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 summaries

Daily ride counts

Distribution of cab fare amount

Distribution of payment type

Adding more detail to summaries

Relationship between trip duration and total fare

Faceting daily rides

Tip amount distribution faceted by payment type

Visualizing subsets

Comparing fare distribution by payment type

Visualizing all subsets

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.

Faceting with TrelliscopeJS

Trelliscope faceting gapminder by country

Interacting with the TrelliscopeJS displays

Interacting with the display

Additional TrelliscopeJS features

Customizing the gapminder display

Examining the new cognostics

Adding your own cognostics

Adding custom cognostics

Interpreting custom cognostics

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

Grouping and nesting

Stock price display

Exploring the display

Cognostics

Adding cognostics

Cognostics from nested data frames

Navigating stock plots with new cognostics

Trelliscope options

Customizing the stock display

Visualizing databases of images

Visualizing Pokemon

The most powerful Pokemon

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.

BIXI-fietsdata van Montreal

Aantal ritten per dag

Tijdstip van de dag onderzoeken

Effect van lidmaatschap en weekdag

Samenvatting: visualisatie-terugblik

Dagelijkse grafieken

Alle dagen bekijken

Gegevensset met top 100-routes

Data uitbreiden: samenvattende statistieken per route

De data visualiseren: Aantallen per uur van de dag

De visualisatie evalueren

Au revoir

Case Study: Exploring Montreal BIXI Bike Data

Bikes

Pokémon

Top 100 routes

Stocks

Taxi rides

Nu je je visualisatievaardigheden met ggplot2 hebt aangescherpt, is het tijd voor grotere datasets. In deze cursus leer je verschillende technieken om big data te visualiseren, met speciale aandacht voor de schaalbare visualisatietechniek van facetten. Je leert hoe je deze techniek toepast met de Trelliscope-aanpak zoals geïmplementeerd in het R-pakket trelliscopejs. Trelliscope sluit naadloos aan op standaard R-workflows en levert interactieve visualisaties waarmee je je gegevens tot in detail visueel kunt verkennen. Aan het einde van deze cursus kun je eenvoudig interactieve, verkennende weergaven van grote datasets maken die jou en je collega’s helpen nieuwe inzichten uit je data te halen.

Introduction to the Tidyverse

Leer hoe je big data visualiseert in R met ggplot2 en trelliscopejs.

Grote datasets visualiseren met Trelliscope in R

Leer hoe je big data kunt visualiseren in R met behulp van ggplot2 en trelliscopejs.

Big Data in R

Interactieve datavisualisatie in R

Author, TrelliscopeJS

Summary visualization recap

Daily rides

Hourly rides over time

Hourly rides over time + membership

Diving deeper

Case Study: Exploring Montreal BIXI Bike Data - Samenvatting: visualisatie-terugblik

Samenvatting: visualisatie-terugblik

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