Getting Started with R for Data Science [Video]

Mykola Kolisnyk, Richard Skeggs
Friday, September 30, 2016

1 customer reviews

Unleash the powerful capabilities of R to work effectively with data

Mapt Subscription

FREE

€29.98/m after trial

Video

€79.56

RRP €93.58

Save 14%

What do I get with a Mapt Pro subscription?

Unlimited access to all Packt’s 5,000+ eBooks and Videos
Early Access content, Progress Tracking, and Assessments
1 Free eBook or Video to download and keep every month after trial

What do I get with an eBook?

Download this book in EPUB, PDF, MOBI formats
DRM FREE - read and interact with your content when you want, where you want, and how you want
Access this title in the Mapt reader

What do I get with Print & eBook?

Get a paperback copy of the book delivered to you
Download this book in EPUB, PDF, MOBI formats
DRM FREE - read and interact with your content when you want, where you want, and how you want
Access this title in the Mapt reader

What do I get with a Video?

Download this Video course in MP4 format
DRM FREE - read and interact with your content when you want, where you want, and how you want
Access this title in the Mapt reader

€0.00

€79.56

€29.98p/m after trial

RRP €93.58

Subscription

Video

Start 30 Day Trial

Code Files

Preview in Mapt

Video Details

ISBN 139781785884252

Course Length1 hours 39 minute

Video Description

The R language is a powerful open source functional programming language. R is becoming the go-to tool for data scientists and analysts. Its growing popularity is due to its open source nature and extensive development community.

This course will take you on a journey to become an efficient data science practitioner as you thoroughly understand the key concepts of R. Starting from the absolute basics, you will quickly be introduced to programming in R. You will see how to load data into R for analysis, and get a good understanding of how to write R scripts. We will delve into data types in R, and you'll gain the ability to read and write data to and from databases as well as files. You will also get to know how to perform basic analysis of the data.

By the end of the course, you will know how data science can be applied in practical conditions.

Style and Approach

This is a hands-on introductory course to help you analyze, interpret, and optimize data in R. We cover a range of topics with a brief discussion, followed by a simple example of the implementation.

Introducing R

The Course Overview

What is R?

The Structure of the Language

Data Structures within R

Writing a Simple Program in R

Getting Data into R

The Structure of a DataFrame

Creating a DataFrame from a CSV File

Creating a DataFrame from a Zip File

Creating a DataFrame from a Database

Cleaning and Blending Data

The Tools Available for Cleaning Data

Dealing with Null Values

Standardizing Date Formats

Blending Multiple DataFrames

Cooking the Codebook

What Is a Codebook and Why Create One?

Creating the Codebook Using Standard R API Functionality

Manually Creating a Custom Codebook

Data Mining and Analysis

Introduction to Data Mining and Analysis

The Tools and Techniques for Creating the Story

Regression Analysis with R

Clustering Data with R

Classifying Data with R

A Picture Paints a Thousand Words

Data Visualization Tools

Creating Static Visualization Plots

Creating Interactive Plots

Publishing the Graphics

What’s Next?

What You Will Learn

Write R code that can be executed outside RStudio
Get data from numerous sources such as files, databases, and even Twitter
Clean data before the analysis phase begins
Load libraries into RStudio for use within the analysis phase
Perform data cleaning on a dataset
Create a codebook so that the data can be presented in a summary
Understand how to use visualization to understand data and tell a story

Authors

Mykola Kolisnyk

Mykola Kolisnyk has been working in test automation since 2004. He has been involved with various activities including creating test automation solutions from scratch, leading test automation teams, and working as a consultant with test automation processes. During his working career, he has had experience with different test automation tools such as Mercury WinRunner, MicroFocus SilkTest, SmartBear TestComplete, Selenium-RC, WebDriver, Appium, SoapUI, BDD frameworks, and many other different engines and solutions. He has had experience with multiple programming technologies based on Java, C#, Ruby, and so on, and with different domain areas such as healthcare, mobile, telecoms, social networking, business process modeling, performance and talent management, multimedia, e-commerce, and investment banking.

Richard Skeggs

Richard Skeggs is not new to big data as he has over 15 years of experience in creating big data repositories and solutions for large multinational organizations in Europe. Having become a single father, he has changed his focus and is now working within the academic and research community. Richard has special interest in big data and is currently undertaking research within the field. His research interests revolve around machine learning, data retrieval, and complex systems.