Menggabungkan semuanya

Sekarang Anda memiliki peta Corvallis yang bagus, tetapi bagaimana cara menampilkan lokasi penjualan rumah di atasnya?

Mirip dengan ggplot(), Anda dapat menambahkan layer data ke pemanggilan ggmap() (misalnya + geom_point()). Namun, penting untuk dicatat bahwa ggmap() menetapkan peta sebagai himpunan data bawaan dan juga menetapkan pemetaan estetika bawaan.

Artinya, jika Anda ingin menambahkan layer dari selain peta (misalnya sales), Anda perlu menyatakan secara eksplisit argumen mapping dan data pada geom tersebut.

Seperti apa tampilannya? Anda sudah melihat cara membuat plot dasar dari data penjualan:

ggplot(sales, aes(lon, lat)) + 
  geom_point()

Cara lain yang setara untuk menyatakan plot yang sama adalah:

ggplot() + 
  geom_point(aes(lon, lat), data = sales)

Di sini, kita menyatakan data dan pemetaan di pemanggilan geom_point() alih-alih di ggplot(). Keuntungan menyatakan plot dengan cara ini adalah Anda dapat menukar ggplot() dengan pemanggilan ggmap() dan mendapatkan peta sebagai latar belakang plot.

Latihan ini adalah bagian dari kursus

Memvisualisasikan Data Geospasial di R

Petunjuk latihan

Paket ggmap telah dimuat untuk Anda dan corvallis_map dari latihan sebelumnya tersedia di workspace Anda.

Pertama, lihat head() dari data sales. Dapatkah Anda melihat kolom yang menentukan lokasi rumah?
Gantilah pemanggilan ggplot() dengan pemanggilan ggmap() menggunakan corvallis_map.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Look at head() of sales


# Swap out call to ggplot() with call to ggmap()
ggplot() +
  geom_point(aes(lon, lat), data = sales)

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: Pengantar data spasial Exercise 2: Mengambil peta latar belakang Exercise 3: Menggabungkan semuanya

Latihan Saat Ini

Exercise 4: Wawasan melalui estetika Exercise 5: Opsi get_map() dan ggmap() yang berguna Exercise 6: Peta yang berbeda Exercise 7: Memanfaatkan kekuatan ggplot2 Exercise 8: Alternatif yang cepat Exercise 9: Jenis umum data spasial Exercise 10: Menggambar poligon Exercise 11: Peta koroplet Exercise 12: Data raster sebagai 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: 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