Building a plot in layers

Now that you know a bit more about tmap(), let's build up your previous plot of population in layers and make a few tweaks to improve it. You start with a tm_shape() layer that defines the data you want to use, then add a tm_fill() layer to color-in your polygons using the variable population:

tm_shape(countries_spdf) +
  tm_fill(col = "population")

Probably the biggest problem with the resulting plot is that the color scale isn't very informative: the first color (palest yellow) covers all countries with population less than 200 million! Since the color scale is associated with the tm_fill() layer, tweaks to this scale happen in this call. You'll learn a lot more about color in Chapter 3, but for now, know that the style argument controls how the breaks are chosen.

Your plot also needs some country outlines. You can add a tm_borders() layer for this, but let's not make them too visually strong. Perhaps a brown would be nice.

The benefit of using spatial objects becomes really clear when you switch the kind of plot you make. Let's also try a bubble plot where the size of the bubbles correspond to population. If you were using ggplot2, this would involve a lot of reshaping of your data. With tmap, you just switch out a layer.

Cet exercice fait partie du cours

Visualizing Geospatial Data in R

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Instructions

Add style = "quantile" to tm_fill(). This chooses the breaks in the color scale based on equal numbers of observations in each interval.
To the same plot, add a tm_borders() layer with col = "burlywood4".
Create new plot the same as the first, but instead of tm_fill() add a tm_bubbles() layer with size mapped to population.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

library(sp)
library(tmap)

# Add style argument to the tm_fill() call
tm_shape(countries_spdf) +
  tm_fill(col = "population") +
  # Add a tm_borders() layer 
  

# New plot, with tm_bubbles() instead of tm_fill()

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Cet exercice fait partie du cours

Visualizing Geospatial Data in R

IntermédiaireNiveau de compétence

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 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

Exercice en cours

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