ggplot2 grafiklerine özel sürekli renk paleti ekleme

ggplot2 grafiklerine özel bir sürekli ölçek eklemenin en esnek yolu scale_color_gradientn() veya scale_fill_gradientn() kullanmaktır. Hangisini kullanacağını nasıl bilirsin? Haritaladığın estetikle fonksiyonu eşleştir. Örneğin, 1. Bölümdeki tahmini ev fiyatı grafiğinde filli fiyata eşlemiştin, bu yüzden scale_fill_gradientn() kullanman gerekir.

Bu iki fonksiyon, paletini tanımlayan bir renk vektörünü verdiğin colors argümanını alır. Esneklik de burada devreye girer. Paletini istediğin şekilde üretebilirsin: otomatik olarak RColorBrewer veya viridisLite gibi paketlerle ya da renkleri adla veya hex koduyla elle belirterek.

scale___gradientn() fonksiyonları bu renklerin değişkeninin değerlerine nasıl eşleneceğini yönetir; yine de values argümanıyla kontrol edebilirsin.

Hadi 1. Bölümdeki tahmini ev fiyatı ısı haritan için bazı alternatif renk ölçekleriyle oynayalım (hesaplama süresini azaltmak için harita arka planını kaldırdık, böylece grafikleri hızlıca görebilirsin).

Bu egzersiz

R ile Coğrafi Verileri Görselleştirme

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

library(RColorBrewer)
# 9 steps on the RColorBrewer "BuPu" palette: blups


# Add scale_fill_gradientn() with the blups palette
ggplot(preds) +
  geom_tile(aes(lon, lat, fill = predicted_price), alpha = 0.8)

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: raster paketi Exercise 2: Raster nesnesi nedir?Exercise 3: Bazı yararlı yöntemler Exercise 4: Daha karmaşık bir nesne Exercise 5: Raster nesnelerini kullanan bir paket Exercise 6: Renk ölçekleri Exercise 7: Doğru paleti seç Exercise 8: ggplot2 grafiklerine özel sürekli renk paleti ekleme

Geçerli Egzersiz

Exercise 9: tmap'te özel palet Exercise 10: Renk ölçekleri hakkında daha fazlası Exercise 11: Bir aralık ölçeği örneği Exercise 12: Ayrışan ölçek örneği Exercise 13: Nitel bir örnek

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