Lab 5 Tutorial | PAF 516

What You Will Build

In this lab you will compare economic hardship across two ACS time periods — 2009–2013 and 2015–2019 — to identify where conditions are worsening, improving, or stable. Both periods use 2010 TIGER/Line tract boundaries so GEOIDs match perfectly. You will apply pooled standardization, test whether changes exceed sampling noise using margin-of-error significance testing, and overlay LISA hot spots from Lab 4 on the change map.

The tutorial walks through every step with fully working code. Knit it first to see the finished product, then go back and run chunk by chunk to understand how it works.

Learning Objectives

Pull ACS data from two time periods (2013 and 2019) using tidycensus
Build a 3-variable economic hardship index with pooled standardization
Compute change scores on a common scale so years are directly comparable
Test statistical significance of changes using MOE-based z-tests
Create diverging color palette maps centered on zero change
Overlay LISA hot spots on change maps to connect spatial and temporal patterns
Build side-by-side comparison figures with patchwork

Before You Begin

1 — File and Folder Setup

Create this folder structure before downloading anything. Move the downloaded .qmd into PAF516/Lab5/ before opening it in RStudio.

PAF516/
  Lab1/  Lab2/  Lab3/  Lab4/
  Lab5/
    Lab5_Tutorial.qmd            ← downloaded tutorial (do not edit)
    Lab5_Tutorial.html           ← auto-generated when you knit
    Lab5_Assignment_Howell.qmd   ← your renamed assignment copy
    Lab5_Assignment_Howell.html  ← submit this to Canvas
  Lab6/  ...

2 — Install Packages (Run Once)

Do this before knitting

Open Lab5_Tutorial.qmd in RStudio
Click inside the packages chunk (labeled #| label: packages)
Press Cmd+Return (Mac) or Ctrl+Enter (PC) to run just that chunk
Wait — first run downloads and installs packages and may take 5–10 minutes
After it finishes, future knits will be fast — the renv block only runs once automatically

What is renv?

renv records the exact version of every package used so that code that works today still works next semester.

3 — Census API Key

The lab pulls live data from the Census Bureau. If you don’t have a key yet:

Register free at api.census.gov/data/key_signup.html
Check email for your key (arrives within a few minutes)
In the RStudio console run: census_api_key("YOUR_KEY_HERE", install = TRUE)
Restart R: Session → Restart R

Running the Tutorial

Option A — Knit First (Recommended)

Click the Render button (blue arrow, top of editor) or press Cmd+Shift+K / Ctrl+Shift+K. This produces Lab5_Tutorial.html with all results — change score summaries, diverging color maps, MOE significance maps, LISA overlay, and side-by-side comparison figures. Review the output to see what the completed analysis looks like.

Option B — Run Chunk by Chunk (Best for Learning)

Place your cursor inside any code chunk and press the run shortcut. Output appears inline. Fix any errors before moving on. This is how you understand what each step does.

Action	Mac	PC
Run current chunk	`Cmd+Return`	`Ctrl+Enter`
Run all chunks above	`Cmd+Option+P`	`Ctrl+Alt+P`
Knit / Render	`Cmd+Shift+K`	`Ctrl+Shift+K`

What’s Inside

Step	What It Does
Step 1	Define 3-variable hardship index and pull 2013 and 2019 Maricopa County tract data
Step 2	Reshape long-to-wide and compute poverty rate and unemployment rate
Step 3	Pooled standardization — compute z-scores on the combined 2013+2019 scale
Step 4	Join periods by GEOID and compute change scores (2019 − 2013)
Step 5	MOE significance test — identify tracts where poverty change exceeds sampling noise
Step 6	Change maps with diverging palette (blue = improved, red = worsened) and significance overlay
Step 7	Side-by-side three-panel comparison: 2013, 2019, and change
Step 8	LISA overlay — outline 2019 hot spots on the change map
Step 9	Histogram of change score distribution

Download

Lab 5 Tutorial File

Download Lab5_Tutorial.qmd

Right-click → Save Link As. Save directly to your PAF516/Lab5/ folder. Do not open in the browser.