r plot usa map with different cluster of states

Mac Grediser

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27-11-2024

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

Data visualization is crucial for understanding complex relationships within datasets. When working with geographical data, like clustering states based on shared characteristics, visualizing these clusters on a US map provides a powerful and intuitive way to communicate findings. This article demonstrates how to create such a visualization in R, using readily available packages and techniques.

1. Necessary Packages and Data

First, we need to install and load the necessary R packages. These packages handle map plotting and data manipulation. We'll assume your state-level data is in a data frame. If not, you'll need to import it first (e.g., using read.csv()).

# Install packages if you haven't already
if(!require(ggplot2)){install.packages("ggplot2")}
if(!require(usmap)){install.packages("usmap")}
if(!require(dplyr)){install.packages("dplyr")}

# Load packages
library(ggplot2)
library(usmap)
library(dplyr)

Let's create a sample dataset for demonstration purposes. Replace this with your own data, ensuring you have a column with state abbreviations (state) and a column indicating cluster membership (cluster).

# Sample data (replace with your own)
data <- data.frame(
  state = state.abb,
  cluster = sample(1:3, 50, replace = TRUE) # Simulates 3 clusters
)

2. Preparing the Data

The usmap package works best with state abbreviations. Ensure your data frame has a column containing these abbreviations. We'll use dplyr for data manipulation.

# Merge your data with the state map data
map_data <- us_map(regions = "states") %>%
  left_join(data, by = "state")

3. Creating the Plot

Now, let's create the map using ggplot2. We'll use the fill aesthetic to color states according to their cluster assignment.

# Create the plot
plot <- ggplot(map_data, aes(map_id = fips, fill = factor(cluster))) +
  geom_map(map = map_data, color = "black") +
  expand_limits(x = map_data$x, y = map_data$y) +
  coord_map() +
  scale_fill_viridis_d(name = "Cluster", option = "D") + # Use a colorblind-friendly palette
  theme_minimal() +
  labs(title = "US State Clusters", fill = "Cluster") +
  theme(panel.background = element_blank())

# Display the plot
print(plot)

This code:

• Uses geom_map to plot the states.
• Sets the fill aesthetic to the cluster column, coloring states by cluster.
• Uses scale_fill_viridis_d for a visually appealing and accessible color palette. You can replace this with other palettes if preferred.
• Adds a title and legend for clarity.
• Uses theme_minimal() for a clean look.

4. Customization and Refinements

This is a basic example. You can customize the plot further:

• Change the color palette: Explore different palettes from RColorBrewer or other packages.
• Add labels: Add state abbreviations or names to the map using geom_text.
• Adjust the theme: Use different ggplot2 themes for varying aesthetics.
• Highlight specific clusters: Use different levels of transparency or bolder outlines for clusters of interest.
• Interactive plots: Consider using packages like plotly to create interactive maps for exploration.

5. Example with Real-World Data

This example uses simulated data. To visualize real clustering results, replace the sample data with your actual results from a clustering algorithm (e.g., k-means, hierarchical clustering) applied to your state-level data. Remember to ensure your data is properly formatted with state abbreviations and cluster assignments.

By following these steps, you can effectively visualize state clusters on a US map in R, providing a clear and insightful representation of your data analysis. Remember to adapt the code to your specific data and desired visualization style.