Homework 5

Author

Your Name

Published

Invalid Date

Before your start the assignment

  • Update the header - put your name in the author argument and put today’s date in the date argument.
  • Click the “Render” button in RStudio and then open the rendered 5-hw5.html page.
  • Then go back and try changing the theme argument in the header to something else - you can see other available themes here. Notice the difference when you render now!

Overview of HW5

In this assignment, we will revisit the midwest dataset from Homework 1 and explore how geography adds context and insight. Recall that in Homework 1, you created scatterplots to explore the relationships between education level and poverty level among Midwest counties in 2000. In this homework, you will see how maps can help you explore the geographical context of those relationships. You will practice creating static maps (point, choropleth, faceted) and interactive maps, while learning to join attribute data with geographic shapes. You will also practice using {tidycensus} to access census data from the 2020 Census and compare it with the 2000 Census data.

Skills practiced:

  • Joining tabular data to geographic shapefiles using sf and tigris
  • Creating static maps: choropleth, point, and faceted maps using tmap
  • Creating interactive maps using leaflet with tooltips and legends
  • Comparing census data over time using tidycensus
  • Visualizing and interpreting regional variation in socioeconomic indicators
  • Using centroids to create point maps from polygon data
  • Designing maps with clear legends, color scales, and map layouts

Setup

Load the necessary libraries and datasets. You will need to install the following packages if you haven’t already:

library(tidyverse)
library(tmap)
library(sf)
library(tigris)
library(tidycensus)
library(leaflet)

options(tigris_use_cache = TRUE)
data(midwest, package = "ggplot2")

Q1: Explore the Dataset (Review from HW1)

Briefly describe what each row in the midwest dataset represents. Identify three public health variables and three geographic identifiers in the dataset. Then, list 2–3 research questions you could explore using maps.

NoteYour Answer

Type your response here.

Q2: Prepare Spatial Data

Use tigris::counties(cb = TRUE, year = 2020) to download county shapefiles for the entire U.S. Filter for the five Midwest states in the midwest dataset. Join with the midwest dataset using state acronym and county names.

hint: you may need to pay attention to the lower case/upper case in county names when you perform the join, use tolower() or toupper() to standardize the case. When you join a sf object and the midwest dataset, you may need to use left_join() from the dplyr package, and put the sf object as the first argument and the midwest dataset as the second argument. Read the documentation on ?left_join for how to join tables based on multiple columns and what to do when column names are different.

# your code here

Q3: Choropleth Map — % Below Poverty

Make a choropleth map using tmap to show percbelowpoverty. Use a sequential palette and a clear legend title. What patterns do you observe across states?

# your code here
NoteYour Answer

Type your response here.

Q4: Point Map — Total Population by County

Create a point map using county centroids. Use the midwest_sf object and convert it to a point layer using st_centroid(). Plot the result using tm_dots(), and encode poptotal with either color or size.

🔍 Hint: midwest_pts <- st_centroid(midwest_sf). Use either size or color to encode poptotal. What does this show about population distribution?

# your code here
NoteYour Answer

Type your response here.

Q5: Faceted Map — College Education by State

Make a faceted choropleth map of percollege, using tm_facets(by = "state"). What differences do you observe across states? Does education attainment appear higher in some states compared to others?

# your code here
NoteYour Answer

Type your response here.

Q6: Compare Poverty and College Education Together

Create two maps comparing percbelowpoverty and percollege. What insights do you gain by viewing these variables in parallel?

# your code here
NoteYour Answer

Type your response here.

Q7: Compare with Decennial Census (tidycensus)

Use the tidycensus package to retrieve total population by county from the 2020 Decennial Census. Join it to midwest_sf and compare it with the poptotal values in the original midwest dataset (which reflects 2000 population). Map the absolute or percent difference between 2000 and 2020 populations.

🔍 Hint: use get_decennial() with variable = "P001001" and year = 2020, and set geometry = FALSE.

What counties saw the most growth or decline in population over time?

# your code here
NoteYour Answer

Type your response here.

BONUS: Interactive Map — Leaflet

Make an interactive map using leaflet. Color counties by percbelowpoverty, and add a tooltip with county name and both poverty and education rates. Include a legend.

# your code here
NoteYour Answer

Type your response here.

How I used GenAI?

Describe how GenAI is used in producing this homework, include prompt, date, model version, and link to chat history.

NoteYour Answer

Type your response here.

Save and Push Your Work

Remember to save your .qmd and render the HTML output before committing to GitHub:

git add .
git commit -m "Complete Homework 5"
git push