After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
Course Description
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.
Prerequisites
Curriculum
Course outline
1
General strategies for visualizing big data
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.
- Visualizing summaries50 XP
- Daily ride counts100 XP
- Distribution of cab fare amount100 XP
- Distribution of payment type100 XP
- Adding more detail to summaries50 XP
- Relationship between trip duration and total fare100 XP
- Faceting daily rides100 XP
- Tip amount distribution faceted by payment type100 XP
- Visualizing subsets50 XP
- Comparing fare distribution by payment type100 XP
- Visualizing all subsets50 XP
2
ggplot2 + TrelliscopeJS
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.
3
Trelliscope in the Tidyverse
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!
4
Case Study: Exploring Montreal BIXI Bike Data
The Montreal BIXI bike network 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.
R
Visualizing Big Data with Trelliscope in R
Course
Complete
