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.

Dati delle bici BIXI di Montreal

Numero di corse giornaliere

Analizzare l'ora del giorno

Effetto di membership e giorno della settimana

Riepilogo delle visualizzazioni: recap

Grafici giornalieri

Uno sguardo a tutti i giorni

Insieme di dati delle prime 100 tratte

Arricchire i dati: statistiche riassuntive delle rotte

Visualizzare i dati: conteggi per ora del giorno

Valutare la visualizzazione

Arrivederci

Case Study: Exploring Montreal BIXI Bike Data

Bikes

Pokémon

Top 100 routes

Stocks

Taxi rides

Dopo aver affinato le tue competenze di visualizzazione con ggplot2, è il momento di affrontare insiemi di dati più grandi. In questo corso imparerai diverse tecniche per visualizzare big data, con particolare attenzione alla tecnica scalabile del faceting. Vedrai come mettere in pratica questo approccio usando Trelliscope, così come implementato nel pacchetto R trelliscopejs. Trelliscope si integra senza problemi nei normali flussi di lavoro in R e produce visualizzazioni interattive che ti permettono di esplorare visivamente i tuoi dati in dettaglio. Al termine del corso, saprai creare con facilità visualizzazioni esplorative interattive di grandi insiemi di dati che aiuteranno te e i tuoi colleghi a ottenere nuove informazioni dai vostri dati.

Introduction to the Tidyverse

Impara a visualizzare big data in R con ggplot2 e trelliscopejs.

Visualizzare Big Data con Trelliscope in R

Impara a visualizzare i big data in R usando ggplot2 e trelliscopejs.

Big Data in R

Visualizzazione interattiva dei dati in R

Author, TrelliscopeJS

Top 100 routes dataset

Studying routes

Route frequency

Data for the top 100 routes

Getting ready for visualization

Let's visualize!

Case Study: Exploring Montreal BIXI Bike Data - Insieme di dati delle prime 100 tratte

Insieme di dati delle prime 100 tratte

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