Book Image

Julia Programming Projects

By : Adrian Salceanu
Book Image

Julia Programming Projects

By: Adrian Salceanu

Overview of this book

Julia is a new programming language that offers a unique combination of performance and productivity. Its powerful features, friendly syntax, and speed are attracting a growing number of adopters from Python, R, and Matlab, effectively raising the bar for modern general and scientific computing. After six years in the making, Julia has reached version 1.0. Now is the perfect time to learn it, due to its large-scale adoption across a wide range of domains, including fintech, biotech, education, and AI. Beginning with an introduction to the language, Julia Programming Projects goes on to illustrate how to analyze the Iris dataset using DataFrames. You will explore functions and the type system, methods, and multiple dispatch while building a web scraper and a web app. Next, you'll delve into machine learning, where you'll build a books recommender system. You will also see how to apply unsupervised machine learning to perform clustering on the San Francisco business database. After metaprogramming, the final chapters will discuss dates and time, time series analysis, visualization, and forecasting. We'll close with package development, documenting, testing and benchmarking. By the end of the book, you will have gained the practical knowledge to build real-world applications in Julia.
Table of Contents (19 chapters)
Title Page
Copyright and Credits
About Packt

Chapter 2. Creating Our First Julia App

Now that you have a working Julia installation and your IDE of choice is ready to run, it's time to put them to some good use. In this chapter, you'll learn how to apply Julia for data analysis—a domain that is central to the language, so expect to be impressed!

We will learn to perform exploratory data analysis with Julia. In the process, we'll take a look at RDatasets, a package that provides access to over 700 learning datasets. We'll load one of them, the Iris flowers dataset, and we'll manipulate it using standard data analysis functions. Then we'll look more closely at the data by employing common visualization techniques. And finally, we'll see how to persist and (re)load our data.

But, in order to do that, first we need to take a look at some of the language's most important building blocks.

We will cover the following topics in this chapter:

  • Declaring variables (and constants)
  • Working with Strings of characters and regular expressions
  • Numbers and...