Schedule

Submit each of your homework answers, as an html file, via this Google form before midnight on the day before the class for which the homework was assigned.

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.

See the home pages for positron.tutorials, 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.

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 positron_answers.html. Avoid using the name which will be assigned to the file if you download the same answers twice (or more), stuff like positron_answers (2).html. You can just change the name of the file by hand if this happens.

Week 1: June 16

Introduction to the tools we use for doing data science.

Monday

Monday’s homework must be completed before midnight on Sunday. I ensure this by only sending the Zoom link for class to students who submitted the homework.

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

  • From the r4ds.tutorials package, complete the “Introduction” (00-introduction) tutorial.

  • From the positron.tutorials package, complete the “Positron and Code” (01-code) tutorial. Make sure that you have already completed the “Introduction” tutorial above before you start the “Positron and Code” tutorial.

Wednesday

  • From the positron.tutorials package, complete the “Positron and Quarto” (02-quarto) and “Terminal” (03-terminal) tutorials.

  • Read Chapters 1 - 3 from R for Data Science (2e).

  • From the r4ds.tutorials package, complete the “Data visualization” (01-data-visualization) and “Data transformation” (03-data-transformation) tutorials.

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.

Thursday

Going forward, we recommend that you read the chapters in R for Data Science (2e) associated with each tutorial we assign from the r4ds.tutorials package. However, we stop listing them explicitly.

  • From the positron.tutorials package, complete the “Positron and GitHub Introduction” (04-github-1) tutorial.

  • From the r4ds.tutorials package, complete the “Data tidying” (05-data-tidying).

Week 2: June 23

Finish learning about tools like Quarto and GitHub Pages. Start learning about models and inference.

Monday

Wednesday

Thursday

Week 3: June 30

Identify the data which you will use for your Data Project. The U.S. Census is a great source.

Monday

Note that chapters 4 and higher from the Primer are a mess, using packages/functions which we no longer recommend. We are re-writing the Primer this summer. In the meantime, don’t feel obligated to read any more of it, although you do have to complete the tutorials, which are up-to-date. We just list the chapters in this schedule for your reference.

  • Complete the “Models” tutorial (041-models) from the primer.tutorials package.

  • Read chapters 1 – 3 from from Analyzing US Census Data: Methods, Maps, and Models in R by Kyle Walker.

  • From the tidycensus.tutorials package, complete the “An introduction to tidycensus” (02-introduction) and “Wrangling Census data with Tidyverse tools” (03-wrangling) tutorials.

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

Wednesday

Thursday

  • Complete the “Three Parameters: Causal” tutorial (061-three-parameters-causal) from the primer.tutorials package.

Week 4: July 7

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

  • 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.

  • From the r4ds.tutorials package, complete the “Exploratory data analysis” (10-exploratory-data-analysis) and the “Communication” (11-communication) tutorials.

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

Wednesday

  • From the r4ds.tutorials package, complete the “Logical vectors” (`12-logical-vectors) and the “Numbers” (13-numbers) tutorials.

  • 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.

Week 5: July 14

Renewed focus on models for inference.

Monday

  • Complete the “Mechanics” tutorial (071-mechanics) from the primer.tutorials package.

  • From the r4ds.tutorials package, complete the “Strings” (14-strings), “Regular expressions” (15-regular-expressions), and “Factors” (16-factors) tutorials.

Wednesday

  • Complete the “Four Parameters: Categorical” tutorial (081-four-parameters-categorical) from the primer.tutorials package.

  • From the r4ds.tutorials package, complete the “Dates and times” (17-dates-and-times) and “Missing values” (18-missing-values) tutorials.

Thursday

  • Complete the “Five Parameters” tutorial (091-five-parameters) from the primer.tutorials package.

  • From the r4ds.tutorials package, complete the “Joins” (19-joins) tutorial.

Week 6: July 21

Monday

  • Complete the “N Parameters” tutorial (101-n-parameters) from the primer.tutorials package.

  • From the r4ds.tutorials package, complete the “Spreadsheets” (20-spreadsheets) and “Databases” (21-databases) tutorials.

Wednesday

  • Complete the “Cumulative” tutorial (111-cumulative) from the primer.tutorials package.

  • From the r4ds.tutorials package, complete the “Arrow” (22-arrow) and “Hierarchical data” (23-hierarchical-data) tutorials.

Thursday

  • Complete the “Stops” tutorial (131-stops) from the primer.tutorials package.

  • Fom the r4ds.tutorials package, complete the “Web scraping” (24-web-scraping) tutorial.

Week 7: July 28

Monday

  • From the r4ds.tutorials package, complete the “Functions” (25-functions) tutorial.

Wednesday

  • From the r4ds.tutorials package, complete the “Iteration” (26-iteration) tutorial.

Thursday

  • From the r4ds.tutorials package, complete the “A field guide to base R” (27-a-field-guide-to-base-R) tutorial.

Week 8: August 4

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, 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.