Palet kustom di tmap

Berbeda dengan ggplot2, di mana pengaturan skala warna kustom dilakukan dengan pemanggilan scale_, warna pada layer tmap ditentukan di layer tempat warna tersebut dipetakan. Misalnya, perhatikan plot variabel age_18_24 dari prop_by_age:

tm_shape(prop_by_age) +
  tm_raster(col = "age_18_24")

Karena warna dipetakan dalam pemanggilan tm_raster(), penentuan palet juga dilakukan di pemanggilan ini. Anda cukup menentukan sebuah vektor warna pada argumen palette. Ini adalah alasan lain mengapa layak mempelajari cara membuat vektor warna. Meskipun paket yang berbeda dapat memiliki pintasan yang sangat berbeda untuk menentukan palet dari paket warna, umumnya mereka selalu menyediakan cara untuk memasukkan sebuah vektor warna.

Mari gunakan beberapa palet dari latihan sebelumnya pada plot ini.

Latihan ini adalah bagian dari kursus

Memvisualisasikan Data Geospasial di R

Petunjuk latihan

Pada plot pertama, gunakan palet blups sebagai pengganti bawaan.
Pada plot kedua, gunakan palet vir sebagai pengganti bawaan.
Pada plot ketiga, gunakan rev(mag) sebagai pengganti bawaan. rev() hanya membalik urutan sebuah vektor, sehingga ini menggunakan warna yang sama tetapi dalam urutan terbalik.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Generate palettes from last time
library(RColorBrewer)
blups <- brewer.pal(9, "BuPu")

library(viridisLite)
vir <- viridis(9)
mag <- magma(9)

# Use the blups palette
tm_shape(prop_by_age) +
  tm_raster("age_18_24") +
  tm_legend(position = c("right", "bottom"))

# Use the vir palette
tm_shape(prop_by_age) +
  tm_raster("age_18_24") +
  tm_legend(position = c("right", "bottom"))

# Use the mag palette but reverse the order
tm_shape(prop_by_age) +
  tm_raster("age_18_24") +
  tm_legend(position = c("right", "bottom"))

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 Exercise 9: Palet kustom di tmap

Latihan Saat Ini

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