Adding Interactivity to ggplot2 Maps with Plotly

Visualizing spatial data through maps is widely used to represent geographical information. Maps allow us to understand geographic distributions, compare regions, and analyze data within a spatial context. Whether it’s displaying population density, sales figures, or climate patterns, maps provide a visual representation that helps us draw meaningful insights from spatial data. Hence, adding interactivity to such maps can enhance the data visualization and help further explore the data.

The need for interactive maps

Interactive maps take data visualization to a whole new level by providing a dynamic experience that allows us to delve deeper into the data. We can interpret the data better using interactive maps through different features such as zooming and clicking the linked information. The ggplot2 package excels at displaying all types of data and is the go-to package for most applications.

Let’s create maps using the ggplot2 package and make them interactive with the plotly package.

Getting started with choropleth maps in ggplot2

A choropleth map is a map made up of colored polygons. It represents spatial patterns and variations of a specific attribute across different regions. The ggplot2 package offers the geom_polygon() function for creating choropleth maps.

Here, we’ll use the maps package, which contains the outlines of various geographical locations such as continents, countries, states, and counties.

First, let’s import the maps package using the following command:

The maps package also includes different plotting functions to create the maps, but we’ll use the ggplot2 package function called map_data() to create our maps. The map_data() function provides a convenient way to obtain map data from various sources, including the maps package.

Let’s see the following example where we’ll store all the necessary data required for plotting a map of the USA in ggplot2, including the longitude and latitude coordinates of the boundary lines in an object called us_data.

• Lines 1–2: We use the map_data() function to retrieve the geographical data of the United States of America and use the <- operator to store it in us_data. We also print the first few rows of the data using the head() function.

Once we obtain the map data, we can use our ggplot2 package to create a static map. Here, we will use the geom_polygon() function for creating polygon maps.

• Line 1: We initialize