Sotto-selezionare i neighborhood

Non ti servono tutti quei neighborhood extra a New York, quindi farai una sotto-selezione mantenendo solo quelli nel New York County. Sai già come fare!

neighborhoods è uno SpatialPolygonsDataFrame e nel Capitolo 2 hai imparato a sotto-selezionare in base alla colonna nello slot data. La chiave era creare un vettore logico, poi sotto-selezionare lo SpatialPolygonsDataFrame come un data frame.

Come puoi identificare i neighborhood giusti? Prova con:

head(neighborhoods@data)

CountyFIPS è un codice numerico che identifica la contea. Se riesci a trovare il codice per il New York County, puoi mantenere solo le righe con quel valore.

Questo esercizio fa parte del corso

Visualizzare dati geospaziali in R

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Istruzioni dell'esercizio

L'oggetto nyc_tracts_merge ha anch'esso i codici di contea nella colonna COUNTYFP. Trova i valori con unique() per individuare il codice del New York County.
Sotto-seleziona neighborhoods aggiungendo una condizione logica che verifichi se neighborhoods$CountyFIPS ha il valore corretto.
Modifica il grafico per usare manhat_hoods al posto di neighborhoods.
Aggiungi un layer tm_text(), mappando text a "NTAName".

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

library(tmap)

# Find unique() nyc_tracts_merge$COUNTYFP


# Add logical expression to pull out New York County
manhat_hoods <- neighborhoods[___, ]

tm_shape(nyc_tracts_merge) +
  tm_fill(col = "estimate") +
  tm_shape(water) +
  tm_fill(col = "grey90") +
  # Edit to use manhat_hoods instead
  tm_shape(neighborhoods) +
  tm_borders() +
  # Add a tm_text() layer

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: 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 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: Lettura dei dati spaziali Exercise 2: Lettura di uno shapefile Exercise 3: Lettura di un file raster Exercise 4: Ottenere dati usando un pacchetto Exercise 5: Sistemi di riferimento di coordinate Exercise 6: Unire dati con CRS/proiezioni differenti Exercise 7: Conversione da un CRS/proiezione a un altro Exercise 8: Aggiungere dati agli oggetti spaziali Exercise 9: Il modo sbagliato Exercise 10: Verificare la corrispondenza dei dati Exercise 11: Unire gli attributi dei dati Exercise 12: Un primo grafico Exercise 13: Rifinire una mappa Exercise 14: Sotto-selezionare i neighborhood

Esercizio in corso

Exercise 15: Aggiungere le etichette dei quartieri Exercise 16: Sistemare la legenda e alcuni ritocchi finali Exercise 17: Riepilogo