Subsetting the neighborhoods

You don't need all those extraneous neighborhoods in New York, so you'll subset out just the neighborhoods in New York County. You already know how!

neighborhoods is a SpatialPolygonsDataFrame and you learned back in Chapter 2 how to subset based on the column in the data slot. The key was creating a logical vector, then subsetting the SpatialPolygonsDataFrame like a data frame.

How can you identify the right neighborhoods? Check out:

head(neighborhoods@data)

The CountyFIPS is a numeric code that identifies the county. If you can figure out the code for New York County, you can keep just the rows with that value.

This exercise is part of the course

Visualizing Geospatial Data in R

Exercise instructions

The nyc_tracts_merge object also has country codes in the column COUNTYFP. Find the unique() values to find the code for New York County.
Subset neighborhoods by adding a logical that tests if neighborhoods$CountyFIPS has the right value.
Edit your plot to use manhat_hoods instead of neighborhoods.
Add a tm_text() layer, mapping text to "NTAName".

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

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 and Run Code

This exercise is part of the course

Visualizing Geospatial Data in R

IntermediateSkill Level

4.6+

Start Course for Free

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: 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

Current Exercise

Exercise 15: Adding neighborhood labels Exercise 16: Tidying up the legend and some final tweaks Exercise 17: Wrap up