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  3. Visualizing Geospatial Data in R
  • 1

    Basic mapping with ggplot2 and ggmap

    Free

    We'll dive in by displaying some spatial data -- property sales in a small US town -- using ggplot2 and we'll introduce you to the ggmap package as a quick way to add spatial context to your plots. We'll talk about what makes spatial data special and introduce you to the common types of spatial data we'll be working with throughout the course.

  • 2

    Point and polygon data

    You can get a long way with spatial data stored in data frames, but it makes life easier if they are stored in special spatial objects. In this chapter we'll introduce you to the spatial object classes provided by the sp package, particularly for point and polygon data. You'll learn how to explore and subset these objects by exploring a world map. The reward for learning about these object classes: we'll show you the package tmap which requires spatial objects as input, but makes creating maps really easy! You'll finish up by making a map of the world's population.

  • 3

    Raster data and color

    While the sp package provides some classes for raster data, the raster package provides more useful classes. You'll be introduced to these classes and their advantages and then learn to display them. The examples continue with the theme of population from Chapter 2, but you'll look at some much finer detail datasets, both spatially and demographically. In the second half of the chapter you'll learn about color -- an essential part of any visual display, but especially important for maps.

  • 4

    Data import and projections

    In this chapter you'll follow the creation of a visualization from raw spatial data files to adding a credit to a map. Along the way, you'll learn how to read spatial data into R, more about projections and coordinate reference systems, how to add additional data to a spatial object, and some tips for polishing your maps.

Initializing

Exercise

Walking the hierarchy

Let's practice accessing slots by exploring the way polygons are stored inside SpatialDataFrame objects. Remember there are two ways to access slots in an S4 object: 

x@slot_name # or...
slot(x, "slot_name")

So, to take a look at the polygons slot of countries_spdf you simply do countries_spdf@polygons. You can try it, but you'll get a long and not very informative output. Let's look at the high level structure instead.

Try running the following code in the console:

str(countries_spdf@polygons, max.level = 2)

Still a pretty long output, but scroll back to the top and take a look. What kind of object is this? It's just a list, but inside its elements are another kind of sp class: Polygons. There are 177 list elements. Any guesses what they might represent?

Let's dig into one of these elements.

Instructions

100 XP
  • Create a new variable called one that contains the 169th element of the list in the polygons slot of countries_spdf. Use double bracket subsetting (i.e. [[...]] to extract this element.
  • Print one.
  • Call summary() on one. What slots does this object have?
  • Call str() on one with max.level = 2.