Book description
This step-by-step guide demonstrates how to build simple-to-advanced applications through examples in R using modern tools.
About This Book
- Get a firm hold on the fundamentals of R through practical hands-on examples
- Get started with good R programming fundamentals for data science
- Exploit the different libraries of R to build interesting applications in R
Who This Book Is For
This books is for aspiring data science professionals or statisticians who would like to learn about the R programming language in a practical manner. Basic programming knowledge is assumed.
What You Will Learn
- Discover techniques to leverage R's features, and work with packages
- Perform a descriptive analysis and work with statistical models using R
- Work efficiently with objects without using loops
- Create diverse visualizations to gain better understanding of the data
- Understand ways to produce good visualizations and create reports for the results
- Read and write data from relational databases and REST APIs, both packaged and unpackaged
- Improve performance by writing better code, delegating that code to a more efficient programming language, or making it parallel
In Detail
R is a high-level statistical language and is widely used among statisticians and data miners to develop analytical applications. Often, data analysis people with great analytical skills lack solid programming knowledge and are unfamiliar with the correct ways to use R. Based on the version 3.4, this book will help you develop strong fundamentals when working with R by taking you through a series of full representative examples, giving you a holistic view of R.
We begin with the basic installation and configuration of the R environment. As you progress through the exercises, you'll become thoroughly acquainted with R's features and its packages. With this book, you will learn about the basic concepts of R programming, work efficiently with graphs, create publication-ready and interactive 3D graphs, and gain a better understanding of the data at hand. The detailed step-by-step instructions will enable you to get a clean set of data, produce good visualizations, and create reports for the results. It also teaches you various methods to perform code profiling and performance enhancement with good programming practices, delegation, and parallelization.
By the end of this book, you will know how to efficiently work with data, create quality visualizations and reports, and develop code that is modular, expressive, and maintainable.
Style and Approach
This is an easy-to-understand guide filled with real-world examples, giving you a holistic view of R and practical, hands-on experience.
Table of contents
- Preface
-
Introduction to R
- What R is and what it isn't
- Comparing R with other software
- The interpreter and the console
- Tools to work efficiently with R
- How to use this book
- Tracking state with symbols and variables
- Working with data types and data structures
- Divide and conquer with functions
- Complex logic with control structures
- The examples in this book
- Summary
-
Understanding Votes with Descriptive Statistics
- This chapter's required packages
- The Brexit votes example
- Cleaning and setting up the data
- Summarizing the data into a data frame
- Getting intuition with graphs and correlations
- Creating a new dataset with what we've learned
- Building new variables with principal components
- Putting it all together into high-quality code
- Summary
- Predicting Votes with Linear Models
- Simulating Sales Data and Working with Databases
-
Communicating Sales with Visualizations
- Required packages
- Extending our data with profit metrics
- Building blocks for reusable high-quality graphs
- Starting with simple applications for bar graphs
- Graphing disaggregated data with boxplots
- Scatter plots with joint and marginal distributions
- Developing our own graph type – radar graphs
- Exploring with interactive 3D scatter plots
- Looking at dynamic data with time-series
- Looking at geographical data with static maps
- Navigating geographical data with interactive maps
- Summary
-
Understanding Reviews with Text Analysis
- This chapter's required packages
- What is text analysis and how does it work?
- Preparing, training, and testing data
- Building the corpus with tokenization and data cleaning
- Training models with cross validation
- Improving our results with TF-IDF
- Adding flexibility with N-grams
- Reducing dimensionality with SVD
- Extending our analysis with cosine similarity
- Digging deeper with sentiment analysis
- Testing our predictive model with unseen data
- Retrieving text data from Twitter
- Summary
-
Developing Automatic Presentations
- Required packages
- Why invest in automation?
- Literate programming as a content creation methodology
- The basic tools for an automation pipeline
- A gentle introduction to Markdown
- Header Level 1
- Extending Markdown with R Markdown
- Developing graphs and analysis as we normally would
- Building our presentation with R Markdown
- Summary
-
Object-Oriented System to Track Cryptocurrencies
- This chapter's required packages
- The cryptocurrencies example
- A brief introduction to object-oriented programming
- Introducing three object models in R – S3, S4, and R6
- The architecture behind our cryptocurrencies system
- Starting simple with timestamps using S3 classes
- Implementing cryptocurrency assets using S4 classes
- Implementing our storage layer with R6 classes
- Retrieving live data for markets and wallets with R6 classes
- Finally introducing users with S3 classes
- Helping ourselves with a centralized settings file
- Saving our initial user data into the system
- Activating our system with two simple functions
- Some advice when working with object-oriented systems
- Summary
-
Implementing an Efficient Simple Moving Average
- Required packages
- Starting by using good algorithms
- How fast is fast enough?
- Calculating simple moving averages inefficiently
- Understanding why R can be slow
- Measuring by profiling and benchmarking
- Easily achieving high benefit - cost improvements
- Using parallelization to divide and conquer
- Using C++ and Fortran to accelerate calculations
- Looking back at what we have achieved
- Other topics of interest to enhance performance
- Summary
-
Adding Interactivity with Dashboards
- Required packages
- What is functional reactive programming and why is it useful?
- Designing our high-level application structure
- Inserting a dynamic data table
- Introducing interactivity with user input
- Adding a summary table with shared data
- Adding a simple moving average graph
- Adding interactivity with a secondary zoom-in graph
- Styling our application with themes
- Other topics of interest
- Summary
- Required Packages
Product information
- Title: R Programming By Example
- Author(s):
- Release date: December 2017
- Publisher(s): Packt Publishing
- ISBN: 9781788292542
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