Veri özniteliklerini birleştirme

merge() varsayılan olarak her iki veri kümesinde de adı aynı olan sütunlara göre birleştirir. Senin durumunda bu uygun değil, çünkü kimlik sütununun adı bir veri kümesinde tract, diğerinde TRACTCE. Bunu ele almak için merge() fonksiyonunun by.x ve by.y adlı isteğe bağlı argümanları var; bunlarla iki veri kümesinde birleştirilecek sütun adlarını sırasıyla belirtebilirsin.

merge() yeni bir Spatial___DataFrame nesnesi döndürür; bu yüzden sonucu tmap ile çizerek inceleyebilirsin.

Haydi birleştirmeyi yapalım.

Bu egzersiz

R ile Coğrafi Verileri Görselleştirme

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

merge() kullan; önce mekansal nesne nyc_tracts, sonra veri çerçevesi nyc_income gelsin. by.x = "TRACTCE" ve by.y = "tract" belirt. Sonucu nyc_tracts_merge içinde sakla.
nyc_tracts_merge üzerinde summary() çalıştırarak nesnenin bir SpatialPolygonsDataFrame olduğunu ve verilerin nyc_income içindeki gerekli estimate sütununu da içerdiğini doğrula.
tm_shape() kullan ve bir tm_fill() katmanı ekleyerek nyc_tracts_merge için renkleri estimatea eşleyen bir koroplet harita oluştur.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

library(sp)
library(tmap)

# Merge nyc_tracts and nyc_income: nyc_tracts_merge


# Call summary() on nyc_tracts_merge


# Choropleth with col mapped to estimate

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: 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: Uzamsal verileri içe aktarma Exercise 2: Bir shapefile'ı içeri aktarma Exercise 3: Bir raster dosyasını içeri aktarma Exercise 4: Bir paket kullanarak veri alma Exercise 5: Koordinat referans sistemleri Exercise 6: Farklı CRS/projeksiyonlardan verileri birleştirme Exercise 7: Bir CRS/projeksiyondan diğerine dönüştürme Exercise 8: Mekânsal nesnelere veri ekleme Exercise 9: Yanlış yol Exercise 10: Verilerin eşleşeceğini kontrol etme Exercise 11: Veri özniteliklerini birleştirme

Geçerli Egzersiz

Exercise 12: İlk grafik Exercise 13: Bir haritayı cilalamak Exercise 14: Mahalleleri alt kümelemek Exercise 15: Mahalle etiketlerini ekleme Exercise 16: Göstergeyi toparlama ve son rötuşlar Exercise 17: Özetle