Veri özniteliklerine göre alt kümeleme

Veri özniteliklerine göre alt kümeleme, veri çerçevendeki sütunlardan bir mantıksal ifade oluşturup bunu Spatial___DataFrame nesnesini alt kümelerken kullanmanın birleşimidir. Bu, normal bir veri çerçevesini alt kümelerken yaptığın işleme benzer.

Bir sütundan mantıksal bir vektör oluştur, örneğin Asya’daki ülkeler:

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

Ardından bu mantıksalı kullanarak Spatial___DataFrame nesnesinin satırlarını seç:

countries_spdf[in_asia, ]

Yeni Zelanda’yı alt kümeleyip çizebilir misin?

Bu egzersiz

R ile Coğrafi Verileri Görselleştirme

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

name sütununun "New Zealand"'e eşit olup olmadığını test eden is_nz adlı bir mantıksal vektör oluştur.
is_nz'yi kullanarak countries_spdf'i alt kümeleyip nz adlı yeni bir uzamsal nesne oluştur.
nz'yi çiz.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Create logical vector: is_nz


# Subset countries_spdf using is_nz: nz


# Plot nz

Kodu Düzenle ve Çalıştır

Bu egzersiz

R ile Coğrafi Verileri Görselleştirme

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.6+

Kursa Ücretsiz Başlayın

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: sp nesnelerine giriş Exercise 2: Haydi bir mekânsal nesneye göz atalım Exercise 3: Bir mekânsal nesnenin içinde ne var?Exercise 4: Daha karmaşık bir mekânsal nesne Exercise 5: sp ve S4 Exercise 6: Hiyerarşide gezinmek Exercise 7: Tavşan deliğinde biraz daha derine Exercise 8: Daha fazla sp sınıfı ve yöntemi Exercise 9: İndekse göre alt kümeleme Exercise 10: sp nesnelerindeki veriye erişim Exercise 11: Veri özniteliklerine göre alt kümeleme

Geçerli Egzersiz

Exercise 12: sp nesneleriyle çalışan bir paket: tmap Exercise 13: tmap'e giriş Exercise 14: Grafiği katmanlarla oluşturma Exercise 15: Grönland neden bu kadar büyük görünüyor?Exercise 16: Bir tmap grafiğini kaydetme

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