Creating an Interactive Web Map

Similar to the packages in the tidyverse, the leaflet package makes use of the pipe operator (i.e., %>%) from the magrittr package to chain function calls together. This means we can pipe the result of one function into another without having to store the intermediate output in an object. For example, one way to find every car in the mtcars data set with a mpg >= 25 is to pipe the data through a series of functions.

mtcars  %>% 
    mutate(car = rownames(.))  %>% 
    select(car, mpg)  %>% 
    filter(mpg >= 25)

To create a web map in R, you will chain together a series of function calls using the %>% operator. Our first function leaflet() will initialize the htmlwidget then we will add a map tile using the addTiles() function.

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Interactive Maps with leaflet in R

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Instrucciones del ejercicio

Load the leaflet library.
Call the leaflet() function.
Pipe the output of leaflet() into addTiles().
Experiment with zooming and panning your first interactive web map.

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# Load the leaflet library
library(___)

# Create a leaflet map with default map tile using addTiles()
___() %>%
    ___()

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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

Ejercicio actual

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!