Welcome to STATS 32!

Zoom etiquette (subjecct to change as we figure this out)

• Leave your video off
• Stay muted unless asking a question - there are 40-50 of us here, and we don’t want to be interrupted by background noise
• To ask a question, click “raise hand” and wait for me to call on you
• Please email me feedback after lectures! When lecturing, I can usually get a feel for whether or not people are engaged, confused or bored from body language. Since I can’t see you, I’m relying on you to tell me.

What is R?

• A scripting language designed specifically for statistical data analysis

What are it’s strengths?

• The syntax and logic are uniquely suited for statistics analysis - though somewhat quirky, it allows you to express common statistical ideas clearly and succinctly, where this might be awkward in (say) python’s sklearn or statsmodels
• A huge ecosystem of statistical and data-analytic libraries and packages with excellent documentation that usually includes mathematical explanations and justifications of procedures (a rare find!)
• Beautiful and flexible data visualisation tools in ggplot and relatives
• A (relatively) recently modernised interface in the tidyverse, which is the focus of this course
• Great for writing up analysis through RMarkdown - all lecture slides will be written this way!

print("Hello, world!")

## [1] "Hello, world!"

• It’s free!

What are you better off finding elsewhere?

• Processing really big datasets - R is not a computational beast, and is best suited to medium-sized datasets. For efficiency and parallelisation, you probably want something like Hadoop
• Big-ol tensor multiplication - if you’re training neural networks, TensorFlow is the way to go
• Automating regular tasks - R is a domain-specific programming language, and while it can technically do anything that a general-purpose language can do, the results are invariably horrifying

Administrative details

• The course website is the comprehensive guide to all things procedural
• Lectures: Weeks 2-5, April 6 to April 29, TTh 12:30 - 1:50pm
• Office hours: Weeks 2-6, April 7 to May 5, W 9:00 - 11:00am
• Grading: Credit/no credit basis
  • Final project (80%), due Fri, May 7
  • Final project proposal (20%), due Fri, April 23
  • Much more detail can be found on the course website
• While not using a specific textbook, the course runs parallel to Hadley Wickham’s free online book R for Data Science. It’s very accesibly written, and I encourage you to read as much of it as you can!
• In the current climate, things can change unexpectedly, so please monitor the course changes page closely

Structure of the course

Before the session

• Read the assigned and suggested texts - you will get much more out of each lecture if you do this!
• View the recorded lecture corresponding to that session.

During the session

• I briefly present the relevant theory and technical details
• In breakout rooms, work through the lab for the session – these will let you practice all the concepts covered in lecture and let you practice with the relevant commands and R programming idioms.

After the session

• Do the assigned programming questions (these will not be graded, but will address details and subtelties that we don’t have time for in lecture)
• Play around with the session’s dataset!

Fun R stuff

• R shiny dashboard for correlations between various stats for NBA 2017-18 season
• Making XKCD-like graphs
• Animated plots using various R packages
• Visualizations projected on the earth as a globe
• Visualizing soccer world cup goals

R-bloggers

Blog aggregator of content contributed by bloggers who write about R

The R Journal

Bi-annual open-access journal: Features short to medium length articles covering topics of interest to R users and developers

R-exercises

Website with both tutorials and exercises

DataCamp

Website for learning data science, R included (some courses free, some not)

Using RStudio

Rmarkdown

What is a variable?

• An “envelope” or “bucket” where information can be stored and referenced
• An abstraction that allows us to deal with the underlying structure and relatioships
• Each “bucket” has a name on the outside, contains information inside
  • Variable name: name of the bucket
  • Variable value: information on the inside
• Variable assignment: process of putting information inside a bucket

Variable types

• Every programming language has the notion of data types
• The object’s type determines what operations can be done with it
  • E.g. You can add 2 numbers, but can you add a number and a letter?
• Common types
  • integer (…, -1, 0, 1, 2, …)
  • double (real numbers),
  • character (“a”, “R is cool”, “the quick brown fox…”, “1”)
  • Boolean (TRUE or FALSE)

Confusion: 123 vs. “123”

How to differentiate between numeric variables and character variables which consist of digits?

• Should I treat it as a number like I do in math?
  • e.g. Does it make sense to add 2 of them together?
  • e.g. Does it make sense to compare their relative sizes?
• Numeric variables: counts, age, …
• Character variables: Phone numbers, ZIP codes, …

Let’s get our hands dirty!

Today’s dataset: Uber and Green taxi trips in New York

• rides.csv: Time, location, duration, etc. of rides made in Uber and Green taxi cabs in 2014 and 2015
• weather.csv: Temperature, precipitation, snowfall, etc. in New York in 2014 and 2015

Some questions

• How do rides in the two companies compare?
• How does demand change in space and time?
• Does weather affect cab rides? If so, by how much? And is the effect different for different companies?