Mapping Polygons

1. Mapping Polygons

Now that we are familiar with spatial polygon data frames, let's get back to making some maps. In this section, we'll be creating choropleth maps that color zip codes based on their total and mean incomes.

2. Plotting Polygons

Similar to how we plotted thousands of colleges with just a few lines of R code, we can map all of the zip codes in North Carolina by piping our spatial data into a chain of leaflet functions. By default this creates a map with blue boundaries that may be too thick for the number of polygons we are plotting. We’ll use a few options to the addPolygons() function to see if we can improve our boundaries.

3. addPolygons()

We'll focus on four arguments to customize the appearance of our polygons: weight, color, label, and highlightOptions. We can use the weight and color arguments to customize the appearance of our polygons. For example, we can change the boundaries to be 1 pixel and the polygon color to be gray. With the label argument, we can create labels just like we did for our circle markers. Lastly, the highlight argument can be used to customize the appearance of a polygon when a user hovers over it.

4. addPolygons()

With just a few arguments, we have created a customized map of North Carolina that highlights zip codes on hover and presents users with the total income in any zip code in the state. Our next step is to color the zip codes.

5. Coloring Numeric Data

There are three common approaches when mapping color palettes to numeric data. First, colorNumeric, which maps continuous data to an interpolated palette. Then colorBin and colorQuantile, which color the numeric data based on a specified number of groups using the cut function and the quantile function, respectively.

6. colorNumeric()

Let's take a closer look at how the colorNumeric() function works. First, we supply the name of the colors that we want to use to create the palette and the domain, which is a vector that describes the possible values that could be mapped to the colors. While creating a palette, we can use the previewColors function to take a look at how our palette will represent sample values from within the domain. For example, we can see how the zip codes with mean incomes from $100,000 to $600,000 will be represented.

7. Choropleth Map

We can apply our palette by using the color argument. Remember, we'll need to use the tilde operator to create a one-sided equation.

8. Choropleth Map

This palette creates a map that is colored by mean income, but is mostly light blue. This is because the data is right-skewed, meaning there are a small number of zip codes with very large mean incomes.

9. Choropleth Example

If we are interested in learning more about the variation in income across zip codes, we can log transform the mean income variable. Log transforming pulls those large values closer to the mean and yields a more symmetrically distributed variable.

10. Logging

As for the map, log transforming the mean income variable increases the variation in the color gradient across zip codes and enables us to better visualize the distribution of average income across the state.

11. Let's practice!

Now it's your turn to plot a few polygons.

Interactive Maps with leaflet in R

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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 Exercise 2: Introduction to Spatial Data Exercise 3: Exploring Spatial Data Exercise 4: Joining Spatial Data Exercise 5: Mapping Polygons

Current Exercise

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!