Più a fondo nella tana del coniglio

Nell’ultimo esercizio, lo SpatialPolygonsDataFrame aveva un elenco di Polygons nello slot polygons, e ognuno di quegli oggetti Polygons aveva a sua volta uno slot Polygons. Quindi, molti poligoni… ma non sei ancora in fondo alla gerarchia!

Diamo un’altra occhiata al 169° elemento nello slot Polygons di countries_spdf. Esegui questo codice dall’esercizio precedente:

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

Lo slot Polygons contiene un elenco con 10 elementi. Che oggetti sono? Continuiamo a scavare…

Questo esercizio fa parte del corso

Visualizzare dati geospaziali in R

Visualizza il corso

Istruzioni dell'esercizio

Chiama str() con max.level = 2 sullo slot Polygons di one.
Chiama str() con max.level = 2 sul 6° elemento dello slot Polygons di one. Vedi qualcosa che sembra poter essere dati spaziali?
Chiama plot() sullo slot coords del 6° elemento dello slot Polygons di one. Riconosci quali dati contiene questo oggetto?

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

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

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Visualizzare dati geospaziali in R

IntermediárioNível de habilidade

4.6+

Inizia il corso 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: Introduzione agli oggetti sp Exercise 2: Diamo un'occhiata a un oggetto spaziale Exercise 3: Cosa c'è dentro un oggetto spaziale?Exercise 4: Un oggetto spaziale più complesso Exercise 5: sp e S4 Exercise 6: Percorrere la gerarchia Exercise 7: Più a fondo nella tana del coniglio

Esercizio in corso

Exercise 8: Altre classi e metodi di sp Exercise 9: Subset per indice Exercise 10: Accedere ai dati negli oggetti sp Exercise 11: Sottoinsiemi basati su attributi dei dati Exercise 12: tmap, un pacchetto che lavora con oggetti sp Exercise 13: Introduzione a tmap Exercise 14: Costruire un grafico a livelli Exercise 15: Perché la Groenlandia è così grande?Exercise 16: Salvare un grafico tmap

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