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 de tidyverse

Groeperen en nesten

Weergave van aandelenkoersen

De weergave verkennen

Cognostics

Cognostics toevoegen

Cognostics uit geneste data frames

Navigeren door aandelengrafieken met nieuwe cognostics

Trelliscope-opties

De aandelendisplay aanpassen

Databases met afbeeldingen visualiseren

Pokemon visualiseren

De krachtigste Pokémon

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.

Montreal BIXI bike data

Number of daily rides

Examining time-of-day

Effect of membership and weekday

Summary visualization recap

Daily plots

Looking at all days

Top 100 routes dataset

Augmenting the data: Route summary statistics

Visualizing the data: Counts by hour-of-day

Evaluating the visualization

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.

Trelliscope in de tidyverse

Create Your Free Account