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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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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

More advanced scatter plots

Breeze-viz offers a scatter function that adds a significant degree of customization to scatter plots. In particular, we can use the size and color of the marker points to add additional dimensions of information to the plot.

The scatter function takes, as its first two arguments, collections of x and y points. The third argument is a function mapping an integer i to a Double indicating the size of the ith point. The size of the point is measured in units of the x axis. If you have the sizes as a Scala collection or a Breeze vector, you can use that collection's apply method as the function. Let's see how this works in practice.

As with the previous examples, we will use the REPL, but you can find a sample program in BreezeDemo.scala:

scala> val fig = new Figure("Advanced scatter example")
fig: breeze.plot.Figure = breeze.plot.Figure@220821bc

scala> val plt = fig.subplot(0)
plt: breeze.plot.Plot = breeze.plot.Plot@668f8ae0

scala>...
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