Sottoinsiemi basati su attributi dei dati

Creare sottoinsiemi basati su attributi dei dati significa combinare la creazione di un vettore logico a partire dalle colonne del tuo data frame con il sottoinsieme dell'oggetto Spatial___DataFrame. È simile a come crei sottoinsiemi in un normale data frame.

Crea un vettore logico da una colonna, ad esempio i paesi in Asia:

in_asia <- countries_spdf$region == "Asia"
in_asia

Poi usa il vettore logico per selezionare le righe dell'oggetto Spatial___DataFrame:

countries_spdf[in_asia, ]

Riesci a estrarre la Nuova Zelanda come sottoinsieme e a tracciarla?

Questo esercizio fa parte del corso

Visualizzare dati geospaziali in R

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Istruzioni dell'esercizio

Crea un vettore logico chiamato is_nz che verifica se la colonna name è uguale a "New Zealand".
Crea un nuovo oggetto spaziale chiamato nz usando is_nz per creare un sottoinsieme di countries_spdf.
Traccia nz.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Create logical vector: is_nz


# Subset countries_spdf using is_nz: nz


# Plot nz

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Visualizzare dati geospaziali in R

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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: 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 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

Esercizio in corso

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