Mathematical Annotation in R

In this article we demonstrate how to include mathematical symbols and formulas in plots created with R. This can mean adding a formula in the title of the plot, adding symbols to axis labels, annotating a plot with some math, and so on. R provides a \(\LaTeX\)-like language for defining mathematical expressions. It is documented […]

ROC Curves and AUC for Models Used for Binary Classification

This article assumes basic familiarity with the use and interpretation of logistic regression, odds and probabilities, and true/false positives/negatives. The examples are coded in R. ROC curves and AUC have important limitations, and I encourage reading through the section at the end of the article to get a sense of when and why the tools […]

Data Scientist as Cartographer: An Introduction to Making Interactive Maps in R with Leaflet

Note: This version of the article contains static images of maps generated with Leaflet. To view a version with interactive maps, click here. A striking feature of many maps from early in the history of cartography is their linearity. Being primarily for travel (and given the technological limitations on how faithfully geographies could be understood […]

Getting Started with Shiny

What is Shiny? Shiny is an R package that facilitates the creation of interactive web apps using R code, which can be hosted locally, on the shinyapps server, or on your own server. Shiny apps can range from extremely simple to incredibly sophisticated. They can be written purely with R code or supplemented with HTML, […]

How to apply a graduated color symbology to a layer using Python for QGIS 3

I was recently working on a project in QGIS 3 with a member of UVA Health’s Oncology department. This person wanted to take a set of patient data (after identifying info had been removed) and after doing some other stuff, apply a graduated color scheme to the results, shading them from light to dark based […]

Visualizing the Effects of Proportional-Odds Logistic Regression

Proportional-odds logistic regression is often used to model an ordered categorical response. By “ordered”, we mean categories that have a natural ordering, such as “Disagree”, “Neutral”, “Agree”, or “Everyday”, “Some days”, “Rarely”, “Never”. For a primer on proportional-odds logistic regression, see our post, Fitting and Interpreting a Proportional Odds Model. In this post we demonstrate […]

Look People are Going to Think… (Debate Rhetoric Redux)

I’m still looking at the rhetoric from the presidential debates, this time focusing on the first general election debate between Hillary Clinton and Donald Trump. I turned the data frame from last week into a corpus, did some pre-processing with the tm package (remove capitalization, punctuation, stopwords, stemming the words and then completing stems with […]

Setting up Color Palettes in R

Plotting with color in R is kind of like painting a room in your house: you have to pick some colors. R has some default colors ready to go, but it’s only natural to want to play around and try some different combinations. In this post we’ll look at some ways you can define new […]

Getting Started with Hurdle Models

Hurdle Models are a class of models for count data that help handle excess zeros and overdispersion. To motivate their use, let’s look at some data in R. The following data come with the AER package. It is a sample of 4,406 individuals, aged 66 and over, who were covered by Medicare in 1988. One […]