Plotting DataCamp HQ

1. Plotting DataCamp HQ

We have been creating maps with a single layer: a base map. To plot data on our maps, we can add layers to the base map similar to how you add a layer in ggplot2.

2. Plotting a Point

One of the most common layers to add are location markers, which you can add by piping the result of addTiles() into the addMarkers() function. There are several options for supplying the data for your markers. We'll focus on two approaches: using numeric columns from a data frame and using numeric vectors with a length of 1. For example, if we plot DataCamp's New York headquarters by passing the coordinates to addMarkers() as numeric vectors with one element, our web map will plot a blue pin. You may have noticed that our map is zoomed in, but we didn't use the setView or fitBounds(). When you add markers to your map without setting the view, leaflet will automatically set the boundaries of the base map based on the markers that you're plotting. If you're plotting a single marker, leaflet will center the map on that marker. If you're plotting multiple markers, leaflet will set the bounds so that they're all visible.

3. Plotting Multiple Points

If you want to add multiple markers, we can use a data frame or a tibble to pass the coordinates to the addMarkers() function. For example, to plot both DataCamp’s New York and Belgium offices, we can use a tibble with the coordinates in our addMarkers() function call.

4. Plotting Multiple Points II

An alternate approach to mapping points from a data frame is to pipe the data. Then addMarkers() will search the columns names for names that are most likely your coordinates and the leaflet package will send you a message to let you know if a match was found.

5. Pop-ups

It is often helpful to provide users with information about the points we have mapped. A common way to do this is by adding pop-ups that will appear whenever a user clicks on a marker. We can add pop-ups by specifying the popup argument in the addMarkers() function. For example, we can have the text from the hq column appear whenever a marker is clicked.

6. Pop-ups II

For this example with just two markers it may be more useful to add pop-ups to the map without the markers. We can do this by replacing addMarkers() with the addPopups() function. If we had more than a few markers, this approach may be problematic.

7. Storing leaflet Maps as Objects

Once we have our map shaped up to our liking we can store it in R object. Similar to how you store ggplots as R objects, you can add and edit layers of a leaflet map once it's stored as an object. For example, we can store our base map with a particular view as an object called m. Then we can pipe m into the addMarkers() function to plot DataCamp's office on the map.

8. Let's practice!

Now it's your turn to create a leaflet object that plots DataCamp's headquarters!

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

Current Exercise

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!