This step-by-step guide demonstrates how to build simple-to-advanced applications through examples in R using modern tools.
Key Features
- 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
Book Description
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.
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
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.
Table of Contents
- Introduction to R
- Analyzing Brexit Votes with Descriptive Statistics
- Analyzing Brexit Votes with Linear Models
- Extracting and Visualizing Data From Company Products
- Analyzing Text Data From Company Products
- Building and Object-Oriented Stock Trades Evaluation System
- Improving the Performance of Our Stock Trades Evaluation System
- Building Dashboards For Our Stock Trades Evaluation System
- Improving Performance With Delegation and Parallelization
- Adding Interactivity With Dashboards
- Appendix
Omar Trejo Navarro is a data consultant. He co-founded Datata, is actively working on CVEST, and maintains a personal website (OTRENAV). He is an applied mathematics and economics double major from ITAM in Mexico City, where he continues to work as a research assistant. He does software development with a focus on data platforms, data science, and web applications. He has worked with clients from all over the world, and is a keen supporter of open source, open data, and open science in general.
