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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

By : Tony Fischetti
3.5 (2)
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Data Analysis with R, Second Edition

Data Analysis with R, Second Edition

3.5 (2)
By: Tony Fischetti

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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Version control

A very compelling benefit to our neat hierarchical organization scheme is that it lends itself to easy integration with version control systems. Version control systems, at a basic level, allow one to track changes/revisions to a set of files, and easily roll back to previous states of the set of files.

A simple (and inadequate) approach is to compress your analysis project at regular intervals, and post-fix the filename of each compressed copy with a timestamp. This way, if you make a mistake, and would like to revert to a previous version, all you have to do is delete your current project and un-compress the project from the time you want to roll back to.

A far more sane solution is to use a remote file synchronization service that features revision tracking. The most popular of these services at the time of writing is Dropbox, though there are others such as...

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Data Analysis with R, Second Edition
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