Subset berdasarkan atribut data

Melakukan subset berdasarkan atribut data merupakan kombinasi dari membuat nilai logika dari kolom-kolom data frame Anda dan melakukan subset pada objek Spatial___DataFrame. Ini mirip dengan cara Anda melakukan subset pada data frame biasa.

Buatlah nilai logika dari sebuah kolom, misalnya negara-negara di Asia:

in_asia <- countries_spdf$region == "Asia"
in_asia

Lalu, gunakan nilai logika tersebut untuk memilih baris-baris dari objek Spatial___DataFrame:

countries_spdf[in_asia, ]

Dapatkah Anda melakukan subset untuk New Zealand dan membuat plot-nya?

Latihan ini adalah bagian dari kursus

Memvisualisasikan Data Geospasial di R

Petunjuk latihan

Buat vektor logika bernama is_nz yang menguji apakah kolom name sama dengan "New Zealand".
Buat objek spasial baru bernama nz dengan menggunakan is_nz untuk melakukan subset countries_spdf.
Plot nz.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Create logical vector: is_nz


# Subset countries_spdf using is_nz: nz


# Plot nz

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

Latihan Saat Ini

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