Lebih jauh menyelami hierarki

Pada latihan sebelumnya, SpatialPolygonsDataFrame memiliki daftar Polygons di slot polygons, dan setiap objek Polygons tersebut juga memiliki slot Polygons. Jadi, banyak poligon… tetapi Anda belum sampai di dasar hierarkinya!

Mari kita lihat lagi elemen ke-169 di slot Polygons dari countries_spdf. Jalankan kode dari latihan sebelumnya:

one <- countries_spdf@polygons[[169]]
str(one, max.level = 2)

Slot Polygons memiliki daftar di dalamnya dengan 10 elemen. Objek apakah ini? Mari kita gali lebih dalam….

Latihan ini adalah bagian dari kursus

Memvisualisasikan Data Geospasial di R

Petunjuk latihan

Panggil str() dengan max.level = 2 pada slot Polygons dari one.
Panggil str() dengan max.level = 2 pada elemen ke-6 dari slot Polygons dari one. Apakah Anda melihat sesuatu yang terlihat seperti data spasial?
Panggil plot() pada slot coords dari elemen ke-6 dari slot Polygons dari one. Apakah Anda mengenali data apa yang dimuat objek ini?

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

one <- countries_spdf@polygons[[169]]

# str() with max.level = 2, on the Polygons slot of one


# str() with max.level = 2, on the 6th element of the one@Polygons


# Call plot on the coords slot of 6th element of one@Polygons

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

Latihan Saat Ini

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)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