Agenda for today

• Course overview: Why learn R?
• Class logistics
• Variable types
• Installing R, RStudio and packages

The big data explosion

• John Naisbitt (1982): “We are drowning in information but starved for knowledge.”
  
• We need computing tools to make sense of data

What is R?

• S programming language: Developed in the mid-1970s in Bell Labs
  • An interactive approach to statistical computing: “… to turn ideas into software, quickly and faithfully”
  • Specifically designed for data analysis and graphics
• R is a version of S, with some ideas inspired by Scheme
• Created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand
• Conceptualized in 1992, announced on S-news mailing list in Aug 1993, initial version released in 1995, 1st stable version in 2000
• Currently developed by the R Development Core Team

Ross Ihaka & Rob Gentleman

Why learn R?

Reason #1: R was specifically designed for statistics and data analysis.

• Many features to aid data analysis which other programming languages don’t have
  
• Interactive, well-suited for exploratory data analysis and rapid prototyping
  
• Good graphical capabilities

Example: Map of 2016 U.S. presidential elections

Example: Spotify Top 100 Songs in 2017

Why learn R?

Reason #2: It’s popular!

(Source: stack overflow)

Why learn R?

Reason #3: It’s easy to get started with R.

• Open source: free!
• Several online tutorials and resources
• A lot of documentation (i.e. how-to pages)
• Active community developing “packages”
• RStudio
  • Open-source integrated development environment (IDE) for R
    
  • Several features which make programming and data analysis easier

Stack Overflow

Q&A site for programmers

Packages

(Source: Revolution Analytics)

Why learn R?

Reason #4: Analyses done in R are reproducible.

• R scripts (.R): Lines of R code meant to be run together
• R markdown files (.Rmd): Document format that combines code and text into a presentable document

R script

R script

# load packages and get dataset
library(ggplot2)
data(mtcars)

# plot of miles per gallon vs. horsepower, colored by no. of cylinders
ggplot(data = mtcars, aes(x = hp, y = mpg, col = factor(cyl))) +
    geom_point() +
    labs(title = "Miles per gallon vs. horsepower")

R markdown: input

(Source: Github: kjytay/FIFA-world-cup-2018)

R markdown: output

(Source: Github: kjytay/FIFA-world-cup-2018)

Why learn R?

Reason #5: Community

• Active online community (package development, online discussion and Q&A)
  
• Physical meet-ups
  • Full list here
  • Bay Area useR Group
  • R-Ladies San Francisco
  • rstudio::conf
  • useR!

How about you?

In the next minute, introduce yourself to someone around you!

• Name
• One interesting fact about yourself
• Why do you want to learn R?

Course objectives

By the end of this course, students will be able to:

• Navigate the R ecosystem at a basic level (RStudio, CRAN, R markdown, documentation)
  
• Open, manipulate, plot and save data
  
• Perform simple data modeling and statistical analyses in R

Note: Course is for undergraduates!!

Tentative overview of the course

• Week 1: Introduction to RStudio, basic objects in R
• Week 2: Data visualization
• Week 3: Transforming and cleaning data
• Week 4: Importing and publishing data
• Week 5: Applications (map-making, data modeling)

Class logistics

Class logistics

• Bring your laptop to class!
• Course website: https://web.stanford.edu/~kjytay/courses/stats32-aut2019/
• Office hours: Fridays 10am - 12pm, Sequoia Hall Room 105 (except Week 1: 1030am - 1230pm)
• Recommended text: R for Data Science by Garrett Grolemund and Hadley Wickham

Assignments

• Final project proposal (due Oct 16): 20%
• Final project (due Nov 2): 80%
• Programming exercises (ungraded)

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, …

Optional material

List of useful packages

• dplyr: Transform data
• ggplot2: Make nice plots
• readr: Import data into R
• tidyr: Clean data
• stringr: Tools for working with character strings and regular expressions
• lubridate: Make working with dates and times easier
• caret: Tools for training regression and classification models
• maps, ggmap: Tools for plotting spatial data
• shiny: Make interactive web apps

Other 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)