1. Aprender
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  3. Visualizing Big Data with Trelliscope in R
  • 1

    General strategies for visualizing big data

    Gratuito

    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.

  • 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.

Initializing

Exercicio

Grouping and nesting

Let's practice nesting data frames, getting familiar with the output, and computing on nested data frames.

Instruções 1/3

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Nest the stocks data by symbol, creating a new output datset by_symbol. Recall that you first need to group_by() with dplyr and then nest() with tidyr.