CMPSC301: Data Science

Activity 01: R Foundations and Randomness

Assigned and Due

• Assigned: Friday, 4th September 2026

• Due and Expiration: Wednesday, 9th September 2026 by classtime.

Note: There is no class on Monday, 7th September thanks to Labor day!!

• Estimated Time: Approximately 40 minutes

Note: the expiration date is the last date you can submit your work for a grade.

Table of contents

• CMPSC301: Data Science
  • Assigned and Due
  • Table of contents
  • Deliverables
  • Project Goals
  • Introduction to R Fundamentals
  • Part 1: Tutorial 1 - Variable Workout
  • Part 2: Tutorial 2 - Campus Event Data Challenge
  • Part 3: Tutorial 3 - Can You Beat Randomness?
  • Reflection Questions
  • Checking Your Work

Note: Parts of this work were enhanced by Claude.

Deliverables

You are to complete and push to your repository the following files:

• tutorials/tutorial_01_variable_workout/variables.R - Completed R source code
• tutorials/tutorial_02_event_data/events.R - Completed R source code
• tutorials/tutorial_03_randomness/randomness.R - Completed R simulation code
• writing/reflection.md - Reflection document with answers to all questions

Project Goals

• To practice creating and updating numeric, character, and logical variables in R.
• To use descriptive variable names and the <- assignment operator.
• To create numeric vectors with c() and analyze them with sum(), mean(), min(), and max().
• To use vectorized arithmetic and logical indexing to answer a small data question.
• To simulate random outcomes with sample() and replicate().
• To use proportions and a histogram to reason about probability and evidence.
• To develop the habit of testing R code in small, meaningful steps.

Introduction to R Fundamentals

R is designed for working with data. A variable gives a useful name to one value, while a vector stores a sequence of values of the same type. In this activity, you will use variables to describe information and vectors to answer questions about a fictional campus event.

Use the Week 1 course slides as a reference while you work. In particular, review variable assignment, data types, vector creation with c(), vectorized operations, summary functions, comparisons, and logical indexing.

Part 1: Tutorial 1 - Variable Workout

In this tutorial, you will create numeric, character, and logical variables and then use each variable in an expression.

Location: tutorials/tutorial_01_variable_workout/

What you will do:

1. Create variables that describe your commute, a data-science interest, and your R experience.
2. Update or transform each variable using arithmetic, toupper(), and !.
3. Add one variable of your own and use it in an expression or function.

Getting Started:

Load your code using RStudio: tutorials/tutorial_01_variable_workout/variables.R Or, load it using VSCode to edit, and then run it later using the command:

 Rscript tutorials/tutorial_01_variable_workout/variables.R

Complete every TODO section in variables.R. Run the script after each small change and read the printed output.

Part 2: Tutorial 2 - Campus Event Data Challenge

In this tutorial, you will analyze attendance counts from a fictional five-day campus data-science event. The program uses only base R, so no packages are required.

Location: tutorials/tutorial_02_event_data/

What you will do:

1. Create a numeric vector with c().
2. Use sum(), mean(), min(), and max() to summarize attendance.
3. Apply a vectorized adjustment to every attendance value.
4. Use a comparison and logical indexing to identify high-attendance days.
5. Complete a short challenge using your own numeric vector.

Getting Started:

Load your code using RStudio: tutorials/tutorial_02_event_data/events.R Or, load it using VSCode to edit, and then run it later using the command:

Rscript tutorials/tutorial_02_event_data/events.R

Complete every TODO section in events.R. Your script should run without errors when you finish.

Tips:

• R indexes vectors starting at 1: attendance[1] is the first value.
• attendance + 10 adds 10 to every value in the vector.
• attendance[attendance >= 50] keeps only values that meet the condition.
• Use print() to inspect a variable when you are unsure of its value.

Part 3: Tutorial 3 - Can You Beat Randomness?

In this probability extension, you will simulate 10,000 experiments of 20 fair coin flips and investigate how often an experiment contains seven heads in a row. You will also explore weighted dice in an interactive Shiny app.

Location: tutorials/tutorial_03_randomness/

What you'll do:

1. Simulate coin flips with sample().
2. Repeat an experiment with replicate().
3. Calculate counts and proportions with sum() and mean().
4. Interpret a histogram of the number of heads across many experiments.
5. Use the Shiny app to compare a fair die with a weighted die.

Getting Started:

Load your code using RStudio: tutorials/tutorial_03_randomness/randomness.R Or, load it using VSCode to edit, and then run it later using the command:

 Rscript tutorials/tutorial_03_randomness/randomness.R

Complete every TODO section in randomness.R.

Then, check out the Shiny App in which you can complete experiments using virtual dice to roll.

To Run the Shiny App, use Rstudio to load the file, tutorials/tutorial_03_randomness/app.R

To run the Shiny App (a data dashboard) you might need to install a library in R to help the app work. Rstudio could conveniently handle this task for you. The code for the Shiny App provides a complete exploration tool rather than a code-completion task. Run it several times with the same settings, then change the number of rolls and the chance of a drawing a six on the die. Notice what changes and what stays consistent. As a data scientist, you will be watching how values change after making small changes to your analysis.

Reflection Questions

After completing all three tutorials, answer the reflection questions in the writing/reflection.md file. These questions will help you connect the code you wrote with the R concepts from this week's slides.

Checking Your Work

To check if your work meets the assignment requirements, you can use GatorGrade:

gatorgrade --config config/gatorgrade.yml

This will automatically verify:

• All required files exist
• All TODO markers have been removed
• The reflection document is complete
• You have made at least 3 commits to your repository

Note: Make sure to commit your changes regularly throughout the activity using:

git add .
git commit -m "Descriptive message about your changes"
git push

If you have any questions, please ask your friendly Technical Leader, or your instructor.