Software Tools for Data Analysis
STA 9750
Michael Weylandt
Week 4 – Thursday 2026-02-26
Last Updated: 2026-02-26

STA 9750 Week 4

Today: Project Proposal Presentation + Enrichment: Additional Review of R

These slides can be found online at:

https://michael-weylandt.com/STA9750/slides/slides04.html

In-class activities (if any) can be found at:

https://michael-weylandt.com/STA9750/labs/lab04.html

Upcoming TODO

Upcoming student responsibilities:

Date	Time	Details
2026-03-01	11:59pm ET	Mini-Project Peer Feedback #00 Due
2026-03-05	6:00pm ET	Pre-Assignment #05 Due
2026-03-12	6:00pm ET	Pre-Assignment #06 Due
2026-03-13	11:59pm ET	Mini-Project #01 Due
2026-03-19	6:00pm ET	Pre-Assignment #07 Due
2026-03-22	11:59pm ET	Mini-Project Peer Feedback #01 Due
2026-03-26	6:00pm ET	Mid-Semester Check-In Slides Due

Course Project Proposals

Today: Course project proposal presentations (Official Description)

• 6 minute presentation
• Key topics:
  • Animating Question
  • Team Roster
• Also discuss: Possible specific questions, data sources, analytical plan, anticipated challenges

Most important: team names!

Previous: Rat Pack, Subway Surfers, Going for Gold, etc.

After Proposals

Extra Review of R Basics - 100% Optional

• Variables, Vectors, Types, Control Flow

Mini-Project #00

MP#00 due last Friday - Course Infrastructure Set-Up

• Setup RStudio Project
• Create GitHub Account and Repo
• Connect Local Computer to GitHub
• Deploy GitHub Pages

I enjoyed getting to know (a bit more about) you!

Mini-Project #00

Mini-Project #00 peer feedback assigned

• Due 2026-03-01
• 3 peer feedback assignments per student
  • Give 3 comments, get 3 comments

Peer Feedback Instructions can be found online

Mini-Project #00

Interactive script automates this process:

source("https://michael-weylandt.com/STA9750/load_helpers.R")
mp_pf_perform(0, github="YOUR_GITHUB_ID")

Will request the secret code I gave you via Piazza when you completed MP#00

Will ask you a series of questions and save your responses in a specifically formatted file

MP#00 Peer Feedback

Ungraded assignment, so simple feedback:

• One strength, one weakness, one suggestion for improvement (3x)

After completion, upload bspf file to Brightspace (for privacy / confidentiality)

MP PF Cycle

Aims of Mini-Project Peer Feedback:

• Learn to read and evaluate code
• In analysis, rarely right and wrong; definitely better and worse
• Learn tricks to improve your own site

“Good artists copy; great artists steal.” – Steve Jobs

Most coding is reading - most reading is reading your own old code

Course Support

Asynchronous Support: Piazza

• All registered now in Piazza!
• Sub-1 Hour average time to response

Synchronous Support: Office Hours

• Wednesdays (in person) and Thursdays (Zoom) at 5pm

Pre-Assignments

Pre-Assignment #05 - Due 2026-03-05 at 6:00pm ET

• Day before class at 6:00pm
• Available on course website + Brightspace after 9pm
• Unlimited re-tries so make sure you get 30/30!

Quick Review

Values

Basic “things” in R (“scalars”):

• Numeric values: 10, 3.14, 0.0002, 1.234e5, 3 + 4i
  • R distinguishes between integer and numeric / double but you don’t need to
• Character values: "Baruch", "Michael Weylandt",
  • Arbitrary length - matched quotes (double or single)
  • Mix quotes to put quotes inside a string: "He said to me: 'Code is great!'"
• Logical values: TRUE, FALSE (no quotes)

Values

Use the class() function to see types:

class(3)

[1] "numeric"

class(3.14159)

[1] "numeric"

class("I love R!")

[1] "character"

Variables and Assignment

We can assign a name to a value:

x <- 3

Now anywhere we use x, the value 3 automatically is introduced:

x^2

[1] 9

Variables and Assignment

Variable names must be:

• One word (no spaces)
• All alpha, numeric, or underscores
• Start with a letter

Avoid special “reserved words”: if, else, for, etc

Variables and Assignment

Assignment (<-) is the last operation, so can use to save results for later use

five_factorial <- 5 * 4 * 3 * 2 * 1
five_factorial

[1] 120

Then

six_factorial <- 6 * five_factorial
six_factorial

[1] 720

Vectors

An ordered collection of the same type is called a vector:

• Create with c (“concatenate”):

x <- c(1, 2, 3)

class(x)

[1] "numeric"

length(x)

[1] 3

Vectors

Vectors are everywhere in R:

• The “single” values we saw earlier are just vectors of length 1

length(3)

[1] 1

Vectors

Access specific elements of a vector with []:

x <- c("a", "b", "c")
x[2]

[1] "b"

Or give a vector of indices:

x[c(2, 1, 3)]

[1] "b" "a" "c"

x[c(2, 1, 2, 1, 3, 3)]

[1] "b" "a" "b" "a" "c" "c"

Vectors

Negative indices drop:

x[-2]

[1] "a" "c"

This is different from Python: not count backwards!

Functions

Functions take input (“arguments”) and produce results and side-effects:

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

[1] 1 2 3 4

x is an input to the function sqrt

sqrt doesn’t “see” the name x; it sees the vector 1, 4, 9, 16

Not 100% true, but close enough!

Functions

Some examples:

• Print to screen with some formatting: print
• “Pure” print to screen (no formatting): cat
• Combine strings: paste
• Math: sin, sqrt
• Load a package: library

Vectorization

Where possible, functions are vectorized:

x <- c(1, 2, 3)
y <- c(4, 5, 6)

x * y

[1]  4 10 18

Operations occur “in parallel” on matched elements

Control Flow

Two useful operations for small code snippets:

if(condition){
  do_if_true
} else { # Optional - can omit this 'side'
  do_if_false
}

This is a conditional operator:

• Used to run code sometimes
  • Download a missing file, but not a file already present
  • Throw an error if something bad happens
  • Turn off parts of code with (if(FALSE){})
• Everything between { and } is handled
• else branch is optional

Compound Conditions

Can do ‘compound’ or nested if/else:

if(x > 10){
  cat("x is very positive.")
} else if(x > 0) {
  cat("x is a little positive.\n")
} else {
  cat("x is negative.\n")
}

Control Flow

Two useful operations for small code snippets:

for(element in vector){
  process_one_at_a_time(element)
}

Goes through vector taking out one element at a time:

nums <- c(1, 2, 3, 4, 5)
for(n in nums){
  cat(n, "squared is", n^2, "\n")
}

1 squared is 1 
2 squared is 4 
3 squared is 9 
4 squared is 16 
5 squared is 25 

Don’t focus on these too much - better alternatives coming soon!

Control Flow

By default, the last line of a function is the returned value:

my_absolute_value <- function(x){
  if(x > 0){
    x
  } else {
    -x
  }
}

my_absolute_value(-3)

[1] 3

my_absolute_value(3)

[1] 3

Control Flow

Can override with return statement - instantly returns and ‘exits’ function:

say_hello <- function(name, scream=FALSE, quiet=FALSE){
  text <- paste("Hello", name)
  
  if(scream){
    text <- paste(toupper(text), "!!!")
  }
  
  if(quiet){return(text)} # Stop here if quiet and skip print
  
  print(text)
}

say_hello("Michael")

[1] "Hello Michael"

say_hello("Michael", scream=TRUE)

[1] "HELLO MICHAEL !!!"

say_hello("Michael", quiet=TRUE)

[1] "Hello Michael"

Control Flow

Overly complicated \(\text{sign}(x)\) function:

sign <- function(x){
  if(x > 0){
    1
  } else {
    if(x < 0){
      -1
    } else {
      0
    }
  }
}

Control Flow

Somewhat better \(\text{sign}(x)\) function:

sign <- function(x){
  if(x > 0){
    1
  } else if(x < 0){
      -1
  } else {
      0
  }
}

Control Flow

Decent \(\text{sign}(x)\) function:

sign <- function(x){
  if(x > 0) return(1) # Use 'return' to stop function here
  if(x < 0) return(-1)
  return(0)
}

Would be even better to vectorize

Optional Review

Programming exercises to practice these concepts

Proposal Presentations

Presentation Order

Presentation Number	Team
1	MUO+KN+CM+ID+KM
2	JE+JABB+MTP+JA+AS
3	HHS+KK+FC+DN
4	XC+ML+ER+RJSN
5	LR+MOG+APTL+TN

Wrap-Up

Orientation

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

Next Time

Data Frames:

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

Upcoming Work

Upcoming work from course calendar

• Weekly Pre-Assignment
• Mini-Project #00 peer feedback
• Mini-Project #01 due on 2026-03-13 at 11:59pm ET

Life Tip of the Week

Making the most of Amazon

1. Free Trial and Discounted Rate Amazon Prime for Students
2. Amazon Prime Visa by Chase: No annual fee + 5% cash back (or more) on all Amazon/Whole Foods/Chase Travel + 2% on gas, restaurants, and transit
3. Camel Camel Camel
   • Price history for all Amazon items (see if you’re getting a good deal)
   • Price drop alert emails (get custom messages when an item goes on sale)

Musical Treat

Live Video

Optional Review

Exercise #01

Q: Write a function f that does the following:

f(c(1, 2, 3))

The vector has 3 elements and is of type numeric

f(c("a", "b", "c"))

The vector has 3 elements and is of type character

f(1:5)

The vector has 5 elements and is of type integer

Hint: Use the cat function to print to screen.

f <- function(x){
  cat("The vector has", length(x), "elements and is of type", class(x))
}

Exercise #02

Q: Write a vectorized function to tell if numbers are even.

is_even(3)

[1] FALSE

is_even(c(3, 4, 5))

[1] FALSE  TRUE FALSE

is_even(1:10)

 [1] FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE

Hint: Use the %% operator to get remainders from division

is_even <- function(x)  (x %% 2) == 0

Exercise #03

Q: Write a function to count the even elements of a vector:

count_even(3)

[1] 0

count_even(c(3, 4, 5))

[1] 1

count_even(1:10)

[1] 5

Hint: Combine Q2 with the “sum of logical = count” trick.

count_even <- function(x)  sum(is_even(x))

Exercise #04

Q: The seq() function lets us construct sequences. What is the average (mean) of the first 23 odd numbers?

Hint: Read the documentation for ?seq before trying this question

mean(seq(from=1, by=2, length.out=23))

[1] 23

Exercise #05

Q: The alternating harmonic series is defined as:

\[1 - \frac{1}{2} + \frac{1}{3} - \frac{1}{4} + \frac{1}{5} + \dots\]

Show that this series converges to \(\ln(2)\) by taking a partial sum of the first one million elements.

Hint: Use recycling to get the signs right:

seq(1, 5) * c(1, -1)

[1]  1 -2  3 -4  5

sum(1/seq(1, 1e6) * c(1, -1))

[1] 0.6931467

Compare to:

log(2)

[1] 0.6931472

Exercise #06

Q: Write a function that computes the mean of a vector (Don’t use the built-in mean function)

Hint: Use the sum and length functions

my_mean <- function(x){
  sum(x) / length(x)
}

my_mean(8)

[1] 8

my_mean(1:10)

[1] 5.5

Exercise #07

Q: Write a vectorized function that returns \(\sqrt{x}\) if \(x\) is positive and 0 otherwise.

pos_sqrt(c(-4, -1, 1, 4, 9, -9))

[1] 0 0 1 2 3 0

Hint: The pmax (“parallel max”) function may be useful here:

x <- c(1, 3, 5,  7)
y <- c(5, 3, 10, 1)
pmax(x, y)

[1]  5  3 10  7

pos_sqrt <- function(x) sqrt(pmax(0, x))

Alternative - use the ifelse function for vectorized conditionals (but this has a warning because it tries to do both conditions)

Exercise #08

Q: Write a function that takes in a vector of characters and returns the longest.

long_string(c("a", "bc", "def"))

[1] "def"

long_string(c("My", "name", "is", "Michael"))

[1] "Michael"

Hint: The nchar and which.max functions may be helpful here.

long_string <- function(x)  x[which.max(nchar(x))]

Exercise #09

Q: Write a function that computes the factorial of a value. (Don’t use the built-in factorial function)

Hint: Use the seq and prod functions

my_factorial <- function(n){
  return(prod(seq(1, n)))
}

my_factorial(8)

[1] 40320

Exercise #10

Q: Write a function that computes the factorial of a value using a loop (instead of the prod function).

my_factorial <- function(n){
  fact <- 1
  for(x in seq(1, n)){
    fact <- fact * x
  }
  return(fact)
}

my_factorial(8)

[1] 40320

Exercise #11

Q: Show that: \[\sum_{k=0}^{\infty} \frac{1}{k!} = e\]

Use the built-in factorial function since it is vectorized.

sum(1/factorial(seq(0, 10000)))

[1] 2.718282

Exercise #12

Q: Write a function that takes a vector and returns the maximum element. (Don’t use the built-in max function.)

Hint: You will need to use a loop and a conditional here.

Hint: We started factorial at 1; start max at -Inf (why?)

my_max <- function(x){
  max_val <- -Inf
  for(v in x){
    if(v > max_val) max_val <- v
  }
  return(max_val)
}

my_max(c(1, -1, 3, -4, 10, -3))

[1] 10