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    R Programming Fundamentals: Deal with data using various modeling techniques
    R Programming Fundamentals: Deal with data using various modeling techniques
    R Programming Fundamentals: Deal with data using various modeling techniques
    Ebook310 pages1 hour

    R Programming Fundamentals: Deal with data using various modeling techniques

    By Kaelen Medeiros

    ()

    Read preview

    About this ebook

    Study data analysis and visualization to successfully analyze data with R




    Key Features



    • Get to grips with data cleaning methods


    • Explore statistical concepts and programming in R, including best practices


    • Build a data science project with real-world examples





    Book Description



    R Programming Fundamentals, focused on R and the R ecosystem, introduces you to the tools for working with data. To start with, you'll understand you how to set up R and RStudio, followed by exploring R packages, functions, data structures, control flow, and loops.







    Once you have grasped the basics, you'll move on to studying data visualization and graphics. You'll learn how to build statistical and advanced plots using the powerful ggplot2 library. In addition to this, you'll discover data management concepts such as factoring, pivoting, aggregating, merging, and dealing with missing values.







    By the end of this book, you'll have completed an entire data science project of your own for your portfolio or blog.





    What you will learn



    • Use basic programming concepts of R such as loading packages, arithmetic functions, data structures, and flow control


    • Import data to R from various formats such as CSV, Excel, and SQL


    • Clean data by handling missing values and standardizing fields


    • Perform univariate and bivariate analysis using ggplot2


    • Create statistical summary and advanced plots such as histograms, scatter plots, box plots, and interaction plots


    • Apply data management techniques, such as factoring, pivoting, aggregating, merging, and dealing with missing values, on the example datasets





    Who this book is for



    R Programming Fundamentals is for you if you are an analyst who wants to grow in the field of data science and explore the latest tools.

    LanguageEnglish
    PublisherPackt Publishing
    Release dateSep 27, 2018
    ISBN9781789616101
    R Programming Fundamentals: Deal with data using various modeling techniques

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      Book preview

      R Programming Fundamentals - Kaelen Medeiros

      R Programming Fundamentals

      R Programming Fundamentals

      Deal with data using various modeling techniques

      Kaelen Medeiros

      BIRMINGHAM - MUMBAI

      R Programming Fundamentals

      Copyright © 2018 Packt Publishing

      All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

      Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.

      Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

      Acquisitions Editors: Aditya Date, Bridget Neale

      Content Development Editor: Madhura Bal

      Production Coordinator: Ratan Pote

      First published: September 2018

      Production reference: 1270918

      Published by Packt Publishing Ltd.

      Livery Place

      35 Livery Street

      Birmingham

      B3 2PB, UK.

      ISBN 978-1-78961-299-8

      www.packtpub.com

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      Contributors

      About the Author

      Kaelen Medeiros is a content quality developer at DataCamp, where she works to improve course content and tracks quality metrics across the company. She also works as a data scientist/developer for HealthLabs, who develop automated methods for analyzing large amounts of medical data. She received her MS in biostatistics from Louisiana State University Health Sciences Center in 2016. Outside of work, she has one cat, listens to way too many podcasts, and enjoys running.

      Packt is Searching for Authors Like You

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      Table of Contents

      Title Page

      Copyright and Credits

      R Programming Fundamentals

      Packt Upsell

      Why Subscribe?

      Packt.com

      Contributors

      About the Author

      Packt is Searching for Authors Like You

      Preface

      Who This Book is for

      What This Book Covers

      To Get the Most Out of This Book

      Download the Example Code Files

      Conventions Used

      Get in Touch

      Reviews

      Introduction to R

      Using R and RStudio, and Installing Useful Packages

      Using R and RStudio

      Executing Basic Functions in the R Console

      Setting up a New Project 

      Installing Packages

      Activity: Installing the Tidyverse Packages

      Variable Types and Data Structures

      Variable Types

      Numeric and Integers

      Character

      Dates

      Activity: Identifying Variable Classes and Types

      Data Structures

      Vectors

      Lists

      Matrices

      Dataframes

      Activity: Creating Vectors, Lists, Matrices, and Dataframes

      Basic Flow Control

      If/else

      For loop

      While loop

      Activity: Building Basic Loops

      Data Import and Export

      Excel Spreadsheets

      Activity: Exporting and Importing the mtcars Dataset

      Getting Help with R

      Package Documentation and Vignettes

      Activity: Exploring the Introduction to dplyr Vignette

      RStudio Community, Stack Overflow, and the Rest of the Web

      Summary

      Data Visualization and Graphics

      Creating Base Plots

      The plot() Function

      Factor Variables

      Model Objects

      Plotting More Than One Plot at a Time

      Creating and Plotting a Linear Model Object

      Titles and Axis Labels

      Changing the Color of Base Plots

      Activity: Recreating Plots with Base Plot Methods

      ggplot2

      ggplot2 Basics

      Histogram

      Creating Histograms using ggplot2

      Bar Chart

      Creating a Bar Chart with ggplot2 using Two Different Methods

      Scatterplot

      Creating a Scatterplot of Two Continuous Variables

      Boxplot

      Creating Boxplots using ggplot2

      Activity: Recreating Plots Using ggplot2

      Digging into aes()

      Bar Chart

      Using Different Bar Chart Aesthetic Options

      Facet Wrapping and Gridding

      Utilizing Facet Wrapping and Gridding to Visualize Data Effectively

      Boxplot + coord_flip()

      Scatterplot

      Utilizing Different Aesthetics for Scatterplots, Including Shapes, Colors, and Transparencies

      Activity: Utilizing ggplot2 Aesthetics

      Extending the Plots with Titles, Axis Labels, and Themes

      Interactive Plots

      Plotly

      Shiny

      Exploring Shiny and Plotly

      Summary

      Data Management

      Factor Variables

      Creating Factor Variables in a Dataset

      Using ifelse() Statements

      Using the recode() Function

      Examining and Changing the Levels of Pre-existing Factor Variables

      Creating an Ordered Factor Variable

      Activity: Creating and Manipulating Factor Variables

      Summarizing Data

      Data Summarization Tables

      Tables in R

      Creating Different Tables Using the table() Function

      Using dplyr Methods to Create Data Summary Tables

      Activity: Creating Data Summarization Tables

      Summarizing Data with the Apply Family

      Using the apply() Function to Create Numeric Data Summaries

      Activity: Implementing Data Summary

      Splitting, Combining, Merging, and Joining Datasets

      Splitting and Combining Data and Datasets

      Splitting and Unsplitting Data with Base R and the dplyr Methods

      Splitting Datasets into Lists and Then Back Again

      Combining Data

      Combining Data with rbind()

      Combining Matrices of Objects into Dataframes

      Splitting Strings

      Using stringr Package to Manipulate a Vector of Names

      Combining Strings Using Base R Methods

      Activity: Demonstrating Splitting and Combining Data

      Merging and Joining Data

      Demonstrating Merges and Joins in R

      Activity: Merging and Joining Data

      Summary

      Solutions

      Chapter 1: Introduction to R

      Activity: Installing the Tidyverse Packages

      Activity: Identifying Variable Classes and Types

      Activity: Creating Vectors, Lists, Matrices, and Dataframes

      Activity: Building Basic Loops

      Activity: Exporting and Importing the mtcars Dataset

      Activity: Exploring the Introduction to dplyr Vignette

      Chapter 2: Data Visualization and Graphics

      Activity: Recreating Plots with Base Plot Methods

      Activity: Recreating Plots Using ggplot2

      Activity: Utilizing ggplot2 Aesthetics

      Chapter 3: Data Management

      Activity: Creating and Manipulating Factor Variables

      Activity: Creating Data Summarization Tables

      Activity: Implementing Data Summary

      Activity: Demonstrating Splitting and Combining Data

      Activity: Merging and Joining Data

      Other Books You May Enjoy

      Leave a Review - Let Other Readers Know What You Think

      Preface

      Demand for data scientists is growing exponentially and demand in the US is expected to increase by 28 percent by the year 2020, with this trend reflected across the world. R is a tool often used by data scientists to clean, examine, analyze, and report on data. It is a great starting point for those familiar with analysis in Excel or MS SQL and is an excellent place to begin to learn programming fundamentals.

      This book begins by addressing the setup of R and RStudio on the machine and progresses from there, demonstrating how to import datasets, clean them, and explore their contents. It balances theory and exercises, and contains multiple open-ended activities that use real-life business scenarios for you to practice and apply your newly acquired skills in a highly relevant context. We have included over 50 practical activities and exercises across 11 topics, along with a mini project that will allow you to begin your data science project portfolio. With this book, we have created a definitive guide to beginning data science in R. 

      Who This Book is for

      This book is for analysts who are looking to grow their data science skills beyond the tools they have used before, such as MS Excel and other statistical tools.

      What This Book Covers

      Chapter 1, Introduction to R, deals with installation of R, RStudio, and other useful packages, and talks about variable types and data structures.  The chapter then introduces the different kinds of loops that can be used in R, explains how to import and export data, and also talks about getting help with R programming.

      Chapter 2, Data Visualization and Graphics, covers the basic plots built into R and how to create them, and then introduces ggplot, a popular graphics package in R. Finally, the chapter briefly talks about two tools, Shiny and Plotly, that can be used to design interactive plots.

      Chapter 3, Data Management, discusses how to create and manipulate factor variables, examine data using tables, apply the family of functions to generate summaries, and split, combine, merge, or join datasets in R.

      The Appendix contains the solutions to all the activities within the chapters.

      To Get the Most Out of This Book

      You will require a computer system with at least an i3 processor, 2 GB RAM, 10 GB of storage space, and an internet connection. Along with this, you would require the following software:

      Operating System: Windows 8 64-bit

      R and Rstudio

      Browsers (Google Chrome and Mozilla Firefox - latest versions)

      Download the Example Code Files

      You can download the

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