code

code — the scripts from class

This folder holds the R and Python scripts we work through in class. A script is just a text file full of code, saved so you can run it again later; its name ends in .R or .py.

You'll come here when a class session or a textbook chapter points you at a file. You don't need to read the whole folder, and you never need to run every file — open the one for the session you're on and leave the rest alone.

The tidy, written-out version of every analysis lives in the textbook at timothyfraser.com/sigma. What's in here is the in-class working copy of the same material.

How to run one of these

You'll do this in Posit Cloud, the browser version of R you set up in the first workshop. Nothing to install.

1. Open the course project. In the Files window (bottom right), click the code folder, then click the file you want.
2. It opens in the Scripts window (upper left).
3. Highlight a line or two with your cursor, then press CTRL and ENTER simultaneously — or click the Run button above the script. (On a Mac, use the Mac equivalent, Command and Return.)
4. The result shows up in the Console (bottom left). Read it, then move down and run the next couple of lines.

The .py scripts work the same way, and it matters more there: they're written REPL-style, so running one top to bottom prints almost nothing. Open an interactive Python console and send a few lines at a time, exactly as you would in R.

Go a few lines at a time. Don't run the whole file top to bottom. Most of these scripts were typed live, in class, while working through a problem with students. They read that way on purpose: false starts, a first attempt that a better one replaces a few lines later, and the occasional bit of pseudo-code that was only ever meant for the board. Running the file straight through will throw errors that aren't your fault. Running it a few lines at a time is how it's meant to be used — and it's how you actually see what each line does.

If you get an error

Errors are normal. You will get them constantly, and so does everyone else, including your instructor. Nearly all of them are one of three things:

• there is no package called ... or could not find function ... — the script needs a package (a bundle of extra commands someone else wrote and shared) that isn't installed yet. Run workshops/packages.R once, at the start of the term, and this goes away. That's normal, not something you broke.
• cannot open file 'workshops/onsen.csv' — R is looking in the wrong folder. workshops/README.md has the one-click fix.
• a missing parenthesis or quotation mark — the most common error there is. Count your ( against your ), and your " against your ".

If it's none of those, bring the error to recitation or office hours. Paste the whole message; it usually says more than it looks like.

R or Python?

Both tracks are first-class, and you only need one. Pick the language that fits your background and stay with it.

A script and its twin in the other language share a name: 01_workshop.R and 01_workshop.py are the same class session, each written in the style of its own textbook chapter. Not every session has both twins.

What the file names mean

The number at the front is the class session number — the number on the slide deck that was up on screen. 11_workshop.R is the code from Workshop 11. It is not the textbook chapter number, and not the calendar week.

Part of the name	What it means
11_	the class session number, zero-padded. 11_workshop.R goes with Workshop 11.
07b_	a session split across two decks: 07b_workshop.R goes with Lesson 7B.
_workshop	the main in-class coding session for that slot
_lesson	a shorter walkthrough of a single technique
_recitation	extra practice, worked in recitation
_solutions	a worked answer key, shared on purpose
_<topic>	added only when one session draws on two chapters and the names would collide (session 9: 09_workshop* is maximum likelihood, 09_recitation* is fault tree analysis)
00_, ZZ_	side topics and demos that don't belong to any one session
.R / .py	the R track and the Python track
apps/	a folder, not a session — the Shiny dashboards, plumber APIs, and parameterized report

Every script opens with a short header naming itself, the author, the session it belongs to, and the textbook chapter it pairs with. If you're not sure what a file is for, read its first four lines.

What the _solutions files are

A file ending in _solutions is a worked answer key — the same exercise with the answers filled in. They're shared deliberately, so use them. The order that actually teaches you something is: try it yourself, get stuck, then open the solutions file and compare line by line. Reading the answer before you've tried it feels efficient and teaches you almost nothing.

Finding the file you want

The table in the top-level README.md lists every textbook chapter by title, with the scripts and datasets that go with it. That's the fastest way in.

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_{Maintainers: conventions for this folder are in CLAUDE.md.}