TidyX 33: Beer, Web Scraping, and State Maps

This week, Ellis Hughes and I discuss a TidyTuesday submission from Richard Bamattre. Richard visualized craft beers by state using the {statebins} package. This approach produces a very clean method of displaying data across the USA.

We follow up our discussion of Richard’s R code by giving a short web scraping tutorial to acquire more data about beer production across the USA and then create our own state map visualization.

To watch our screen cast, CLICK HERE.

To access our code, CLICK HERE.

TidyX 32: {shiny}, {golem}, and our interview with Eric Nantz

This week, Ellis Hughes and I are joined by Eric Nantz.

Not only is Eric a statistician and a massive contributor to the R community, but he is also the host of the R-Podcast and the Shiny Developer Series, two brilliant resources for those who work in R.

Eric joined us to walk through his {shiny} app for this week’s TidyTuesday data of the DataSaurus data set. Eric also introduces us to the {golem} package, which helps speed up the development process by creating a framework for you to use, right from the start.

Click the video in Eric’s tweet below to see a quick demo of his app.

To watch the entire screen cast, CLICK HERE.

To get Eric’s code for this cool app, CLICK HERE.

TidyX 31.1 (Bonus Episode) – NCAA Women’s Basketball Tournament Finish Probabilities

Earlier this week, Ellis Hughes and I discussed how to use {reactable} to build interactive tables within R. We did this using the NCAA Women’s Basketball Tournament data provided by the TidyTuesday Project.

We actually recorded a short (~10 min) bonus episode where we used {reactable} to construct a table of tournament finish probabilities based on initial seeding.

To watch the screen cast, CLICK HERE.

To access our code, CLICK HERE.

TidyX 31: NCAA Women’s Basketball Championship Tournament & reactable tables

This week, Ellis Hughes and I used this week’s TidyTuesday data set, which was NCAA Women’s Basketball Championship Tournament Finishes, to build a {reactable} table. {reactable} is an incredibly flexible R package for making interactive data tables. Our table includes using unix hex codes, sparklines, and nested results tables for each university.

To watch our screen cast, CLICK HERE.

To access our code, CLICK HERE.

TidyX 30: Twitter Scraping & Sentiment Analysis

This week, Ellis Hughes and I show how to scrape tweets using the {rtweet} package.

First, we scrape tweets containing the hashtag, Debate2020. We then walk through how to do a sentiment analysis to evaluate positive and negative sentiment based on the words used by those making the tweets and show how this sentiment varies between countries.

To watch our screen cast, CLICK HERE.

To access our code, CLICK HERE.