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  • Book Overview & Buying Data Analysis with R, Second Edition
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Data Analysis with R, Second Edition

Data Analysis with R, Second Edition - Second Edition

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Data Analysis with R, Second Edition

Data Analysis with R, Second Edition

3.5 (2)

Overview of this book

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. 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 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.
Table of Contents (19 chapters)
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Enter MCMC – stage left


As mentioned earlier, we started with the coin flip examples because of the ease of determining the posterior distribution analytically-primarily because of the beta distribution's self-conjugacy with respect to the binomial likelihood function.

It turns out that most real-world Bayesian analyses require a more complicated solution. In particular, the hyper-parameters that define the posterior distribution are rarely known. What can be determined is the probability density in the posterior distribution for each parameter value. The easiest way to get a sense of the shape of the posterior is to sample from it many thousands of times. More specifically, we sample from all possible parameter values and record the probability density at that point.

How do we do this? Well, in the case of just one parameter value, it's often computationally tractable to just randomly sample willy-nilly from the space of all possible parameter values. For cases where we are using Bayesian...

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Data Analysis with R, Second Edition
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