Nog dieper het konijnenhol in

In de vorige oefening had de SpatialPolygonsDataFrame een lijst met Polygons in de polygons-slot, en elk van die Polygons-objecten had ook weer een Polygons-slot. Dus: heel veel polygons… maar je bent nog niet onderaan de hiërarchie!

Laten we nog eens kijken naar het 169e element in de Polygons-slot van countries_spdf. Voer deze code uit van de vorige oefening:

one <- countries_spdf@polygons[[169]]
str(one, max.level = 2)

De Polygons-slot bevat een lijst met 10 elementen. Wat zijn dit voor objecten? Laten we verder graven…

Deze oefening maakt deel uit van de cursus

Georuimtelijke data visualiseren in R

Cursus bekijken

Oefeninstructies

Roep str() aan met max.level = 2 op de Polygons-slot van one.
Roep str() aan met max.level = 2 op het 6e element van de Polygons-slot van one. Zie je iets dat eruitziet als ruimtelijke data?
Roep plot() aan op de coords-slot van het 6e element van de Polygons-slot van one. Herken je welke data dit object bevat?

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

one <- countries_spdf@polygons[[169]]

# str() with max.level = 2, on the Polygons slot of one


# str() with max.level = 2, on the 6th element of the one@Polygons


# Call plot on the coords slot of 6th element of one@Polygons

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Georuimtelijke data visualiseren in R

SkillTag.level.intermediateSkillTag.label

4.6+

Begin de cursus gratis

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: Kennismaken met sp-objecten Exercise 2: Laten we een ruimtelijk object bekijken Exercise 3: Wat zit er in een ruimtelijk object?Exercise 4: Een complexer ruimtelijk object Exercise 5: sp en S4 Exercise 6: Door de hiërarchie lopen Exercise 7: Nog dieper het konijnenhol in

Huidige oefening

Exercise 8: Meer sp-klassen en -methoden Exercise 9: Subsetten op index Exercise 10: Gegevens benaderen in sp-objecten Exercise 11: Subsetten op basis van data-attributen Exercise 12: tmap, een pakket dat met sp-objecten werkt Exercise 13: Introductie tot tmap Exercise 14: Een plot in lagen opbouwen Exercise 15: Waarom is Groenland zo groot?Exercise 16: Een tmap-plot opslaan

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