Data Analysis with R

Description

Key FeaturesLoad, manipulate and analyze data from different sourcesGain a deeper understanding of fundamentals of applied statisticsA practical guide to performing data analysis in practiceBook 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 learnNavigate the R environmentDescribe and visualize the behavior of data and relationships between dataGain a thorough understanding of statistical reasoning and samplingEmploy hypothesis tests to draw inferences from your dataLearn Bayesian methods for estimating parametersPerform regression to predict continuous variablesApply powerful classification methods to predict categorical dataHandle missing data gracefully using multiple imputationIdentify and manage problematic data pointsEmploy parallelization and Rcpp to scale your analyses to larger dataPut best practices into effect to make your job easier and facilitate reproducibilityAbout the AuthorTony Fischetti is a data scientist at College Factual, where he gets to use R everyday to build personalized rankings and recommender systems. He graduated in cognitive science from Rensselaer Polytechnic Institute, and his thesis was strongly focused on using statistics to study visual short-term memory.Tony enjoys writing and and contributing to open source software, blogging at onthelambda.com, writing about himself in third person, and sharing his knowledge using simple, approachable language and engaging examples.The more traditionally exciting of his daily activities include listening to records, playing the guitar and bass (poorly), weight training, and helping others.Table of ContentsRefresheRThe Shape of DataDescribing RelationshipsProbabilityUsing Data to Reason About the WorldTesting HypothesesBayesian MethodsPredicting Continuous VariablesPredicting Categorical VariablesSources of DataDealing with Messy DataDealing with Large DataReproducibility and Best Practices