Membangun plot secara bertahap (layer)

Sekarang setelah Anda lebih mengenal tmap(), mari kita bangun kembali plot jumlah penduduk sebelumnya secara bertahap dan lakukan beberapa penyesuaian untuk meningkatkannya. Anda memulai dengan lapisan tm_shape() yang menentukan data yang akan digunakan, lalu menambahkan lapisan tm_fill() untuk mewarnai poligon menggunakan variabel population:

tm_shape(countries_spdf) +
  tm_fill(col = "population")

Kemungkinan masalah terbesar pada plot yang dihasilkan adalah skala warnanya kurang informatif: warna pertama (kuning paling pucat) mencakup semua negara dengan penduduk kurang dari 200 juta! Karena skala warna terkait dengan lapisan tm_fill(), penyesuaian skala ini dilakukan pada pemanggilan tersebut. Anda akan mempelajari lebih banyak tentang warna di Bab 3, tetapi untuk saat ini, ketahuilah bahwa argumen style mengontrol bagaimana batas kelas (breaks) dipilih.

Plot Anda juga memerlukan garis batas negara. Anda dapat menambahkan lapisan tm_borders() untuk ini, tetapi jangan sampai terlalu mencolok. Mungkin warna cokelat akan terlihat bagus.

Manfaat menggunakan objek spasial menjadi sangat jelas ketika Anda mengganti jenis plot yang dibuat. Mari kita coba juga plot gelembung (bubble) di mana ukuran gelembung merepresentasikan jumlah penduduk. Jika Anda menggunakan ggplot2, ini akan memerlukan banyak penataan ulang data. Dengan tmap, Anda cukup mengganti satu lapisan.

Latihan ini adalah bagian dari kursus

Memvisualisasikan Data Geospasial di R

Petunjuk latihan

Tambahkan style = "quantile" ke tm_fill(). Ini memilih batas kelas pada skala warna berdasarkan jumlah observasi yang sama di setiap interval.
Pada plot yang sama, tambahkan lapisan tm_borders() dengan col = "burlywood4".
Buat plot baru yang sama seperti yang pertama, tetapi alih-alih tm_fill() tambahkan lapisan tm_bubbles() dengan size dipetakan ke population.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

library(sp)
library(tmap)

# Add style argument to the tm_fill() call
tm_shape(countries_spdf) +
  tm_fill(col = "population") +
  # Add a tm_borders() layer 
  

# New plot, with tm_bubbles() instead of tm_fill()

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: Pengenalan objek sp Exercise 2: Mari melihat sebuah objek spasial Exercise 3: Apa isi sebuah objek spasial?Exercise 4: Objek spasial yang lebih rumit Exercise 5: sp dan S4 Exercise 6: Menelusuri hierarki Exercise 7: Lebih jauh menyelami hierarki Exercise 8: Lebih banyak kelas dan metode sp Exercise 9: Subset berdasarkan indeks Exercise 10: Mengakses data dalam objek sp Exercise 11: Subset berdasarkan atribut data Exercise 12: tmap, paket yang bekerja dengan objek sp Exercise 13: Pengenalan tmap Exercise 14: Membangun plot secara bertahap (layer)

Latihan Saat Ini

Exercise 15: Mengapa Greenland terlihat begitu besar?Exercise 16: Menyimpan plot tmap

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