Last updated: 2019-06-16

Checks: 1 1

Knit directory: stats topics/

This reproducible R Markdown analysis was created with workflowr (version 1.4.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.


The R Markdown file has unstaged changes. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.

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:


Unstaged changes:
    Modified:   analysis/index.Rmd

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 ffc3849 Zhengyang Fang 2019-06-16 start
html ffc3849 Zhengyang Fang 2019-06-16 start
Rmd 35d816a Zhengyang Fang 2019-06-16 Start workflowr project.

Topics of statistics

Explain interesting statistics topics with critical concept and examples. Hopefully they are easy to understand. If you have any questions or suggestions, welcome to email me at [zyfang@uchicago.edu].

Multivariate statistics

Multiple testing

Time series analysis

Machine learnings

Optimization