r usarrests data plot usmap

Breiz Heurope

3 min read

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06-03-2025

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Visualizing US Arrests: An Interactive Map Using usmap and R

This article demonstrates how to create an interactive map of US arrest data using the usmap package in R. We'll visualize arrest rates, allowing for easy comparison between states. This project combines data analysis with compelling data visualization, making complex information readily understandable. Understanding arrest data distribution across the US can provide insights into crime patterns and inform policy decisions.

Getting Started: Packages and Data

First, we need to install and load the necessary R packages. We'll use usmap for the map, ggplot2 for enhanced plotting, and dplyr for data manipulation. You'll also need to obtain arrest data; for this example, we'll assume you have a CSV file named "arrest_data.csv" with state names and arrest rates. Replace "arrest_data.csv" with your actual file name.

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

# Load the libraries
library(usmap)
library(ggplot2)
library(dplyr)

# Load your arrest data
arrest_data <- read.csv("arrest_data.csv")

Ensure your arrest_data.csv file has a column named "state" containing two-letter state abbreviations (e.g., "CA", "NY", "TX") and a column with the arrest rate data, for example named "arrest_rate".

Data Cleaning and Preparation

Before plotting, let's ensure your data is correctly formatted and ready for use with usmap. This might involve checking for missing values or inconsistencies in state abbreviations.

# Clean and prepare data (example - adapt as needed)
arrest_data <- arrest_data %>%
  mutate(state = toupper(state)) # Ensure state abbreviations are uppercase

This code snippet converts state abbreviations to uppercase to match the usmap package's requirements. Adjust this section based on the specifics of your dataset. Dealing with missing data is crucial for accurate visualization. Consider using imputation techniques or filtering out incomplete entries.

Creating the Interactive Map

Now, we'll use plot_usmap to create the interactive map. We'll use the values argument to specify the arrest rates for each state, and color to define the color palette.

plot_usmap(data = arrest_data, values = "arrest_rate", color = "red") + 
  scale_fill_continuous(low = "lightgray", high = "darkred", name = "Arrest Rate", label = scales::comma) + 
  labs(title = "US Arrest Rates by State", subtitle = "Interactive Map") +
  theme(legend.position = "right")

This code generates a choropleth map where each state's color intensity reflects its arrest rate. The scale_fill_continuous function defines the color gradient, and labs adds a title and subtitle. The legend is placed on the right for easy interpretation.

Enhancing the Visualization

We can further improve the map's clarity and visual appeal using ggplot2's features. Adding a title, subtitle, and a legend improves readability. Experiment with different color palettes for optimal visual impact.

# Example using ggplot2 for more customization
plot_usmap(data = arrest_data, values = "arrest_rate", color = "red") +
  geom_map(map = usmap::us_map,
           aes(map_id = state, fill = arrest_rate),
           color = "#ffffff", linewidth = 0.1) +
  expand_limits(x = 0, y = 0) +
  scale_fill_viridis_c(option = "D", name = "Arrest Rate", label = scales::comma) +
  labs(title = "US Arrest Rates by State", subtitle = "Data Visualization with ggplot2") +
  theme(legend.position = "right", panel.background = element_blank())

This uses geom_map for better control over map aesthetics and scale_fill_viridis_c for a perceptually uniform color palette. Experiment with different option values in scale_fill_viridis_c for varied color schemes.

Addressing potential issues

• Data inconsistencies: Ensure your data accurately represents arrest rates and is consistent across states. Missing data can skew results.
• Data source credibility: Use reliable and verifiable sources for your arrest data. Cite your sources appropriately.
• Map interpretation: Remember that correlation doesn't equal causation. High arrest rates in a state might reflect various factors, not solely crime prevalence.

This comprehensive guide shows how to effectively visualize US arrest data using R and the usmap package. Remember to replace placeholder filenames and column names with your actual data. By adapting and expanding upon this code, you can create informative and visually appealing maps for a wide range of geographic data analysis. This method allows for a dynamic and interactive exploration of crime statistics across the United States.