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 dengan TrelliscopeJS

Pemfaktoran Trelliscope gapminder menurut negara

Berinteraksi dengan tampilan TrelliscopeJS

Berinteraksi dengan tampilan

Fitur tambahan TrelliscopeJS

Menyesuaikan tampilan gapminder

Menelaah cognostics baru

Menambahkan kognostik Anda sendiri

Menambahkan cognostics kustom

Menafsirkan kognostik kustom

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.

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

Setelah mengasah keterampilan visualisasi Anda dengan mempelajari ggplot2, kini saatnya menangani himpunan data yang lebih besar. Dalam kursus ini, Anda akan mempelajari berbagai teknik untuk memvisualisasikan big data, dengan fokus khusus pada teknik visualisasi skala besar berupa faceting. Anda akan mempelajari cara menerapkan teknik ini menggunakan pendekatan Trelliscope yang diimplementasikan dalam paket R trelliscopejs. Trelliscope terhubung mulus ke alur kerja R standar dan menghasilkan visualisasi interaktif yang memungkinkan Anda menjelajahi data secara mendalam. Di akhir kursus, Anda akan mampu dengan mudah membuat tampilan eksplorasi interaktif untuk himpunan data besar yang akan membantu Anda dan rekan kerja mendapatkan wawasan baru dari data Anda.

Introduction to the Tidyverse

Pelajari cara visualisasi big data di R dengan ggplot2 dan trelliscopejs.

Visualizing Big Data with Trelliscope in R

Pelajari cara memvisualisasikan big data di R menggunakan ggplot2 dan trelliscopejs.

Menambahkan kognostik Anda sendiri

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