A Cluster Approach

Rather than using layers to improve the usability of our map, we could elect to cluster the colleges by clustering groups of nearby colleges together to reduce the number of points on the map. Zooming in will cause the clusters to break apart and the individual colleges to appear. This can be a useful tactic for mapping a large number of points in a user-friendly manner.

We can cluster all of our colleges by setting the clusterOptions argument of addCircleMarkers() as follows.

ipeds %>% 
  leaflet() %>%
    addTiles() %>% 
    addCircleMarkers(clusterOptions = markerClusterOptions())

The ipeds data, htmltools library, and color palette pal have been loaded for you.

Diese Übung ist Teil des Kurses

Interactive Maps with leaflet in R

Kurs anzeigen

Anleitung zur Übung

Sanitize any html in our labels.
Color code colleges by sector using the pal color palette.
Cluster all colleges using clusterOptions.

Interaktive Übung

Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

ipeds %>% 
    leaflet() %>% 
        addTiles() %>% 
        # Sanitize any html in our labels
        addCircleMarkers(radius = 2, label = ___(name),
                         # Color code colleges by sector using the `pal` color palette
                         color = ___(sector_label),
                         # Cluster all colleges using `clusterOptions`
                         ___ = ___())

Code bearbeiten und ausführen

Interactive Maps with leaflet in R

Geringe SchwierigkeitSchwierigkeitsgrad

4.8+

44 reviews

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!