Data Analysis with R: A comprehensive guide to manipulating, analyzing, and visualizing data in R, 2nd Edition

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 domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly.

Key Features
• Load, wrangle, and analyze your data using R - the world's most powerful statistical programming language
• Gain a deeper understanding of fundamentals of applied statistics and implement them using practical use-cases
• A comprehensive guide specially designed to take your understanding of R for data analysis to a new level

Book Description
Starting with the basics of R and statistical reasoning, this book dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples.

Packed with engaging problems and exercises, this book begins with a review of R and its syntax. 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.

What you will learn
• Navigate the R environment
• Describe and visualize the behavior of data and relationships between data
• Gain a thorough understanding of statistical reasoning and sampling
• Employ hypothesis tests to draw inferences from your data
• Learn Bayesian methods for estimating parameters
• Use bootstrapping and an alternative to parametric hypothesis testing
• Perform regression to predict continuous variables
• Apply powerful classification methods to predict categorical data
• Perform time series forecasting with Exponential Smoothing methods
• Handle missing data gracefully using multiple imputation
• Identify and manage problematic data points
• Use regular expressions to clean data sets
• Employ parallelization and Rcpp to scale your analyses to larger data
• Put best practices into effect to make your job easier and facilitate reproducibility

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