Georgia State University

Andrew Heiss

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

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

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

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

 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

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

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

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

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

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,

 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

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

 wrong

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

 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?

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

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

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

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

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

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

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)  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

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

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. 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

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

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

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! 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

(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

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

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"](https://doi.org/10.1017/S1049096516000160) 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](https://rmarkdown.rstudio.com/) +
[**flexdashboard**](https://rmarkdown.rstudio.com/flexdashboard/)
[dashboard to show the status of the
pipeline](https://twitter.com/andrewheiss/status/1201892963451842562)
interactively. I manage the data for the dashboard in a Google Sheet,
knit the dashboard, and create an HTML file with the latest statistics.

The combination of [R Markdown](https://rmarkdown.rstudio.com/) +
[**flexdashboard**](https://rmarkdown.rstudio.com/flexdashboard/) +
[**ggplotly**](https://plot.ly/ggplot2/) makes it incredibly easy to
create interactive dashboards, but you have to reknit and recompile the
dashboard any time any data changes, which I'm not a fan of doing.
Wouldn't it be amazing if there was a way to get the latest data into a
dashboard document without any reknitting?

Fortunately there *is* a way! [**flexdashboard**'s integration with
Shiny](https://rmarkdown.rstudio.com/flexdashboard/shiny.html) makes it
possible!

Here's a minimal working example of feeding data from a Google Sheet
into a Shiny-based flexdashboard. It has to be hosted on a Shiny server
somewhere (like [shinyapps.io](https://www.shinyapps.io/) or on your own
server), but you shouldn't have to reknit ever again!

::: {#step-1-create-a-google-sheet .section .level2}
## Step 1: Create a Google Sheet {#step-1-create-a-google-sheet .anchored anchor-id="step-1-create-a-google-sheet"}

Go to Google Drive, create a new spreadsheet, and put this data in it:

| Category | Count |
|:--------:|:-----:|
| A | 4 |
| B | 3 |
| C | 8 |
| D | 2 |

You can keep it private, but then you have to deal with OAuth tokens to
get it to work with a Shiny server, so it's easiest here to make it
publicly accessible. If you want to work with private data, check the
[**googlesheets4** documentation on
authentication](https://googlesheets4.tidyverse.org/#auth). Click on the
"Share" button in the top right corner and make the document accessible
via a link. You should also make the sheet public by going to File \>
Publish to the web and publish as a CSV. You can also just use [this
sheet
here](https://docs.google.com/spreadsheets/d/1RKNn84xVLUanhGyX8DPvDSd8zt4GX_eBmK2ZX2nf0BI/edit)
if you don't want to create your own (but this is read only and you
won't be able to edit it and see your changes live).
:::

::: {#step-2-build-a-static-dashboard .section .level2}
## Step 2: Build a static dashboard {#step-2-build-a-static-dashboard .anchored anchor-id="step-2-build-a-static-dashboard"}

We'll make a barebones dashboard with a couple big numbers at the top
showing a count of categories and the sum of the category counts, and an
interactive bar chart at the bottom showing the counts in each category.
Check the fantastic documentation for
[**flexdashboard**](https://rmarkdown.rstudio.com/flexdashboard/) for
more details and examples.

Save this as an R Markdown file and knit it---the resulting HTML file is
a complete self-contained dashboard.

::: {#cb1 .sourceCode style="background: #f1f3f5;"}
```` {.sourceCode .markdown .code-with-copy}
---
title: "Example dashboard with static data from Google"
output:
 flexdashboard::flex_dashboard:
 orientation: rows
 theme: yeti
---


```{r setup, include=FALSE}
library(tidyverse)
library(googlesheets4)
library(flexdashboard)
library(plotly)

# Make googlesheets4 not try to authenticate, since we're using a public sheet
sheets_deauth()
```

```{r get-data, message=FALSE, include=FALSE}
# The 1RKNn84xVLUanhGyX8DPvDSd8zt4GX_eBmK2ZX2nf0BI comes from the shared link
# URL from Google Sheets. You can also refer to sheets by name if you're
# authenticated into your account. See the googlesheets4 documentation for more
# details
remote_data <- read_sheet("1RKNn84xVLUanhGyX8DPvDSd8zt4GX_eBmK2ZX2nf0BI")
```

## Row

### Categories {.value-box}

```{r}
# Find the number of unique categories
n_categories <- remote_data %>% distinct(Category) %>% nrow()

# Show the number in a special valueBox (note the {.value-box} CSS class
# above—that applies the CSS class to the HTML output and makes it render
# correctly)
valueBox(value = n_categories, icon = "fas fa-users")
```

### Total {.value-box}

```{r}
# Get a total of all the counts
total <- sum(remote_data$Count)

# Show the number in a valueBox
valueBox(value = total, icon = "fas fa-cubes")
```

## Row

###

```{r}
# Make a basic column plot
my_plot <- ggplot(remote_data, aes(x = Category, y = Count)) +
 geom_col(aes(text = Count)) +
 theme_minimal()

# Show the plot with plotly
ggplotly(my_plot, tooltip = "text")
```
````
:::
:::

::: {#step-3-make-static-dashboard-dynamic .section .level2}
## Step 3: Make static dashboard dynamic {#step-3-make-static-dashboard-dynamic .anchored anchor-id="step-3-make-static-dashboard-dynamic"}

The only problem with this dashboard is that it's now the same every
time you visit it. If you update the data in the Google Sheet, those
changes won't be reflected in the dashboard unless you reknit the
document.

We can make the dashboard dynamic with some basic shinyfication: we can
put the data reading into a function that gets rerun when the page is
reloaded, and we can put `valueBox()` and `ggplotly()` inside functions
that connect them to the more dynamic data. This only requires a few
changes---here's the same static dashboard with the Shiny bits added.

::: {#cb2 .sourceCode style="background: #f1f3f5;"}
```` {.sourceCode .markdown .code-with-copy}
---
title: "Example dashboard with dynamic data from Google"
output:
 flexdashboard::flex_dashboard:
 orientation: rows
runtime: shiny # Make this use a Shiny backend
---


```{r setup, include=FALSE}
library(tidyverse)
library(googlesheets4)
library(flexdashboard)
library(plotly)

# Make googlesheets4 not try to authenticate, since we're using a public sheet
sheets_deauth()
```

```{r get-data, message=FALSE, include=FALSE}
# Create a function that reads the data from Google. I've seen other examples
# that make this a reactive(), but I don't really know what that is since I
# rarely use Shiny :shrug:
#
# I'm also not completely sold that this is the right approach, since this feels
# kind of roundabout (create a function, run the function), but it doesn't work
# if you just do remote_data <- read_sheet(). Also :shrug: for now.
#
# ALSO flexdashboard can use a special global chunk to speed up expensive data
# loading (https://rmarkdown.rstudio.com/flexdashboard/shiny.html#loading_data),
# which I assume also includes loading data remotely from Google, but if you
# name this chunk global, the dynamic data loading stops working. Once again,
# big :shrug:.
load_remote_data <- function() {
 read_sheet("1RKNn84xVLUanhGyX8DPvDSd8zt4GX_eBmK2ZX2nf0BI")
}

remote_data <- load_remote_data()
```

## Row

### Categories {.value-box}

```{r}
n_categories <- remote_data %>% distinct(Category) %>% nrow()

# Put valueBox() inside renderValueBox({})
renderValueBox({
 valueBox(value = n_categories, icon = "fas fa-users")
})
```


### Total {.value-box}

```{r}
total <- sum(remote_data$Count)

# Put valueBox() inside renderValueBox({})
renderValueBox({
 valueBox(value = total, icon = "fas fa-cubes")
})
```

## Row

###

```{r}
my_plot <- ggplot(remote_data, aes(x = Category, y = Count)) +
 geom_col(aes(text = Count)) +
 theme_minimal()

# Put ggplotly() inside renderPlotly({})
renderPlotly({
 ggplotly(my_plot, tooltip = "text")
})
```
````
:::
:::

::: {#step-4-check-the-dynamicness .section .level2}
## Step 4: Check the dynamicness {#step-4-check-the-dynamicness .anchored anchor-id="step-4-check-the-dynamicness"}

Edit the Google Sheet, refresh the Shiny app, and you should see
everything change in real time!
:::

::: {#step-5-publish-somewhere .section .level2 .page-columns .page-full}
## Step 5: Publish somewhere {#step-5-publish-somewhere .anchored anchor-id="step-5-publish-somewhere"}

The easiest way to publish the Shiny-based dashboard is to click on the
"Publish" menu in the top right corner of the RStudio preview window and
then go through the menus to publish the document at shinyapps.io.
[Here's mine.](https://andrewheiss.shinyapps.io/dynamic-flexdashboard/)

::: {.quarto-figure .quarto-figure-center .page-columns .page-full}
<figure class="figure page-columns page-full">
<img
src="https://www.andrewheiss.com/blog/2020/01/01/flexdashboard-dynamic-data/shiny.png"
class="img-fluid figure-img" />
<figcaption>flexdashboard-based Shiny app on shinyapps.io</figcaption>
</figure>
:::

------------------------------------------------------------------------

And that's it! An R Markdown-based flexdashboard that dynamically loads
data from a Google Sheet. There are probably better ways to load the
data (like somehow using the `global` chunk?), but this works well
enough!
:::

::: {#quarto-appendix .default}
::: {.section .quarto-appendix-contents}
## Citation {#citation .anchored .quarto-appendix-heading}

<div>

::: {.quarto-appendix-secondary-label}
BibTeX citation:
:::

``` {.sourceCode .code-with-copy .quarto-appendix-bibtex}
@online{heiss2020,
 author = {Heiss, Andrew},
 title = {Create a Dynamic Dashboard with {R,} Flexdashboard, and
 {Shiny}},
 pages = {undefined},
 date = {2020-01-01},
 url = {https://www.andrewheiss.com/blog/2020/01/01/flexdashboard-dynamic-data},
 doi = {10.59350/8vtys-s0p68},
 langid = {en}
}
```

::: {.quarto-appendix-secondary-label}
For attribution, please cite this work as:
:::

::: {#ref-heiss2020 .csl-entry .quarto-appendix-citeas}
Heiss, Andrew. 2020. "Create a Dynamic Dashboard with R, Flexdashboard,
and Shiny." January 1, 2020. <https://doi.org/10.59350/8vtys-s0p68>.
:::

</div>
:::
:::