cloned the project to a local computer this website is rendered in the docs subdirectory of the project directory.

If you are using workflowr to publish the research website it will also be rendered online to GitHub Pages.

This page acts as a table of contents for the website. There are links to the web pages generated from the analysis notebooks.


01_read_data

Read the example datasets, calculate some check counts, reformat the data for analysis, and save to an R data file.

02_check_data

Check the ranges of the variables in the example data.

03_plot_independent_distributions

Check the ranges of the variables in the example data.