Maps

Author

Affiliation

Prof Emily Kurtz

Carleton College
Stat 220 - Spring 2026

library(tidyverse)
library(maps)
library(mapproj)
library(ggthemes)

Create Original Map

states <- map_data("state")
ggplot(states, aes(x=long, y=lat, group=group)) + 
    geom_polygon(color="gold2", fill="navyblue")

Warm Up

Edit the code so that each state is a different color.

Hint: what layer should we edit? How will we edit it? You could do this in either the ggplot() function or the geom_polygon() function.

states <- map_data("state")
ggplot(states, aes(x=long, y=lat, group=group)) + 
    geom_polygon(color="gold2", fill="navyblue")

Activity: ACS data

Load Data

acs_state_data = read_csv("https://raw.githubusercontent.com/stat220kurtz/stat220kurtz.github.io/refs/heads/main/data/acs_state_data_2022_5y.csv")
acs_state_data$state = tolower(acs_state_data$NAME)

Variable information:

med_age: median age
med_income: median income
race_X: estimated population of (self-reported) race
born_in_state: estimated population who were born in state
X_age_married: average age at first marriage for self-reported male and female respondents
hs_diploma, associate_degree, prof_degree, etc.: estimated number with a high school diploma, associate’s degree, professional degree, etc.
internet_X: estimated number of people who have internet services

ACS Data Starter Map

Important note: we’re using geom_map as a shortcut here to make a chloropleth map. This saves us the step of encoding the lat/long borders of states, and lets us directly fill the state colors using acs_state_data only. If you’re asked to make a chloropleth map with data that only includes, e.g., state name, use this starter code.

ggplot(acs_state_data) + 
  geom_map(
    aes(map_id = state, fill = NAME),
    map = states
  ) +
  expand_limits(x = states$long, y = states$lat) +
  coord_map() +
  theme_map()

Exercise 1

Comment out the expand_limits line using #. What happened?

Exercise 2

Open up the Data visualization with ggplot2 cheat sheet linked at the Cheatsheets section of the course website (expand the Computing section).

Edit your chloropleth map by:

Choosing a different variable in acs_state_data to map to fill
Choosing a different color scale - think about whether a sequential or diverging scale makes more sense for your visual
Updating the title, and legend title, and any other relevant paramters in labs()

Exercise 3

If you didn’t already choose a variable that records raw counts in the previous exercise (e.g. race_x), make another map that plots data with raw counts, and proceed to the step below. If you did choose such a variable, further update your map from the previous exercise by:

Plotting the variable you chose per capita. You can do this by dividing the variable chosen in the previous problem by total_pop directly in the aes() function.

--- title: Maps --- ```{r} library(tidyverse) library(maps) library(mapproj) library(ggthemes) ``` # Create Original Map ```{r} states <- map_data("state") ggplot(states, aes(x=long, y=lat, group=group)) + geom_polygon(color="gold2", fill="navyblue") ``` ## Warm Up Edit the code so that each state is a different color. Hint: what layer should we edit? How will we edit it? You could do this in either the `ggplot()` function or the `geom_polygon()` function. ```{r} states <- map_data("state") ggplot(states, aes(x=long, y=lat, group=group)) + geom_polygon(color="gold2", fill="navyblue") ``` # Activity: ACS data ## Load Data ```{r} acs_state_data = read_csv("https://raw.githubusercontent.com/stat220kurtz/stat220kurtz.github.io/refs/heads/main/data/acs_state_data_2022_5y.csv") acs_state_data$state = tolower(acs_state_data$NAME) ``` Variable information: - `med_age`: median age - `med_income`: median income - `race_X`: estimated population of (self-reported) race - `born_in_state`: estimated population who were born in state - `X_age_married`: average age at first marriage for self-reported male and female respondents - `hs_diploma`, `associate_degree`, `prof_degree`, etc.: estimated number with a high school diploma, associate's degree, professional degree, etc. - `internet_X`: estimated number of people who have internet services ## ACS Data Starter Map **Important note:** we're using `geom_map` as a shortcut here to make a chloropleth map. This saves us the step of encoding the lat/long borders of states, and lets us directly fill the state colors using `acs_state_data` *only*. If you're asked to make a chloropleth map with data that only includes, e.g., state name, use this starter code. ```{r} ggplot(acs_state_data) + geom_map( aes(map_id = state, fill = NAME), map = states ) + expand_limits(x = states$long, y = states$lat) + coord_map() + theme_map() ``` ### Exercise 1 Comment out the `expand_limits` line using `#`. What happened? ### Exercise 2 Open up the Data visualization with ggplot2 cheat sheet linked at the Cheatsheets section of the course website (expand the Computing section). Edit your chloropleth map by: - Choosing a different variable in `acs_state_data` to map to `fill` - Choosing a different color scale - think about whether a sequential or diverging scale makes more sense for your visual - Updating the title, and legend title, and any other relevant paramters in `labs()` ### Exercise 3 If you didn't already choose a variable that records raw counts in the previous exercise (e.g. `race_x`), make another map that plots data with raw counts, and proceed to the step below. If you did choose such a variable, further update your map from the previous exercise by: - Plotting the variable you chose *per capita.* You can do this by dividing the variable chosen in the previous problem by `total_pop` directly in the `aes()` function.