Course Schedule

Course Schedule

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Week One (August 18 - 24): Course Overview

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• Lecture 1: Course Overview
• Course Syllabus

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Week Two (August 25 - 31): Quarto / RMD / ggplot

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HW2 (Due Tuesday August 27 - submit via gradescope)

Video Resources:

• Installing/Updating R and R Studio (3:39)
• Quarto Documents (8:26)
• ggplot Intro (11:03)

Reading Resources:

• R 4 DS Ch. 28.1 & 28.2: Quarto
• R 4 DS Ch. 1: Visualization

Tuesday August 27:

• Lecture 2: ggplot

Thursday August 29:

• Lab 1 (QMD Source Code) (Due Thursday September 5 - submit 1 per group via gradescope)
• Lab 1 key (QMD Source Code)

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Week Three (September 1 - September 7): Coding basics and importing data

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HW3 (Due Tuesday September 3 - submit via gradescope)

Video Resources:

• R basics and style + importing data (11:19)

Reading Resources:

• R 4 DS Ch. 2: Basics
• R 4 DS Ch. 4: Code Style
• R 4 DS Ch. 7: Data import

Tuesday September 3:

• Lecture 3: R basics

Thursday September 5:

• Lab 2 (QMD Source Code) (Due Thursday September 12 - submit 1 per group via gradescope)
• Lab 2 key (QMD Source Code)

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Week Four (September 8 - September 14): dplyr & Tidy Data & Joining Data

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HW4 (QMD Source) (Due Tuesday September 10 - submit via gradescope)

Video Resources:

• dplyr (21:01) R Script

Reading Resources:

• R 4 DS Ch. 3: Data Transform
• R 4 DS Ch. 5: Data Tidy
• dplyr + tidyr cheatsheet

Tuesday September 10:

• Lecture 4: Data Wrangling
• Lecture 4: Exercise Script

Thursday September 12:

• Lab 3 (QMD Source Code) (Due Thursday September 19 - submit 1 per group via gradescope)
• Lab 3 key (QMD Source Code)

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Week Five (September 15 - September 21): ggplot layers

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HW5 (QMD Source) (Due Tuesday September 17 - submit via gradescope)

Video Resources:

• ggplot + EDA (28:05) R Script

Reading Resources:

• R 4 DS Ch. 9: Layers
• R 4 DS Ch. 10: EDA
• ggplot cheatsheet

Tuesday September 17:

• Lecture 5: ggplot
• Exercise Script

Thursday September 19:

• Lab 4 (QMD Source Code) (Due Thursday September 26 - submit 1 per group via gradescope)

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Week Six (September 22 - September 28): Advanced Data Visualization

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HW6 (Due Tuesday September 24 - submit via gradescope)

Video Resources:

• data viz principles (22:05)
• data viz slides RMD Script

Reading Resources:

• R 4 DS Ch. 11: Communication

Tuesday September 24:

• Lecture 6: Nice Graphs
• Exercise Script

Thursday September 26:

• Lab 5 (QMD Source Code) (Due Thursday October 3 - submit 1 per group via gradescope)

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Week Seven (September 29 - October 5): Advanced Data Wrangling: Strings, Factors, Date/Time

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HW7 (QMD Source) (Due Tuesday October 1 - submit via gradescope)

Video Resources:

• Dates & Strings (12:06) (QMD Source)

Reading Resources:

• R 4 DS Ch. 16: Factors
• R 4 DS Ch. 17: Dates and times
• stringr cheatsheet
• lubridate cheatsheet

Optional Reading:

• R 4 DS Ch. 12: Logical vectors (review, skim)
• R 4 DS Ch. 13: Numbers (review, skim)
• R 4 DS Ch. 14: Strings (skim)
• R 4 DS Ch. 15.3: Regular Expressions (skim)

Tuesday October 1:

• Lecture 7: Strings and Dates
• Exercise Script

Thursday October 3:

• Lab 6 (QMD Source Code) (Due Tuesday October 8 - submit 1 per group via gradescope)

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Week Eight (October 6 - October 12): Midterm Exam

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Previous Take Home Exams

• Midterm Spring 2020 Questions 1 & 2
• Midterm Fall 2020 Part 1: 1 & 2 (in class), & Part 2
• Final Fall 2020 Questions 1 & 3
• Final Spring 2020 Question 2 & 4 (ignore SAS)

Previous In Class Exam

• Midterm Spring 2020 Questions 1, 3, 5, 6, 7

Tuesday October 8:

• In class Review
• Take home exam assigned 2024 midterm (QMD Source Code) (Due Tuesday October 15 at 925 AM)

Thursday October 10:

• In class exam.

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Week Nine (October 13 - October 19): R Shiny

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Tuesday October 15:

• Take Home Exam Due key PDF (Key QMD)
• Project Overview
• R Shiny Demo

Thursday October 17:

• Lab 7 (QMD Source File) (Lab 7 Key QMD)

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Week Ten (October 20 - October 26): Maps

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HW8 (Due Tuesday October 22 - submit PDF screenshot via gradescope)

Tuesday October 22:

• Leaflet Overview
• Leaflet Overview (R)
• Leaflet Demo (quarto source)

Thursday October 24:

• R mapping Demo (quarto source)
• R mapping key

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Week Eleven (October 27 - November 2): Conditional Logic, Loops, & Simulations

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HW9 (Due Tuesday October 29 - submit PDF screenshot via gradescope)

Tuesday October 29:

• Loops Demo (Quarto Source Document)

Thursday October 31:

• Lab 8: Monte Carlo Overview
• Lab 8: Monte Carlo R source
• Lab 8: Key - R code

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Week Twelve (November 3 - November 9): R Functions and Packages

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Tuesday November 5th:

• Intermediate Dashboard Submission
• R4DS: Ch 25: Functions
• Functions Demo (QMD Source code)
• Functions Demo Key

Thursday November 7th:

• R Packages Overview
• Lab 9: Functions and Packages (Quarto Source Code)
• Lab 9: Key

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Week Thirteen (November 10 - November 16): Predictive Modeling

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Tuesday November 12th:

• Intermediate Submission Feedback Due
• Predictive Modeling Demo (Quarto Source Code)

Thursday November 14th:

• Lab 10: Predictive Modeling (QMD Source)
• Lab 10 Key

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Week Fourteen (November 17 - November 23): Project Presentations

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Tuesday November 19th:

• Project Demos

Thursday November 21st:

• Project Demos

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Week Fifteen (November 24 - 30): Fall Break

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Monday November 25:

• Project Dashboard Submission Due at 11:59 PM (Submit to D2L)

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Week Sixteen (December 1 - December 7): Debugging/Review

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Tuesday December 3:

• Lab 11: Debugging (QMD source) (Due Thursday Dec. 5, submit to gradescope)

Thursday December 5:

• Class Review
• Final Exam Take Home Assigned (Submit to gradescope. Due Friday December 13 at 8:00 AM)

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Week Seventeen (December 8 - December 14): Finals Week

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Tuesday December 10:

• Final Exam In Class: Tuesday December 10 8:00am-9:50am
• Final Exam Take Home: Due Friday December 13 at 8:00 AM (QMD Source code)

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Course Syllabus

A PDF of the syllabus can be downloaded with this link.

This course provides an overview of statistical computation and data visualization.

Learning Outcomes:

Students will:

1. To become literate in statistical programming using R and SAS,

2. To learn to effectively communicate through visual presentation of data, and

3. To understand and imitate good programming practices.

Prerequisites

• STAT 337, or equivalent.

Textbooks and Resources (all optional or free)

1. R for Data Science, 2nd Edition, by Hadley Wickham, Mine Cetinkaya-Rundel, and Garret Grolemund. Free at https://r4ds.hadley.nz

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1. ModernDive: An introduction to Statistical and Data Sciences via R, by Chester Ismay and Albert Kim. Free at http://moderndive.com

2. Visualize This: The FlowingData Guide to Design, Visualization, and Statistics, by Nathan Yau, 2011.

3. The Art of R Programming: A Tour of Statistical Software Design, by Norman Matloff, 2011,

4. R cheatsheets. https://www.rstudio.com/resources/cheatsheets/

Course Policies

The course will be taught from a flipped perspective. Prior to attending class on Tuesdays, students will watch short online videos and submit homework assignments. Tuesdays will review online video lectures and include interactive computing components. Thursdays will be group labs which focus on implementing the programming covered during the week.

Grading Policy

• 5% of your grade will be determined by short weekly homework assignments that correspond to watching online videos. Homework will be collected via gradescope.

• 25% of your grade will be determined by group labs. Labs will be in-class group assignments conducted every Thursday. The labs will be designed to be completed in 75 minutes; however, there may be times that groups need to finish labs outside of class time. Labs will be submitted via gradescope.

• 25% of your grade will be determined by a midterm exam. The midterm exam will have two parts: an in class exam and a take home portion.

• 25% of your grade will be determined by a final exam. The final exam will have two parts: an in class exam and a take home portion.

• 20% of your grade will be determined by a course project. The course project will focus on advanced data visualization to create a dashboard using R Shiny.

Collaboration

University policy states that, unless otherwise specified, students may not collaborate on graded material. Any exceptions to this policy will be stated explicitly for individual assignments. If you have any questions about the limits of collaboration, you are expected to ask for clarification.

In this class students are encouraged to collaborate on labs and homework assignments, but exams should be completed without collaboration. Additionally the course project can be completed in groups of up to 3 students.

Citing Sources

When submitting work, please cite all sources and inspiration for your submission.

Academic Misconduct

Section 420 of the Student Conduct Code describes academic misconduct as including but not limited to plagiarism, cheating, multiple submissions, or facilitating others’ misconduct. Possible sanctions for academic misconduct range from an oral reprimand to expulsion from the university.

Disabilities Policy

Federal law mandates the provision of services at the university-level to qualified students with disabilities. If you have a documented disability for which you are or may be requesting an accommodation(s), you are encouraged to contact the Office of Disability Services and the instructor as soon as possible.