Software Tools for Data Analysis
STA 9750
Michael Weylandt
Week 3 – Tuesday 2026-09-15, Thursday 2026-09-17
Last Updated: 2026-09-17

STA 9750 Week 3

Today: Lecture #03: Coding in R: Variables, Control Flow, Packages, Function Calls

These slides can be found online at:

https://michael-weylandt.com/STA9750/slides/slides03.html

In-class activities can be found at:

https://michael-weylandt.com/STA9750/labs/lab03.html

Upcoming TODO

Upcoming student responsibilities - Tuesday Section:

Date	Time	Student Responsibility
2026-09-22	6:00pm ET	Pre-Assignment #04 Due
2026-09-24	11:59pm ET	Team Roster Submission
2026-09-25	11:59pm ET	Mini-Project #00 Due
2026-09-29	6:00pm ET	Team Contract Due
2026-09-29	6:00pm ET	Project Proposal Presentation Slides Due
2026-10-05	11:59pm ET	Mini-Project Peer Feedback #00 Due
2026-10-06	6:00pm ET	Pre-Assignment #05 Due
2026-10-13		Classes on Monday Schedule – Columbus Day Conversion Day

Upcoming TODO

Upcoming student responsibilities - Thursday Section:

Date	Time	Student Responsibility
2026-09-24	6:00pm ET	Pre-Assignment #04 Due
2026-09-24	11:59pm ET	Team Roster Submission
2026-09-25	11:59pm ET	Mini-Project #00 Due
2026-10-01	6:00pm ET	Team Contract Due
2026-10-01	6:00pm ET	Project Proposal Presentation Slides Due
2026-10-05	11:59pm ET	Mini-Project Peer Feedback #00 Due
2026-10-08	6:00pm ET	Pre-Assignment #05 Due
2026-10-15	6:00pm ET	Pre-Assignment #06 Due

Other Important Dates

Other Important Dates:

Date	Instructor Action
2026-09-15	Course Project Description Finalized
2026-09-22	Mini-Project Released #01
2026-09-28	Mini-Project Peer Feedback Assigned #00
2026-10-02	Estimated Return of Peer Comments #00
2026-10-11	Estimated Return of Meta-Review Comments #00
2026-10-13	Classes on Monday Schedule – Columbus Day Conversion Day #01

Course Project

Course project description is now online

Detailed discussion of:

Project structure
Key deadlines
Grading rubrics

Please send me questions!

Team rosters due at 2026-09-24 - keep posting online to find a team.

Course Project

Teams: 4-6 classmates
Stages:
- Proposal (in class presentation)
- Mid-semester check-in (in class presentation)
- Final: in class presentation, individual report, summary report
Structure:
- Shared “Overarching Question” (OQ)
- Individual “Specific Question” (SQ, one per teammate)

Full description online

Course Project

Conceit:

Consultants hired by client interested in qualitative OQ
You break OQ into several quantitative SQs
Combine results from SQs to answer OQ

Process:

Proposal: Sales pitch for consultants - here’s the OQ we will answer
Mid-Semester: Scope of work - here’s the SQs and data we will use
End-of-Semester: Presentation of Results
- Group Report: “Executive Summary”
- Individual Reports: “Technical Appendices”

Course Project

Two weeks: project proposal presentations

Major goal:

What is the overarching / motivating question for your project?

See instructions for more

Post slides and team contract to Brightspace before class - you will present from your computer

Overarching Question

Example Topic: NYC Apartment Prices

Very Bad: Is there a relationship between rent and location?
- Problems: Binary, Obvious, Limited room for exploration
Bad: What is the correlation between rent and location?
- Problems: Simple (one numeric answer), Linear, Limited room for exploration
Good: What is the relationship between rent and location?
- Strengths: Allows more interesting analysis, subgroups / differential impact
- Weaknesses: Very broad (lots has been said about this), not clear how to split up
Great: What are the key drivers of NYC rents and do they suggest multiple market segments with different relative priorities?
- Strengths: Allows project to be split up (different factors), clear intent to integrate findings, working hypothesis (multiple segments)

Finding Data

Advice:

Start early!
NYC Open Data is great
- See also: FRED, Federal Open Data, Nasa EarthData
- Kaggle is not really a primary source
- Ask on Teams for pointers
- Lots of data hidden in Wikipedia
Nothing paid or private without express instructor submission
Everyone loves spatial data!
Avoid overdone topics (NYC rents, NYC schools) - new topics => low-hanging fruit

Special Presentation

Data Science Resources at the Baruch Libraries

Presenter: Jason Amey (Baruch Business Librarian and Former STA 9750 Student)

Course Support

Asynchronous Support: Teams Discussion Board

67 students registered on Teams
Still waiting for 20

Synchronous Support: Office Hours

Tuesdays & Thursdays at 5pm (Zoom)

Pre-Assignments

Pre-assignment quizzes

30 point quiz
- My intent is for everyone to get 30/30
- Take as many times as needed
- Settings tweaked to only re-show incorrect questions
I sometimes give feedback through Brightspace, so check ‘feedback’ if you are alerted to any

Today

Brief Review of Quarto Render + Git Commit Cycle
Introduction to R Programming
Key Ideas in R: Data Frames, Functions, Packages and Control Flow
Wrap Up

Git Review

Render + Commit Cycle

In Mini-Project #00, your goal is to get rendered HTML to GitHub

‘Kick-the-tires’ on Set-Up
Things will go wrong - better now than later

Once you do this once, cycle becomes easier

Git Workflow

Three key commands:

git add: add some changes to a ‘box’
git commit: seal the ‘box’
git push: send the ‘box’ to GitHub

Git pane in RStudio shows uncommited changes, not files.

If a file ‘vanishes’ after a commit, that’s good!

Render + Commit

For ongoing changes / updates:

Render button in RStudio - generate new / change HTML
Check boxes next to all changed files - Stage updates
Hit Commit and use modal to make a Commit - Commit a new version in Git
Push - Send new version to GitHub. Website will update automatically

Render + Commit

Tips:

Go to your GitHub.com - see if expected files are present
When in doubt, better to include everything in docs
Cycle rapidly - find problems ASAP

Storage is cheap - your time isn’t

Getting Started with `R`

Programming in R

It’s now time for us to start writing code in R

No more copy and trust

Goals:

Modify existing code to new applications
Write code to use existing libraries
Read and debug code

Execution Model

Three models of executing code:

Line-by-line at Console
- REPL: Read Evaluate Print Loop
- Best for transient, one-off actions; trying new things
Script writing in a separate file
- Write in a separate (.R) file
- Executes in same session; persistent state
- Best for longer analyses with complex commands, developing code
Code in a Quarto document
- Write code in chunks inside a qmd file
- Executes in a fresh session
- Best for documenting and conveying analysis, archiving results

Arithmetic in R

Basic arithmetic in R runs as expected

1 + 2 + 3 + 4 + 5

[1] 15

PEMDAS Ordering: Parentheses, Exponents, Multiplication/Division, Addition/Subtraction

Arithmetic in R

\[3^{2 * 5 - 1} / 24^5\]

3^(2 * 5 - 1) / 24^5

[1] 0.002471924

\[\frac{1^1 + 2^2 + 3^3}{3^1 + 2^2 + 1^3}\]

(1^1 + 2^2 + 3^3) / (3^1 + 2^2 + 1^3)

[1] 4

When in doubt, extra parentheses don’t hurt

Function Calls

To go beyond basic arithmetic, need to invoke functions

\[ \cos(\pi) + \tan\left(\frac{\pi}{4}\right) + \sqrt{\sin(\pi/2)} - e^1\]

cos(pi) + tan(pi / 4) + sqrt(sin(pi/2)) - exp(1)

[1] -1.718282

Function Calls

All function calls have a fundamental syntax:

name()

e.g.,

R.Version()

To get help with any function in R, type ?name

Function Calls

Most interesting functions require input:

name(argument)

Here, the argument is input to the function:

cos(pi)

Multiple arguments are separated by commas

atan2(-1, 1)

Function Calls

Type a name without () to see its implementation

IQR

function (x, na.rm = FALSE, type = 7) 
diff(quantile(as.numeric(x), c(0.25, 0.75), na.rm = na.rm, names = FALSE, 
    type = type))
<bytecode: 0x11737fa88>
<environment: namespace:stats>

and

sd

function (x, na.rm = FALSE) 
sqrt(var(if (is.vector(x) || is.factor(x)) x else as.double(x), 
    na.rm = na.rm))
<bytecode: 0x117635110>
<environment: namespace:stats>

Conceptually sqrt vs sqrt(4) is “concept of square rooting” vs “the actual square root of 4, i.e., 2”

Function Calls

Most important for users are the first few lines (args)

Define optional and required inputs

args(log)

function (x, base = exp(1))

Two arguments:

x: the input
base: optional 2nd argument with default \(e\) (natural log)

Function Calls

Every argument has a name, but not always required

R is usually smart about knowing what you meant

These are equivalent:

log(10)
log(10, exp(1))
log(x = 10)
log(x = 10, exp(1))
log(x = 10, base = exp(1))
log(base = exp(1), x = 10)
log(base = exp(1), 10)

Vector Semantics

Often when dealing with data, we want to transform related data similarly:

E.g., change all temperatures in data set from F to C

Dangerous to only do part

R has vectorized semantics - whenever possible, do same operation to all numbers together

1:10 # A vector

 [1]  1  2  3  4  5  6  7  8  9 10

sqrt(1:10) # Keeps same vector structure

 [1] 1.000000 1.414214 1.732051 2.000000 2.236068 2.449490 2.645751 2.828427
 [9] 3.000000 3.162278

Vector Semantics

“Vectorization” comes from math (linear algebra):

\[\begin{pmatrix} 1 \\ 2 \\ 3 \end{pmatrix} + \begin{pmatrix} 4 \\ 5 \\ 6 \end{pmatrix} = \begin{pmatrix} 5 \\ 7 \\ 9 \end{pmatrix}\]

but extends beyond just vector addition

Vector Semantics

Most functions in R try to vectorize, but not always possible

sqrt(1:10)

 [1] 1.000000 1.414214 1.732051 2.000000 2.236068 2.449490 2.645751 2.828427
 [9] 3.000000 3.162278

cospi((1:8) / 4)

[1]  0.7071068  0.0000000 -0.7071068 -1.0000000 -0.7071068  0.0000000  0.7071068
[8]  1.0000000

But

sum(1:10)

[1] 55

and

rev(1:10)

 [1] 10  9  8  7  6  5  4  3  2  1

Vector Index Printing

The [1] you sometimes see is R just letting you know where in a vector you are:

1:10

 [1]  1  2  3  4  5  6  7  8  9 10

Each line gets a new index:

sqrt(1:10)

 [1] 1.000000 1.414214 1.732051 2.000000 2.236068 2.449490 2.645751 2.828427
 [9] 3.000000 3.162278

More complex objects have alternate print styles:

matrix(1:9, nrow=3, ncol=3)

     [,1] [,2] [,3]
[1,]    1    4    7
[2,]    2    5    8
[3,]    3    6    9

Print width is controlled by getOption("width").

Variables

Often, we want to save a several values as a single ‘thing’

x <- 1:5

This is an assignment operator. Vector formed by 1:5 is labeled x

Naming x later gives us this vector

[1] 1 2 3 4 5

Can be used in functions

sqrt(x)

[1] 1.000000 1.414214 1.732051 2.000000 2.236068

Vectors and Variables

If you need to write a vector ‘by hand’, use the c function:

x <- c(1, 4, 9, 16, 25)
x

[1]  1  4  9 16 25

then

sqrt(x)

[1] 1 2 3 4 5

We won’t usually hand-write vectors like this:

Data comes in vectors (e.g., spreadsheet columns)

Vector Access

Use [] operator to get individual elements of a vector:

x <- sqrt(1:10)
x[4]

[1] 2

Can do more complex indexing, but we won’t use it much:

x[1:5]

[1] 1.000000 1.414214 1.732051 2.000000 2.236068

x[6:10]

[1] 2.449490 2.645751 2.828427 3.000000 3.162278

x[-1]

[1] 1.414214 1.732051 2.000000 2.236068 2.449490 2.645751 2.828427 3.000000
[9] 3.162278

In-Class Activity

Weekly Lab

Lab #03

New topics to cover:

classes of objects
Using Packages
Comments
Writing functions
Control Flow

Object classes

Everything in R has a type or class:

Integer, Double (i.e. 64 bit number allowing decimals), Character, …

All vectors elements must have the same class - this is the vector’s class

x <- 1:5
y <- sqrt(x)

class(x)

[1] "integer"

class(y)

[1] "numeric"

class(letters)

[1] "character"

Using Packages

A package is a set of code (and data) packaged up for distribution and use

R has many helpful packages - these are distributed via CRAN (presently 25055)

Using packages is a two-step process:

Get package from CRAN to your computer (one time)
Loading into R (every time)

Think of regular software: you download MS Office once but need to start it whenever you want to use it

Using Packages

The install.packages function will download and install a package:

install.packages("ggplot2")

If that package uses other packages, R will sort that out automatically

When ready to use a package, use the library() command to ‘start’ it:

library(ggplot2)

Now I have access to everything in that package

Comments

Comments are text inside the code that R ignores

Everything following a # gets ignored

Compare

tan(45 * pi / 180) Compute the tangent of 45 degrees

Error in parse(text = input): <text>:1:20: unexpected symbol
1: tan(45 * pi / 180) Compute
                       ^

with

tan(45 * pi / 180) # Compute the tangent of 45 degrees

[1] 1

Comments

The best comments don’t just say what you are doing. They say why you are doing something in the way it is being done

More discussion of comments later as we write more complex code

Recycling Rules

Alignment by default:

x <- 1:3
y <- 4:6
x + y

[1] 5 7 9

Recycling by default:

x <- 1
y <- 4:6
x + y

[1] 5 6 7

Recycle warning when vectors don’t fit together cleanly:

x <- 1:2
y <- 4:6
x + y

Warning in x + y: longer object length is not a multiple of shorter object
length

[1] 5 7 7

Recycling Warning

x <- 1:2
y <- 4:6
x + y

Warning in x + y: longer object length is not a multiple of shorter object
length

[1] 5 7 7

Not a problem per se, but often a sign that something has gone wrong.

scalar + vector is usually safe
2 vectors of same size is usually safe
vectors of different size is usually a programming mistake

Warnings vs Errors

Warnings: heuristics pointing at typical problem
- Code still executed without a problem
- Try to fix these unless you’re certain it’s not a problem
Errors: code failed to execute
- You have to fix these to run your code

Changing Functions

Most built-in functions can’t / shouldn’t be changed.

Some allow alternate behavior via additional arguments:

log(10) # Default is natural (base e) logarithm

[1] 2.302585

log(10, base=10)

[1] 1

If you want different behavior, write your own function:

cosd <- function(x){
    ## Cosine in degrees
    cos(x * pi / 180)
}
cosd(90)

[1] 6.123234e-17

Always try ?name to see documentation.

Wrap-Up

Review

Introduction to R:

Arithmetic
Variables and Vectors
Functions: Calls, Arguments, Defining
Packages
Help System

Orientation

Communicating Results (quarto) ✅
R Basics ⬅️
Data Manipulation in R
Data Visualization in R
Getting Data into R
Statistical Modeling in R

Next Time

Communicating Results (quarto) ✅
R Basics ✅
Data Manipulation in R
- Single-Table Data Manipulation ⬅️
- Multi-Table Data Manipulation
Data Visualization in R
Getting Data into R
Statistical Modeling in R

Data Frames:

Organizing several ‘connected’ vectors into a table
table operations with dplyr

Life Tip of the Week

Register to Vote

If you want to vote in the upcoming election, it’s time to register to vote:

https://www.vote.nyc/page/register-vote

Early voting begins at the end of October: need to be registered by then

Forms must be received not just mailed

Next Time

On the Ballot

Governor / Lt. Governor
Comptroller, Attorney General
Federal & State Representatives
Various Judge
5 City Charter Amendments (Official NYC-CFB Voter Guide)

for more see https://findmypollsite.vote.nyc/.

Software Tools for Data AnalysisSTA 9750Michael WeylandtWeek 3 – Tuesday 2026-09-15, Thursday 2026-09-17Last Updated: 2026-09-17

STA 9750 Week 3

Upcoming TODO

Upcoming TODO

Other Important Dates

Course Project

Course Project

Course Project

Course Project

Overarching Question

Finding Data

Special Presentation

Course Support

Pre-Assignments

Today

Today

Git Review

Render + Commit Cycle

Git Workflow

Render + Commit

Render + Commit

Getting Started with R

Programming in R

Execution Model

Arithmetic in R

Arithmetic in R

Function Calls

Function Calls

Function Calls

Function Calls

Function Calls

Function Calls

Vector Semantics

Vector Semantics

Vector Semantics

Vector Index Printing

Variables

Vectors and Variables

Vector Access

In-Class Activity

Weekly Lab

Object classes

Using Packages

Using Packages

Comments

Comments

Recycling Rules

Recycling Warning

Warnings vs Errors

Changing Functions

Wrap-Up

Review

Orientation

Next Time

Life Tip of the Week

Register to Vote

Next Time

On the Ballot

From the Mayor

Musical Treat

Software Tools for Data Analysis
STA 9750
Michael Weylandt
Week 3 – Tuesday 2026-09-15, Thursday 2026-09-17
Last Updated: 2026-09-17

Getting Started with `R`