RUBYCONFTH I came to Thailand for the first time. I've wanted to see the Chao Phraya River. I was able to achieve it yesterday. Thank you for holding the RubyConfTH
about me (@284km) a programmer came from Japan, Tokyo I made Ruby2.6 standard CSV library 1.5x-3x faster a creator of a visualization library named Charty a member of Asakusa.rb
About Charty Charty is a open-source Ruby library for visualizing your data in a simple way. For example, Charty outputs these graphs. We can easily plot using Charty
Charty using Ruby, Java, Python and JavaScript. These days, there is no de facto standard visualization tool for Ruby On the other hand, each above languages has its own good plotting library Thus, Charty is visualizing your data by standing on the shoulders of giants
Characteristics of Charty Charty has 2 abstract layer Data Abstraction Layer (abstract data structure) Plotting Abstraction Layer (abstract backend plotting libraries) (I will explain about it later)
e.g. Pyplot backend require 'charty' charty = Charty::Plotter.new(:pyplot) scatter = charty.scatter do iris.group_by(:label).groups.each do |label, index| records = iris.row[*index] series records[:petal_length].to_a, records[:petal_width].to_a, label: label[0] end xlabel "Petal Length" ylabel "Petal Width" end scatter.render('pyplot.png')
Gruff backend example If we want to use another backend, difference is only one line. require 'charty' charty = Charty::Plotter.new(:gruff) scatter = charty.scatter do iris.group_by(:label).groups.each do |label, index| records = iris.row[*index] series records[:petal_length].to_a, records[:petal_width].to_a, label: label[0] end xlabel "Petal Length" ylabel "Petal Width" end scatter.render('gruff.png')
about Plotting Abstraction Layer From the previous example, the difference is one line to change backend Here is one of the features of Charty We can easily switch backend libraries with almost the same code
More about Plotting Abstraction Layer Currently supported backends is below pyplot gruff Rubyplot google-chart bokeh plotly plotly.js chart.js JFreeChart (Charty now works with JRuby )
How to develop backend I feel that Pyplot has the largest number of graph types that can be output. When we want to add a graph to support, we often implement Pyplot first as a reference implementation. After that, we will implement other libraries.
(other cases) For example, google-charts, bokeh, plotly These were implemented by a pull request that "I'd like to use Charty if this library is supported by the backend" If there is a real User and Real-world use case exists, it depends on the priority with other work, but consider support for a new backend
about Data Abstraction Layer Charty supports these data structures (I will show a demo) daru numo/narray nmatrix ActiveRecord Thus, Charty can respond to various data structures. That's because Charty::Table is abstracted.
Feature summary of Charty Charty has two abstraction layers. Data Abstraction Layer Plotting Abstraction Layer. Thus we can use the data structures we need We can use output libraries we want to use. We can use them in any combination we need with almost no code rewrite.
Introduction of various use cases of Charty Recently, we introduced Charty in our production environment of Web Application, which is our job. This Web Application is a common Rails Application. At that time, we were asking for Charty to output json, not image file. Here is an example using plotly.js (I will show a demo)
This combination is also possible. Because Charty has Data Abstraction Layer to support various data structures. https://github.com/284km/benchmark_driver- output-charty
summary of Data Abstraction Layer currently supports Array Hash daru numo-narray nmatrix ActiveRecord benchmark_driver (Charty Adapter) It can output: image, HTML, JSON format
Now Charty with JRuby is working This impression became my motivation. So, I implemented JFreeChart backend. Thanks to Charles, development progressed.
Charty with JRuby red-data-tools/charty-backends-jfreechart Please use JRubyist. This is not yet complete. If you have important use cases, I can write code. I'm grad to hear everyone's thoughts.
Future plan (we aim for these) Improvement interface (Continued) Support red-arrow for Data Abstraction Layer because Apache Arrow is great. Release stable version Add supported dataset (red-datasets) (e.g. titanic)
284km
aridlehoover
toricky6
prof18
clown0082
chimame
irof
huffon
takefumiyoshii
doggy
eddie
mot_techtalk
minako__ph
eileencodes
zakiwarfel
bryan
schacon
iamctodd
addyosmani
malarkey
marcelosomers
pedronauck
lauravandoore
rmw
vanstee