This week, Ellis Hughes and I begin by working through the code that Natalie O’Shea created to show Kenyan Census data from this week’s TidyTuesday data set. It was an interesting plot because instead of the common network visual of lines and nodes, she plotted circles nested within circles.

After that, we continue working on NHL data by scraping the salaries of skaters and goalies and explore the relationship between team spending and wins using logistic regression (NOTE: we discussed a lot of the ins and outs of logistic regression in TidyX 29).

To watch the screen cast, CLICK HERE.

To access our code, CLICK HERE.

This week, Ellis Hughes and I use a variety of different R techniques to further explore NHL Hockey. Some things we cover:

• Web scraping
• Cleaning up strings
• for loops
• Data visualization
• Data frame joins
• Model building

To watch our screen cast, CLICK HERE.

To access our code, CLICK HERE.

This week, Ellis Hughes and I discuss building maps in {plotly}.

First, we go through the code of Martin Devaux, who made a cool looking plot showing the evolution of public transit expansion costs around the world (data courtesy of TidyTuesday Project). The plot was really interesting and actually threw us for a loop as we initially thought we were looking at a true map! It wasn’t until we got into the code that we realized what was going on and that it wasn’t an actual map at all.

We then move into {plotly} to build a map of NBA travel. The data comes from the Jose Fernandez, who created a cool R package called {airball}.

To watch our screen cast, CLICK HERE.

To access our code, CLICK HERE.

This week, Ellis Hughes and I continue our work in {plotly}. Using the same player shooting data that we used in TidyX 42, we build an interactive funnel plot to visualize player shooting% relative to sample size, allowing us to explore player’s abilities while acknowledging uncertainty in those who have taken less shots.

To watch the screen cast, CLICK HERE.

To access our code, CLICK HERE.

This week, Ellis Hughes and I begin by explaining code from Peter (@DataVizGuy_1648), who made a cool line plot for the Big Mac Index, which used colors to emphasizes the top 5 and bottom 5 countries for the price of a Big Mac, scaled to US Dollars, over time (data courtesy of TidyTuesday Project).

After that, we build on some of the work we did in TidyX 41, as we dive deeper into {plotly} and build an interactive NHL data visualization. We then take our {plotly} visualization and build it into a {shiny} app.

To watch the screen cast, CLICK HERE.

To access our code, CLICK HERE.