28  U.S. Cities

Readings

  1. Using SF package with tidyverse
  2. Chapter 6.2 and 6.4 ggplot2: Elegant Graphics for Data Analysis
    • To see all the methods for an simple features column, run methods(class = 'sfc')
  3. USA Boundaries

    • USAboundaries and USAboundariesData are finicky to install correctly. Consider using this code to install the package:

      install.packages(“USAboundaries”, repos = “https://ropensci.r-universe.dev”, type = “source”)

      install.packages(“USAboundariesData”, repos = “https://ropensci.r-universe.dev”, type = “source”)

  4. (optional/extra) SF R package Read through the section titled, “sfc: simple feature geometry list-column.”

Guided Instruction

This file contains explanations and “fill-in-the-blank” coding exercises (along with solutions at the bottom of the file) to help you prepare for the task. It assumes you have done the reading.

Up to this point, we have dealt with data that fits into the tidy format without much effort. Spatial data has many complicating factors that have made handling spatial data in R complicated.

We will use library(USAboundaries) and library(sf) to make a map of the US and show the top three largest cities in each state. Specifically, you will use library(ggplot2) and the function geom_sf() to recreate the provided image.

We want to create a map of the United States that plots the three largest cities (according to population) in each state - colored according to their rank. We also want to draw the shapes of the Idaho counties on the same plot.

  1. Create a map of the 48 continguous United States where each state is outlined and where the counties of Idaho are outlined. Here are some hints to keep in mind for this task
    • From library(USAboundaries) use us_states() and us_counties() to get the state and county shape geometries
    • Remember that R has some helpful variables called state.name and state.abb
  2. Create a dataset that only contains rows for the top 3 cities in each of the 48 continuous states and has a column that contains their ranking (either a 1, 2, or 3).
    • From library(USAboundaries) use us_cities() to get a starting dataset and then do your wrangling from there.
  3. Use the wrangled data to fully recreate this image (right click image to view in new window if it is too small to see well):

  • The labels don’t have to be in exactly the same spot, just be sure that they aren’t covering up each other or the data.
  • Notice that the color of the points does not map to population size, rather it indicates the city’s population rank (1st, 2nd or 3rd) within the state.
  • Note that fill = NA in geom_sf() will not fill the polygons with a grey color, rather it makes the fill transparent.
  • To add the labels to the image consider using library(ggrepel).
  • The labels may not look correct until you save the map as a larger image.
  1. Save your chart as a .png image. Try using ggsave() with width = 15 and height = 10. What happens if you change the width and height?
  2. Render the .qmd. Push the .png, .qmd, .md, and .html files to your GitHub repository.

Submit

In I-learn submit a link to the .md file on GitHub.