Book description
Learn, by example, the fundamentals of data analysis as well as several intermediate to advanced methods and techniques ranging from classification and regression to Bayesian methods and MCMC, which can be put to immediate use.
About This Book
 Analyze your data using R – the most powerful statistical programming language
 Learn how to implement applied statistics using practical usecases
 Use popular R packages to work with unstructured and structured data
Who This Book Is For
Budding data scientists and data analysts who are new to the concept of data analysis, or who want to build efficient analytical models in R will find this book to be useful. No prior exposure to data analysis is needed, although a fundamental understanding of the R programming language is required to get the best out of this book.
What You Will Learn
 Gain a thorough understanding of statistical reasoning and sampling theory
 Employ hypothesis testing to draw inferences from your data
 Learn Bayesian methods for estimating parameters
 Train regression, classification, and time series models
 Handle missing data gracefully using multiple imputation
 Identify and manage problematic data points
 Learn how to scale your analyses to larger data with Rcpp, data.table, dplyr, and parallelization
 Put best practices into effect to make your job easier and facilitate reproducibility
In Detail
Frequently the tool of choice for academics, R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. The power and domainspecificity of R allows the user to express complex analytics easily, quickly, and succinctly.
Starting with the basics of R and statistical reasoning, this book dives into advanced predictive analytics, showing how to apply those techniques to realworld data though with realworld examples.
Packed with engaging problems and exercises, this book begins with a review of R and its syntax with packages like Rcpp, ggplot2, and dplyr. From there, get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. Solve the difficulties relating to performing data analysis in practice and find solutions to working with messy data, large data, communicating results, and facilitating reproducibility.
This book is engineered to be an invaluable resource through many stages of anyone's career as a data analyst.
Style and approach
An easytofollow step by step guide which will help you get to grips with real world application of Data Analysis with R
Table of contents
 Title Page
 Copyright and Credits
 Packt Upsell
 Contributors
 Preface
 RefresheR
 The Shape of Data
 Describing Relationships
 Probability
 Using Data To Reason About The World
 Testing Hypotheses
 Bayesian Methods
 The Bootstrap
 Predicting Continuous Variables
 Predicting Categorical Variables

Predicting Changes with Time
 What is a time series?
 What is forecasting?
 Creating and plotting time series
 Components of time series
 Time series decomposition
 White noise
 Autocorrelation
 Smoothing
 ETS and the state space model
 Interventions for improvement
 What we didn't cover
 Citations for the climate change data
 Exercises
 Summary
 Sources of Data
 Dealing with Missing Data
 Dealing with Messy Data
 Dealing with Large Data
 Working with Popular R Packages
 Reproducibility and Best Practices
 Other Books You May Enjoy
Product information
 Title: Data Analysis with R  Second Edition
 Author(s):
 Release date: March 2018
 Publisher(s): Packt Publishing
 ISBN: 9781788393720
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