Wonderful Wednesdays

This article is originally published at https://rviews.rstudio.com/

For almost a year now, the PSI Visualization Special Interest Group (VIS SIG) has been conducting a monthly graduate-level seminar on creating effective statistical visualizations that is open to everyone. Wonderful Wednesdays is a unique collegial event. Every month the SIG publishes a link to a new data set and issues a challenge to produce visualizations that effectively illustrate some specific aspects of the data. Anyone can submit an entry coded in the language of their choice. Submissions that are received by the deadline are then critiqued in free webinar that takes place roughly thirty days later. You don’t have to make a submission to attend the webinar.

The process is well organized and straightforward. The figure below illustrates the process and time liness for the for the webinar that will happen this week on Wednesday, January 13th.

Click here to register for the webinar.

Here is the link to the December webinar where the challenge was to visualize the heterogeneity among the data used for a meta-analysis of seven studies undertaken to show a reduction in hypertension.

The following image is a traditional forest plot showing a comparison of the odds ratios for the seven studies. (If you are not familiar with this plot type look here for some tips on how to interpret it.)

This image comes from a shiny app that is critiqued at the by the VIS-SIG statisticians towards the beginning of the webinar. The experts liked the clean look and labeling on the plot, but had mixed feelings about the colored bands which are meant to show regions where the studies overlap. (Darker color indicates more overlap.) Here are the links to the Shiny App and the code and also to the blog post that reviews all of the submissions for the December challenge.

Visit the VIS-SIG Blog to finds posts and code for the submissions for all of Wonderful Wednesday events so far.

Thanks for visiting r-craft.org
This article is originally published at https://rviews.rstudio.com/
Please visit source website for post related comments.