Georgia State University

Andrew Heiss

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

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

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

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 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

I am
<em>
 so beyond thrilled
</em>
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

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

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’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

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
<strong>
 dplyr
</strong>
’s group_by() and summarize() and shows some interesting trends.

Quick and easy ways to deal with long labels in ggplot2

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
<strong>
 blogdown
</strong>
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

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}

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. As part of my dissertation work, I ran a global survey of international NGOs to see how they adjust their programs and strategies when working under authoritarian legal restrictions in dictatorship.

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

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

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
<em>
 Ted Lasso
</em>
, 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

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
<em>
 small
</em>
.  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

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

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,
<em>
 I’m really really bad at probability math
</em>
.  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

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

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

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

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
<em>
 wrong
</em>
wrong, but it is a bit oversimplified.

The ultimate practical guide to multilevel multinomial conjoint analysis with R

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

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

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}

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
<em>
 The Two Towers
</em>
, 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
<em>
 Lord of the Rings
</em>
adventures, he hardly behaves like a regular human 87-year-old. How old is he really?

How old was Aragorn in regular human years?

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

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

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

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

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

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
<em>
 The Book of Why
</em>
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

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

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

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

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

(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)  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

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

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

(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

<em>
 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.
</em>
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

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 +
<strong>
 flexdashboard
</strong>
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

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

Andrew Heiss's blog

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Complete tutorial and code
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[See the full tutorial here.](_book/){target="_blank" rel="noopener"}
You can also see [the tutorial's code
here](https://github.com/andrewheiss/diy-api-plumber-quarto-ojs) and the
[code for the final API
here](https://github.com/andrewheiss/basic-plumber-api).
:::
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For years, I've tracked all sorts of data about myself (and my family)
through Google Forms, [Airtable](https://www.airtable.com/), and devices
like Fitbits to keep track of all sorts of things: personal goals,
progress of research projects, current health status, books read, and so
on.

It's nice to have all this data, but it's hard to use it all
immediately. I often look at it at the end of the year, or every few
months, or whatever, but having an instant snapshot is helpful too.
That's why people invented data dashboards, after all.

I like R a lot, and R has the ability to make dashboards, like with
[{flexdashboard}](https://pkgs.rstudio.com/flexdashboard/) and
[Shiny](https://shiny.posit.co/). I've made several dashboards for
tracking things like health and reading and research---[I even have a
blog post about making one with
{flexdashboard}](https://www.andrewheiss.com/blog/2020/01/01/flexdashboard-dynamic-data/)!

But I've always run into issues with getting live data. With
{flexdashboard}, you can make it grab the most recent version of the
data you're interested in *when you knit the document*, but then to
update the graphs and tables in the document, you have to re-knit it.
With Shiny, there are ways to dynamically grab the latest data, but then
you have to run a whole Shiny server, and that's hard and costs money
*and* it's slow---it can sometimes take a few minutes to reanimate a
hibernating Shiny app!

However, nowadays it's possible to use [Observable
JS](https://quarto.org/docs/computations/ojs.html) chunks in Quarto that
automatically grab live data from the internet and display it, like
this:

::: {.cell}
Show the OJS code

::: {#cb1 .sourceCode .cell-code startfrom="41" source-offset="46" style="background: #f1f3f5;"}
```` {.sourceCode .java .code-with-copy}
```{ojs}
//| echo: fenced
//| code-fold: true
//| code-summary: "Show the OJS code"
d3 = require('d3')

viewof year_to_show = Inputs.radio(["2023", "2024"], {value: "2023", label: "Year to show"})

books = await d3.json(
  "https://api.andrewheiss.com/books_simple?year=" + year_to_show
)

book_noun = (books.count[0] === 1 ? " book read" : " books read")

Plot.plot({
  title: books.count[0] + book_noun + " in " + year_to_show,
  y: {
    label: "Books read",
    grid: false,
    percent: false
  },
  x: {
    label: "Month",
    domain: books.monthly_count.map(d => d.read_month_fct),
  },
  marks: [
    Plot.ruleY([0]),
    Plot.axisX({label: null, ticks: null}),
    Plot.axisY({label: null, ticks: null}),

    Plot.barY(books.monthly_count, {
      x: "read_month_fct", 
      y: "count", 
      fill: "#f3752f",
      tip: {
        format: {
          x: true,
          y: true
        }
      }
    })
  ]
})
```
````
:::

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<div>

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And now that [Quarto supports
dashboards](https://quarto.org/docs/dashboards/), you can create entire
Shiny-like dashboards that can load and display data without needing a
Shiny server. [Like this one!](_book/dashboard/)

The trickiest part of all this, though, is getting data from all around
the internet (Google Sheets, Airtable databases, RSS feeds, Fitbit,
etc.) into an easily accessible, clean, and usable format that you can
feed into things like Observable plots or R.

Fortunately there's a good (and really neat!) solution for this! You can
use the [{plumber} R package](https://www.rplumber.io/) to create your
own API that you can use to grab and clean data from all around the
internet. And to simplify life, you can use other services like
[Make.com](https://www.make.com/en) to deal with the hard work of
regularly checking in on different parts of the internet (checking RSS
feeds, reading Google/Airtable data, logging into services like Fitbit).

In the end, you can have a server like `api.yourname.com` and access
JSON, CSV, or .rds data like
`api.yourname.com/books?start_date=2024-01-01`. You can then use that in
an R file, in a Python script, or in a Quarto document with Observable
JS. It's magical!

To explain and illustrate this whole process, I started out writing a
blog post, but it got long and complex, so I wrote a literal book
instead.

[Access it here for a full tutorial.](_book/){target="_blank"
rel="noopener"}

::: {.page-columns .page-full}
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<figure class="figure page-columns page-full">
<p><img
src="https://www.andrewheiss.com/blog/2024/01/12/diy-api-plumber-quarto-ojs/_book/assets/out/overview.svg"
class="img-fluid figure-img column-page-inset" /></p>
<figcaption>Overview of the process</figcaption>
</figure>
:::
:::
