ggplot2

Visualizing Texas High School SAT Math Scores with Bubble Grids

Two awesome things inspired this post: {ggplot2}’s version 3.0 release on CRAN, including full support for the {sf} package and new functions geom_sf() and coord_sf(), which make plotting data from shapefiles very straightforward. Jonas Scholey’s blog post discussing the use of “bubble grid” maps as an alternative to choropleth maps, which seem to be used more prevalent. As Jonas implies, using color as a visual encoding is not always the best option, a notion with which I strongly agree.

Investigating Ranks, Monotonicity, and Spearman's Rho with R

The Problem I have a bunch of data that can be categorized into many small groups. Each small group has a set of values for an ordered set of intervals. Having observed that the values for most groups seem to increase with the order of the interval, I hypothesize that their is a statistically-significant, monotonically increasing trend. An Analogy To make this abstract problem more relatable, imagine the following scenario.

Analyzing Professional Sports Team Colors with R

When working with the ggplot2 package, I often find myself playing around with colors for longer than I probably should be. I think that this is because I know that the right color scheme can greatly enhance the information that a plot portrays; and, conversely, choosing an uncomplimentary palette can suppress the message of an otherwise good visualization. With that said, I wanted to take a look at the presence of colors in the sports realm.

A Tidy Text Analysis of R Weekly Posts

I’m always intrigued by data science “meta” analyses or programming/data-science. For example, Matt Dancho’s analysis of renown data scientist David Robinson. David Robinson himself has done some good ones, such as his blog posts for Stack Overflow highlighting the growth of “incredible” growth of python, and the “impressive” growth of R in modern times. With that in mind, I thought it would try to identify if any interesting trends have risen/fallen within the R community in recent years.

A Tidy Text Analysis of My Google Search History

While brainstorming about cool ways to practice text mining with R I came up with the idea of exploring my own Google search history. Then, after googling (ironically) if anyone had done something like this, I stumbled upon Lisa Charlotte’s blog post. Lisa’s post (actually, a series of posts) are from a while back, so her instructions for how to download your personal Google history and the format of the downloads (nowadays, it’s in a .