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Home > Web Development > Front End Web Development > Learning Shiny
Learning Shiny
Learning Shiny

Learning Shiny: Make the most of R's dynamic capabilities and implement web applications with Shiny

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Learning Shiny

Chapter 2. First Steps towards Programming in R

This chapter is a general introduction to classes, objects, and functions in R. Now, we now know that R is an object-oriented language. Conceptually, as any other language of its kind, R has three main elements, as follows:

  • Classes
  • Objects
  • Functions

This chapter will cover the following topics:

  • Object-oriented programming (OOP) concepts in R
  • Vectors and arrays
  • Lists
  • Decision making and looping constructs

Object-oriented programming concepts

As in any other object-oriented programming language, a class in R is an abstract definition of an object type with specific attributes associated to it. For instance, for an eventual dog class (which is a general definition of a dog), we could say that it has the color, size, age, and so on attributes.

An object is an instance of a specific class. Continuing with the previous example, we could have a Dog1 object whose attributes can be the following:

  • color: "brown"
  • size: 5.5 inch
  • age: 3 years

In R, the attributes of an object can be accessed by typing attr(object, "attribute"), for instance:

data(iris)
attributes(iris)

In this example, a data frame object (data frame objects will be explained later in this chapter) called iris is loaded, which has the names, row.names, and class attributes (R considers the class of an object an attribute as well). In most cases, many of the values for these attributes in a particular object can be accessed...

Variables in R

Unlike Java or C#, R enables total flexibility in the assignment of variables. This means that you can assign objects of different types to the same variable. This will cause overriding:

var1 <- 10
var1 <- "a string"

In this case, for instance, R will not throw an error for var1. In addition, there is no need to pre-declare the class of the variable.

The assignment of variables in R can be done in the following three ways:

  • <- or ->: These arrows assign the corresponding value to a variable. However, the first alternative is more common:
    var1 <- 10
10 -> var1
  • =: This is similar to <- or ->.

    Tip

    As in most programming languages, it is important to keep in mind that whenever a comparison is needed, == must be used.

  • assign(): This is a function with the name of the variable as its first parameter and the value as its second:
    > assign("var1",6)

For conventional variable assignments, there is no need to use this function. However, it becomes...

Classes in depth

R has the following six fundamental or atomic classes:

  • Character: When assigning a character value to a variable, the corresponding string must be quoted.
  • Numeric: Decimal numbers.
  • Integer: Non-decimal numbers.
  • Complex: Complex numbers.
  • Logical: TRUE/FALSE values.
  • Raw: As explained in the help section of R—the raw type is intended to hold raw bytes. This is very rarely used.

All the rest of the classes that can be built in R are combinations of these six. In the later sections, you will find a list of the most common ones.

Vectors

Vectors are objects that contain elements of only one atomic class. The type of the vector will be the same as the elements it contains (for example, a numeric vector or a character vector). It is important to keep in mind that, as is the case with variables, if a value is added to a vector that does not correspond to the vector type, R will eventually change the vector type in order to adjust it to all the values in the vector instead of throwing...

Element selection

Let's now examine how elements can be selected from various class features.

Selecting elements from vectors

At this point in the chapter, you probably already suspect that in order to select specific items from a vector, the selection condition must be enclosed in [].

There are basically three ways of selecting elements from arrays in R. They are as follows:

  1. By index: A set of integers that indicate the position of the elements to select:
    > LETTERS[c(1,5,6)]
[1] "A" "E" "F"

    Note

    LETTERS is a character vector built-in object in R that contains the entire alphabet in upper case. For lower case, use LETTERS.

    Using negative subscripts removes specific elements from an object (unlike in languages such as Python, where it implies reverse order):

    > LETTERS[-c(1,5,6)]
[1] "B" "C" "D" "G" "H" "I" "J" "K" "L" "M" "N" "O" &quot...

Control structures in R

Control structures in computer programming are statements that decide the execution (or not) of certain pieces of code. In while and if, they are based on a condition that evaluates to TRUE or FALSE, and in for, the statement is executed for every element of the input sequence.

In R, all the control structures have the same coding pattern, as follows:

control_structure(condition or sequence){code block}

The if...else block

The following is a small example of an if...else block in R. You can play with it by changing the value of a:

> a <- 5
> if(a > 0){print("a is greater than 0")} else
+ { print("a is smaller than 0")}
[1] "a is greater than 0"

Note

The else clause must start in the same line where the if clause ends.

With an else...if statement, it would be:

> a <- 10
> if(a < 0 ){
+   print("a is smaller than 0")} else if(a >= 0 & a <= 5)
+     { print("a is between 0 and 5")} else
+  ...

Object-oriented programming concepts

As in any other object-oriented programming language, a class in R is an abstract definition of an object type with specific attributes associated to it. For instance, for an eventual dog class (which is a general definition of a dog), we could say that it has the color, size, age, and so on attributes.

An object is an instance of a specific class. Continuing with the previous example, we could have a Dog1 object whose attributes can be the following:

  • color: "brown"

  • size: 5.5 inch

  • age: 3 years

In R, the attributes of an object can be accessed by typing attr(object, "attribute"), for instance:

data(iris)
attributes(iris)

In this example, a data frame object (data frame objects will be explained later in this chapter) called iris is loaded, which has the names, row.names, and class attributes (R considers the class of an object an attribute as well). In most cases, many of the values for these attributes in a particular object can be accessed by typing either...

Variables in R

Unlike Java or C#, R enables total flexibility in the assignment of variables. This means that you can assign objects of different types to the same variable. This will cause overriding:

var1 <- 10
var1 <- "a string"

In this case, for instance, R will not throw an error for var1. In addition, there is no need to pre-declare the class of the variable.

The assignment of variables in R can be done in the following three ways:

  • <- or ->: These arrows assign the corresponding value to a variable. However, the first alternative is more common:

    var1 <- 10
10 -> var1

  • =: This is similar to <- or ->.

    Tip

    As in most programming languages, it is important to keep in mind that whenever a comparison is needed, == must be used.

  • assign(): This is a function with the name of the variable as its first parameter and the value as its second:

    > assign("var1",6)

For conventional variable assignments, there is no need to use this function. However, it becomes particularly useful...

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Page 1 of 8

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Description

Make the most of R’s dynamic capabilities and implement web applications with Shiny About This Book Present interactive data visualizations in R within the Shiny framework Construct web dashboards in a simple, intuitive, but fully flexible environment Apply your skills to create a real-world web application with this step-by-step guide Who This Book Is For If you are a data scientist who needs a platform to show your results to a broader audience in an attractive and visual way, or a web developer with no prior experience in R or Shiny, this is the book for you. What You Will Learn Comprehend many useful functions, such as lapply and apply, to process data in R Write and structure different files to create a basic dashboard Develop graphics in R using popular graphical libraries such as ggplot2 and GoogleVis Mount a dashboard on a Linux Server Integrate Shiny with non-R-native visualization, such as D3.js Design and build a web application In Detail R is nowadays one of the most used tools in data science. However, along with Shiny, it is also gaining territory in the web application world, due to its simplicity and flexibility. Shiny is a framework that enables the creation of interactive visualizations written entirely in R and can be displayed in almost any ordinary web browser. It is a package from RStudio, which is an IDE for R. From the fundamentals of R to the administration of multi-concurrent, fully customized web applications, this book explains how to achieve your desired web application in an easy and gradual way. You will start by learning about the fundamentals of R, and will move on to looking at simple and practical examples. These examples will enable you to grasp many useful tools that will assist you in solving the usual problems that can be faced when developing data visualizations. You will then walk through the integration of Shiny with R in general and view the different visualization possibilities out there. Finally, you will put your skills to the test and create your first web application! Style and approach This is a comprehensive, step-by-step guide that will allow you to learn and make full use of R and Shiny’s capabilities in a gradual way, together with clear, applied examples.

What you will learn

  • Comprehend many useful functions, such as lapply and apply, to process data in R Write and structure different files to create a basic dashboard Develop graphics in R using popular graphical libraries such as ggplot2 and GoogleVis Mount a dashboard on a Linux Server Integrate Shiny with non-R-native visualization, such as D3.js Design and build a web application

Product Details

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Publication date : Oct 16, 2015
Length: 246 pages
Edition : 1st
Language : English
ISBN-13 : 9781785281990
Category :
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Languages :
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Concepts :
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Shiny

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Publication date : Oct 16, 2015
Length: 246 pages
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Language : English
ISBN-13 : 9781785281990
Category :
Web Development
Languages :
R
Concepts :
Front End Web Development
Tools :
Shiny

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Table of Contents

12 Chapters
1. Introducing R, RStudio, and Shiny Chevron down icon Chevron up icon
1. Introducing R, RStudio, and Shiny
About R
About RStudio
About Shiny
Summary
2. First Steps towards Programming in R Chevron down icon Chevron up icon
2. First Steps towards Programming in R
Object-oriented programming concepts
Variables in R
Classes in depth
Element selection
Control structures in R
Reading data
Summary
3. An Introduction to Data Processing in R Chevron down icon Chevron up icon
3. An Introduction to Data Processing in R
Sorting elements
Basic summary functions
grep and regular expressions
The lapply, vapply, sapply, and apply functions
plyr
The data.table package
reshape2
Summary
4. Shiny Structure – Reactivity Concepts Chevron down icon Chevron up icon
4. Shiny Structure – Reactivity Concepts
Shiny as a package
An introduction to server.R and UI.R
The concept of reactivity
Reactive independent processes within an application
An introduction to global.R
Running a Shiny web application
An overview of simple examples
Summary
5. Shiny in Depth – A Deep Dive into Shiny's World Chevron down icon Chevron up icon
5. Shiny in Depth – A Deep Dive into Shiny's World
UI.R
Optimal usage of server.R and global.R
Shiny options
Summary
6. Using R's Visualization Alternatives in Shiny Chevron down icon Chevron up icon
6. Using R's Visualization Alternatives in Shiny
The graphics package
Plotting options
Legends
Plotting a fully customized plot with the graphics package
Including a plot in a Shiny application
A walk around the googleVis package
ggplot2 – first steps
Summary
7. Advanced Functions in Shiny Chevron down icon Chevron up icon
7. Advanced Functions in Shiny
The validate() function
The isolate() function
The observe() function
The reactiveValues() function
Input updates
Summary
8. Shiny and HTML/JavaScript Chevron down icon Chevron up icon
8. Shiny and HTML/JavaScript
The www directory
Creating UIs from plain HTML
The use of tags in UI.R
Relating HTML/JavaScript and server.R
Summary
9. Interactive Graphics in Shiny Chevron down icon Chevron up icon
9. Interactive Graphics in Shiny
Interaction possibilities within R graphics
D3.js integration
Summary
10. Sharing Applications Chevron down icon Chevron up icon
10. Sharing Applications
runGist/runGitHub/runUrl
shinyapps.io
Summary
11. From White Paper to a Full Application Chevron down icon Chevron up icon
11. From White Paper to a Full Application
Problem presentation
Conceptual design
Pre-application processing
global.R coding
UI.R partial coding
server.R coding
UI.R completion
Styling
Summary
Reference
Index Chevron down icon Chevron up icon
Index

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Rating distribution
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2 star 14.3%
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M. Rumore Jan 26, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I disagree with the other reviewers. This book is an introduction to Shiny and it covers the case where the reader may also be new to R.It provides an excellent description of the Shiny archictecture, the connection between the UI processing and the data-processing backend. The chapters slowly progress through the various Shiny capabilites with straightforward examples which are easy to understand and demonstrate the interactions between user-input, data processing, UI updates, etc. It also covers (briefly) how to include HTML, CSS, javascript into Shiny applications.The final chapter develops an application where all the parts are linked and demonstrated.If you need more details, then you do have to look elsewhere.
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Oscar Jun 11, 2019
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Excelente libro
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Ray O Light Nov 30, 2015
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
This is value for money given the technical content of the book.What I liked about the book is that it goes straight to giving the technical knowledge and understanding of Shiny.Where I would think it could be better is to bring the last chapter forward and start off with an overarching view of using Shiny and its Conceptual design (this would help the reader to associate better web design and using Shiny). Also suggest removing the introduction to R portion, which is too brief and not necessary since it is very likely people who wish to learn about Shiny already know R. (started reading straight from chapter 4).Overall, this book is geared more towards the technical aspect and helps to stir interest in picking up Shiny development.
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Amazon Customer Nov 19, 2015
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There are very rare books written on shiny. It is an introductory book to get you started with Shiny. The book covers very basic examples so do n't expect to have expert skills after reading this book. Chapter 11 From White Paper to a Full Application did nice job in giving an overview of end to end development process.
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MR MW GREGORY Oct 10, 2016
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
Online tutorials provided by R Studio are superior. Provides little additional value for the cost. Probably good if you are new to R and Shiny, not good for advanced users.
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About the author

Profile icon Hernan Resnizky
Hernan Resnizky
contacted on 2 jun 16 Shiny, R
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  • If you experience a problem with using or installing Adobe Reader, the contact Adobe directly.
  • To view the errata for the book, see www.packtpub.com/support and view the pages for the title you have.
  • To view your account details or to download a new copy of the book go to www.packtpub.com/account
  • To contact us directly if a problem is not resolved, use www.packtpub.com/contact-us
What eBook formats do Packt support? Chevron down icon Chevron up icon

Our eBooks are currently available in a variety of formats such as PDF and ePubs. In the future, this may well change with trends and development in technology, but please note that our PDFs are not Adobe eBook Reader format, which has greater restrictions on security.

You will need to use Adobe Reader v9 or later in order to read Packt's PDF eBooks.

What are the benefits of eBooks? Chevron down icon Chevron up icon
  • You can get the information you need immediately
  • You can easily take them with you on a laptop
  • You can download them an unlimited number of times
  • You can print them out
  • They are copy-paste enabled
  • They are searchable
  • There is no password protection
  • They are lower price than print
  • They save resources and space
What is an eBook? Chevron down icon Chevron up icon

Packt eBooks are a complete electronic version of the print edition, available in PDF and ePub formats. Every piece of content down to the page numbering is the same. Because we save the costs of printing and shipping the book to you, we are able to offer eBooks at a lower cost than print editions.

When you have purchased an eBook, simply login to your account and click on the link in Your Download Area. We recommend you saving the file to your hard drive before opening it.

For optimal viewing of our eBooks, we recommend you download and install the free Adobe Reader version 9.