Build beautiful interactive maps, explore your data by zooming into and hovering over plots, and design engaging, dynamic charts in R via multiple different packages.

Build beautiful interactive maps, explore your data by zooming into and hovering over plots, and design engaging, dynamic charts in R.

in R

Interactive Data Visualization

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.

Setting Up Interactive Web Maps

In chapter 2 students will build on the leaflet map they created in chapter 1 to create an interactive web map of every four year college in California. After plotting hundreds of points on an interactive leaflet map, students will learn to customize the markers on their leaflet map. This chapter will also how to color code markers based on a factor variable.

Plotting Points

In chapter 3 students will expand on their map of all four year colleges in California to create a map of all American colleges. First, in section 3.1 students will review and build on the material from Chapter 2 to create a map of all American colleges. Then students will re-plot the colleges on their leaflet map by sector (public, private, or for-profit) using groups to enable users to toggle the colleges that are displayed on the map. In section 3.3 students will learn to add multiple base maps so that users can toggle between multiple map tiles.

Groups, Layers, and Extras

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().

Plotting Polygons

IPEDS All 4-Year Colleges

NC Zipcode Income data

NC Zipcode Polygons

America's Wealthiest Zipcodes

Get ready to have some fun with maps! Interactive Maps with leaflet in R will give you the tools to make attractive and interactive web maps using spatial data and the tidyverse. In this course, you will create maps using the IPEDS dataset, which contains data on U.S. colleges and universities. Along the way, you will customize our maps using labels, popups, and custom markers, and add layers to enhance interactivity. Following the course, you will be able to create and customize your own interactive web maps to reveal patterns in your data.

Introduction to the Tidyverse

Learn how to produce interactive web maps with ease using leaflet.

Interactive Maps with leaflet in R

Data Visualization

In this chapter, you will receive an introduction to basic graphics with plotly. You will create your first interactive graphics, displaying both univariate and bivariate distributions. Additionally, you will discover how to easily convert ggplot2 graphics to interactive plotly graphics. 

Introduction to plotly

In this chapter, you will learn how to customize the appearance of your graphics and use opacity, symbol, and color to clarify your message. You will also learn how to transform axes, label your axes, and customize the hover information of your graphs.

Styling and customizing your graphics

In this chapter, you move past basic plotly charts to explore more-complex relationships and larger datasets. You will learn how to layer traces, create faceted charts and scatterplot matrices, and create binned scatterplots.

Advanced charts

In the final chapter, you use your plotly toolkit to explore the results of the 2018 United States midterm elections, learning how to create maps in plotly along the way.

Case Study

Video game sales and ratings dataset

Wine datasets

Midterm election datasets

Interactive graphics allow you to manipulate plotted data to gain further insight. As an example, an interactive graphic would allow you to zoom in on a subset of your data without the need to create a new plot. In this course, you will learn how to create and customize interactive graphics in plotly using the R programming language. Along the way, you will review data visualization best practices and be introduced to new plot types such as scatterplot matrices and binned scatterplots.

Learn to create interactive graphics entirely in R with plotly.

Interactive Data Visualization with plotly in R

A review of key plotly commands. You will review how to create multiple plot types in plotly and how to polish your charts. Additionally, you will create static versions of the bubble and line charts that you will animate in the next chapter.

Introduction and review of plotly

In this chapter, you will learn how to implement keyframe animation in plotly. You will explore how to create animations, such as Hans Rosling's bubble charts, as well as cumulative animations, such as an animation of a stock's valuation over time.

Animating graphics

When you are exploring unexpected structure in your graphics, it's useful to have selections made on one chart update the other. For example, if you are exploring clusters observed on a scatterplot, it is useful to have the selected cluster update some chart of group membership, such as a jittered scatterplot or sets of bar charts. In this chapter, you will learn how to link your plotly charts to enable linked brushing. Along the way, you will also learn how to add dropdown menus, checkboxes, and sliders to your plotly charts, without the need for Shiny.

Linking graphics

In the final chapter, you will use your expanded plotly toolkit to explore orbital space launches between 1957 and 2018. Along the way, you'll learn how to wrangle data to enable cumulative animations without common starting points, and hone your understanding of the crosstalk package.

Case Study: Space launches

Economic indicators for the 50 states and Washington, D.C. from 1997 to 2017

Complete list of all orbital space launches between 1957 and 2018

The plotly package enables the construction of interactive and animated graphics entirely within R. This goes beyond basic interactivity such as panning, zooming, and tooltips. In this course, you will extend your understanding of plotly to create animated and linked interactive graphics, which will enable you to communicate multivariate stories quickly and effectively. Along the way, you will review the basics of plotly, learn how to wrangle your data in new ways to facilitate cumulative animations, and learn how to add filters to your graphics without using Shiny.

Learn to create animated graphics and linked views entirely in R with plotly.

Intermediate Interactive Data Visualization with plotly in R

Learn to create animated graphics and linked views entirely in R with plotly.



Learn different strategies for plotting big data using ggplot2, including calculating and plotting summary statistics, various techniques to deal with overplotting, and principles of small multiples with faceting, which leads into Trelliscope.

General strategies for visualizing big data

In the previous chapter you saw how faceting can be used as a powerful technique for visualizing a lot of data that can be naturally partitioned in some meaningful way. Now, using the trelliscopejs package with ggplot2, you will learn how to create faceted visualizations when the number of partitions in the data becomes too large to effectively view in a single screen.

ggplot2 + TrelliscopeJS

The ggplot2 + trelliscopejs interface is easy to use, but trelliscopejs also provides a faceted plotting mechanism that gives you much more flexibility in what plotting system you use and how to specify cognostics. You will learn all about that in this chapter!

Trelliscope in the Tidyverse

<a href="https://www.bixi.com" target="_blank">The Montreal BIXI bike network</a> provides open data for every bike ride, including the date, time, duration, and start and end stations of the ride. In this chapter, you will analyze data from over 4 million bike rides in 2017, going between 546 stations. There are many interesting exploratory questions to ask from this data and you will create exploratory visualizations ranging from summary statistics to detailed Trelliscope visualizations that will give you interesting insight into the data.

Case Study: Exploring Montreal BIXI Bike Data

Bikes

Pokémon

Top 100 routes

Stocks

Taxi rides

Having honed your visualization skills by learning ggplot2, it's now time to tackle larger datasets. In this course, you will learn several techniques for visualizing big data, with particular focus on the scalable visualization technique of faceting. You will learn how to put this technique into action using the Trelliscope approach as implemented in the trelliscopejs R package. Trelliscope plugs seamlessly into standard R workflows and produces interactive visualizations that allow you to visually explore your data in detail. By the end of this course, you will be able to easily create interactive exploratory displays of large datasets that will help you and your colleagues gain new insights into your data.

Learn how to visualize big data in R using ggplot2 and trelliscopejs.

Visualizing Big Data with Trelliscope in R

In this chapter we get introduced to rbokeh layers. You will learn how to specify data and arguments to create the desired plot and how to combine multiple layers in one figure.

rbokeh Introduction

In this chapter you will learn how to customize your rbokeh figures using aesthetic attributes and figure options. You will see how aesthetic attributes such as color, transparancy and shape can serve a purpose and add more info to your visualizations. In addition, you will learn how to activate the tooltip and specify the hover info in your figures.

rbokeh Aesthetic Attributes and Figure Options

In this chapter, you will learn how to put your data in the right format to fit the desired figure. And how to transform between the wide and long formats. You will also see how to combine normal layers with regression lines. In addition you will learn how to customize the interaction tools that appear with each figure.

Data Manipulation for Visualization and More rbokeh Layers

In this chapter you will learn how to combine multiple plots in one layout using grid plots. In addition, you will learn how to create interactive maps.

Grid Plots and Maps

Human Development Index dataset

Corruption Perception Index dataset

Tuberculosis Cases dataset

New York Citi Bike Trips dataset

Data visualization is an integral part of the data analysis process. This course will get you introduced to rbokeh: a visualization library for interactive web-based plots. You will learn how to use rbokeh layers and options to create effective visualizations that carry your message and emphasize your ideas. We will focus on the two main pieces of data visualization: wrangling data in the appropriate format as well as employing the appropriate visualization tools, charts and options from rbokeh.

Learn rbokeh: a visualization library for interactive web-based plots.

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Interactive Data Visualization in R

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