The City of Colleges

Based on our map of California colleges it appears that there is a cluster of colleges in and around the City of Angels (e.g., Los Angeles). Let's take a closer look at these institutions on our leaflet map.

The coordinates for the center of LA are provided for you in the la_coords data frame.

la_coords <- data.frame(lat = 34.05223, lon = -118.2437)

Once you create a map focused on LA, try panning and zooming the map. Can you find the cluster of colleges East of LA known as the Claremont Colleges?

When there are hundreds of markers, do you find the pin markers helpful or do they get in your way?

The coordinates of LA have been provided in the la_coords data frame and the ca data frame of California colleges and the map have been loaded for you.

This exercise is part of the course

Interactive Maps with leaflet in R

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Hands-on interactive exercise

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

# Center the map on LA 
map %>% 
   addMarkers(data = ca) %>% 
    ___(lat = la_coords$lat, lng = la_coords$___, zoom = 12)

Edit and Run Code

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!