Course Content for ID529 Data Management and Analytic Workflows in R

What follows are the time-tables and resources for each course day in ID529, a 2-week short-course being offered in January 2023.

The website for the course is online at https://id529.github.io/.

Monday January 9th

• 1:30-1:40 Welcome to ID529
• 1:40-2:10 Introductory Lecture: Course Details
• 2:10-2:40 Live Demonstration: Modern Data Science Practices in an R Project Based Workflow
• 2:40-2:55 Activity: Q&A + Group reflection on principles shown in the demonstration
• 2:55-3:10 Lecture: Introduce the Final Presentations Format
• 3:10-3:15 Break
• 3:15-3:50: Intro to RStudio and R
• 3:50-4:05: Discussion and Self-Introductions
• 4:05-4:45: Lecture: Intro to Git and GitHub
• 4:45-5:00: Demo of how to do the homework
• 5:00-5:05: Positive Affirmations
• 5:05-5:30 Activity: Setup GitHub accounts + work on the homework + chat with classmates

Recommended materials:

• Articles:
  • The Introduction Chapter to R for Data Science: https://r4ds.had.co.nz/explore-intro.html (just one page)
  • Excuse me, do you have a moment to talk about version control? by Jennifer Bryan: https://peerj.com/preprints/3159.pdf
  • Tutorial: Getting Started with R and RStudio: https://www.dataquest.io/blog/tutorial-getting-started-with-r-and-rstudio/
  • Quickstart from GitHub: https://docs.github.com/en/get-started/quickstart/hello-world
• Videos:
  • RStudio for the Total Beginner, HRAnalytics101: https://www.youtube.com/watch?v=FIrsOBy5k58
  • If you haven’t already installed R and RStudio, you’ll want to do that, and you can do that by following the instructions here:
    • Install R: https://vimeo.com/203516510
    • Install RStudio: https://vimeo.com/203516968

Homework:

• Write a bio for yourself and include a picture!
• Complete the intro to course survey
• Check that your R and RStudio installations are working on your computer

Tuesday January 10th

• 1:30-2:00 Lecture: Intro to R Programming (including conditionals, control flow, etc.)
• 2:00-2:15 Activity: learnr Tutorial on Conditionals and Control Flow
• 2:15-2:45 Lecture: Data Dictionaries and Documentation
• 2:45-3:00 Activity: Q&A + Discussion
• 3:00-3:05 Break
• 3:05-3:35 Lecture: Reading in data of various formats
• 3:35-4:30 Lecture: Intro to ggplot2 (common types of figures, faceting, legends, patchwork, and saving figures)
• 4:30-4:50 Discussion: What are the ingredients to a ggplot? What makes an effective data visualization?
• 4:50-5:10 Demonstration of how to do the homework
• 5:10-5:30 Time to work on homework, chat with classmates, peruse recommended materials, engage in self-affirmation

Recommended materials:

• Our learnr tutorial on Conditionals, Control Flow, and Logic in R
• Smithsonian Data Management and Best Practices — Describing Your Data: Data Dictionaries
  • https://library.si.edu/sites/default/files/tutorial/pdf/datadictionaries20180226.pdf
• U.S. Geological Survey, Data Dictionaries
  • https://www.usgs.gov/data-management/data-dictionaries
• Skim the readr cheatsheet:
  • https://raw.githubusercontent.com/rstudio/cheatsheets/main/data-import.pdf
  • Keep in mind, your goal shouldn’t be to memorize everything, but rather to get a sense of what functionality is available to you, and how you could reference this cheatsheet or follow up on its contents to make use of it.
• Fundamentals of Data Visualization, Chapter 2, Visualizing data: Mapping data onto aesthetics:
  • https://clauswilke.com/dataviz/aesthetic-mapping.html
• Fundamentals of Data Visualization, Chapter 17, The principle of proportional ink:
  • https://clauswilke.com/dataviz/proportional-ink.html
• [Video] Introduction to ggplot2 https://www.youtube.com/watch?v=UiuA5sBEcFk
• [Video] BeginneR Workshop https://www.youtube.com/watch?v=7kuPnVZcot0 [lecture starts around 20:00]
  • Check the video description for the code files
• [Video] Intro to Git and GitHub https://www.youtube.com/watch?v=u4LIpYC0Yaw

Truly extra reading for those interested in advancing their conceptual understanding of ggplot2 and the possibilities in data visualization:

• A Layered Grammar of Graphics, by Hadley Wickham http://vita.had.co.nz/papers/layered-grammar.pdf
• Start to get familiar with the ggplot2 book. We recommend starting with subsection 1.2 “What is the grammar of graphics?” here: https://ggplot2-book.org/introduction.html#what-is-the-grammar-of-graphics
• The R Graph Gallery: https://r-graph-gallery.com/index.html

Homework:

• Read in a dataset of your choice [we will give you some example datasets you can use] and create a figure using ggplot2. We want to see students include titles, subtitles, captions, data sources, legends, etc.

What you can expect from the instructional team:

• We will be reading your bios and working on sorting you into groups for the final presentation topics.

Wednesday January 11th

• 1:30-2:00 Lecture: R Projects
• 2:00-2:50 Lecture: Intro to dplyr
• 2:50-3:10 Activity: Work on dplyr learnr tutorial in groups
• 3:10-3:15 Break
• 3:15-3:35 Lecture: Cleaning Text Data
• 3:35-4:05 Lecture: Writing Functions
• 4:05-4:30 Activity: Write functions together
• 4:30-4:45 Activity: Discussion Q&A from ggplot2 Homework
• 4:45-4:55 Survey: Checking in on Pacing
• 4:55-5:10 Demonstration of how to do the homework
• 5:10-5:30 Time to work on homework, chat with classmates, peruse recommended materials

Recommended reading:

• On Projects:
  • Tidyverse Blog, Project-Oriented Workflow by Jenny Bryan: https://www.tidyverse.org/blog/2017/12/workflow-vs-script/
  • R for Data Science, Chapter 8: https://r4ds.had.co.nz/workflow-projects.html
  • Project-Oriented Workflow, Jenny Bryan https://www.tidyverse.org/blog/2017/12/workflow-vs-script/
• On dplyr, cleaning text, and writing functions
  • R for Data Science, Chapter 5 Data Transformation: https://r4ds.had.co.nz/transform.html
  • R for Data Science, Chapter 14 Strings: https://r4ds.had.co.nz/strings.html
  • R for Data Science, Chapter 19 Functions: https://r4ds.had.co.nz/functions.html

Homework:

• No homework

Thursday January 12th

• 1:30-2:00 Lecture: Diverse Data Sources (APIs [tidycensus, WHO, World Bank, qualtRics], scraping web data, tabulizer, tesseract, datapasta)
• 2:00-2:20 Discussion: What kind of sources are students interested in using in their research or future work?
• 2:20-2:50 Lecture: How to handle factors and date-times
• 2:50-3:00 Break
• 3:00-3:30 Lecture: Regression Modeling: How to fit regression models, extracting model summaries from fit model objects, visualizing model estimates
• 3:30-4:15 Activity: Working with Regression Models in R
• 4:15-4:45 Lecture: Creating maps in R
• 4:45-5:00 Lecture: Reproducible Examples for Getting Help
• 5:00-5:30 Time to work on final presentation materials together, peruse recommended materials, chat with classmates

Homework 4:

• Choose one of the prespecified geospatial datasets and create a map in R
• Fit and report on a regression model including categorical (factor) variables
• Peer Review for Homework 3

Available/Recommended Reading (Remember! You don’t have to read all of this! Just focus on what’s most useful to you):

• Tidy Data by Hadley Wickham https://vita.had.co.nz/papers/tidy-data.pdf
• Diverse Data Sources
  • The readme to the datapasta package: https://github.com/MilesMcBain/datapasta
  • Analyzing US Census Data by Kyle Walker, Chapter 2: An introduction to tidycensus: https://walker-data.com/census-r/an-introduction-to-tidycensus.html
  • The readr cheatsheet: https://raw.githubusercontent.com/rstudio/cheatsheets/main/data-import.pdf
  • Working with Qualtrics Data - Part 1: Importing Data, ROpenSci https://ropensci.org/blog/2022/08/02/working-with-qualtrics-data-importing/
• Handling factors and date-times in R:
  • Chapter 15: Factors, R for Data Science by Hadley Wickham and Garrett Grolemund https://r4ds.had.co.nz/factors.html
  • Chapter 16: Dates and Times, R for Data Science by Hadley Wickham and Garrett Grolemund https://r4ds.had.co.nz/dates-and-times.html
  • Forcats cheatsheet https://raw.githubusercontent.com/rstudio/cheatsheets/main/factors.pdf
  • Lubridate cheatsheet https://raw.githubusercontent.com/rstudio/cheatsheets/main/lubridate.pdf
• Working with Regression Models:
  • Introduction to broom https://broom.tidymodels.org/articles/broom.html
  • sjPlot:
    • Summary of Regression Models as HTML Table https://strengejacke.github.io/sjPlot/articles/tab_model_estimates.html
    • Plotting Estimates of Regression Models https://strengejacke.github.io/sjPlot/articles/plot_model_estimates.html
    • How to Easily Visualize Data And Model Results https://yuzar-blog.netlify.app/posts/2022-08-01-sjplot/
  • A nice introduction to linear model diagnostics plots: https://book.stat420.org/model-diagnostics.html
  • Interpretation of R’s lm() output: https://stats.stackexchange.com/questions/5135/interpretation-of-rs-lm-output
• Mapping:
  • Chapter 8 Plotting Spatial Data, Spatial Data Science https://r-spatial.org/book/08-Plotting.html
    • This focuses more on sf which is the most modern and increasingly most popular paradigm for working with spatial data in R
  • Chapter 9 Making Maps with R, Geocomputation with R https://geocompr.robinlovelace.net/adv-map.html
    • This chapter has a lot of focus on tmap, a package for creating thematic maps

Friday January 13th

• 1:30-2:00 Lecture: Why reproducibility and robustness are important principles in science and data analysis and acknowledging the pressures in academia that push people away from reproducible science
• 2:00-2:15 Discussion
• 2:15-2:45 Lecture: Visualizing and Reporting on Regression Models
• 2:45-3:05 Lecture: Data Linkage Methods
• 3:05-3:25 Activity: Working with Joins
• 3:25-3:35 Break
• 3:35-4:00 Activity: Hallway QR Code Challenges
• 4:00-4:15 Lecture: Introduction of the Brown et al (partial) reproduction of Onikye et al’s results
• 4:15-4:30 Homework demonstration
• 4:30-5:25 Time to work on homework + chat together + work on final projects
• 5:25-5:30 Giveaway for a Copy of R for Data Science
• 5:30 Meet Hodu!

Homework 5

• Read the Brown et al reproduction of Onikye et al, run their code, and fill out the worksheet
  • https://open.lnu.se/index.php/metapsychology/article/view/2071
  • https://osf.io/j32yw/
• Peer review for homework 4

Available/Recommended Reading:

• Reproducibility:
  • Draw Me A Project https://masalmon.eu/2021/06/30/r-projects/
  • Reproducibility of Scientific Results https://plato.stanford.edu/entries/scientific-reproducibility/
  • Best Practices for Scientific Computing https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1001745
  • Good Enough Practices for Scientific Computing https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005510
  • Replicability, Robustness, and Reproducibility in Psychological Science https://pure.uvt.nl/ws/portalfiles/portal/59415163/MTO_Nuijten_replicability_robustness_and_reproducibility_Annual_Review_of_Psy_2022.pdf
  • A manifesto for reproducible science https://www.nature.com/articles/s41562-016-0021
• Regression Modeling:
  • Introduction to Poisson Regression, Beyond Multiple Linear Regression https://bookdown.org/roback/bookdown-BeyondMLR/ch-poissonreg.html
  • Poisson Regression https://rpubs.com/franzbischoff/poisson_regression
  • Logistic Regression, Beyond Multiple Linear Regression https://bookdown.org/roback/bookdown-BeyondMLR/ch-logreg.html

Tuesday January 17th

• 1:30-1:50 Activity: Discussion of Onikye et al reproduction article
• 1:50-2:10 Lecture: Introduction to R Packages
• 2:10-2:30 Demonstration of how to create R packages that standardize data loading and cleaning processes
• 2:30-3:00 Lecture: How to use R Markdown to produce reproducible reports including tables, visualizations, and inline-quantitative statements.
• 3:00-3:10 Break
• 3:10-3:30 Activity: Experiment with different R Markdown features
• 3:30-3:50 Lecture: Advice for Debugging
• 3:50-4:10 Activity: Debugging
• 4:10-4:30 Activity: Getting Help Online and Debugging
• 4:30-4:45 Demonstration of how to do the homework
• 4:45-5:30 Time to do the homework, work on the final project together, peruse recommended materials

Recommended Reading:

• R Packages:
  • Read Karl Broman’s Why write an R package?
  • Familiarize yourself with what’s in the R Packages book: https://r-pkgs.org/ — having a rough familiarity with the different parts will be helpful. Our suggestion here is to try and approach this more in terms of “what are the ingredients in a good R Package?” rather than trying to learn how to craft all of those ingredients from the ground up immediately.
• R Markdown:
  • Check out the Get Started for R Markdown, especially the ~1 minute video intro on the first page: https://rmarkdown.rstudio.com/lesson-1.html
  • How R Helps Airbnb Make the Most of Its Data
  • If you find yourself loving R Markdown, you may find the R Markdown Cookbook useful, but it is incredibly comprehensive and we’d suggest it’s better to reference as you need it than to try to read it cover-to-cover.

Homework:

• Use R Markdown to document some exploratory data analysis.

Wednesday January 18th

• 1:30-2:00 Lecture: A Data Analysis from Start to Finish
• 2:00-2:30 Lecture: Longitudinal Data Analysis
• 2:30-3:00 Lecture: Best practices for reporting on missing data
• 3:00-3:30 Lecture: Intro to accessible exploratory data analysis methods: Correlation, principal components analysis, variable importance
• 3:30-3:40 Break
• 3:40-4:00 Discussion: What are the ethical principles involved in data analysis? What are the risks involved?
• 4:00-4:30 Lecture: Clean Code and Considerate Coding
• 4:30-5:30 Free time to work together on the final project, chat with classmates, peruse recommended materials

Recommended reading:

• Harms and Ethics in Data Science and Machine Learning:
  • The Data Science Ethics chapter from the Modern Data Science with R book: https://mdsr-book.github.io/mdsr2e/ch-ethics.html

Thursday January 19th

• 1:30-2:00 Lecture/Demo: Principles for Data Analysis from Start to Finish
• 2:00-2:30 Lecture: Functional Programming
• 2:30-3:00 Discussion + Demo: When does it make sense to use functional programming?
• 3:00-3:10 Break
• 3:10-3:40 Lecture: [Students’ Choice]
• 3:40-4:10 Lecture: How to Keep Growing as a Programmer (and stay up to date)
• 4:10-4:20 Details about turning in your final presentations
• 4:20-5:30 Positive affirmations, free time to work together on final projects

Recommended materials:

• Hadley Wickham on Many Models: https://youtu.be/cU0-NrUxRw4

Friday January 20th

• 1:30-4:30 Final Presentations:
  • Students will be divided into 10 groups with 10-12 minutes presentation time and 3-5 minutes for feedback from the instructional team and Q&A from the audience.
  • 15 minutes × 10 groups = ~2.5 hours
  • we’ll make sure to take some breaks between every few groups
• 4:30-5:00 Lecture: Recap of Key Takeaways
• 5:00-5:30:
  • Make sure you’ve uploaded your presentation!
  • Feedback and Course Evaluations

Enjoy being done with the class and go on to do great things with your newly learned R skills!