Mastering Plot Usmap: A Comprehensive Guide to Creating Interactive Maps in R

Understanding Plot Usmap

Plot usmap is a powerful tool for creating interactive maps in R using the USMap package. It provides an easy-to-use interface for customizing the appearance and behavior of your map. However, like any other package, it has its own set of challenges and quirks.

Prerequisites

Before we dive into the world of plot usmap, let’s cover some essential prerequisites:

R Packages

The following R packages are required to work with plot usmap:

usmap: This is the main package for creating interactive maps in R.
maps: This package provides a set of map data that can be used with plot usmap.

To install these packages, run the following command in your R console:

install.packages("usmap")
install.packages("maps")

Data

Plot usmap requires some form of geographic data to work. This can be in the form of a shapefile or a GeoJSON file.

Creating a Map with Plot Usmap

Here’s an example of how to create a simple map using plot usmap:

library(usmap)
library(tidyverse)

# Load your data into a tibble format
df <- tibble(
    state = c("Alabama", "Alaska", "Arizona"),
    population = c(4.779, 739153, 7.421)
)

# Create the map
toy_example <- plot_usmap(data = df, values = "population")

# Customize the map
toy_example + 
  scale_fill_continuous(low = "white", high = "blue", name = "Population", label = scales::comma) +
  labs(title = "US Map with Population Data", subtitle = "2020 Estimates") +
  theme(legend.position = "right")

This example creates a simple map of the United States using the population data.

Adding Layers to the Map

Plot usmap allows you to add multiple layers to your map. These can include geographic features, shapefiles, and even raster images.

Here’s an example of how to add a layer to the map:

library(usmap)
library(tidyverse)

# Load your data into a tibble format
df <- tibble(
    state = c("Alabama", "Alaska", "Arizona"),
    population = c(4.779, 739153, 7.421),
    area = c(51.903, 663268, 131878)
)

# Create the map
toy_example <- plot_usmap(data = df, values = "population")

# Add a layer to the map using shapefile data
shp_file <- usmap::read_shapefile("path/to/shapefile.shp")
toy_example + 
  geom_shape(data = shp_file, aes(x = long, y = lat), color = "red") +
  labs(title = "US Map with Population and Shapefile Data", subtitle = "2020 Estimates")

This example adds a layer to the map using shapefile data.

Plot Usmap on Secure Server

The question at hand is why plot usmap isn’t displaying properly when run on a secure server. The answer, as mentioned in the Stack Overflow post, is that the data wasn’t compiling properly on the server and the map output is the result of a column full of NAs.

This can occur for several reasons:

Data Corruption: The data may have been corrupted during transfer or storage.
NA Values: NA values can cause issues with plot usmap, especially when used as a value in the plot_usmap function.
Server Configuration: The server’s configuration may not be suitable for running plot usmap.

To resolve this issue, it’s essential to identify and address any data corruption or NA values that might be causing the problem. Additionally, ensure that the server’s configuration is optimized for running plot usmap.

Resolving Data Corruption

Data corruption can occur due to various reasons such as disk errors or network issues during transfer. To resolve this issue:

Check Disk Errors: Run a disk check on your server to identify and fix any errors.
Verify Network Connection: Ensure that the network connection between your server and data source is stable.

Handling NA Values

NA values can be problematic when used as a value in plot usmap. To handle this:

Remove NA Values: Remove any rows with NA values from your dataset before passing it to plot usmap.
Replace NA Values: Replace NA values with suitable alternatives such as zero or missing values.

Server Configuration

The server’s configuration plays a crucial role in running plot usmap. To optimize the configuration:

Check Plot Usmap Requirements: Ensure that the server meets the requirements for running plot usmap.
Adjust Server Resources: Adjust server resources such as RAM and CPU to ensure smooth performance.

Best Practices

Here are some best practices to keep in mind when working with plot usmap:

Data Preparation

Before passing data to plot usmap, ensure that it is properly formatted and cleaned. Remove any unnecessary columns or rows, and handle NA values accordingly.

# Clean the data
df <- df[, -which(is.na(df))]

Map Customization

Customize your map using various options provided by plot usmap. Use scale_fill_continuous to change the fill color, labs to add a title and subtitle, and theme to adjust the appearance.

# Customize the map
toy_example + 
  scale_fill_continuous(low = "white", high = "blue", name = "Population", label = scales::comma) +
  labs(title = "US Map with Population Data", subtitle = "2020 Estimates") +
  theme(legend.position = "right")

Error Handling

Error handling is crucial when working with plot usmap. Catch any errors that occur and handle them accordingly.

tryCatch(
  # Code that may raise an error
  toy_example,
  
  # Handle the error
  function(e) {
    print("An error occurred:", e)
  }
)

Conclusion

Plot usmap is a powerful tool for creating interactive maps in R. However, it has its own set of challenges and quirks. By understanding how to handle data corruption, NA values, and server configuration, you can optimize your plot usmap experience.

Remember to follow best practices such as proper data preparation, map customization, and error handling to ensure smooth performance.

Last modified on 2025-04-25