Lecture Notes: Creating Maps with ggplot2

Introduction to ggplot2 Maps

• Tip #13: Focus on creating maps with ggplot.
• Importance of maps: Great visuals for publications and reports.
• Key concepts: ggplot, map data, coordinate systems (latitude and longitude).
• Example map: Voting patterns in the U.S. for the 1976 election.

Getting Started

1. Sign Up: Register for weekly R tips.
2. Access GitHub Repo: Perform a git pull to get all the code.
3. Open File: Locate and open 01_03_ggplot2_maps.R.

Libraries to Load

• Tidyverse: Core library for data manipulation and visualization.
• Maps Library: Provides access to map data.
• Map Projection Library: Ensures correct projection of maps.

Accessing Map Data

• Use map_data() function from ggplot2 to get map datasets.
• Plan to create a world map using this function.

Creating a World Map

1. Load world map data using:

   world_map <- map_data("world")
   

2. Visualize using ggplot:
   • Use geom_map() to create a blank canvas.
   • Map latitude and longitude with appropriate aesthetics (aes).
3. Projection: Add coordinate system for accurate visual representation.
   • Use coord_map() to apply an orthogonal projection.

Focusing on the United States

• Narrow down to U.S. states:

  usa_map <- map_data("state")
  

• Visualize using ggplot similar to world map.
• Ensure to add coordinate system for accuracy.

Integrating Data with Maps

• Introduce the concept of combining map visuals with data.
• Example: Republican voting data by state for 1976 election.

Data Wrangling Steps

1. Access voting data from the maps library:

   vote_data <- map_data("vote_repub") 
   

2. Filter for 1976 data:
   • Create a tibble, select necessary columns (state and 1976 results).
   • Rename columns and convert units to proportions.
3. Join Data: Use left_join() to combine voting data with U.S. map data:

   voting_data <- left_join(usa_map, vote_data, by = "state")
   

Creating a Chloropleth Map

• Use ggplot to visualize voting data:
  • Aesthetics:
    • aes(x = long, y = lat, group = group, fill = republican_proportion).
  • Add layers using geom_polygon() for states.
• Customize color gradient:
  • Define color scale from dark blue (Democrat) to red (Republican).
  • Use scale_fill_gradient2() to set the midpoint color as white.

Final Adjustments

• Theme customizations:
  • Use theme_minimal() to clean up the visual.
  • Add labels for clarity and context.
  • Adjust title and legend position.

Conclusion

• Final output: A clear and visually appealing map showing voting patterns in 1976.
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