Creating Multiple Choropleth Maps from Each Column in a Data Frame using R and ggplot2

Introduction

In this article, we will explore how to create multiple choropleth maps from each column in a data frame using the popular R programming language and the ggplot2 library. Specifically, we’ll be discussing how to generate 48 hourly maps of the US for each hour of observation in a data frame.

Background

A choropleth map is a type of thematic map that uses color or shade to represent different values of a variable across different geographic areas. In this case, we have a data frame containing 48 hourly observations from each state, with state names in column 1 and columns 2:49 containing the observations for each hour.

We’ve already managed to create a single choropleth map using the following code:

map_hours <- colnames(qso_per_hour[2:49]) # drop state column, get just hour column names
this_map <- map_hours[18]  # hour 18 as an example

ggplot(qso_per_hour, aes_string(map_id="states", fill=this_map)) +
    geom_map(map=states_map, colour="black") # other formatting code trimmed

However, we want to create multiple choropleth maps for each column in the data frame and save them off as individual graphics files. In this article, we’ll explore how to achieve this.

The Problem with Using a For Loop

When we try to use a for loop to create multiple choropleth maps, we encounter an issue:

map_hours <- colnames(qso_per_hour[2:49]) # drop state column, get just hour column names
for (this_map in map_hours) {
    ggplot(qso_per_hour, aes_string(map_id="states", fill=this_map)) +
        geom_map(map=states_map, colour="black") # other formatting code trimmed
}

The problem here is that the ggplot function doesn’t work well within a for loop. The issue seems to be related to how R handles the graphics rendering process.

Solution: Using Print() Function

To resolve this issue, we can use the print() function to display each map individually:

map_hours <- colnames(qso_per_hour[2:49]) # drop state column, get just hour column names
for (this_map in map_hours) {
    print(ggplot(qso_per_hour, aes_string(map_id="states", fill=this_map)) +
           geom_map(map=states_map, colour="black") # other formatting code trimmed)
}

By wrapping the ggplot function within a print() statement, we can ensure that each map is displayed correctly.

Alternative Solution: Using Lapply()

Another alternative to using a for loop is to use the lapply() function from the utils package:

library(utils)
map_hours <- colnames(qso_per_hour[2:49]) # drop state column, get just hour column names
maps <- lapply(map_hours, function(this_map) {
    ggplot(qso_per_hour, aes_string(map_id="states", fill=this_map)) +
        geom_map(map=states_map, colour="black") # other formatting code trimmed
})

In this example, lapply() applies the anonymous function to each element in the map_hours vector and returns a list of maps.

Creating Graphics Files

To save each map as an individual graphics file, we can use the ggsave() function:

library(ggplot2)
map_hours <- colnames(qso_per_hour[2:49]) # drop state column, get just hour column names
for (this_map in map_hours) {
    ggsave("map_".paste(this_map, ".png", sep=""), print(ggplot(qso_per_hour, aes_string(map_id="states", fill=this_map)) +
           geom_map(map=states_map, colour="black") # other formatting code trimmed))
}

In this example, ggsave() saves the current plot as a PNG file with a name that includes the map hour.

Conclusion

In conclusion, creating multiple choropleth maps from each column in a data frame using R and ggplot2 is achievable through various means. We’ve explored how to use a for loop, the print() function, and alternative solutions like lapply(). By understanding these techniques, you can efficiently create and display multiple choropleth maps as individual graphics files.

Example Use Case

Suppose we have a data frame called qso_per_hour with 48 hourly observations from each state. We want to create a choropleth map for each hour of observation. Using the code presented above, we can achieve this by running the following R script:

library(ggplot2)
library(utils)

# Load necessary libraries

# Create data frame (assuming 'qso_per_hour' exists)
# ...

map_hours <- colnames(qso_per_hour[2:49]) # drop state column, get just hour column names

for (this_map in map_hours) {
    print(ggplot(qso_per_hour, aes_string(map_id="states", fill=this_map)) +
           geom_map(map=states_map, colour="black") # other formatting code trimmed)
}

# Save each map as an individual graphics file
for (this_map in map_hours) {
    ggsave("map_".paste(this_map, ".png", sep=""), print(ggplot(qso_per_hour, aes_string(map_id="states", fill=this_map)) +
           geom_map(map=states_map, colour="black") # other formatting code trimmed))
}

This script will create a choropleth map for each hour of observation and save it as an individual PNG file.

Last modified on 2025-03-19