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Scala for Data Science

You're reading from   Scala for Data Science Leverage the power of Scala with different tools to build scalable, robust data science applications

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Product type Paperback
Published in Jan 2016
Publisher
ISBN-13 9781785281372
Length 416 pages
Edition 1st Edition
Languages
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Author (1):
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Pascal Bugnion Pascal Bugnion
Author Profile Icon Pascal Bugnion
Pascal Bugnion
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Toc

Table of Contents (17) Chapters Close

Preface 1. Scala and Data Science FREE CHAPTER 2. Manipulating Data with Breeze 3. Plotting with breeze-viz 4. Parallel Collections and Futures 5. Scala and SQL through JDBC 6. Slick – A Functional Interface for SQL 7. Web APIs 8. Scala and MongoDB 9. Concurrency with Akka 10. Distributed Batch Processing with Spark 11. Spark SQL and DataFrames 12. Distributed Machine Learning with MLlib 13. Web APIs with Play 14. Visualization with D3 and the Play Framework A. Pattern Matching and Extractors Index

Drawing plots with NVD3

D3 is a library that offers low-level components for building interactive visualizations in JavaScript. By offering the low-level components, it gives a huge degree of flexibility to the developer. The learning curve can, however, be quite steep. In this example, we will use NVD3, a library which provides pre-made graphs for D3. This can greatly speed up initial development. We will place the code in the file repoGraph.js and expose a single method, build, which takes, as arguments, a model and a div and draws a pie chart in that div. The pie chart will aggregate language use across all the user's repositories.

The code for generating a pie chart is nearly identical to the example given in the NVD3 documentation, available at http://nvd3.org/examples/pie.html. The data passed to the graph must be available as an array of objects. Each object must contain a label field and a size field. The label field identifies the language, and the size field is the total size...

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