Meet the toolkit

Lecture 3

2024-05-20

About the syllabus

The basics

• Class meets Monday through Thursday
• In-class exercises most days
• A course project completed in pieces throughout the term. We’ll workshop these pieces in class before they’re due.

Grading

Category	Percentage
In-class exercises	35%
Project component 1: proposal	10%
Project component 2: descriptive statistics	10%
Project component 3: results	10%
Project component 4: presentation	10%
Project component 5: final paper	25%

Late work and regrades

• Project components and in-class exercises may be submitted up to 3 days late
• If you submit at least 80% of the daily exercises on time, you will receive full credit for the “in-class exercises” portion of your grade!
• I’ll only change a grade if I’ve made a clear error

Academic honesty

You must be the principal author of your own work!

Basically:

• You can collaborate with other students, but don’t copy their code directly
• You can use online resources (like StackOverflow, but if you use code from these sources, make that clear)
• Don’t let AI write your code

Toolkit: Computing

Learning goals

By the end of the course, you will be able to…

• gain insight from data
• gain insight from data, reproducibly
• gain insight from data, reproducibly, using modern programming tools and techniques
• gain insight from data, reproducibly and collaboratively, using modern programming tools and techniques
• gain insight from data, reproducibly (with literate programming and version control) and collaboratively, using modern programming tools and techniques

Today’s slides adapted from Dr. Mine Çetinkaya-Rundel’s Intro to Data Science (STA199) course

Reproducible data analysis

Reproducibility checklist

What does it mean for a data analysis to be “reproducible”?

Near-term goals:

• Are the tables and figures reproducible from the code and data?
• Does the code actually do what you think it does?
• In addition to what was done, is it clear why it was done?

Long-term goals:

• Can other people check your work?
• Can the code be used for other data?
• Can you extend the code to do other things?

Toolkit for reproducibility

• Scriptability \(\rightarrow\) R / RStudio
• Literate programming (code, narrative, output in one place) \(\rightarrow\) Quarto
• Version control \(\rightarrow\) Git / GitHub

R and RStudio

R and RStudio

• R is an open-source statistical programming language
• R is also an environment for statistical computing and graphics
• It’s easily extensible with packages

• RStudio is a convenient interface for R called an IDE (integrated development environment), e.g. “I write R code in the RStudio IDE”
• RStudio is not a requirement for programming with R, but it’s very commonly used by R programmers and data scientists

R vs. RStudio

Source: Modern Dive.

A short list (for now) of R essentials

• Functions are (most often) verbs, followed by what they will be applied to in parentheses:

do_this(to_this)
do_that(to_this, to_that, with_these)

• Packages are installed with the install.packages() function and loaded with the library function, once per session:

install.packages("package_name")
library(package_name)

R essentials (continued)

• Columns (variables) in data frames are accessed with $:

dataframe$var_name

• Object documentation can be accessed with ?

?mean

Tour: R and RStudio

Quarto

Quarto

• A text editor, like Microsoft Word or Google Docs–but it does code too!
• Fully reproducible reports – each time you render the analysis is run from the beginning

Tour: Quarto

How will we use Quarto?

• Every assignment / report / project / etc. is a Quarto document
• You’ll always have a template Quarto document to start with
• The amount of scaffolding in the template will decrease over the semester

Toolkit: Version control, organization, collaboration

Git and GitHub

• Git is a version control system – like “Track Changes” features from Microsoft Word, on steroids
• It’s not the only version control system, but it’s a very popular one

• GitHub is the home for your Git-based projects on the internet – like DropBox but much, much better

• We will use GitHub as a platform for web hosting and collaboration (and as our course management system!)

How it all fits together

Install time!

• Setting up is the hardest part…
• Then go to our course webpage , click on “Computing” in the side bar, and then click on “Setup”.
• Today’s exercise: Work through these installation steps with a partner on the same operating system