Schedule

Note

All times and dates in the schedule and other course material correspond to Tallahassee’s Eastern Time Zone.

Week 1: No class due to weather

Assignments due: Nothing due
Class meets: No class

FSU has canceled all classes for January 9 so our first meeting will be January 16. This is not an extra week off! Get started on the assignments due next week because they will take considerable time to complete.

Week 2: Why R is worth learning

Assignments due: January 15
Class meets: January 16

Due Monday night:

  1. Join the class’s DataCamp by following the instructions at this link. You must sign up using your @fsu.edu email address. Please enter your first and last names in your DataCamp profile to make it easier for us to track your progress.
  2. Complete the DataCamp course, “Introduction to R”
  3. Configure your email and Canvas settings according to these instructions
  4. Take this introductory survey, which I will use to understand your background and what you hope to get out of this class.

  • 1 Budget your time: DataCamp courses often take around four hours to complete. They can be completed in multiple sessions over the course of the week.

    • Due Tuesday before class begins:

    1. Read this short Reuters article by Elizabeth Culliford
    2. Join the class’s Posit Cloud workspace with this link. Come to class with your RStudio Cloud password ready so you can log in on the lab’s computers.

    Suggested reading:

    Note

    In this class, the suggested readings are optional. Read them if you want a stronger understanding of the material or if you get stuck on a related assignment.

    R for Data Science (2e) intro, chapter 1, and chapter 2

  • 2 Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund. R for Data Science (2e)

    • Week 3: Getting to know your data

    Assignments due: January 22
    Class meets: January 23

    Due:

    • DataCamp Course, “Introduction to the Tidyverse”

    Due Tuesday before class begins:

    1. Join the class’s Posit Cloud workspace with this link. Come to class with your RStudio Cloud password ready so you can log in on the lab’s computers.

    Suggested reading:

    R for Data Science (2e) chapters 3 & 4

    Week 4: Importing and inspecting your data

    Assignments due: January 29
    Class meets: January 30

    Due:

    • DataCamp:
      1. “Introduction to Importing Data in R” (all chapters)
      2. “Intermediate Importing Data in R” (just chapter 5, “Importing data from statistical software packages”)

    Suggested reading:

    R for Data Science (2e) chapter 7

    Week 5: Describing and visualizing your data

    Assignments due: February 05
    Class meets: February 06

    Due:

    • DataCamp Course, “Exploratory Data Analysis in R”

    Suggested reading:

    R for Data Science (2e) chapters 9, 10, and 11

    Week 6: Survey analysis

    Assignments due: February 12
    Class meets: February 13

    Due:

    • DataCamp Course, “Analyzing Survey Data in R”

    Suggested reading:

    Week 7: Cause and effect

    Assignments due: February 19
    Class meets: February 20

    Due:

    • On Canvas, submit the assignments
      • Midterm 1: Load your data
      • Programming Workshop 5
    • DataCamp Course, “Visualizing Geospatial Data in R”

    Suggested reading:

    Using Geospatial Data in R, by Stefan Jünger

    Week 8: Improving our plots

    Assignments due: February 26
    Class meets: February 27

    Due:

    • On Canvas, submit Programming Workshop 6
    • DataCamp Course, “Analyzing US Census Data in R”

    Suggested:

    Week 9: How to effectively present your work

    Assignments due: March 04
    Class meets: March 05

    Instead of a Programming Workshop today, we’ll use class time to work through problems you are encountering with your Midterm Project data.

    Due:

    Suggested:

    Week 10: No class, Spring Break

    Assignments due: Nothing due
    Class meets: No class

    Week 11: Midterm Presentations

    Assignments due: March 18
    Class meets: March 19

    Due:

    Suggested reading:

    (None. Note the above readings are required and the advice is expected to be reflected in your presentations.)

    Week 12: Modeling Data 1–Estimation and Interpretation

    Assignments due: March 25
    Class meets: March 26

    Due:

    • On Canvas, submit the assignment, Midterm 4: Full Report.
    • DataCamp Course, “Introduction to Regression in R”

    Suggested reading:

    • R for Data Science (2e) chapters 22 & 23
    • This guide to getting a campaign+data job.
      • Note 1: this guide is written by progressivedatajobs.org and thus focuses on “progressive” jobs. But if you want a job working for a conservative group, just mentally replace each use of the word progressive with conservative (and vice versa) and it will still be excellent advice.
      • Note 2: I’m suggesting this reading this week because it provides a great overview of the skills you should learn if you want a data-focused job. At this point in the semester, you should be able to understand a lot of the skills it is describing even if you have yet to learn those skills. You can use the advice to focus what you learn the rest of this semester (and into the future).

    Week 13: Modeling Data 2–Binary Outcomes

    Assignments due: April 01
    Class meets: April 02

    Due:

    • On Canvas, submit Programming Workshop 8
    • DataCamp Course, “Modeling with Data in the Tidyverse”

    Suggested reading:

    R for Data Science (2e) chapters 24 & 25

    Week 14: Modeling Data 3–Prediction

    Assignments due: April 08
    Class meets: April 09

    Due:

    Suggested reading:

    Week 15: Working with missing data

    Assignments due: April 15
    Class meets: April 16

    Instead of a Programming Workshop today, we’ll use class time to work through problems you are encountering with your Final Project data.

    Due:

    • No DataCamp this week to give you more time for your analysis draft.
    • On Canvas, submit the assignments:
      • Final 2: Analysis Draft
      • Programming Workshop 10

    Suggested reading:

    Skim Text Mining with R, by Julia Silge and David Robinson

    Week 16: Where to go from here and final project presentations

    Assignments due: April 22
    Class meets: April 23

    Due:

    • On Canvas:
      • provide peer feedback to two classmates for their Final 2: Analysis Draft submissions.
      • submit the assignment, Final 3: Presentation Slides. Arrive prepared to present your project to the class using the slides submitted to Canvas.

    Week 17: No class, Finals Week

    Assignments due: April 29
    Class meets: No class

    Due:

    • On Canvas, submit the assignment, Final 4: Full Report.