Book Image

Julia Cookbook

By : Raj R Jalem, Jalem Raj Rohit
Book Image

Julia Cookbook

By: Raj R Jalem, Jalem Raj Rohit

Overview of this book

Want to handle everything that Julia can throw at you and get the most of it every day? This practical guide to programming with Julia for performing numerical computation will make you more productive and able work with data more efficiently. The book starts with the main features of Julia to help you quickly refresh your knowledge of functions, modules, and arrays. We’ll also show you how to utilize the Julia language to identify, retrieve, and transform data sets so you can perform data analysis and data manipulation. Later on, you’ll see how to optimize data science programs with parallel computing and memory allocation. You’ll get familiar with the concepts of package development and networking to solve numerical problems using the Julia platform. This book includes recipes on identifying and classifying data science problems, data modelling, data analysis, data manipulation, meta-programming, multidimensional arrays, and parallel computing. By the end of the book, you will acquire the skills to work more effectively with your data.
Table of Contents (12 chapters)

Aesthetic customizations


As we have already gone through how to plot the most important visualizations and their customizations in the Gadfly library, we will also see how to customize them even further. The Gadfly library allows the analyst to almost completely tweak and customize their visualizations so that they can be better fitted to the dataset properties are very flexible for our purposes.

Getting ready

We will use the Gadfly library, which we used in the preceding recipes. So, to install the library, you can follow the installation steps mentioned in the previous recipes.

How to do it...

  1. The limits of the axes can be customized or transformed to the logarithmic scale with the Scale.x_log parameter in the plot() function. This would help in visualizing exponentially increasing data or data in different scales. We will scale the x axis in this example. This can be done as follows:

    plot(x = rand(10), y = rand(10), Scale.x_log)
    

  2. The minimum and maximum values in the plot or in a particular...