Data Analysis With R: Load, Wrangle, and Analyze Your Data Using the World's Most Powerful Statistical Programming Language

ISBN: 1785288148

Author: Fischetti, Tony

Condition: Used

Key Features Load, manipulate and analyze data from different sources Gain a deeper understanding of fundamentals of applied statistics A practical guide to performing data analysis in practice Book DescriptionFrequently 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. With over 7,000 user contributed packages, it's easy to find support for the latest and greatest algorithms and techniques.Starting with the basics of R and statistical reasoning, Data Analysis with R 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 Perform regression to predict continuous variables Apply powerful classification methods to predict categorical data Handle missing data gracefully using multiple imputation Identify and manage problematic data points Employ parallelization and Rcpp to scale your analyses to larger data Put best practices into effect to make your job easier and facilitate reproducibility Table of Contents RefresheR The Shape of Data Describing Relationships Probability Using Data to Reason About the World Testing Hypotheses Bayesian Methods Predicting Continuous Variables Predicting Categorical Variables Sources of Data Dealing with Messy Data Dealing with Large Data Reproducibility and Best Practices