De buurten subselecteren

Je hebt al die overbodige buurten in New York niet nodig, dus je selecteert alleen de buurten in New York County. Je weet al hoe dat moet!

neighborhoods is een SpatialPolygonsDataFrame en in Hoofdstuk 2 heb je geleerd hoe je kunt subselecteren op basis van de kolom in de data-slot. De sleutel was een logische vector maken en vervolgens het SpatialPolygonsDataFrame subselecteren zoals een data frame.

Hoe identificeer je de juiste buurten? Bekijk:

head(neighborhoods@data)

CountyFIPS is een numerieke code die de county aangeeft. Als je de code voor New York County weet te vinden, kun je alleen de rijen met die waarde behouden.

Deze oefening maakt deel uit van de cursus

Georuimtelijke data visualiseren in R

Cursus bekijken

Oefeninstructies

Het object nyc_tracts_merge heeft ook county-codes in de kolom COUNTYFP. Zoek de unique()-waarden om de code voor New York County te vinden.
Subselecteer neighborhoods door een logische test toe te voegen die controleert of neighborhoods$CountyFIPS de juiste waarde heeft.
Pas je plot aan om manhat_hoods te gebruiken in plaats van neighborhoods.
Voeg een tm_text()-laag toe en map text naar "NTAName".

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

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

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: 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: Ruimtelijke data inlezen Exercise 2: Een shapefile inlezen Exercise 3: Een rasterbestand inlezen Exercise 4: Gegevens ophalen met een package Exercise 5: Coördinatenreferentiesystemen Exercise 6: Data samenvoegen met verschillende CRS/projecties Exercise 7: Converteren van de ene CRS/projectie naar een andere Exercise 8: Gegevens toevoegen aan ruimtelijke objecten Exercise 9: De verkeerde manier Exercise 10: Controleren of data overeenkomt Exercise 11: Gegevensattributen samenvoegen Exercise 12: Een eerste plot Exercise 13: Een kaart verfraaien Exercise 14: De buurten subselecteren

Huidige oefening

Exercise 15: Labels voor buurten toevoegen Exercise 16: De legenda opruimen en nog wat laatste tweaks Exercise 17: Afronding