In this episode, Ellis Hughes and I discuss the use of data tables to accompany your visuals in rmarkdown reports.

First we go over code provided by Ted Laderas, who used the gt package to create some nice data tables on the Animal Crossings data set from the TidyTuesday Project.

Then, we apply a similar approach to NFL Kicker data and show how you can add conditional formatting to your table to aid in data presentation.

For the screen cast, CLICK HERE.

For our code, CLICK HERE.

This week, Ellis Hughes and I discuss plotting lines for a single group with grey traced lines in the background representing the rest of the population.

First, with data provided by the TidyTuesday Project, we go over how Jake Kaupp used this approach to visualize some of the top grossing Broadway shows over the past couple decades.

Then, we apply this same approach to historic NBA data to show how different teams have trended in a variety of game statistics and produce a plot like this:

To watch our screen cast, CLICK HERE.

To access our code, CLICK HERE.

In today’s screen cast, Ellis Hughes and I discuss how to get started using git.

We go over:

1) How to get started with git.
2) The value of git when working on projections with colleagues.
3) How version control works when yourself or colleagues make updates to the project.
4) How to push your updates to github.

To watch the screen cast, CLICK HERE.

This week, Ellis Hughes and I discuss heatmaps and facet wrapping using a coding example provide by Danielle Barnas for the TidyTuesday Project.

After going through Danielle’s code we apply heatmap visualizations to NBA data from the 2019 season. We show how to use facets to visualize different grouping of your data that may be useful when presenting to decision makers or colleagues.

To watch the screen cast, CLICK HERE.

For our code, CLICK HERE.

This week on the TidyX screen cast, Ellis Hughes and I explain the lollipop plot code created by Priya Shukla, who used data on Rap album ratings supplied by the TidyTuesday Project.

After that, we take the concept of lollipop plots and apply it to data from the National Women’s Soccer league (accessed using the nwslR package) to look at home advantage and average fan attendance. We finish by creating an animated lollipop plot showing the successful shooting percentage for each team across the 2019 season.

To watch this week’s screen cast, CLICK HERE.

To access the code for this week’s episode, CLICK HERE.