A problem that we often run into when building reports is that the amount of data can make the report itself rather long. RMarkdown can assist us in streamlining the data sharing process by creating an clickable Table of Contents at the top of the report and by adding tabs within the report that are designated to specific data or research questions. Therefore, this week, Ellis Hughes and I discuss how you can create multiple tabs within your RMarkdown report.

To watch our screen cast, CLICK HERE.

To access our code, CLICK HERE.

Picking up where we left off last week, Ellis Hughes and I dive into controlling code chunks and figure options within your RMarkdown reports.

We cover:

1) How to show or hide your code in the report
2) How to show or hide the output of your code in a report
3) How to show code without having R run it in the report
4) Setting figure height, width, and alignment
5) Setting a figure caption

The first RMarkdown file covers the basics <CLICK HERE>

The second RMarkdown walks through a more nuanced example, where we scrape data from basketball-reference.com and create a {ggplot2} plot of Team Offensive and Defensive Rebounds <CLICK HERE>

To watch the screen cast, CLICK HERE.

This week, Ellis Hughes and I begin a new series on RMarkdown. We will be going over the functionality of RMarkdown files and how you can quickly and easily build reports.

This first episode covers some of the basics of RMarkdown files:

• Formatting text
• Creating headers
• Creating bulleted or numbered lists
• Running code
• Writing statistical equations
• Adding links, image files, and figures into the report

To watch our screen cast, CLICK HERE.

To access our code, CLICK HERE.

This week, Ellis Hughes and I delve into our mailbag to answer a question posed to us inquiring how we would go about creating tables for research publication in R. Most of the time it seems that people will get their model outputs and transpose them into Excel to build a table. But, in R you can go directly from the model output straight to a nice table that meets that journal’s requirements.

In this episode, we build a regression model and use {broom} to tidy the model outputs into a clean data frame. When then take that output and walk through different ways of using {gt} to customize your table for publication. We wrap up by covering how to build a plot of the interquartile range and annotate that plot to provide more context using {ggplot2}.

To watch our screen cast, CLICK HERE.

To access our code, CLICK HERE.