Building off our screen cast from last week, where we discussed the basics of reactivity in a {shiny} app, Ellis Hughes and I expand on the concept and begin constructing the pieces of our NBA dashboard. For inspiration purposes, we are building elements similar to Five Thirty Eight’s CARMELO NBA Player Projection webpage.

We start by creating the table and percentile charts in an interactive web app:

But before we get into that, we break down the code of @MrOchiwar, who made some really nice line plots of Nigerian crop yields over time, using data provided by the TidyTuesday project.

To watch the screen cast, CLICK HERE.

To access our code, CLICK HERE.

This week, Ellis Hughes and I begin a series on building Shiny apps. We’ve built several Shiny apps in the past (for example TidyX 3 or TidyX 24) but always feel rushed to explain some of the fundamental coding that is necessary for people to get off the ground. As such, and because the syntax for Shiny apps can be a little funky, we decided to create a series where we of episodes on Shiny apps where we will take a data set and iteratively build a Shiny app from scratch.

In this week’s episode we help get you off the ground with your first Shiny app by walking you through my very own starter template. When I sit down to develop a Shiny app (which is pretty much weekly at this point) I always start with this exact template just to help me get going and get the data showing up in a webpage. From there I begin to customize the app based on need.

To watch the screen cast, CLICK HERE.

For our code, CLICK HERE.

This week, Ellis Hughes and I break down code that Jared Braggins wrote to construct waffle plots describing plant extinction in Africa using data from TidyTuesday.

For those that are unfamiliar, a waffle plot is a great way for showing counts of data and the relative contribution of one value to a group. This type of plot lends itself well to the number of targets each receiver on an NFL team receives relative to other receivers on that team. So, we took Jared’s idea, used the {waffle} package, and constructed our own waffle plot. We then extended this out into an interactive {shiny} app so that the user can select a team of interest and get returned back a waffle plot for that team along with a team of data providing the receiver statistics for that given season. Finally, we provide the option to download the output as a PDF report directly from the {shiny} app. When it is all done, it looks a little something like this:

To watch the screen cast, CLICK HERE.

To obtain our code, CLICK HERE.

This week, Ellis Hughes and I delve into our mailbag and answer a viewer’s question about writing for loops and custom functions. To explain these two topics we walk through our script on how to optimize the exponent in Bill James’ Pythagorean Win Formula for baseball.

To watch our screen cast, CLICK HERE.

To get our code, CLICK HERE.

If you have any questions or topics you’d like to have explained, feel free to reach out to us a tidy.explained@gmail.com or connect with us on Twitter at @tidy_explained.

This week, Ellis Hughes and I go over how to create dumbbell plots. Dumbbell plots are a nice way of showing differences between two time points or between two variables that are on the same scale within a group or within individual.

To explain dumbbell plots, we first go through code provided by Kelly Cotton, who used this data visualization technique on the latest TidyTuesday data set to show changes in energy production over time within different European countries.

Ellis and I then take Kelly’s approach and apply it to NBA players to show differences between their Assists per Game and Shots per Game within the 2019 season. A report we termed the “Ball Hog Report”.

To watch our screen cast, CLICK HERE.

To access our code, CLICK HERE.