Teaching data science with the tidyverse and Quarto

posit::conf(2023)
Teaching Data Science Masterclass

Mine Çetinkaya-Rundel

Welcome

Introductions

Dr. Mine Çetinkaya-Rundel

Profeesor, Statistical Science, Duke University

Developer Educator, Posit

Tracy Teal,
Open Source Program Director, Posit

Michele Peruzzi,
Assistant Professor, Biostatistics, University of Michigan

George Stagg,
Software Engineer, Posit

Workshop materials

One link for all materials

🔗 https://pos.it/teach-ds-conf23

Schedule

Time	Activity
09:00 - 10:30	Teaching data science with the tidyverse and Quarto
10:30 - 11:00	Coffee break
11:00 - 12:30	Teaching data science with Git and GitHub
12:30 - 13:30	Lunch break
13:30 - 15:00	Computing infrastructure for teaching data science
15:00 - 15:30	Coffee break
15:30 - 17:00	Organizing, publishing, and sharing of course materials

WiFi

Username: Conf 2023

Password: conf2023

If you have any difficulty with your connection please let us (myself and or the TAs) know so we can escalate issues if needed.

Code of Conduct

• The Code of Conduct and COVID policies can be found at https://posit.co/code-of-conduct.

• Please review them carefully. You can report Code of Conduct violations in person, by email, or by phone. Please see the policy linked above for contact information.

Other useful info

• There are gender-neutral bathrooms located are among the Grand Suite Bathrooms.

• There are two meditation/prayer rooms: Grand Suite 2A and Grand Suite 2B. Open Sunday - Tuesday 7:30 a.m. - 7:00 p.m., Wednesday 8:00 a.m. - 6:00 p.m.

• The lactation room is located in Grand Suite 1. Open Sunday - Tuesday 7:30 a.m. - 7:00 p.m., Wednesday 8:00 a.m. - 6:00 p.m.

• Participants who do not wish to be photographed have red lanyards; please note everyone’s lanyard colors before taking a photo and respect their choices.

Asking for help (Stickies)

I’m stuck

I’m done

I have a general question

Discord

You should have received an email with an invitation and instructions for joining the conference’s discord server.

This workshop has a private channel under Workshops:

#teaching-data-science-masterclass

This is a great place to ask questions, share responses to exercises, post resources, memes, or most anything else before, during, and after the workshop.

Computational Environment

Posit Cloud

You can use the following link to join the workshops Posit Cloud space,

pos.it/teach-ds-conf23-cloud

Once you have joined, navigate to Projects on the top menu.

Using your own system

If you’d like to use your own system, please see posit-conf-2023.github.io/teach-ds-masterclass/#install

Introductory data science curriculum design

Doing data science

Wickham, H., Çetinkaya-Rundel, M., & Grolemund, G. (2023). R for Data Science, 2nd Edition.

Teaching data science

Day one

Day one

• Go to Posit Cloud and start the project titled UN Votes

Day one

• Go to Posit Cloud and start the project titled UN Votes

• Open the Quarto document called unvotes.qmd

Day one

• Go to Posit Cloud and start the project titled UN Votes

• Open the Quarto document called unvotes.qmd

• Render the document

Day one

• Go to Posit Cloud and start the project titled UN Votes

• Open the Quarto document called unvotes.qmd

• Render the document

• Review the data visualization you just produced

Your turn!

UN Votes

pos.it/teach-ds-conf23-cloud

---

• Change Turkey to a different country and render the document again

• Review how the voting patterns of the country you picked compare to the United States and the United Kingdom

• Then, introduce yourself to your neighbor and discuss why you chose your country and what, if anything, surprising or expected you saw in the visualization you produced

• In the remaining time, continue the introduction:

  • Affiliation and/or where you’re joining from (geographically)
  • Where you are in your teaching (or learning) journey
  • Your favorite thing to teach

10:00

Rest of semester

Components

1. Content in three examples
2. Pedagogy
3. Tooling

Fisheries of the world

Starting point

Data

fisheries <- read_csv("data/fisheries.csv")
continents <- read_csv("data/continents.csv")

Your turn!

Fisheries of the world

---

Open the Posit Cloud project titled Fisheries of the world and work through the exercises that guide you through visualizing percentage of aquaculture across continents. Render the document after each exercise.

10:00

Concepts

fisheries |>
  select(country)

# A tibble: 216 × 1
   country            
   <chr>              
 1 Afghanistan        
 2 Albania            
 3 Algeria            
 4 American Samoa     
 5 Andorra            
 6 Angola             
 7 Antigua and Barbuda
 8 Argentina          
 9 Armenia            
10 Aruba              
# ℹ 206 more rows

continents

# A tibble: 245 × 2
   country           continent
   <chr>             <chr>    
 1 Afghanistan       Asia     
 2 Åland Islands     Europe   
 3 Albania           Europe   
 4 Algeria           Africa   
 5 American Samoa    Oceania  
 6 Andorra           Europe   
 7 Angola            Africa   
 8 Anguilla          Americas 
 9 Antigua & Barbuda Americas 
10 Argentina         Americas 
# ℹ 235 more rows

• data joins

fisheries <- left_join(fisheries, continents)

Concepts

fisheries |>
  filter(is.na(continent))

# A tibble: 3 × 5
  country                          capture aquaculture   total continent
  <chr>                              <dbl>       <dbl>   <dbl> <chr>    
1 Democratic Republic of the Congo  237372        3161  240533 <NA>     
2 Hong Kong                         142775        4258  147033 <NA>     
3 Myanmar                          2072390     1017644 3090034 <NA>     

fisheries <- fisheries |>
  mutate(
    continent = case_when(
      country == "Democratic Republic of the Congo" ~ "Africa",
      country == "Hong Kong"                        ~ "Asia",
      country == "Myanmar"                          ~ "Asia",
      .default = continent
    )
  )

• data joins
• data science ethics

Concepts

• data joins
• data science ethics
• critique
• improving data visualizations

Concepts

• data joins
• data science ethics
• critique
• improving data visualizations
• mapping

Student work

Project: Regional differences in average GPA and SAT

Question: Exploring the regional differences in average GPA and SAT score across the US and the factors that could potentially explain them.

Team: Mine’s Minions

Teaching with Quarto

Why Quarto?

• Reproducibility: Train new analysts whose only workflow is a reproducible one
• Pedagogy:
  • Code + output + prose together
  • Syntax highlighting FTW!
  • Familiar-feeling authoring with the visual editor without having to learn a bunch of new markdown syntax
• Efficiency: Consistent formatting -> easier grading
• Extendability: Use with Python, and Julia, and Observable, and more

Tips for starting with Quarto

• Minimal YAML
• Minimal chunk options
• Use well scaffolded Quarto documents
• Render early and often!

Demo: Quarto 💙 visual editor

UN Votes

---

• Document YAML
• Code chunks with YAML style options
• YAML completion and diagnostics

Scottish COVID briefings

Data

Concepts

• web scraping
• text parsing
• data types
• regular expressions

Concepts

• web scraping
• text parsing
• data types
• regular expressions
• functions
• iteration

Concepts

• web scraping
• text parsing
• data types
• regular expressions
• functions
• iteration
• data visualization
• interpretation

Concepts

• web scraping
• text parsing
• data types
• regular expressions
• functions
• iteration
• data visualization
• interpretation
• text analysis

Concepts

robotstxt::paths_allowed("https://www.gov.scot")

[1] TRUE

• web scraping
• text parsing
• data types
• regular expressions
• functions
• iteration
• data visualization
• interpretation
• text analysis
• data science ethics

Student work

Project: Factors Most Important to University Ranking

Question: Explore how various metrics (e.g., SAT/ACT scores, admission rate, region, Carnegie classification) predict rankings on the Niche College Ranking List.

Team: 2cool4school

Spam mail

Concepts

• logistic regression

• prediction

Concepts

	Email is spam	Email is not spam
Email labelled spam	True positive	False positive (Type 1 error)
Email labelled not spam	False negative (Type 2 error)	True negative

• logistic regression

• prediction

• decision errors

• sensitivity / specificity

• intuition around loss functions

Student work

Project: Predicting League of Legends success

Question: Is a gold or experienced lead a better predictor of which team wins after 10 minutes into the game?

Team: Blue Squirrels

Teaching with the tidyverse

Building a curriculum

• Start with library(tidyverse)

• Teach by learning goals, not packages

Teaching consistently

• Challenge: Google and Stack Overflow can be less useful – demo problem solving

• Counter-proposition: teach all (or multiple) syntaxes at once – trying to teach two (or more!) syntaxes at once will slow the pace of the course, introduce unnecessary syntactic confusion, and make it harder for students to complete their work.

• “Disciplined in what we teach, liberal in what we accept”

Postel, J. (1980). DoD standard internet protocol. ACM SIGCOMM Computer Communication Review, 10(4), 12-51. datatracker.ietf.org/doc/html/rfc760

Keeping up with the tidyverse

• Blog posts highlight updates, along with the reasoning behind them and worked examples

• Teaching the tidyverse blog post series provides a roughly annual review

• Lifecycle stages and badges

Community

• The encouraging and inclusive tidyverse community is one of the benefits of the paradigm

• Each package comes with a website, each of these websites are similarly laid out, and results of example code are displayed, and extensive vignettes describe how to use various functions from the package together

Discussion

Do you teach with the tidyverse?

• If yes, what are some highlights of your teaching experience and what are some challenges?

• If no, what is your approach and, if you’ve considered the tidyverse but decided against it, why?

Any other discussion points of interest?

---

Discuss with your partner for a few minutes first, before sharing with the big group.

More resources

An educator’s perspective of the tidyverse

Çetinkaya-Rundel, M., Hardin, J., Baumer, B., McNamara, A., Horton, N., & Rundel, C. (2022). An educator’s perspective of the tidyverse. Technology Innovations in Statistics Education, 14(1). http://dx.doi.org/10.5070/T514154352.

We are all converts to the tidyverse and have made a conscious choice to use it in our research and our teaching. We each learned R without the tidyverse and have all spent quite a few years teaching without it at a variety of levels from undergraduate introductory statistics courses to graduate statistical computing courses. This paper is a synthesis of the reasons supporting our tidyverse choice, along with benefits and challenges associated with teaching statistics with the tidyverse.

Teaching (with) Quarto

Session at Joint Statistical Meetings, 2023

https://mine-cetinkaya-rundel.github.io/teach-with-quarto

The share their experiences teaching Quarto to students in a wide range of courses that span from first-year introductory data science to Master’s level statistical computing courses. The talks also touch on multiple languages and tools as these courses teach R and Python using both RStudio and Jupyter Notebooks.

Photo credits

• Fisheries of the world: Photo by Paul Einerhand on Unsplash

• Scottish COVID briefings: Photo by jim Divine on Unsplash

• Spam mail: Photo by Alexander Grey on Unsplash