Spatial Data

1. Plotting Polygons

So far in the course, we have focused on plotting points using a latitude longitude coordinate pair. There are several additional spatial features we can map, like polygons.

2. Spatial Data

We stored point coordinates and information describing the point in a single row of the data frame. This was sufficient for points with each row representing a single point. However, polygons have many points, which would require us to have the data repeat. Rather than repeating the information that describes the polygon across multiple rows, we can leverage a different type of object from the sp package called the spatial polygons data frame.

3. SpatialPolygonsDataFrame

Let's use RStudio's object viewer to take a look at a spatial polygons data frame called shp. This data frame contains information on North Carolina zip codes. You'll notice that there are five slots in the shp object: data, polygons, plotOrder, bbox, and projection string.

4. SpatialPolygonsDataFrame

The data slot has one observation for each polygon. The polygons slot holds the coordinates needed to plot each polygon. The plot order slot describes the order for plotting each of the polygons. The bounding box describes the rectangle that all of the polygons fit within. And lastly, the projection string contains the coordinate reference system.

5. Working with Spatial Data in R

Let's take a closer look at the data slot. You can access the information in any of the five slots using the @ symbol. The data slot in this example stores zip codes and the area of each polygon. Since the information in the data slot is stored as a data frame, we can join on additional variables using dpylr. For instance, we can join on income information from the IRS including the mean income per zip code.

6. Working with Spatial Data in R

This works just like joining any two data frames and yields a data frame in the data slot with five variables and 808 observations.

7. Our SpatialPolygonsDataFrame

In the exercises, you will use income information to color zip codes by mean income, but first let's take a look at a single polygon. Similar to how we mapped points, we can pipe our polygon data into a call to leaflet(). Then instead of using addMarkers(), we can use addPolygons(). If we want to add more context to our map, we can plot this zip code on top of a base map just like we did when mapping colleges.

8. Let's practice!

Now it's your turn to explore a spatial polygons data frame.

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Interactive Maps with leaflet in R

BeginnerSkill Level

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Chapter 1 will introduce students to the htmlwidgets package and the leaflet package. Following this introduction, students will build their first interactive web map using leaflet. Through the process of creating this first map students will be introduced to many of the core features of the leaflet package, including adding different map tiles, setting the center point and zoom level, plotting single points based on latitude and longitude coordinates, and storing leaflet maps as objects. Chapter 1 will conclude with students geocoding DataCamp’s headquarters, and creating a leaflet map that plots the headquarters and displays a popup describing the location.

Exercise 1: Introduction to leaflet Exercise 2: Loading the leaflet Library Exercise 3: Creating an Interactive Web Map Exercise 4: Map Tiles Exercise 5: Provider Tiles Exercise 6: Adding a Custom Map Tile Exercise 7: Setting the Default Map View Exercise 8: A Map with a View I Exercise 9: A Map with a Narrower View Exercise 10: Plotting DataCamp HQ Exercise 11: Mark it Exercise 12: Adding Popups and Storing your Map

In Chapter 4 students will learn to map polygons, which can be used to define geographic regions (e.g., zip codes, states, countries, etc.). Chapter 4 will start by plotting the zip codes in North Carolina that fall in the top quartile of mean family incomes. Students will learn to customize the polygons with color palettes and labels. Chapter 4 will conclude with adding a new layer to the map of every college in America that displays every zip code with a mean income of $200,000 or more during the 2015 tax year. Through the process of mapping zip codes students will learn about spatial data generally, geoJSON data, the @ symbol, and the addPolygons() function. Furthermore, students will have an opportunity to practice applying many of the options that they learned about in the previous chapters, such as popups and labels, as well as new ways to customize their maps, such as the highlight option in addPolygons().

Exercise 1: Spatial Data

Current Exercise

Exercise 2: Introduction to Spatial Data Exercise 3: Exploring Spatial Data Exercise 4: Joining Spatial Data Exercise 5: Mapping Polygons Exercise 6: addPolygons() Function Exercise 7: NC High Income Zips Exercise 8: addPolygon() Options Exercise 9: Let's do some Logging Exercise 10: Putting it All Together Exercise 11: Wealthiest Zip Codes in America Exercise 12: Final Map Exercise 13: Thank you!