Intro to STAT 331 / 531 + Intro to R

Week 1 Day 1

Tuesday, September 24

Today we will…

Introductions

Hi, I’m Dr. Theobold!

  • I am originally from Colorado, but went to grad school in Montana.

  • My favorite things are being outside, drinking coffee, and watching women’s basketball.

A picture of my wife and I backpacking in Yosemite. We are standing in front of a large blue lake (called Thousand Islands Lake), with some snow covered, scraggly peaks in the background.

  • I love cats!

  • I’ve been programming in R since 2014!

  • I absolutely love teaching this course!

A picture of my two kitties, snuggling on the couch. Both kitties are facing each other, with their noses touching.

On a personal note…

I prefer you refer to me as “Dr. Theobold”, or “Dr. T”, or “Professor Theobold.”

Please don’t call me “Allison” or “Professor.”


I use they / them pronouns. If you are not sure what this means, I’d encourage you to read more about gender identity and pronouns. 🙂

A picture of a very pink fruit smoothie, with sprinkles on top.

Our Classroom Learning Assistant!

We will be joined in class by Jasmine.

Jasmine is…

  • a forth-year Statistics major pursuing a Data Science minor,
  • originally from Irvine, CA,
  • someone who loves sushi, hanging out with friends, watching Netflix, and working out.

You!

I am looking forward to reading your introductions on Discord!


Please read the intros of your classmates so you can discover who you will be learning with this quarter!

Course Layout

Beginning of the Week

  • Look over the coursework page
  • Read the required chapter(s)
  • Watch the required video(s)
  • Complete the Check-ins
  • Review chapter content
  • Ask clarifying questions
  • Discuss example analyses
  • Work in pairs to complete the Practice Activity
  • Finish whatever remains of the Practice Activity
  • Complete any additional reading / videos / Check-ins

End of the Week

  • Debrief Practice Activity
  • Warm-up review
  • Start Lab Assignment
  • Finish remaining problems of the Lab Assignment
  • Complete Challenge Assignment

Labs & Challenges Due Sunday by Midnight

To keep everyone on track with the coursework, your Lab and Challenges are due by midnight every Sunday. You are permitted to request up to four (4) deadline extensions by filling out the deadline extension form.

R Basics

Data Types

  • A value is a basic unit of stuff that a program works with.

  • Values are allowed to have different data types:

  1. logical / boolean: FALSE / TRUE or 0 / 1 values.
  1. integer: whole numbers.
  1. double / float / numeric: decimal numbers.
  1. character / string - text values.

Variables

are names that refer to values.

  • A variable is like a container that holds something - when you refer to the container, you get whatever is stored inside.

  • We assign values to variables using the syntax object_name <- value.

    • This can be read as “object name gets value.”
message <- "So long and thanks for all the fish"
year <- 2025
the_answer <- 42.5
earth_demolished <- FALSE

Data Structures

every element has the same data type

  • Vector: a one-dimensional column of homogeneous data.

  • Matrix: the next step after a vector - it’s a set of homogenous data arranged in a two-dimensional, rectangular format.

elements can be of different types

  • List: a one-dimensional column of heterogeneous data.

  • Dataframe: a two-dimensional set of heterogeneous data arranged in a rectangular format.

Indexing

We use square brackets ([]) to access elements within data structures.

  • In R, we start indexing from 1.
vec[4]    # 4th element
vec[1:3]  # first 3 elements
mat[2, 6]  # element in row 2, col 6
mat[ , 3]   # all elements in col 3
li[[5]]    # 5th element
li$elementName # all elements in "elementName"
df[1, 2]     # element in row 1, col 2
df[17, ]     # all elements in row 17
df$colName  # all elements in the col named "colName"

Logic

We can combine logical statements using and, or, and not.

  • (X AND Y) requires that both X and Y are true.

  • (X OR Y) requires that one of X or Y is true.

  • (NOT X) is true if X is false, and false if X is true.

x <- c(TRUE, FALSE, TRUE, FALSE)
y <- c(TRUE, TRUE, FALSE, FALSE)
x & y
[1]  TRUE FALSE FALSE FALSE
x | y
[1]  TRUE  TRUE  TRUE FALSE
!x
[1] FALSE  TRUE FALSE  TRUE

Group Programming Warm-up

Broken Circles

To set the stage for your future group collaborations, we will complete an activity that explores how working in groups can be more / less successful.

Let’s start by dividing the class into groups of 6 students.

An image of whit e circles with slices cut out. The pieces are labeled A, B, C, D, and E to indicate how they should be assembled to create a complete circle.

Activity Guidelines

  1. No talking or other noises!
  2. No pointing or hand signals!
  3. No taking pieces. You may only give pieces to others.

Your group is finished once everyone has a complete circle.

Discussion

  • What do you think this game was about? What was its purpose?
  • What did your group did that made you cooperate more successfully?
  • What did your group did that made cooperation harder?
  • What are some behaviors that could be implemented in the future to make cooperation easier?

A Data Puzzle

A Data Puzzle

Arrange the different pieces (tables and graphs) based on the actions that led to each new table / graph.


Once you have arranged each of the tables and graphs, answer the following questions:

  1. What is the most common type of work location - remote or in-person?
  2. Does work location type vary by company size?

Groupworthy Data Science

Groupworthy Data Science

The purpose of the study is to understand how an instructor’s pedagogy impacts the equity of group collaborations, specifically as they relate to pair programming.

Before class on Thursday…

Before class on Thursday…

  • Complete the Version Control coursework

  • Check-ins 1.6 - 1.9: Getting version control set-up

    • Due Thursday (9/26) by the start of class

  • Introduce yourself on Discord!

    • Due by Friday (9/27)