Menambahkan palet warna kontinu kustom ke plot ggplot2

Cara paling fleksibel untuk menambahkan skala kontinu kustom ke plot ggplot2 adalah dengan scale_color_gradientn() atau scale_fill_gradientn(). Bagaimana cara menentukan mana yang digunakan? Cocokkan fungsinya dengan estetika yang Anda petakan. Misalnya, pada plot prediksi harga rumah di Bab 1, Anda memetakan fill ke harga, sehingga Anda perlu menggunakan scale_fill_gradientn().

Kedua fungsi ini menerima argumen colors tempat Anda memberikan sebuah vektor warna yang mendefinisikan palet Anda. Di sinilah letak fleksibilitasnya. Anda dapat membuat palet dengan cara apa pun yang Anda pilih, secara otomatis menggunakan sesuatu seperti RColorBrewer atau viridisLite, atau secara manual dengan menentukan warna berdasarkan nama atau kode hex.

Fungsi scale___gradientn() menangani bagaimana warna-warna tersebut dipetakan ke nilai variabel Anda, meskipun tersedia pengendalian melalui argumen values.

Mari bereksperimen dengan beberapa skala warna alternatif untuk peta panas prediksi harga rumah dari Bab 1 (kami menghapus latar belakang peta untuk mengurangi waktu komputasi, sehingga Anda dapat melihat plot Anda dengan cepat).

Latihan ini adalah bagian dari kursus

Memvisualisasikan Data Geospasial di R

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

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)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Memvisualisasikan Data Geospasial di R

SkillTag.level.intermediateSkillTag.label

4.6+

Mulai Kursus 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: Paket raster Exercise 2: Apa itu objek raster?Exercise 3: Beberapa metode yang berguna Exercise 4: Objek yang lebih rumit Exercise 5: Paket yang menggunakan objek Raster Exercise 6: Skala warna Exercise 7: Pilih palet yang tepat Exercise 8: Menambahkan palet warna kontinu kustom ke plot ggplot2

Latihan Saat Ini

Exercise 9: Palet kustom di tmap Exercise 10: Lebih lanjut tentang skala warna Exercise 11: Contoh skala interval Exercise 12: Contoh skala divergen Exercise 13: Contoh kualitatif

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