Introducing sp objects

1. Introducing sp objects

So far, the spatial data you've seen has been in data frames. Data frames are nice because you already know how to manipulate and visualize them, but data frames aren't ideal for spatial data.

2. Data frames aren't great for storing spatial data

Data frames are fine for storing coordinates, but there is no natural way to store information about the coordinate reference system. This may not seem like much of a problem when you are only working with one coordinate system and you know which one. But as soon as you have data in more than one coordinate system, or you are sharing data with others, you need to have a system for storing and manipulating the coordinate reference system. We saw in our polygon example, of sales summarized to wards, that the ward level data was repeated as many times as there were points needed to describe the polygon. This is a bit inefficient, really we should only need to store the polygon level data once per polygon. And finally, we saw to store polygons, which may have many pieces, and may have polygons inside polygons, we had to store a lot of extra information about the order to draw points, and which points belong to which polygons. We had to force something that is naturally hierarchical into a rectangular format. For these reasons it makes sense to have special objects for storing spatial data.

3. The sp package

In this chapter we'll focus on the objects that the sp package defines. The package provides special classes for storing spatial objects and methods for manipulating these objects. While sp does provide classes for raster data, we'll see a better set of objects for raster data in the next chapter. Here we'll focus on point, line and polygon data. There's always a bit of overhead to master a new type of object, but the benefit is that the sp classes have become a standard in R for storing spatial data. Many packages that work with spatial data will expect the data in an sp object. Putting in the time to understand them now will pay off whenever you run into one of these packages.

4. Let's practice!

Rather than tell you about the objects sp defines right now, we're going to give you some to explore on your own.

This exercise is part of the course

Visualizing Geospatial Data in R

IntermediateSkill Level

4.5+

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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.

Exercise 1: Introduction to spatial data Exercise 2: Grabbing a background map Exercise 3: Putting it all together Exercise 4: Insight through aesthetics Exercise 5: Useful get_map() and ggmap() options Exercise 6: Different maps Exercise 7: Leveraging ggplot2's strengths Exercise 8: A quick alternative Exercise 9: Common types of spatial data Exercise 10: Drawing polygons Exercise 11: Choropleth map Exercise 12: Raster data as a heatmap

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.

Exercise 1: Introducing sp objects

Current Exercise

Exercise 2: Let's take a look at a spatial object Exercise 3: What's inside a spatial object?Exercise 4: A more complicated spatial object Exercise 5: sp and S4 Exercise 6: Walking the hierarchy Exercise 7: Further down the rabbit hole Exercise 8: More sp classes and methods Exercise 9: Subsetting by index Exercise 10: Accessing data in sp objects Exercise 11: Subsetting based on data attributes Exercise 12: tmap, a package that works with sp objects Exercise 13: Introduction to tmap Exercise 14: Building a plot in layers Exercise 15: Why is Greenland so big?Exercise 16: Saving a tmap plot

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.

Exercise 1: The raster package Exercise 2: What's a raster object?Exercise 3: Some useful methods Exercise 4: A more complicated object Exercise 5: A package that uses Raster objects Exercise 6: Color scales Exercise 7: Pick the right palette Exercise 8: Adding a custom continuous color palette to ggplot2 plots Exercise 9: Custom palette in tmap Exercise 10: More about color scales Exercise 11: An interval scale example Exercise 12: A diverging scale example Exercise 13: A qualitative example

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.

Exercise 1: Reading in spatial data Exercise 2: Reading in a shapefile Exercise 3: Reading in a raster file Exercise 4: Getting data using a package Exercise 5: Coordinate reference systems Exercise 6: Merging data from different CRS/projections Exercise 7: Converting from one CRS/projection to another Exercise 8: Adding data to spatial objects Exercise 9: The wrong way Exercise 10: Checking data will match Exercise 11: Merging data attributes Exercise 12: A first plot Exercise 13: Polishing a map Exercise 14: Subsetting the neighborhoods Exercise 15: Adding neighborhood labels Exercise 16: Tidying up the legend and some final tweaks Exercise 17: Wrap up