En güçlü Pokemon

Artık görselleştirmemiz hazır olduğuna göre, türü (type_1) "fire" olan 1. nesil (generation_id) Pokemonlar içinde saldırı gücü açısından en güçlü olan hangisi, cevaplayabilir misin?

Bu egzersiz

R ile Trelliscope kullanarak Büyük Veriyi Görselleştirme

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

İnteraktif egzersizlerimizden biriyle teoriyi pratiğe dökün

Egzersizi başlat

Bu egzersiz

R ile Trelliscope kullanarak Büyük Veriyi Görselleştirme

kursunun bir parçasıdır

InicianteNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

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.

Exercise 1: Visualizing summaries Exercise 2: Daily ride counts Exercise 3: Distribution of cab fare amount Exercise 4: Distribution of payment type Exercise 5: Adding more detail to summaries Exercise 6: Relationship between trip duration and total fare Exercise 7: Faceting daily rides Exercise 8: Tip amount distribution faceted by payment type Exercise 9: Visualizing subsets Exercise 10: Comparing fare distribution by payment type Exercise 11: Visualizing all subsets

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.

Exercise 1: Faceting with TrelliscopeJS Exercise 2: Trelliscope faceting gapminder by country Exercise 3: Interacting with the TrelliscopeJS displays Exercise 4: Interacting with the display Exercise 5: Additional TrelliscopeJS features Exercise 6: Customizing the gapminder display Exercise 7: Examining the new cognostics Exercise 8: Adding your own cognostics Exercise 9: Adding custom cognostics Exercise 10: Interpreting custom cognostics

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!

Exercise 1: Tidyverse içinde Trelliscope Exercise 2: Gruplama ve iç içe yerleştirme Exercise 3: Hisse fiyatı gösterimi Exercise 4: Görselleştirmeyi keşfetme Exercise 5: Kognostikler Exercise 6: Kognostikler ekleme Exercise 7: İç içe geçmiş veri çerçevelerinden kognostikler Exercise 8: Yeni kognostiklerle hisse grafiklerinde gezinme Exercise 9: Trelliscope seçenekleri Exercise 10: Hisse senedi görünümünü özelleştirme Exercise 11: Görsel veritabanlarını görselleştirme Exercise 12: Pokemon'u görselleştirme Exercise 13: En güçlü Pokemon

Geçerli Egzersiz

<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.

Exercise 1: Montreal BIXI bike data Exercise 2: Number of daily rides Exercise 3: Examining time-of-day Exercise 4: Effect of membership and weekday Exercise 5: Summary visualization recap Exercise 6: Daily plots Exercise 7: Looking at all days Exercise 8: Top 100 routes dataset Exercise 9: Augmenting the data: Route summary statistics Exercise 10: Visualizing the data: Counts by hour-of-day Exercise 11: Evaluating the visualization Exercise 12: Au revoir