Schedule

Key resources: R for Data Science (2e) by Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund, Preceptor’s Primer for Bayesian Data Science by David Kane, Analyzing US Census Data: Methods, Maps, and Models in R by Kyle Walker, along with their their associated tutorials. Homework should be completed before the class meeting for which it is assigned.

See the home pages for the r4ds.tutorials, primer.tutorials, and tidycensus.tutorials for installation instructions.

Always reinstall the relevant package before starting a new tutorial. Example for primer.tutorials:

remove.packages("primer.tutorials")
remotes::install_github("PPBDS/primer.tutorials")

We are always fixing mistakes. You want to use the latest version.

Completed tutorials are submitted via this Google form. If you do not complete the tutorials on time, you will be removed from the class. Send in your tutorial answers saved in html format. Please try to ensure that the name of the file is the default name, like getting-started_answers.html. Avoid using the name which will be assigned to the file if you download the same answers twice (or more), stuff like getting-started_answers (2).html. You can just change the name of the file by hand if this happens.

Registration

To register for the class and reserve your spot, please read Getting Started and follow all the associated instructions. Complete the “Getting Started with Tutorials” tutorial from the tutorial.helpers package. Submit your answers, as an html file, via this Google form.

Week 1: Data

Looking at the data is the first step in data science. Note that Monday’s homework must be completed before Monday’s class. I ensure this by only sending the Zoom link for class to students who submitted the homework.

Monday

• Read the Introduction from R for Data Science (2e).

• From the r4ds.tutorials package, complete the “Introduction” tutorial. Submit your answers via the usual Google form.

Thursday

• Read Chapters 1 – 4 from R for Data Science (2e).

• From the r4ds.tutorials package, complete four tutorials: “Data Visualization,” “RStudio and Code,” “Data Transformation,” and “RStudio and Github.” Submit your answers via the usual Google form.

Reminder: All work must be completed before class. Failure to submit your tutorial answers will result in you being removed from the course. It is not fair to your fellow students, with whom you will be working in small groups, for you to not be prepared for class.

Week 2: Models

Now that we know how to visualize data that is already clean, we need to learn how to clean up messy data.

Monday

• Read Chapters 5, 6, 7, and 8 from R for Data Science (2e), and from the r4ds.tutorials package, complete the tutorials associated with the readings: “Data Tidying,” “Terminal,” “Data Import,” and “Getting Help.”

Thursday

• Read Chapters 9, 10 and 11 from R for Data Science (2e), and from the r4ds.tutorials package, complete the tutorials associated with the readings: “Layers,” “Exploratory Data Analysis,” and “Communication.”

• Read chapters 1 and 2 from from Analyzing US Census Data: Methods, Maps, and Models in R by Kyle Walker. The U.S. Census is a great source of data for your projects.

• From the tidycensus.tutorials package, complete the “An introduction to tidycensus” tutorial.

Week 3: Inference

Monday

• Read chapters 12, 13, 28 and 29 from R for Data Science (2e) and, from the r4ds.tutorials package, complete the “Logical vectors,” “Numbers,” and “Quarto Websites” tutorials.

• Come to class with the url for at least one data source you will be using for your Data Project.

Thursday

• Read chapters 14, 15 and 16 from R for Data Science (2e) and, from the r4ds.tutorials package, complete the “Strings,” “Regular expressions” and “Factors” tutorials.

Week 4: Wisdom

The main focus on the 4th week is individual presentations of your Data Project. Do you love soccer or wine or NYC politics? The Data Project provides you with an opportunity to study that topic in depth. Your Data Project will be, for most of you, the first item in your professional portfolio, something so impressive that you will be eager to show it to graduate schools or potential employers.

Monday

• Read chapter 17 from R for Data Science (2e) and, from the r4ds.tutorials package, complete the “Dates and times” tutorial.

• Complete the “Data Project” tutorial from the primer.tutorials package. Be sure to reinstall the package before starting the tutorial.

Wednesday

• Read chapter 18 from R for Data Science (2e) and, from the r4ds.tutorials package, complete the “Missing values” tutorial.

• Practice presentations and feedback. You must have your presentation ready to go!

Thursday

• Data Project presentations. You must invite someone, and bcc your TF when you do so. The presentations are public.

Week 5: Justice

Monday

• Read Chapter 1 Rubin Causal Model and Chapter 2 Probability.

• From the primer.tutorials package, complete “Rubin Causal Model” and “Probability” tutorials.

• Read chapter 19 from R for Data Science (2e) and, from the r4ds.tutorials package, complete the “Joins” tutorial.

Wednesday

• Read Chapter 3: Sampling and, from the primer.tutorials package, complete the “Sampling” tutorial.

• Read chapter 20 from R for Data Science (2e) and, from the r4ds.tutorials package, complete the “Spreadsheets” tutorial.

Thursday

• Read Chapter 4: Models and, from the primer.tutorials package, complete the “Models” tutorial.

• Read chapter 21 from R for Data Science (2e) and, from the r4ds.tutorials package, complete the “Databases” tutorial.

Week 6: Courage

Monday

• Read Cardinal Virtues.

• Read Chapter 5: Two Parameters and, from the primer.tutorials package, complete the “Two Parameters” tutorial.

• Read Chapter 6: Three Parameters: Causal and, from the primer.tutorials package, complete the “Three Parameters: Causal” tutorial.

Wednesday

• Read Chapter 7: Mechanics and, from the primer.tutorials package, complete the “Mechanics” tutorial.

• Read Chapter 8: Four Parameters: Categorical and, from the primer.tutorials package, complete the “Four Parameters: Categorical” tutorial.

Thursday

• Read chapter 22 from R for Data Science (2e) and, from the r4ds.tutorials package, complete the “Arrow” tutorial.

• Read chapter 23 from R for Data Science (2e) and, from the r4ds.tutorials package, complete the “Hierarchical data” tutorial.

Week 7: Temperance

Monday

• Read Chapter 9: Five Parameters and, from the primer.tutorials package, complete the “Five Parameters” tutorial.

• Read Chapter 10: N Parameters and, from the primer.tutorials package, complete the “N Parameters” tutorial.

Wednesday

• Read chapter 24 from R for Data Science (2e) and, from the r4ds.tutorials package, complete the “Web scraping” tutorial.

• Read chapter 25 from R for Data Science (2e) and, from the r4ds.tutorials package, complete the “Functions” tutorial.

Thursday

• Read chapter 26 from R for Data Science (2e) and, from the r4ds.tutorials package, complete the “Iteration” tutorial.

• Read chapter 27 from R for Data Science (2e) and, from the r4ds.tutorials package, complete the “A field guide to base R” tutorial.

Week 8: Projects

The last week will consist of projects that the students will make.

Monday

• From the primer.tutorials package, complete the “Project” tutorial before class, or you will be removed the course. There are no extensions for this assignment.

• You must have a draft of your final project ready to go, including a Quarto website and your paragraph introduction.

Wednesday

• Practice presentations and feedback. You must have your presentation ready to go!

Thursday

• Final project presentations. You must invite someone, and bcc your TF when you do so.