Menyeleksi subset neighborhoods

Anda tidak memerlukan semua wilayah lingkungan lain di New York, jadi Anda akan mengambil subset hanya lingkungan di New York County. Anda sudah tahu caranya!

neighborhoods adalah SpatialPolygonsDataFrame dan Anda telah mempelajari di Bab 2 cara mengambil subset berdasarkan kolom di slot data. Kuncinya adalah membuat vektor logika, lalu mengambil subset SpatialPolygonsDataFrame seperti mengambil subset data frame.

Bagaimana cara mengidentifikasi lingkungan yang tepat? Coba jalankan:

head(neighborhoods@data)

CountyFIPS adalah kode numerik yang mengidentifikasi county. Jika Anda dapat mengetahui kode untuk New York County, Anda dapat menyisakan hanya baris dengan nilai tersebut.

Latihan ini adalah bagian dari kursus

Memvisualisasikan Data Geospasial di R

Petunjuk latihan

Objek nyc_tracts_merge juga memiliki kode county pada kolom COUNTYFP. Cari nilai unique() untuk menemukan kode New York County.
Ambil subset neighborhoods dengan menambahkan ekspresi logika yang menguji apakah neighborhoods$CountyFIPS memiliki nilai yang benar.
Sunting plot Anda untuk menggunakan manhat_hoods alih-alih neighborhoods.
Tambahkan layer tm_text(), memetakan text ke "NTAName".

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

library(tmap)

# Find unique() nyc_tracts_merge$COUNTYFP


# Add logical expression to pull out New York County
manhat_hoods <- neighborhoods[___, ]

tm_shape(nyc_tracts_merge) +
  tm_fill(col = "estimate") +
  tm_shape(water) +
  tm_fill(col = "grey90") +
  # Edit to use manhat_hoods instead
  tm_shape(neighborhoods) +
  tm_borders() +
  # Add a tm_text() layer

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: 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: Membaca data spasial Exercise 2: Membaca shapefile Exercise 3: Membaca berkas raster Exercise 4: Mengambil data menggunakan sebuah paket Exercise 5: Sistem referensi koordinat Exercise 6: Menggabungkan data dari CRS/proyeksi yang berbeda Exercise 7: Mengonversi dari satu CRS/proyeksi ke yang lain Exercise 8: Menambahkan data ke objek spasial Exercise 9: Cara yang keliru Exercise 10: Memeriksa kecocokan data Exercise 11: Menggabungkan atribut data Exercise 12: Plot pertama Exercise 13: Memoles peta Exercise 14: Menyeleksi subset neighborhoods

Latihan Saat Ini

Exercise 15: Menambahkan label lingkungan Exercise 16: Merapikan legenda dan beberapa penyesuaian akhir Exercise 17: Ringkasan