Georgia State University

Positron’s Quarto support is really quite robust and it’s full of nice little features like previewing a citation using whatever bibliography style your document is using (Chicago, APA, etc.) when you hover over a citation key:  …or knowing about the different headings inside your document for tab-completed cross references:  …or even knowing about those headings in other documents in a project:  That’s all neat, but for long-form writing, like

Open files in external programs with Positron or Visual Studio Code

Andrew Heiss

On Bluesky the other day, I came across this neat post that suggested using a histogram as a plot legend to provide additional context for the data being shown:  Here’s a closer comparison of those two maps (click to zoom):  This histogram legend is especially useful for choropleth maps where units like counties are sized differently, which can create an illusion of a different distribution.

How to use a histogram as a legend in {ggplot2}

I am

 so beyond thrilled

to announce that I’ll be joining the Andrew Young School of Policy Studies at Georgia State University in Fall 2019 as an assistant professor in the Department of Public Management and Policy. I’ll be teaching classes in statistics/data science, economics, and nonprofit management in beautiful downtown Atlanta, and we’ll be moving back to the South. I am so so excited about this!

The academic job search finally comes to an end

As part of Elon Musk’s weird Department of Government Efficiency’s unconstitutional rampage through the federal government, USAID’s ForeignAssistance.gov was taken offline on January 31, 2025.

Using USAID data to make fancy world maps with Observable Plot

Last week I was making some final revisions to a paper where we used a neat conjoint experiment to test the effect of a bunch of different treatments on nonprofit donor preference. One of the peer reviewers asked us to compare the characteristics of our experimental sample with the general population so that we could speak a little to the experiment’s generalizability.

Guide to comparing sample and population proportions with CPS data, both classically and Bayesianly

Tip  This is now an R package named reconPlots. (Skip to the tl;dr complete example; see this mini project on GitHub)  So far, teaching at BYU has been delightful. I’ve been using static course-specific websites for the two classes I’m teaching this semester—data visualization and telling stories with data—and it’s been fantastic. Everything is self-contained and automated and magic and I’m a huge fan of blogdown.

Create supply and demand economics curves with ggplot2

Even though I’ve been teaching R and statistical programming since 2017, and despite the fact that I do all sorts of heavily quantitative research,

 I’m really really bad at probability math

. Like super bad. The last time I truly had to do set theory and probability math was in my first PhD-level stats class in 2012.

Calculating birthday probabilities with R instead of math

.no-stripe .gt_table tr.odd {     --bs-table-striped-bg: transparent; }   .gt_footnote {     text-align: left !important; }  In my causal inference class, I spend just one week talking about the Rubin causal model and potential outcomes.

Demystifying causal inference estimands: ATE, ATT, and ATU

My longstanding workflow for writing, citing, and PDF management   When I started my first master’s degree program in 2008, I decided to stop using Word for all my academic writing and instead use plain text Markdown for everything. Markdown itself had been a thing for 4 years, and MultiMarkdown—a pandoc-like extension of Markdown that could handle BibTeX bibliographies—was brand new.

How to migrate from BibDesk to Zotero for pandoc-based writing

The Natural Earth Project   The Natural Earth Project provides high quality public domain geographic data with all sorts of incredible detail, at three resolutions: high (1:10m), medium (1:50m), and low (1:110m). I use their data all the time in my own work and research, and the {rnaturalearth} package makes it really easy to get their data into R for immediate mapping. I mean, look at this!

How to move Crimea from Russia to Ukraine in maps with R

’Tis the season for Spotify Wrapped stats and I love it, both for seeing what everyone listens to and because it’s such a cool way of presenting data.

Apple Music Wrapped with R

This year, I’ve helped build the Idaho Secretary of State’s office’s election results website for both the primary and general elections. Working with election data is a complex process, with each precinct reporting results to their parent counties, which all use different systems and software and candidate identifiers.

Guide to generating and rendering computational markdown content programmatically with Quarto

At the end of June 2024, Posit released a beta version of its next-generation IDE for data science: Positron. This follows Posit’s general vision for language-agnostic data analysis software: RStudio PBC renamed itself to Posit PBC in 2022 to help move away from a pure R focus, and Quarto is pan-lingual successor to R Markdown.

Fun with Positron

I’ve used Garrick Aden-Buie’s tidyexplain animations since he first made them in 2018. They’re incredibly useful for teaching—being able to see which rows left_join() includes when merging two datasets, or which cells end up where when pivoting longer or pivoting wider is so valuable.

Visualizing {dplyr}’s mutate(), summarize(), group_by(), and ungroup() with animations

Complete tutorial and code See the full tutorial here. You can also see the tutorial’s code here and the code for the final API here.

DIY API with Make and {plumber}

tl;dr If you want to skip the explanation and justification for why you might want separate bibliographies, you can skip down to the example section, or just go see some example files at GitHub. Why use separate bibliographies? In academic articles, it’s common to have a supplemental appendix with extra tables, figures, robustness checks, additional math, proofs, and other details.

How to create separate bibliographies in a Quarto document

In my research, I study international nongovernmental organizations (INGOs) and look at how lots of different institutional and organizational factors influence INGO behavior. For instance, many authoritarian regimes have passed anti-NGO laws and engaged in other forms of legal crackdown, which has forced NGOs to change their programming strategies and their sources of funding.

The ultimate practical guide to conjoint analysis with R

In one of the assignments for my data visualization class, I have students visualize the number of essential construction projects that were allowed to continue during New York City’s initial COVID shelter-in-place order in March and April 2020. It’s a good dataset to practice visualizing amounts and proportions and to practice with

 dplyr

’s group_by() and summarize() and shows some interesting trends.

Quick and easy ways to deal with long labels in ggplot2

.first-block { padding-right: 10px; }


Read the previous post first! This post is a sequel to the previous one on Bayesian propensity scores and won’t make a lot of sense without reading that one first. Read that one first! In my previous post about how to create Bayesian propensity scores and how to legally use them in a second stage outcome model, I ended up using frequentist models for the outcome stage.

How to create a(n almost) fully Bayesian outcome model with inverse probability weights

In a research project I’ve been working on for several years now, we’re interested in the effect of anti-NGO legal crackdowns on various foreign aid-related outcomes: the amount of foreign aid a country receives and the proportion of that aid dedicated to contentious vs. non-contentious causes or issues. These outcome variables are easily measurable thanks to the AidData project, but they post a tricky methodological issue.

A guide to modeling outcomes that have lots of zeros with Bayesian hurdle lognormal and hurdle Gaussian regression models

At the end of my previous post on beta and zero-inflated-beta regression, I included an example of a multilevel model that predicted the proportion of women members of parliament based on whether a country implements gender-based quotas for their legislatures, along with a few different control variables. I also included random effects for year and region in order to capture time- and geography-specific trends.

A guide to correctly calculating posterior predictions and average marginal effects with multilievel Bayesian models

In the data I work with, it’s really common to come across data that’s measured as proportions: the percent of women in the public sector workforce, the amount of foreign aid a country receives as a percent of its GDP, the percent of religious organizations in a state’s nonprofit sector, and so on.  When working with this kind of data as an outcome variable (or dependent variable) in a model, analysis gets tricky if you use standard models like

A guide to modeling proportions with Bayesian beta and zero-inflated beta regression models

Update #1 An update to knitr has made it a ton easier to embed fonts in SVG files from R. Jump to the update to see how.


Update #2 Also, it’s possible to change TikZ fonts and not use Computer Modern for everything! Jump to the second update to see how.

How to automatically convert TikZ images to SVG (with fonts!) from knitr

The world of econometrics has been roiled over the past couple years with a bunch of new papers showing how two-way fixed effects (TWFE; situations with nested levels of observations, like country-year, state-month, etc.) estimates of causal effects from difference-in-differences-based natural experiments can be biased when treatment is applied at different times.

Exploring Pamela Jakiela’s simple TWFE diagnostics with R

Regression is the core of my statistics and program evaluation/causal inference courses. As I’ve taught different stats classes, I’ve found that one of the regression diagnostic statistics that students really glom onto is . Unlike lots of regression diagnostics like AIC, BIC, and the joint F-statistic,  has a really intuitive interpretation—it’s the percent of variation in the outcome variable explained by all the explanatory variables.

Exploring R² and regression variance with Euler/Venn diagrams

Downloads Jump to the downloads and get your own free pattern and template files! Apparently I now only produce cross stitch content. Thanks, pandemic. In preparation for season 2 of the incredible

 Ted Lasso

, I made a cross stitch version of the AFC Richmond crest, and I’m really happy with how it turned out!

AFC Richmond / Ted Lasso cross stitch pattern

Downloads Jump to the downloads and get your own free pattern and template files! In the data science world, hex stickers with logos of developers’ favorite packages are all the rage.

Hex sticker/logo cross stitch pattern

Downloads Jump to the downloads and get your own free pattern! For my latest pandemic art medium (on this, the three hundred and nineteenth day of sheltering in place), I decided to teach myself how to cross stitch. I did a ton of needlepoint as a kid and teen, but was always afraid of cross stitch because it was so much smaller and more delicate.

Bayesian (cross stitch) sampler

Since my last two blog posts on binary and continuous inverse probability weights (IPWs) and marginal structural models (MSMs) for time-series cross-sectional (TSCS) panel data, I’ve spent a ton of time trying to figure out why I couldn’t recover the exact causal effect I had built in to those examples when using panel data. It was a mystery, and it took weeks to figure out what was happening.

Marginal structural models for panel data with GEE and multilevel models

This is written for instructors in the Department of Public Management and Policy at the Andrew Young School of Policy Studies at Georgia State University, but it’s hopefully widely applicable too.

With more than 100 universities moving their teaching online (including Emory just last night), it’s looking more and more inevitable that GSU will make a similar switch any time now.

Emergency online teaching resources

(See this notebook on GitHub)  I’ve been teaching program evaluation in the MPA/MPP program at GSU for the past semester and a half, and since I was given free rein over how to teach it, I decided to make it as modern and cutting edge as possible.

Ways to close backdoors in DAGs

(See this notebook on GitHub)  A year ago, I wrote about how to use R to solve a typical microeconomics problem: finding the optimal price and quantity of some product given its demand and cost. Doing this involves setting the first derivatives of two functions equal to each other and using algebra to find where they cross.

Chidi’s budget and utility: doing algebra and calculus with R and yacas

This semester, I used the new ModernDive textbook to teach introductory statistics for executive MPA students at BYU, and it’s been absolutely delightful. The book’s approach to teaching statistics follows a growing trend (led by Mine Çetinkaya-Rundel, Alison Hill, and others) of emphasizing data and simulations instead of classical probability theory and complex statistical tests.

How to test any hypothesis with the infer package

(See this notebook on GitHub)  Missing data can significantly influence the results of normal regression models, since the default in R and most other statistical packages is to throw away any rows with missing variables. To avoid unnecessarily throwing out data, it’s helpful to impute missing values. One of the best ways to do this is to build a separate regression model to make predictions that fill in the gaps in data.

Meld regression output from multiple imputations with tidyverse

(See this notebook on GitHub)

 tl;dr

: Use functions like Deriv::Deriv(), splinefun(), approxfun(), and uniroot() to do things with derivatives in R, both with actual functions and with existing empirical data A typical microeconomics problem involves finding the optimal price and quantity of a product, given its demand and cost across different quantities.

Fun with empirical and function-based derivatives in R

I’ve been finishing up a project that uses ordered Beta regression (Kubinec 2022), a neat combination of Beta regression and ordered logistic regression that you can use for modeling continuous outcomes that are bounded on either side (in my project, we’re modeling a variable that can only be between 1 and 32, for instance). It’s possible to use something like zero-one-inflated Beta regression for outcomes like this, but that kind of model

Guide to understanding the intuition behind the Dirichlet distribution

I recently posted a guide (mostly for future-me) about how to analyze conjoint survey data with R. I explore two different estimands that social scientists are interested in—causal average marginal component effects (AMCEs) and descriptive marginal means—and show how to find them with R, with both frequentist and Bayesian approaches. However, that post is a little wrong. It’s not

 wrong

wrong, but it is a bit oversimplified.

The ultimate practical guide to multilevel multinomial conjoint analysis with R

As I explained in my previous blog post, in June 2023 I drove my family across the country in a 5,000-mile roundtrip odyssey from Atlanta, Georgia to just-south-of-Salt Lake City, Utah, and back again.

Road trip analysis! How to use and play with Google Location History in R

In a couple days, I’m going to drive across the country to Utah, my home state. I haven’t been out west with my whole family in four years—not since 2019 when we moved from Spanish Fork, Utah to Atlanta, Georgia. According to Google Maps, it’s a mere 1,900 miles (3,000 kilometers) through the middle of the United States and should take 28 hours, assuming no stops.

How to make fancy road trip maps with R and OpenStreetMap

I always forget how to deal with logged values in ggplot—particularly things that use the natural log.

How to use natural and base 10 log scales in ggplot2

As a field, statistics is really bad at naming things. Take, for instance, the term “fixed effects.” In econometrics and other social science-flavored statistics, this typically refers to categorical terms in a regression model.

Marginal and conditional effects for GLMMs with {marginaleffects}

Downloadable cheat sheets! You can download PDF, SVG, and PNG versions of the diagrams and cheat sheets in this post, as well as the original Adobe Illustrator and InDesign files, at the bottom of this post Do whatever you want with them!

Visualizing the differences between Bayesian posterior predictions, linear predictions, and the expectation of posterior predictions

Now that I’m on the tenure track, I’ve been looking for a way to keep track of my different research projects so I can get them all finished and published. Matt Lebo’s “Managing Your Research Pipeline” presents a neat way of quantifying and tracking the progress of your research, and I recently adopted it for my own stuff. I even made a fancy R Markdown +

 flexdashboard

dashboard to show the status of the pipeline interactively.

Create a dynamic dashboard with R, flexdashboard, and Shiny

GSU uses Microsoft’s Office365 for e-mail, which is fine. My previous institutions—Duke and BYU—both use it too, and it’s pretty standard. GSU also enforces 2-factor authentication (2FA) with Duo, which is also fine. Everybody should use some sort of 2FA for all their important accounts! However, for whatever reason, GSU’s version of Duo’s 2FA doesn’t allow you to generate app-specific passwords for things like e-mail.

Convert Markdown to rich text (with syntax highlighting!) in any macOS app

(See this notebook on GitHub)  Taking a sample from two groups from a population and seeing if there’s a significant or substantial difference between them is a standard task in statistics.

Half a dozen frequentist and Bayesian ways to measure the difference in means in two groups

tl;dr

: Skip to complete example In one of my current research projects, I use Bayesian modeling (with Stan and rstanarm) and multiple imputation (with Amelia) to measure how international aid agencies change their funding allocations to countries that impose legal restrictions on NGOs.

Create a cheap, disposable supercomputer with R, DigitalOcean, and future

This post combines two of my long-standing interests: causal inference and Bayesian statistics. I’ve been teaching a course on program evaluation and causal inference for a couple years now and it has become one of my favorite classes ever.

How to use Bayesian propensity scores and inverse probability weights

(See this notebook on GitHub)

 tl;dr

: Use the functions in broomify-amelia.R to use broom::tidy(), broom::glance(), and huxtable::huxreg() on lists of multiply imputed models. The whole reason I went into the rabbit hole of the mechanics of merging imputed regression results in the previous post was so I could easily report these results in papers and writeups.

Show multiply imputed results in a side-by-side regression table with broom and huxtable

In a project I’m working on, I need to generate predictions from a logistic regression model.

Manually generate predicted values for logistic regression with matrix multiplication in R

.magic-block {   font-style: normal; user-select: none; padding: 0 0.1em 0 0.15em; line-height: 1;   font-size: 120%; }  I’ve been working on converting a couple of my dissertation chapters into standalone articles, so I’ve been revisiting and improving my older R code.

A guide to Bayesian proportion tests with R and {brms}

I’ve taught a course on data visualization with R since 2017, and it’s become one of my more popular classes, especially since it’s all available asynchronously online with hours of Creative Commons-licensed videos and materials.

Making Middle Earth maps with R

See this notebook on GitHub. You can (and should) download the project from there if you want to follow along and try this out. tl;dr: Skip to the completed example. I use

 blogdown

to generate the websites for all the courses I teach, and it’s delightful to not have to worry about databases and server configurations.

Automatically zip up subdirectories with Make

In

 The Two Towers

, while talking with Eowyn, Aragorn casually mentions that he’s actually 87 years old. When Aragorn is off running for miles and miles and fighting orcs and trolls and Uruk-hai and doing all his other

 Lord of the Rings

adventures, he hardly behaves like a regular human 87-year-old. How old is he really?

How old was Aragorn in regular human years?

.smaller { font-size: 85%; }


Diagrams! You can download PDF, SVG, and PNG versions of the marginal effects diagrams in this guide, as well as the original Adobe Illustrator file, here: PDFs, SVGs, and PNGs Illustrator .ai file Do whatever you want with them! They’re licensed under Creative Commons Attribution-ShareAlike (BY-SA 4.0). I’m a huge fan of doing research and analysis in public.

Marginalia: A guide to figuring out what the heck marginal effects, marginal slopes, average marginal effects, marginal effects at the mean, and all these other marginal things are

I’ve been teaching a course on program evaluation since Fall 2019, and while part of the class is focused on logic models and the more managerial aspects of evaluation, the bulk of the class is focused on causal inference. Ever since reading Judea Pearl’s

 The Book of Why

in 2019, I’ve thrown myself into the world of DAGs, econometrics, and general causal inference, and I’ve been both teaching it and using it in research ever since.

Do-calculus adventures! Exploring the three rules of do-calculus in plain language and deriving the backdoor adjustment formula by hand

A few days ago, my wife, a bunch of my kids, and I were huddled around a big wall map of the United States, joking about the relative unimportance of Rhode Island, the smallest state in the US. It’s one of the states I never ever think about: …and it’s just so

 small

. Amid the joking, my wife came to Rhode Island’s defense by declaring that even though it’s so small, it has one of the highest proportions of coastline to land borders.

Calculating the proportion of US state borders that are coastlines

The students in my summer data visualization class are finishing up their final projects this week and I’ve been answering a bunch of questions on our class Slack. Often these are relatively standard reminders of how to tinker with specific ggplot layers (chaning the colors of a legend, adding line breaks in labels, etc.), but today one student had a fascinating and tricky question that led me down a realy fun dataviz rabbit hole.

How to fill maps with density gradients with R, {ggplot2}, and {sf}

Pandoc-flavored Markdown makes it really easy to cite and reference things. You can write something like this (assuming you use this references.bib BibTeX file): --- title: "Some title" bibliography: references.bib --- According to @Lovelace:1842, computers can calculate things.

One Simple Trick™ to create inline bibliography entries with Markdown and pandoc

In most of my research, I work with country-level panel data where each row is a country in a specific year (Afghanistan in 2010, Afghanistan in 2011, and so on), also known as time-series cross-sectional (TSCS) data.

A guide to working with country-year panel data and Bayesian multilevel models

In my post on generating inverse probability weights for both binary and continuous treatments, I mentioned that I’d eventually need to figure out how to deal with more complex data structures and causal models where treatments, outcomes, and confounders vary over time.

Generating inverse probability weights for marginal structural models with time-series cross-sectional panel data

My program evaluation class is basically a fun wrapper around topics in causal inference and econometrics. I’m a big fan of Judea Pearl-style “causal revolution” causal graphs (or DAGs), and they’ve made it easier for both me and my students to understand econometric approaches like diff-in-diff, regression discontinuity, and instrumental variables.

Generating inverse probability weights for both binary and continuous treatments

Note Skip to instructions for macOS or Windows R and ggplot can create fantastic graphs, but the default Arial/Helvetica font is too boring and standard.

Andrew Heiss's blog

Working with R, Cairo graphics, custom fonts, and ggplot

Create supply and demand economics curves with ggplot2