Customizing Quarto reports and presentations

Lecture 18

Dr. Benjamin Soltoff

Cornell University
INFO 5001 - Fall 2023

2023-10-30

Announcements

Announcements

• Project exploration due Thursday

ae-16

• Go to the course GitHub org and find your ae-16 (repo name will be suffixed with your GitHub name).
• Clone the repo in RStudio Workbench, open the Quarto document in the repo, and follow along and complete the exercises.
• Render, commit, and push your edits by the AE deadline – end of tomorrow

Quarto

Quarto basics

---
title: Gun deaths
author: Your name
date: today
format: html
---

```{r}
#| label: setup
#| include: false
library(tidyverse)
library(rcis)

youth <- gun_deaths |>
  filter(age <= 65)
```

# Gun deaths by age

We have data about `r nrow(gun_deaths)` individuals killed by guns. Only `r nrow(gun_deaths) - nrow(youth)` are older than 65. The distribution of the remainder is shown below:

```{r}
#| label: youth-dist
#| echo: false
ggplot(data = youth, mapping = aes(x = age)) + 
  geom_freqpoly(binwidth = 1)
```

# Gun deaths by race

```{r}
#| label: race-dist
youth |>
  mutate(race = fct_infreq(race) |> fct_rev()) |>
  ggplot(mapping = aes(y = race)) +
  geom_bar() +
  labs(y = "Victim race")
```

Major components

1. A YAML header surrounded by ---s
2. Chunks of code surounded by ```
3. Text mixed with simple text formatting using the Markdown syntax

Quarto code chunks

Rendering process

Artwork from “Hello, Quarto” keynote by Julia Lowndes and Mine Çetinkaya-Rundel, presented at RStudio Conference 2022. Illustrated by Allison Horst.

Rendering process

Artwork from “Hello, Quarto” keynote by Julia Lowndes and Mine Çetinkaya-Rundel, presented at RStudio Conference 2022. Illustrated by Allison Horst.

🤔 AE: Edit the Quarto document

• Render gun-deaths.qmd as an HTML document
• Add text describing the frequency polygon

05:00

Code chunks

```{r}
#| label: youth-dist
#| message: false
#| warning: false

# code goes here
```

• Naming code chunks
• Code chunk options
• eval: true
• include: true
• echo: true
• message: true or warning: true
• cache: true

Caching with dependencies

• Post-WWII
• Complete history

```{r}
#| cache: true
scdb_case <- read_csv("data/scdb-case.csv") |>
  filter(term >= 1945)
```

```{r}
#| cache: true
scdb_clean <- scdb_case |> 
  mutate(one_vote = majVotes - minVotes == 1)
scdb_clean
```

# A tibble: 9,299 × 53
   caseId docketId caseIssuesId dateDecision decisionType usCite sctCite ledCite
   <chr>  <chr>    <chr>        <chr>               <dbl> <chr>  <chr>   <chr>  
 1 1945-… 1945-00… 1945-001-01… 12/10/1945              1 326 U… 66 S. … 90 L. …
 2 1945-… 1945-00… 1945-002-01… 12/3/1945               1 326 U… 66 S. … 90 L. …
 3 1945-… 1945-00… 1945-003-01… 11/13/1945              1 326 U… 66 S. … 90 L. …
 4 1945-… 1945-00… 1945-004-01… 11/13/1945              1 326 U… 66 S. … 90 L. …
 5 1945-… 1945-00… 1945-005-01… 11/5/1945               1 326 U… 66 S. … 90 L. …
 6 1945-… 1945-00… 1945-006-01… 11/5/1945               1 326 U… 66 S. … 90 L. …
 7 1945-… 1945-00… 1945-007-01… 11/5/1945               2 326 U… 66 S. … 90 L. …
 8 1945-… 1945-00… 1945-008-01… 11/5/1945               1 326 U… 66 S. … 90 L. …
 9 1945-… 1945-00… 1945-009-01… 11/5/1945               1 326 U… 66 S. … 90 L. …
10 1945-… 1945-01… 1945-010-01… 12/10/1945              1 326 U… 66 S. … 90 L. …
# ℹ 9,289 more rows
# ℹ 45 more variables: lexisCite <chr>, term <dbl>, naturalCourt <dbl>,
#   chief <chr>, docket <chr>, caseName <chr>, dateArgument <chr>,
#   dateRearg <chr>, petitioner <dbl>, petitionerState <dbl>, respondent <dbl>,
#   respondentState <dbl>, jurisdiction <dbl>, adminAction <dbl>,
#   adminActionState <dbl>, threeJudgeFdc <dbl>, caseOrigin <dbl>,
#   caseOriginState <dbl>, caseSource <dbl>, caseSourceState <dbl>, …

```{r}
#| cache: true
scdb_case <- read_csv("data/scdb-case.csv")
```

```{r}
#| cache: true
scdb_clean <- scdb_case |> 
  mutate(one_vote = majVotes - minVotes == 1)
scdb_clean
```

# A tibble: 9,299 × 53
   caseId docketId caseIssuesId dateDecision decisionType usCite sctCite ledCite
   <chr>  <chr>    <chr>        <chr>               <dbl> <chr>  <chr>   <chr>  
 1 1945-… 1945-00… 1945-001-01… 12/10/1945              1 326 U… 66 S. … 90 L. …
 2 1945-… 1945-00… 1945-002-01… 12/3/1945               1 326 U… 66 S. … 90 L. …
 3 1945-… 1945-00… 1945-003-01… 11/13/1945              1 326 U… 66 S. … 90 L. …
 4 1945-… 1945-00… 1945-004-01… 11/13/1945              1 326 U… 66 S. … 90 L. …
 5 1945-… 1945-00… 1945-005-01… 11/5/1945               1 326 U… 66 S. … 90 L. …
 6 1945-… 1945-00… 1945-006-01… 11/5/1945               1 326 U… 66 S. … 90 L. …
 7 1945-… 1945-00… 1945-007-01… 11/5/1945               2 326 U… 66 S. … 90 L. …
 8 1945-… 1945-00… 1945-008-01… 11/5/1945               1 326 U… 66 S. … 90 L. …
 9 1945-… 1945-00… 1945-009-01… 11/5/1945               1 326 U… 66 S. … 90 L. …
10 1945-… 1945-01… 1945-010-01… 12/10/1945              1 326 U… 66 S. … 90 L. …
# ℹ 9,289 more rows
# ℹ 45 more variables: lexisCite <chr>, term <dbl>, naturalCourt <dbl>,
#   chief <chr>, docket <chr>, caseName <chr>, dateArgument <chr>,
#   dateRearg <chr>, petitioner <dbl>, petitionerState <dbl>, respondent <dbl>,
#   respondentState <dbl>, jurisdiction <dbl>, adminAction <dbl>,
#   adminActionState <dbl>, threeJudgeFdc <dbl>, caseOrigin <dbl>,
#   caseOriginState <dbl>, caseSource <dbl>, caseSourceState <dbl>, …

Label your chunks

```{r}
#| label: raw-data-cache
#| cache: true
scdb_case <- read_csv("data/scdb-case.csv")
```

```{r}
#| label: processed-data-cache
#| cache: true
#| dependson: raw-data-cache
scdb_clean <- scdb_case |> 
  mutate(one_vote = majVotes - minVotes == 1)
scdb_clean
```

# A tibble: 29,021 × 53
   caseId docketId caseIssuesId dateDecision decisionType usCite sctCite ledCite
   <chr>  <chr>    <chr>        <chr>               <dbl> <chr>  <chr>   <chr>  
 1 1791-… 1791-00… 1791-001-01… 8/3/1791                6 2 U.S… <NA>    1 L. E…
 2 1791-… 1791-00… 1791-002-01… 8/3/1791                2 2 U.S… <NA>    1 L. E…
 3 1792-… 1792-00… 1792-001-01… 2/14/1792               2 2 U.S… <NA>    1 L. E…
 4 1792-… 1792-00… 1792-002-01… 8/7/1792                2 2 U.S… <NA>    1 L. E…
 5 1792-… 1792-00… 1792-003-01… 8/11/1792               8 2 U.S… <NA>    1 L. E…
 6 1792-… 1792-00… 1792-004-01… 8/11/1792               6 2 U.S… <NA>    1 L. E…
 7 1793-… 1793-00… 1793-001-01… 2/19/1793               8 2 U.S… <NA>    1 L. E…
 8 1793-… 1793-00… 1793-002-01… 2/20/1793               2 2 U.S… <NA>    1 L. E…
 9 1793-… 1793-00… 1793-003-01… 2/20/1793               8 2 U.S… <NA>    1 L. E…
10 1794-… 1794-00… 1794-001-01… 2/7/1794               NA 3 U.S… <NA>    1 L. E…
# ℹ 29,011 more rows
# ℹ 45 more variables: lexisCite <chr>, term <dbl>, naturalCourt <dbl>,
#   chief <chr>, docket <chr>, caseName <chr>, dateArgument <chr>,
#   dateRearg <chr>, petitioner <dbl>, petitionerState <dbl>, respondent <dbl>,
#   respondentState <dbl>, jurisdiction <dbl>, adminAction <dbl>,
#   adminActionState <dbl>, threeJudgeFdc <dbl>, caseOrigin <dbl>,
#   caseOriginState <dbl>, caseSource <dbl>, caseSourceState <dbl>, …

Caching guidelines

• Label your code chunks
• Define dependencies
• Never cache chunks that load packages

Inline code

We have data about `r nrow(gun_deaths)` individuals killed by guns.

Only `r nrow(gun_deaths) - nrow(youth)` are older than 65. The distribution of the remainder is shown below:

We have data about 100798 individuals killed by guns.

Only 15687 are older than 65.

🤔 AE: Modify chunk options

• Set echo: false for each code chunk
• Adjust the figure height and width options for the code chunks with plots
• Enable caching for each chunk and render the document. Look at the file structure for the cache. What do you see?

07:00

YAML header

YAML header

---
title: Gun deaths
author: Benjamin Soltoff
date: today
format: html
---

• YAML Ain’t Markup Language
• Standardized format for storing hierarchical data in a human-readable syntax
• Defines how quarto renders your .qmd file

HTML document

---
title: Gun deaths
author: Benjamin Soltoff
date: today
format: html
---

Table of contents

---
title: Gun deaths
author: Benjamin Soltoff
date: today
format:
  html:
    toc: true
    toc-depth: 2
---

Appearance and style

---
title: Gun deaths
author: Benjamin Soltoff
date: today
format:
  html:
    theme: superhero
    highlight-style: github
---

Global options

---
title: "My Document"
format:
  html:
    fig-width: 7
execute:
  echo: true
  message: false
knitr:
  opts_chunk: 
    comment: "#>" 
---

• Default document-level options
• Some options are set with format
• Some options are set with execute
• Some options are set by knitr/jupyter

🤔 AE: Modify YAML options

• Add a table of contents
• Use themes for light and dark mode
• Set relevant code chunk options globally

07:00

PDF document

---
title: Gun deaths
author: Benjamin Soltoff
date: today
format: pdf
---

Presentation

---
title: Gun deaths
author: Benjamin Soltoff
date: today
format: revealjs
---

Quarto supports multiple presentation formats

• revealjs (HTML)
• pptx (PowerPoint)
• beamer (\(\LaTeX\)/PDF)

R scripts

# gun-deaths.R
# 2022-04-18
# Examine the distribution of age of victims in gun_deaths

# load packages
library(tidyverse)
library(rcis)

# filter data for under 65
youth <- gun_deaths |>
  filter(age <= 65)

# number of individuals under 65 killed
nrow(gun_deaths) - nrow(youth)

# graph the distribution of youth
ggplot(data = youth, mapping = aes(x = age)) +
  geom_freqpoly(binwidth = 1)

# graph the distribution of youth, by race
youth |>
  mutate(race = fct_infreq(race) |> fct_rev()) |>
  ggplot(mapping = aes(y = race)) +
  geom_bar() +
  labs(y = "Victim race")

When to use a script

• For troubleshooting
• Initial stages of project
• Building a reproducible pipeline
• It depends

Running scripts

• Interactively
• Programmatically using source()

Recap

• Quarto is an open-source, reproducible document system
• Compatible with R, Python, Julia, Observable, and more
• Supports multiple output formats

It’s Halloween! 🎃