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Visualizing Geospatial Data in R

Intermediate

Updated 12/2024

Learn to read, explore, and manipulate spatial data then use your skills to create informative maps using R.

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RData Visualization4 hours15 videos58 exercises5,000 XP28,064Statement of Accomplishment

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Course Description

Where should you buy a house to get the most value for your money? Your first step might be to make a map, but spatial analysis in R can be intimidating because of the complicated objects the data often live in.

This course will introduce you to spatial data by starting with objects you already know about, data frames, before introducing you to the special objects from the sp and raster packages used to represent spatial data for analysis in R. You'll learn to read, explore, and manipulate these objects with the big payoff of being able to use the tmap package to make maps.

By the end of the course you will have made maps of property sales in a small town, populations of the countries of the world, the distribution of people in the North East of the USA, and median income in the neighborhoods of New York City.

Prerequisites

Introduction to R Introduction to Data Visualization with ggplot2

1

Basic mapping with ggplot2 and ggmap

Introduction to spatial data

Grabbing a background map

Putting it all together

Insight through aesthetics

Useful get_map() and ggmap() options

Different maps

Leveraging ggplot2's strengths

A quick alternative

Common types of spatial data

Drawing polygons

Choropleth map

Raster data as a heatmap

2

Point and polygon data

Introducing sp objects

Let's take a look at a spatial object

What's inside a spatial object?

A more complicated spatial object

Walking the hierarchy

Further down the rabbit hole

More sp classes and methods

Subsetting by index

Accessing data in sp objects

Subsetting based on data attributes

tmap, a package that works with sp objects

Introduction to tmap

Building a plot in layers

Why is Greenland so big?

Saving a tmap plot

3

Raster data and color

The raster package

What's a raster object?

Some useful methods

A more complicated object

A package that uses Raster objects

Color scales

Pick the right palette

Adding a custom continuous color palette to ggplot2 plots

Custom palette in tmap

More about color scales

An interval scale example

A diverging scale example

A qualitative example

4

Data import and projections

Reading in spatial data

Reading in a shapefile

Reading in a raster file

Getting data using a package

Coordinate reference systems

Merging data from different CRS/projections

Converting from one CRS/projection to another

Adding data to spatial objects

The wrong way

Checking data will match

Merging data attributes

A first plot

Polishing a map

Subsetting the neighborhoods

Adding neighborhood labels

Tidying up the legend and some final tweaks

Visualizing Geospatial Data in R

Course
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