Software Tools for Data Analysis
STA 9750
Michael Weylandt
Week 11 – Thursday 2026-04-23
Last Updated: 2026-04-23

STA 9750 Week 11

Today: Lecture #09: Introduction to Web Technologies & Web Scraping

These slides can be found online at:

https://michael-weylandt.com/STA9750/slides/slides11.html

In-class activities can be found at:

https://michael-weylandt.com/STA9750/labs/lab09.html

Upcoming TODO

Upcoming student responsibilities:

Date Time Details
2026-04-24 11:59pm ET Mini-Project #03 Due
2026-04-30 6:00pm ET Pre-Assignment #12 Due
2026-05-03 11:59pm ET Mini-Project Peer Feedback #03 Due
2026-05-07 6:00pm ET Final Project Presentation Slides Due
2026-05-14 6:00pm ET Pre-Assignment #14 Due
2026-05-15 11:59pm ET Mini-Project #04 Due
2026-05-21 11:59pm ET Final Project Summary Report Due [Tentative]
2026-05-21 11:59pm ET Final Project Individual Report Due [Tentative]
2026-05-21 11:59pm ET Final Project Teammate Peer Evaluations Due [Tentative]

STA 9750 Week 11

Today: Lecture #09: Introduction to Web Technologies & Web Scraping

  • Communicating Results (quarto) ✅
  • R Basics ✅
  • Data Manipulation in R ✅
  • Data Visualization in R ✅
  • Getting Data into R ⬅️
    • Files and APIs ✅
    • Web Scraping ⬅️
    • Cleaning and Processing Text
  • Statistical Modeling in R

Today

Today

  • Course Administration
  • Warm-Up Exercise
  • New Material
    • HTML Structure
    • Selectors
    • Parsing HTML with rvest
  • Wrap-Up
    • Life Tip of the Day

Course Administration

Mini-Project #03

MP#03 - Who Goes There? US Internal Migration and Implications for Congressional Reapportionment

Due 2026-04-24 at 11:59pm ET

Topics covered:

  • Data Import
    • Using the tidycensus Package
    • Static files
    • API Usage
  • Data Maipulation and Visualization
    • Slicing and Dicing with dplyr
    • Spatial visualization (optional)

Submissions so far look great!

Mini-Project #04

MP#04 - Going for the Gold

Due 2026-05-15 at 11:59pm ET

Topics covered:

  • Data Import
    • HTTP Request Construction (Last Week)
    • HTML Scraping (Tabular) ⬅️ (Today)
  • \(t\)-tests
  • Putting Everything Together

Course Support

  • Synchronous - MW Office Hours 2x / week:
    • Wednesdays 5pm: In Person
    • Thursdays 5pm: Zoom
  • Asynchronous: Piazza (\(<45\) minute average response time)

Review Exercise

API Exercise

The Open Trivia Database

  • Use the API to build the question bank for a trivia night

See this week’s Lab for details

Breakout Rooms

Breakout Room Team
1 Emissions Impossible (LR+MOG+APTL)
2 Maniac Braniacs (HHS+KK+FC+DN)
3 Water Benders (JE+JABB+MTP+JA+AS)
4 Inspector Gadget (MUO+KN+CM+ID+KM)
5 3-1-Fun! (XC+ML+ER+RJSN)

Working with HTML

HTML

HTML - HyperText Markup Language - is the language used to write web pages

  • We have avoided writing HTML directly, in favor of Markdown

But you will have to read HTML

  • In a web browser, right click and “View Source” on a page

HTML Elements

HTML is written as a nested series of elements:

Important Elements

There are many HTML elements; some important ones are:

  • body: Body (the ‘mean’ of a page)
  • h1, h2, …: Headers
  • div: Division (generic container)
  • p: Paragraph (most text goes in these)
  • table: Table (often how data is displayed)
  • ul, ol: Lists (unordered and ordered)
  • li: List Item (for both list types)
  • a: Anchors (used for linking)

Important Elements

Other useful elements:

  • script: Javascript - the code that implements interactivity
  • style: CSS - formatting and appearance

You won’t use these directly, but they are everywhere

HTML Element Selection

The SelectorGadget can be used to practice selecting elements on web pages

  • #id will select by ID (“name”)
  • type will select all elements of that type
  • .class will select all elements with that class
  • [attribute] will select elements with that attribute
  • [attribute="value"] will select elements with that attribute and value
  • sel1 sel2 will select elements matching sel2 that are inside a sel1
    • e.g., tr .odd will select the odd rows of a gt table

Advanced CSS Selectors

CSS Selector Pseudo-classes can be used to implement more specific logic

  • first-of-type (e.g., table:first-of-type gets the first table)
  • nth-of-type (e.g., h2:nth-of-type(2) gets the second h2)
  • not (e.g., tr:not(.odd) gets all table rows without the odd class)

HTML Element Selection

From the pre-reading:

  1. Star Wars movie titles
    • main h2
  2. Baruch GPS
    • .geo
  3. Wikipedia CUNY Table
    • table or tbody

HTML Anchors

Anchors (a elements) are probably the most important part of HTML

Confusingly, anchors are both links and destinations.

Anchors can reference:

  • Another page (http://URL)
  • A particular part of another page (http://URL#place)
  • A particular part of the same page (#place)

Quarto supports cross-linking with anchors

HTML Anchors

Incoming link anchor:

<a id="#fish"> Content </a>

Links to http://page#fish will go straight to that spot

Outgoing link anchor:

<a href="https://baruch.cuny.edu"> Baruch </a>

Clicking text Baruch will go to Baruch website

HTML Tables

HTML Tables are often used for showing data:

  • <table> - Top level container
  • <thead> and <tbody> - Separate header and body
  • <trow> - Rows
  • <td> - Table data (cells)

Can be much fancier - will see several examples in exercises

rvest

The rvest package can be used to manipulate HTML in R:

  • Get HTML by either read_html or resp_body_html if using httr2
  • Select elements with html_elements("selector")
    • Same syntax as SelectorGadget
    • Use html_element if you only want the first
  • Eventually need to extract content
    • html_text and html_text2 (removes whitespace)
    • html_attr gets attribute values (e.g., link targets)
    • html_table will attempt to parse a table automatically

Example

Using httr2 to get the names of all 5 Mini-Projects

library(rvest)
read_html("https://michael-weylandt.com/STA9750/miniprojects.html") |>
    html_element("#mini-projects") |>
    html_elements("h4") |>
    html_text2()
[1] "Mini-Project #00: Course Set-Up"                                                                           
[2] "Mini-Project #01: Assessing the Impact of SFFA on Campus Diversity One-Year Later"                         
[3] "Mini-Project #02: How Do You Do ‘You Do You’?"                                                             
[4] "Mini-Project #03: Who Goes There? US Internal Migration and Implications for Congressional Reapportionment"
[5] "Mini-Project #04: Going for the Gold"

Breakout Exercise #01

Return to your breakout rooms for Exercise #01

  • Use html_element() to extract the course table
  • Use html_table() to convert to a data frame

JavaScript Websites

JavaScript is a great tool, but R doesn’t know about it

  • What you see might not be what R sees
  • Make sure to consider “raw” HTML
  • Often ‘hijacks’ important elements to add new features
    • Focus on standard HTML elements

Breakout Exercise #02

Return to your breakout rooms for Exercise #02

  • Finding a table in a more complex website
  • Pulling out desired data
  • Making a map of results (optional)

read_html_live

rvest also provides tools for interacting with sites as shown in the browser

  • read_html_live()

User interface is a bit different - we have to load the page explicitly and interact with a persistent object:

wikipage <- read_html_live("https://en.wikipedia.org/wiki/List_of_City_University_of_New_York_institutions")
wikipage$view()
wikipage$html_elements(".jquery-tablesorter")

Should remind you of Python’s obj.method() syntax

This is advanced, so don’t worry too much about it until you need it

Non-Tabular Data

Often, data will not be in a nice table

  • Need to manually build a data.frame
  • Build each data frame and ‘combine’
    • map |> list_rbind() idiom
    • DATA <- rbind(DATA, NEW_DATA) idiom

Loops

Recall the basic structure of a loop in R:

for(item in container){
    do_something_with(item)
}

Accumulate data

all_results <- tibble()
for(item in container){
    new_results <- do_something_with(item)
    new_tibble <- tibble(col1=new_results |> get_val1(), 
                         col2=new_results |> get_val2())
    all_results <- rbind(all_results, new_tibble)
}

Example

Getting info about all pre-assignments

library(rvest)

preassigns <- read_html("https://michael-weylandt.com/STA9750/preassignments.html") |> 
    html_element("#pre-assignments") |>
    html_elements("section")

pa_details <- tibble()
for(pa in preassigns){
    name        = pa |> html_element("h4") |> html_text2()
    description = pa |> html_elements("p:last-of-type") |>  html_text2()
    
    pa_df <- tibble(name = name, description = description)
    
    pa_details <- rbind(pa_details, pa_df)
}

pa_details

Example

Functional syntax is a bit more compact

library(rvest)

preassigns <- read_html("https://michael-weylandt.com/STA9750/preassignments.html") |> 
    html_element("#pre-assignments") |>
    html_elements("section")

parse_pa <- function(pa){
    name <- pa |> html_element("h4") |> html_text2()
    description <- pa |> html_elements("p:last-of-type") |>  html_text2()
    
    tibble(name = name, description = description)
}

pa_details <- preassigns |> map(parse_pa) |> list_rbind()

pa_details

Example

When vectorization is possible (via html_elements) - even cleaner:

library(rvest)

preassigns <- read_html("https://michael-weylandt.com/STA9750/preassignments.html") |> 
    html_element("#pre-assignments") |>
    html_elements("section")

names <- preassigns |> html_elements("h4") |> html_text2()
descriptions <- preassigns |> html_elements("p:last-of-type") |>  html_text2()
    
pa_details <- tibble(name = names, description = descriptions)

pa_details

Breakout Exercise #03

Return to your breakout rooms for Exercise #03

  • Examining a multi-page site
  • Pulling out data from non-tabular elements

Wrap Up

Wrap Up

Processing HTML in R

  • HTML Structure
  • HTML Selectors
  • html_table and <table> elements
  • HTML Anchors and Links

Musical Treat


You might recognize the finale from Fantasia 2000