Data analysis with R : a comprehensive guide to manipulating, analyzing, and visualizing data in R / Tony Fischetti.

Online resource; title from title page (Safari, viewed April 19, 2018).

Cover; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: RefresheR; Navigating the basics; Arithmetic and assignment; Logicals and characters; Flow of control; Getting help in R; Vectors; Subsetting; Vectorized functions; Advanced subsetting; Recycling; Functions; Matrices; Loading data into R; Working with packages; Exercises; Summary; Chapter 2: The Shape of Data; Univariate data; Frequency distributions; Central tendency; Spread; Populations, samples, and estimation; Probability distributions; Visualization methods; Exercises; Summary.

Chapter 3: Describing RelationshipsMultivariate data; Relationships between a categorical and continuous variable; Relationships between two categorical variables; The relationship between two continuous variables; Covariance; Correlation coefficients; Comparing multiple correlations; Visualization methods; Categorical and continuous variables; Two categorical variables; Two continuous variables; More than two continuous variables; Exercises; Summary; Chapter 4: Probability; Basic probability; A tale of two interpretations; Sampling from distributions; Parameters; The binomial distribution.

The normal distributionThe three-sigma rule and using z-tables; Exercises; Summary; Chapter 5: Using Data To Reason About The World; Estimating means; The sampling distribution; Interval estimation; How did we get 1.96?; Smaller samples; Exercises; Summary; Chapter 6: Testing Hypotheses; The null hypothesis significance testing framework; One and two-tailed tests; Errors in NHST; A warning about significance; A warning about p-values; Testing the mean of one sample; Assumptions of the one sample t-test; Testing two means; Assumptions of the independent samples t-test.

Testing more than two meansAssumptions of ANOVA; Testing independence of proportions; What if my assumptions are unfounded?; Exercises; Summary; Chapter 7: Bayesian Methods; The big idea behind Bayesian analysis; Choosing a prior; Who cares about coin flips; Enter MCMC -- stage left; Using JAGS and runjags; Fitting distributions the Bayesian way; The Bayesian independent samples t-test; Exercises; Summary; Chapter 8: The Bootstrap; What's ... uhhh ... the deal with the bootstrap?; Performing the bootstrap in R (more elegantly); Confidence intervals; A one-sample test of means.

Bootstrapping statistics other than the meanBusting bootstrap myths; What have we left out?; Exercises; Summary; Chapter 9: Predicting Continuous Variables; Linear models; Simple linear regression; Simple linear regression with a binary predictor; A word of warning; Multiple regression; Regression with a non-binary predictor; Kitchen sink regression; The bias-variance trade-off; Cross-validation; Striking a balance; Linear regression diagnostics; Second Anscombe relationship; Third Anscombe relationship; Fourth Anscombe relationship; Advanced topics; Exercises; Summary.

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. 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.

Copyright © 2018 Packt Publishing 2018

Includes bibliographical references.

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