Mapping Public Colleges

We can add each sector to our map as a layer providing users with the ability to select which sectors are displayed. To do this we will make use of a new argument to the addCircleMarkers() function, called a group.

leaflet() %>% 
        addProviderTiles("CartoDB") %>% 
        addCircleMarkers(data = public, 
                         label = ~htmlEscape(name), 
                         group = "Public")

We'll integrate another feature into our map from the htmltools library. Wrapping our labels with the htmlEscape() function will sanitize characters that may be interpreted as HTML. This will prevent any of the college names from appearing with unintended formatting.

The color palette pal we created in Chapter 2 has been loaded for you.

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Interactive Maps with leaflet in R

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Instructions

Load the htmltools package.
Filter the ipeds data to create a data frame with only public colleges.
Create a leaflet map of public colleges.
Add the group argument to addCircleMarkers().

Exercice interactif pratique

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# Load the htmltools package
___

# Create data frame called public with only public colleges
public <- ___(ipeds, sector_label == "Public")  

# Create a leaflet map of public colleges called m3 
m3 <- leaflet() %>% 
        addProviderTiles("CartoDB") %>% 
        addCircleMarkers(data = ___, radius = 2, label = ~htmlEscape(name),
                         color = ~pal(sector_label), ___ = "Public")

m3

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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 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!