Pieces of Flair

1. Pieces of Flair

To close out Chapter 3, we'll add a some flair to our leaflet map. We'll start by customizing the search function from the leaflet extras package to search for specific colleges we have on our map. Then we'll explore an alternate approach to presenting a large number of points on a leaflet map, called clustering.

2. The College Search

At the start of Chapter 3, we found the world's largest ball of twine and Carhenge using the addSearchOSM() function from the leaflet extras package. We can take a slightly different approach to allow users to search for specific data on our map. Let's take one last look at the California colleges example to see how this works. First, we need a subset of the IPEDS data that contains only public colleges in California. Then we pipe this data into our leaflet() call, we'll add the Esri map tile, and plot the colleges using circle markers. Then we pipe all of this into the addSearchFeatures() function and tell the function that we want to search for elements on the map in the group called "public".

3. The College Search

We will see a familiar magnifying glass icon, but now when we type in the box it will filter the data elements in the public group. For example, if we want to focus our map on UCLA, we can start to type in the name of the university and then select it from the menu. When we do this, the map will automatically zoom and center on UCLA and the marker that represents the university will be circled. We can specify the zoom level using the options argument.

4. Clustering Colleges

We have tried to address the large number of points on our map by adding the colleges in different layers. This approach has helped make the map more user-friendly, but there are still an overwhelming number of points on the map at times. An alternative solution is to cluster the colleges using the cluster options argument to the addCircleMarkers() function. By default, the clusters are orange, yellow, and green. The colors correspond to the number of the colleges represented by a cluster. Let's take a look at the clusters in action.

5. Clustering Colleges

When you hover over a cluster, its boundaries will appear. Clicking on a cluster will zoom to that area and the clustered colleges will spread out automatically. Eventually the map will show individual colleges in a region. These points will still be color coded and will have all of the other features that we built previously, like labels.

6. Let's practice!

Have fun testing out the searchable and clustered maps in the last few exercises of this chapter.

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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 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!