Statistical Analysis with R
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- An easy introduction for people who are new to R, with plenty of strong examples for you to work through
- This book will take you on a journey to learn R as the strategist for an ancient Chinese kingdom!
- A step by step guide to understand R, its benefits, and how to use it to maximize the impact of your data analysis
- A practical guide to conduct and communicate your data analysis with R in the most effective manner
Book DetailsLanguage : English
Paperback : 300 pages [ 235mm x 191mm ]
Release Date : October 2010
ISBN : 1849512086
ISBN 13 : 9781849512084
Author(s) : John M. Quick
Topics and Technologies : All Books, Big Data and Business Intelligence, Data, Architecture & Analysis, Beginner's Guides, Open Source
Table of Contents
Chapter 1: Uncovering the Strategist's Data Analysis Tool
Chapter 2: Preparing R for Battle
Chapter 3: Exploring the Mysterious Data Analysis Tool
Chapter 4: Collecting and Organizing Information
Chapter 5: Assessing the Situation
Chapter 6: Planning the Attack
Chapter 7: Organizing the Battle Plans
Chapter 8: Briefing the Emperor
Chapter 9: Briefing the Generals
Chapter 10: Becoming a Master Strategist
Appendix: Pop Quiz Answer Key
Download the code and support files for this book.
Please let us know if you have found any errors not listed on this list by completing our errata submission form. Our editors will check them and add them to this list. Thank you.
Errata- 8 submitted: last submission 30 Oct 2013
Errata type: Code | Page number: 70 | Errata date: 01 Nov 10
The code for standard deviation of duration in past head to head conflicts reads as:
sdDurationHeadToHead <- mean(subsetHeadToHead$DurationInDays)
The correct code should be:
sdDurationHeadToHead <- sd(subsetHeadToHead$DurationInDays)
Errata type: Grammar | Page number: 151 | Errata date: 09 Nov 10
The last line of the Introduction reads as:
"It is important to convey your plans with clarity, because the emperor has the power accept or reject your strategy."
Actually it should read as:
"It is important to convey your plans with clarity, because the emperor has the power to accept or reject your strategy."
Errata type: Others | Page number: 50 | Errata date: 16 Nov 10
The line that reads:
to access the city column in soldiersByCity, we could use soldiersByCity[,1], this tells R to retrieve every row within the City column
Should actually say to use soldiersByCity[,2] in order to retrieve the City column
Errata type: Others | Page number: 76 | Errata date: 17 Nov 10
Incorrect variable name in :
So, after step 3, our numericSuccessfullyExecutedHeadToHead variable
Should be :
So, after step 3, our numericExecutionHeadToHead variable
Errata type: Others | Page number: 131 | Errata date: 21 Nov 10
Incorrect mean value in:
"The rating of the Shu army's performance in attacks has ranged from 10 to 100, with a mean of 45"
"The rating of the Shu army's performance in attacks has ranged from 10 to 100, with a mean of 52"
Errata type: code | Page number: 54
mean(soldiersByCity$Soldiers) returns the mean of the entire Soldiers column not just the Shu soldiers.
As for finding the mean for just the Shu soldiers, the subset() function, which is discussed in chapter 5, can be used in tandem with the mean() function. For example, first subset the data to include only the Shu cities, then calculate the mean of the Soldiers column: > shuData <- subset(soldiersByCity, Kingdom == "Shu") > mean(shuData$Soldiers)
Errata type: code | Page number: 70
R Beginner's Guide
John M. Quick Answers to Pop Quiz sections by chapter
Setting your R working directory
Solving the First 4x4 Magic Square
Importing external data
Creating and calling variables
Accessing data within variables
Manipulating variable data
1. Table values (left to right, top to bottom): 10, 20, 30, 40, 50, 60
2. Table values (left to right, top to bottom): 1, 12, 3, 4, 5, 6
Managing the R workspace
Creating a subset from a large dataset
Deriving summary statistics
Quantifying categorical variables
Modeling with simple linear regression
Modeling with multiple linear regression
Comparing and choosing models
Creating custom functions
Incorporating resource constraints into predictions
Assessing the viability of potential strategies
The common steps to all R analyses
Creating a bar chart
Creating a scatterplot
Creating a line chart
Creating a box plot
Creating a histogram
Creating a pie chart
Customizing a bar chart
Customizing a scatterplot
Customizing a line chart
Customizing a box plot
Customizing a histogram
Customizing a pie chart
Building a graphic
Building a graphic with multiple visuals
Using R's help function
Expanding R with packages
What you will learn from this book
- Conduct superior data analysis in R
- Organize and communicate data analysis
- Generate, customize, and export detailed charts, plots, and graphs
- Build your own custom data visualizations
- Program in the R language
- Create your own custom functions
- Extend the functionality of R via external packages
- Manage the R workspace and console
- Import external data into R
- Manipulate data using variables
- Execute a wide array of multi-argument and variable-argument functions
- Develop and employ predictive regression models
- Assess the practical and statistical significance of predictions
- Understand R, its benefits, and how to use it to maximize the impact of your data analyses
R is a data analysis tool, graphical environment, and programming language. Without any prior experience in programming or statistical software, this book will help you quickly become a knowledgeable user of R. Now is the time to take control of your data and start producing superior statistical analysis with R.
This book will take you on a journey as the strategist for an ancient Chinese kingdom. Along the way, you will learn how to use R to arrive at practical solutions and how to effectively communicate your results. Ultimately, the fate of the kingdom depends on your ability to make informed, data-driven decisions with R.
You have unexpectedly been thrust into the role of lead strategist for the kingdom. After you install your predecessor's mysterious data analysis tool, you will begin to explore its fundamental elements. Next, you will use R to import and organize your data. Then, you will use functions and statistical analysis to arrive at potential courses of action. Subsequently, you will design your own functions to assess the practical impacts of your predictions. Lastly, you will focus on communicating your results through the use of charts, plots, graphs, and custom built visualizations. The fate of the kingdom is in your hands. Your rapid development as a master R strategist is the key to future success.
A step by step guide to organize, analyze, and visualize your data in R.
This is a practical, step by step guide that will help you to quickly become proficient in the data analysis using R. The book is packed with clear examples, screenshots, and code to carry on your data analysis without any hurdle.
Who this book is for
If you are a data analyst, business or information technology professional, student, educator, researcher, or anyone else who wants to learn to analyze the data effectively then this book is for you.
No prior experience with R is necessary. Knowledge of other programming languages, software packages, or statistics may be helpful, but is not required.