Each week, Tuesdays will be primarily lecture and class discussion or short activities, and Thursdays will be lab days.

  • You should bring your laptop with you to every class period.
  • Assigned readings for the week should be completed prior to lab. Whether you learn better by reading prior to lecture, or hearing a lecture prior to reading is up to you.
  • Weekly labs are due in GitHub by 5pm on the due date (typically Wednesdays).
  • Weekly homework assignments are due in D2L by 5pm on the due date (typically Wednesdays).

Week 1 (Jan 19–21): Course Overview

Weekly Overview:

  1. Discuss course structure and expectations.
  2. Install R and RStudio.
  3. Provide a brief introduction to R.

Reading and Online Resources:

In-class Materials:

Week 2 (Jan 24–28): Version Control with Git

Weekly Overview:

  1. Create a Github account.
  2. Install Git.
  3. Connect RStudio to Git and GitHub.
  4. Provide a brief introduction to version control with Git.

Reading and Online Resources:

In-class Materials:

In-class Lab:

  • Lab 1 - Due Monday, Jan 31 by 5pm in GitHub

Homework:

Note: In order for RStudio to compile to a PDF document, you need some version of TeX installed on your system. An easy way to install the TeX required for R Markdown compilation to PDF files is through the tinytex package.

Week 3 (Jan 31–Feb 4): Data Visualization with ggplot2

Weekly Overview:

  1. Understand R data structures and basic “base R” graphics functions (leftover from last week).
  2. Describe the “grammar of graphics”.
  3. Visualize data with the ggplot2 package.

Reading and Online Resources:

In-class Materials:

In-class Lab:

  • Lab 2 - Due Monday, Feb 7 by 5pm in GitHub

Homework:

Week 4 (Feb 7–11): Data Transformation with the Tidyverse

Weekly Overview:

  1. Practice wrangling data with the tidyverse “verbs”: filter(), arrange(), select(), mutate(), group_by(), summarise()

Reading and Online Resources:

In-class Materials:

In-class Lab:

  • Lab 3 Prep - Complete this prior to the start of class on Thursday Feb 10!
  • Lab 3 - Due Monday, Feb 14 by 5pm in GitHub

Homework:

Week 5 (Feb 14–18): R Overview and Style

Weekly Overview:

  1. Write code using tidyverse style guidelines
  2. Practice debugging code
  3. Write your own R functions

Reading and Online Resources:

In-class Materials:

In-class Lab:

  • Lab 4 - Due Tuesday, Feb 22 by 5pm in GitHub

Homework:

Week 6 (Feb 21–25): Functions, Loops and Debugging

Weekly Overview:

  1. Write your own R functions (cont)
  2. Use for/while loops in simulation
  3. Debug using built-in tools

Reading and Online Resources:

In-class Materials:

Midterm Exam 1:

  • Midterm Exam 1 will cover material from Weeks 1–4:
    • Overview of R and RStudio
    • Version control with Git and GitHub
    • Data visualization with ggplot2
    • Data wrangling with tidyverse (specifically, dplyr)
    • Labs 1–4 and homeworks 1–3
  • In-class component on Thursday, Feb 24
  • Take-home component due in D2L Monday, Feb 28 by 5:00pm
  • Examples of old exams

Week 7 (Feb 28–Mar 4): Data Wrangling 1 – Tidy Data and Relational Data

Weekly Overview:

  1. Implement merge and join procedures to wrangle multiple data sets
  2. Transform data from wide to long format

Reading and Online Resources:

In-class Materials:

In-class Lab:

  • Lab 5 - Due Monday, Mar 7 by 5pm in GitHub

Homework:

Week 8 (Mar 7–11): Data Wrangling 2 – Strings, Factors, Date/Time

Weekly Overview:

  1. Continue to learn data wrangling techniques
  2. Manipulate character strings
  3. Manipulate time/date objects
  4. Manipulate factor objects

Reading and Online Resources:

In-class Materials:

Homework:

Spring Break (Mar 14–18)

  • No class

Week 9 (Mar 21–25): Web Scraping

Weekly Overview:

  1. Understand the basic structure of HTML
  2. Use the rvest package to scrape data from the web
  3. Explore using functions and iteration applied to web scraping

Reading and Online Resources:

In-class Materials:

In-class Lab:

  • Lab 6 - Due Wednesday, Mar 30 by 5pm in GitHub

Homework:

  • No homework this week due to a longer Lab 6.

Week 10 (Mar 28–Apr 1): Data Visualization Principles + R Shiny Dashboards

Weekly Overview:

  1. Understand and implement principles of data visualization
  2. Develop a beginner understanding of R Shiny
  3. Introduce data visualization project and select data set

Reading and Online Resources:

In-class Materials:

In-class Lab:

  • Lab 7 - Due Wednesday, Apr 6 by 5pm in GitHub

Homework:

Week 11 (Apr 4–8): Modeling Uncertainty

Weekly Overview:

  1. Explore sampling variability through simulation.
  2. Carry out bootstrapping techniques to estimate sampling variability.

Reading and Online Resources:

In-class Materials:

In-class Lab:

  • Lab 8 - Due Wednesday, Apr 13 by 5pm in GitHub

Homework:

Project:

By April 8th at 11:00 p.m., the following must be completed and available in your Github repo:

  1. Topic you are interested in researching (in README.md)
  2. Data source (in README.md)
  3. Two research questions (in README.md)
  4. R script that scrapes a data set of interest (scrape.R)
  5. Scraped and cleaned data set (in the data folder)

Week 12 (Apr 11–15): Review

Weekly Overview:

  1. Review and practice important concepts in statistical computing and visualization using R.

In-class Materials:

Midterm Exam 2:

  • Midterm Exam 2 will cover material from Weeks 5–10
  • In-class component on Thursday, Apr 14
  • Take-home component due in D2L (Rmd) and Gradescope (pdf) Tuesday, Apr 19 by 12:00pm (noon)
  • Examples of old exams

Week 13 (Apr 18–22): Project Week

Weekly Overview:

  1. Continue working on project RShiny dashboard

Reading and Online Resources:

In-class Materials:

  • Project Q&A

Project:

By April 22th at 11:00 p.m., the following must be completed:

  1. “Data Summary & Visualization” portion of RShiny dashboard R code completed in GitHub
  2. RShiny app deployed to https://www.shinyapps.io/
  3. Link to RShiny app posted at the bottom of README.md in Github repo

Optional content on SAS:

Based on the student survey given this week, we will cover predictive modeling, classification, and clustering instead of SAS for the next two weeks. If you would still like to explore SAS, we have a SAS On Demand course set up for you where you can practice with SAS. Here are some additional resources:

Accessing SAS:

SAS video resources:

Dr. Hoegh’s SAS videos and slides:

Week 14 (Apr 25–29): Predictive Modeling, Classification, and Clustering

Weekly Overview:

  1. Introduce (review) predictive modeling methods
  2. Explore classification methods (supervised learning)
  3. Explore clustering methods (unsupervised learning)

Reading and Online Resources:

For this week and next, we will use Modern Data Science with R, 2nd ed., by Baumer, Kaplan and Horton as our reference. We will touch on topics in Chapters 10–12:

In-class Materials:

Videos:

In-class Lab:

  • Lab 9 - Due Wednesday, May 4 by 5pm in GitHub

Homework:

Project:

By April 29th at 11:00 p.m., the following must be completed:

  1. Complete RShiny dashboard (including both “Data Summary & Visualization” and “Discussions”) R code completed in GitHub
  2. Link to RShiny app posted in D2L Project Shiny Apps discussion board

Week 15 (May 2–6): Predictive Modeling, Classification, and Clustering

Weekly Overview:

  1. Continue exploration of predictive modeling, classification, and clustering
  2. Review for final exam

Reading and Online Resources:

  • Continued from last week

In-class Materials:

Project:

By May 6th at 11:00 p.m., the following must be completed:

  1. Post comments on at least two RShiny app discussion posts. Each of your posts should include (a) at least one feature you think works well, (b) at least one feature that you might have done differently, and (c) how you could extend the study (e.g., what other research questions does the analysis inspire?).
  2. Complete project group evaluation in D2L.

Final Exam

Take Home portion (optional): Released Friday, May 6th at 8:00am. Due Monday, May 9 by 11:00pm

In Class portion: Tuesday, May 10 12:00–1:50pm

Your final exam grade will be the higher of the following scores:

  1. In-class final exam score.
  2. Weighted average of your take-home and in-class final exam scores, weighted as 40% in-class, 60% take-home.