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Home > Programming > Data Analysis > Julia Programming Projects
Julia Programming Projects
Julia Programming Projects

Julia Programming Projects: Learn Julia 1.x by building apps for data analysis, visualization, machine learning, and the web

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Profile Icon Adrian Salceanu
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Julia Programming Projects

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...

Technical requirements

The Julia package ecosystem is under continuous development and new package versions are released on a daily basis. Most of the times this is great news, as new releases bring new features and bug fixes. However, since many of the packages are still in beta (version 0.x), any new release can introduce breaking changes. As a result, the code presented in the book can stop working. In order to ensure that your code will produce the same results as described in the book, it is recommended to use the same package versions. Here are the external packages used in this chapter and their specific versions:

CSV@v0.4.3
DataFrames@v0.15.2
Feather@v0.5.1
Gadfly@v1.0.1
IJulia@v1.14.1
JSON@v0.20.0
RDatasets@v0.6.1

In order to install a specific version of a package you need to run:

pkg> add PackageName@vX.Y.Z

For example:

pkg> add IJulia@v1.14.1

Alternatively you can...

Defining variables

We have seen in the previous chapter how to use the REPL in order to execute computations and have the result displayed back to us. Julia even lends a helping hand by setting up the ans variable, which automatically holds the last computed value.

But, if we want to write anything but the most trivial programs, we need to learn how to define variables ourselves. In Julia, a variable is simply a name associated to a value. There are very few restrictions for naming variables, and the names themselves have no semantic meaning (the language will not treat variables differently based on their names, unlike say Ruby, where a name that is all caps is treated as a constant).

Let's see some examples:

julia> book = "Julia v1.0 By Example" 
julia> pi = 3.14 
julia> ANSWER = 42 
julia> my_first_name = "Adrian"
You can follow along...

Comments

Common programming wisdom says the following:

"Code is read much more often than it is written, so plan accordingly."

Code comments are a powerful tool that make the programs easier to understand later on. In Julia, comments are marked with the # sign. Single-line comments are denoted by a # and everything that follows this, until the end of the line, is ignored by the compiler. Multiline comments are enclosed between #= ... =#. Everything within the opening and the closing comment tags is also ignored by the compiler. Here is an example:

julia> #= 
           Our company charges a fixed  
           $10 fee per transaction. 
       =# 
const flatfee = 10 # flat fee, per transaction

In the previous snippet, we can see both multiline and single-line comments in action. A single-line comment can also be placed at the beginning of the line.

...

Strings

A string represents a sequence of characters. We can create a string by enclosing the corresponding sequence of characters between double quotes, as shown in the following:

julia> "Measuring programming progress by lines of code is like measuring aircraft building progress by weight."

If the string also includes quotes, we can escape these by prefixing them with a backslash \:

julia> "Beta is Latin for \"still doesn't work\"."

Triple-quoted strings

However, escaping can get messy, so there's a much better way of dealing with this—by using triple quotes """...""".

julia> """Beta is Latin for "still doesn&apos...

Regular expressions

Regular expressions are used for powerful pattern-matching of substrings within strings. They can be used to search for a substring in a string, based on patterns—and then to extract or replace the matches. Julia provides support for Perl-compatible regular expressions.

The most common way to input regular expressions is by using the so-called nonstandard string literals. These look like regular double-quoted strings, but carry a special prefix. In the case of regular expressions, this prefix is "r". The prefix provides for a different behavior, compared to a normal string literal.

For example, in order to define a regular string that matches all the letters, we can use r"[a-zA-Z]*".

Julia provides quite a few nonstandard string literals—and we can even define our own if we want to. The most widely used are for regular expressions...

Raw string literals

If you need to define a string that does not perform interpolation or escaping, for example to represent code from another language that might contain $ and \ which can interfere with the Julia parser, you can use raw strings. They are constructed with raw"..." and create ordinary String objects that contain the enclosed characters exactly as entered, with no interpolation or escaping:

julia> "This $will error out" 
ERROR: UndefVarError: will not defined

Putting a $ inside the string will cause Julia to perform interpolation and look for a variable called will:

julia> raw"This $will work" 
"This \$will work"

But by using a raw string, the $ symbol will be ignored (or rather, automatically escaped, as you can see in the output).

Numbers

Julia provides a broad range of primitive numeric types, together with the full range of arithmetic and bitwise operators and standard mathematical functions. We have at our disposal a rich hierarchy of numeric types, with the most generic being Number—which defines two subtypes, Complex and Real. Conversely, Real has four subtypes—AbstractFloat, Integer, Irrational, and Rational. Finally, Integer branches into four other subtypes—BigInt, Bool, Signed, and Unsigned.

Let's take a look at the most important categories of numbers.

Integers

Literal integers are represented simply as follows:

julia> 42

The default Integer type, called Int, depends on the architecture of the system upon which...

Technical requirements

The Julia package ecosystem is under continuous development and new package versions are released on a daily basis. Most of the times this is great news, as new releases bring new features and bug fixes. However, since many of the packages are still in beta (version 0.x), any new release can introduce breaking changes. As a result, the code presented in the book can stop working. In order to ensure that your code will produce the same results as described in the book, it is recommended to use the same package versions. Here are the external packages used in this chapter and their specific versions:

CSV@v0.4.3
DataFrames@v0.15.2
Feather@v0.5.1
Gadfly@v1.0.1
IJulia@v1.14.1
JSON@v0.20.0
RDatasets@v0.6.1

In order to install a specific version of a package you need to run:

pkg> add PackageName@vX.Y.Z

For example:

pkg> add IJulia@v1.14.1

Alternatively you can install all the used packages by downloading the Project.toml file provided with the chapter and using pkg> instantiate...

Defining variables

We have seen in the previous chapter how to use the REPL in order to execute computations and have the result displayed back to us. Julia even lends a helping hand by setting up the ans variable, which automatically holds the last computed value.

 

But, if we want to write anything but the most trivial programs, we need to learn how to define variables ourselves. In Julia, a variable is simply a name associated to a value. There are very few restrictions for naming variables, and the names themselves have no semantic meaning (the language will not treat variables differently based on their names, unlike say Ruby, where a name that is all caps is treated as a constant).

Let's see some examples:

julia> book = "Julia v1.0 By Example" 
julia> pi = 3.14 
julia> ANSWER = 42 
julia> my_first_name = "Adrian"

Note

You can follow along through the examples in the chapter by loading the accompanying Jupyter/IJulia notebook provided with this chapter's support files.

The variables...

Comments

Common programming wisdom says the following:

"Code is read much more often than it is written, so plan accordingly."

Code comments are a powerful tool that make the programs easier to understand later on. In Julia, comments are marked with the # sign. Single-line comments are denoted by a # and everything that follows this, until the end of the line, is ignored by the compiler. Multiline comments are enclosed between #= ... =#. Everything within the opening and the closing comment tags is also ignored by the compiler. Here is an example:

julia> #= 
           Our company charges a fixed  
           $10 fee per transaction. 
       =# 
const flatfee = 10 # flat fee, per transaction

In the previous snippet, we can see both multiline and single-line comments in action. A single-line comment can also be placed at the beginning of the line.

Strings

A string represents a sequence of characters. We can create a string by enclosing the corresponding sequence of characters between double quotes, as shown in the following:

julia> "Measuring programming progress by lines of code is like measuring aircraft building progress by weight."

If the string also includes quotes, we can escape these by prefixing them with a backslash \:

julia> "Beta is Latin for \"still doesn't work\"."

Triple-quoted strings

However, escaping can get messy, so there's a much better way of dealing with this—by using triple quotes """...""".

julia> """Beta is Latin for "still doesn't work"."""

 

 

 

 

 

 

Within triple quotes, it is no longer necessary to escape the single quotes. However, make sure that the single quotes and the triple quotes are separated—or else the compiler will get confused:

julia> """Beta is Latin for "still doesn't work"""" 
syntax: cannot juxtapose string literal

The triple quotes come with some extra special powers when used with multiline...

Regular expressions

Regular expressions are used for powerful pattern-matching of substrings within strings. They can be used to search for a substring in a string, based on patterns—and then to extract or replace the matches. Julia provides support for Perl-compatible regular expressions.

The most common way to input regular expressions is by using the so-called nonstandard string literals. These look like regular double-quoted strings, but carry a special prefix. In the case of regular expressions, this prefix is "r". The prefix provides for a different behavior, compared to a normal string literal.

For example, in order to define a regular string that matches all the letters, we can use r"[a-zA-Z]*".

Julia provides quite a few nonstandard string literals—and we can even define our own if we want to. The most widely used are for regular expressions (r"..."), byte array literals (b"..."), version number literals (v"..."), and package management commands (pkg"...").

Here is how we build a regular...

Raw string literals

If you need to define a string that does not perform interpolation or escaping, for example to represent code from another language that might contain$and\ which can interfere with the Julia parser, you can use raw strings. They are constructed withraw"..."and create ordinaryStringobjects that contain the enclosed characters exactly as entered, with no interpolation or escaping:

julia> "This $will error out" 
ERROR: UndefVarError: will not defined

Putting a $ inside the string will cause Julia to perform interpolation and look for a variable called will:

julia> raw"This $will work" 
"This \$will work"

But by using a raw string, the $ symbol will be ignored (or rather, automatically escaped, as you can see in the output).

Numbers

Julia provides a broad range of primitive numeric types, together with the full range of arithmetic and bitwise operators and standard mathematical functions. We have at our disposal a rich hierarchy of numeric types, with the most generic being Number—which defines two subtypes, Complex and Real. Conversely, Real has four subtypes—AbstractFloat, Integer, Irrational, and Rational. Finally, Integer branches into four other subtypes—BigInt, Bool, Signed, and Unsigned.

Let's take a look at the most important categories of numbers.

 

Integers

Literal integers are represented simply as follows:

julia> 42

The default Integer type, called Int, depends on the architecture of the system upon which the code is executed. It can be either Int32 or Int64. On my 64-bit system, I get it as follows:

julia> typeof(42) 
Int64

The Int type will reflect that, as it's just an alias to either Int32 or Int64:

julia> @show Int 
Int = Int64

Overflow behavior

The minimum and maximum values are given by the...

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Key benefits

  • Work with powerful open-source libraries for data wrangling, analysis, and visualization
  • Develop full-featured, full-stack web applications
  • Learn to perform supervised and unsupervised machine learning and time series analysis with Julia

Description

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.

Who is this book for?

Data scientists, statisticians, business analysts, and developers who are interested in learning how to use Julia to crunch numbers, analyze data and build apps will find this book useful. A basic knowledge of programming is assumed.

What you will learn

  • Leverage Julia s strengths, its top packages, and main IDE options
  • Analyze and manipulate datasets using Julia and DataFrames
  • Write complex code while building real-life Julia applications
  • Develop and run a web app using Julia and the HTTP package
  • Build a recommender system using supervised machine learning
  • Perform exploratory data analysis
  • Apply unsupervised machine learning algorithms
  • Perform time series data analysis, visualization, and forecasting
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Publication date : Dec 26, 2018
Length: 500 pages
Edition : 1st
Language : English
ISBN-13 : 9781788292740
Category :
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Languages :
Julia
Concepts :
Data Analysis
Tools :
HTTP

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Product Details

Publication date : Dec 26, 2018
Length: 500 pages
Edition : 1st
Language : English
ISBN-13 : 9781788292740
Category :
Programming
Languages :
Julia
Concepts :
Data Analysis
Tools :
HTTP

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Table of Contents

12 Chapters
Getting Started with Julia Programming Chevron down icon Chevron up icon
Getting Started with Julia Programming
Technical requirements
Why Julia?
Installing Julia
Choosing an IDE
Getting started with Julia
The package system
Summary
Creating Our First Julia App Chevron down icon Chevron up icon
Creating Our First Julia App
Technical requirements
Defining variables
Comments
Strings
Regular expressions
Raw string literals
Numbers
Tuples
Ranges
Arrays
Exploratory data analysis with Julia
Using simple statistics to better understand our data
Loading and saving our data
Summary
Setting Up the Wiki Game Chevron down icon Chevron up icon
Setting Up the Wiki Game
Technical requirements
Data harvesting through web scraping
Understanding HTML documents
Accessing the internet from Julia
Handling HTTP responses
Understanding HTTP responses
Dictionaries
Using the HTTP response
Coding defensively
Handling errors like a pro
Learning about functions
Writing a basic web crawler – take one
Carrying on with the crawler's implementation
Summary
Building the Wiki Game Web Crawler Chevron down icon Chevron up icon
Building the Wiki Game Web Crawler
Technical requirements
Six Degrees of Wikipedia, the gameplay
Organizing our code
Setting up our game's architecture
Building our Wikipedia crawler - take two
Implementing the gameplay
Finishing touches
Learning about Julia's type system
Using article types
Methods
Working with relational databases
Summary
Adding a Web UI for the Wiki Game Chevron down icon Chevron up icon
Adding a Web UI for the Wiki Game
Technical requirements
The game plan
Learning about Julia's web stack
Developing the game's web UI
Starting a new game
Displaying a Wikipedia article page
Showing the solution
Handling any other requests
Wrapping it up
Summary
Implementing Recommender Systems with Julia Chevron down icon Chevron up icon
Implementing Recommender Systems with Julia
Technical requirements
Understanding recommender systems
Classifying recommender systems
Summary
Machine Learning for Recommender Systems Chevron down icon Chevron up icon
Machine Learning for Recommender Systems
Technical requirements
Comparing the memory-based versus model-based recommenders
Machine learning-based recommendations
Testing the recommendations
Learning about hybrid recommender systems
Summary
Leveraging Unsupervised Learning Techniques Chevron down icon Chevron up icon
Leveraging Unsupervised Learning Techniques
Technical requirements
Unsupervised machine learning
Clustering
Data analysis of the San Francisco business
Unsupervised machine learning with clustering
Summary
Working with Dates, Times, and Time Series Chevron down icon Chevron up icon
Working with Dates, Times, and Time Series
Technical requirements
Working with dates and times
Constructing dates and times
Working with date and time accessors
Defining the date ranges
Period types and period arithmetic
Date adjustments
Rounding of dates
Adding support for time zones
Time series data in Julia
Summary
Time Series Forecasting Chevron down icon Chevron up icon
Time Series Forecasting
Technical requirements
A quick look at our data
Understanding time series components
Time series decomposition
Time series stationarity
Time series forecasting
Summary
Creating Julia Packages Chevron down icon Chevron up icon
Creating Julia Packages
Technical requirements
Creating a new Julia package
Defining the requirements for our package
Beginning with test-driven Julia development
Performance testing
Interacting with the GitHub API
Documenting our package
Advanced documentation tips
Registering our package
Summary
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Kota Mori Mar 02, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book covers various tips about the Julia language: from installation, REPL, data types, and all the way through package development. I find the last part -- package development -- is quite unique for this book. I would recommend this book to people who are looking for "Advanced R" and/or "R packages" for Julia; Like the Wickham's books, this book helps one to turn a user into a developer.I would also note that this book does not cover so much about data science and statistics. If you want to learn data analysis in Julia, there would be better choice than this book.
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Jim and Virginia Davidson Jan 24, 2019
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This book is a great place to begin learning Julia if you have some background in programming. I am quite new to Julia, but have programmed in Matlab, Fortran, Python, C#, etc.I have not yet read all the book, but so far, have found it very well done. The text seems accurate based on my experience with Julia, up to date, and the examples and projects are helpful. The writing style is easy to follow. 5 stars for sure!
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dougfort Feb 08, 2020
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The projects are clear, detailed and show production quality code.
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Amazon Customer Feb 07, 2019
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Bom livro sobre a linguagem Julia.
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Massimiliano Bertinetti Aug 11, 2019
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Thank you Adrian for this clear guide for starting with Julia Programming!
Amazon Verified review Amazon

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About the author

Profile icon Adrian Salceanu
Adrian Salceanu
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Adrian Salceanu has been a professional software developer for over 15 years. For the last 10 years, he has been leading agile teams in developing real-time, data-intensive web and mobile products. Adrian is a public speaker and an enthusiastic contributor to the open source community, focusing on high-performance web development. He is the organizer of the Barcelona Julia Users group and the creator of Genie, a high-performance, highly productive Julia web framework. Adrian has a master's degree in computing and a postgraduate degree in advanced computer science.
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Disclaimer:
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