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r plot usa map with different cluster of states

r plot usa map with different cluster of states

2 min read 27-11-2024
r plot usa map with different cluster of states

Visualizing State Clusters in R: A Guide to Plotting US Maps

Data visualization is crucial for understanding complex datasets, and when dealing with geographical data, maps offer an intuitive way to represent information. This article demonstrates how to create a visually appealing map of the United States in R, highlighting different clusters of states based on some underlying characteristic. We'll use the ggplot2 and usmap packages, which provide a powerful and straightforward approach to this task.

Prerequisites:

Before we begin, make sure you have the necessary R packages installed. You can install them using these commands:

install.packages("ggplot2")
install.packages("usmap")

Data Preparation:

Let's assume you have a data frame named state_data containing information about each US state. This data frame should at least include a column named state with state abbreviations (e.g., "CA", "NY", "TX") and a column named cluster indicating the cluster assignment for each state (e.g., 1, 2, 3). This cluster variable could be the result of a clustering algorithm (like k-means) applied to other state-level variables (e.g., population density, GDP per capita, voting patterns).

Example state_data (replace with your actual data):

state_data <- data.frame(
  state = state.abb,
  cluster = sample(1:3, 50, replace = TRUE) #Example:  3 clusters randomly assigned
)

Creating the Map:

Now, let's use ggplot2 and usmap to create the map. The code below generates a choropleth map where each state is colored according to its cluster assignment:

library(ggplot2)
library(usmap)

# Merge your data with the usmap state data
plot_data <- merge(usmap::us_map, state_data, by = "state")

# Create the plot
ggplot(data = plot_data, aes(x = x, y = y, group = group, fill = factor(cluster))) +
  geom_polygon(color = "black", size = 0.1) +
  coord_fixed(1.3) + # Adjust aspect ratio if needed
  scale_fill_viridis_d(name = "Cluster", option = "D") + #Use a colorblind-friendly palette
  theme_void() + #Remove gridlines and axes
  labs(title = "US States Clustered by [Your Variable Name]") +
  theme(legend.position = "right",
        plot.title = element_text(hjust = 0.5)) #Center the title

This code does the following:

  1. Merges: Combines your state_data with the us_map data from the usmap package. This links your cluster information to the geographical coordinates of each state.
  2. Plots: Uses geom_polygon to draw the states, coloring them based on the cluster variable. factor(cluster) ensures that the colors are distinct for each cluster.
  3. Styling: coord_fixed maintains the correct aspect ratio of the map. scale_fill_viridis_d uses a perceptually uniform color palette (Viridis) suitable for presentations and those with colorblindness. theme_void() cleans up the plot's background. Labels are added for clarity.

Customizations:

You can customize the map further:

  • Colors: Replace scale_fill_viridis_d with other color scales from ggplot2 (e.g., scale_fill_brewer, scale_fill_manual) to control the colors explicitly.
  • Labels: Add state labels using geom_text for better readability (though this can clutter the map if there are many states).
  • Titles & Legends: Adjust the title and legend labels to accurately reflect your data and analysis.
  • Themes: Explore different ggplot2 themes to change the overall aesthetic.

Interpreting the Results:

The resulting map provides a clear visual representation of the state clusters. By examining the spatial distribution of clusters, you can gain insights into the underlying patterns driving the clustering. For example, if you clustered states based on economic indicators, you might see geographically contiguous regions showing similar economic profiles.

This guide provides a basic framework. Remember to adapt the code and customize the plot to best reflect your specific data and analysis. By using ggplot2 and usmap, you can create informative and visually compelling maps to effectively communicate your findings.

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