Book Image

Data Analysis with R, Second Edition - Second Edition

Book Image

Data Analysis with R, Second Edition - Second Edition

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 (24 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Index

Other tools for messy data


As we've discussed before, there are countless ways that a dataset could be messy. There are many other messy situations and solutions that we can't discuss at length here. So that you, dear reader, are not left in the dark regarding other custodial solutions, here are some other tools that you may find helpful on your analytics journey.

OpenRefine

Though OpenRefine (formerly Google Refine) doesn't have anything to do with R per se, it is a sophisticated tool for working with and cleaning up messy data. Among its numerous, sophisticated capabilities is the ability to auto-detect misspelled or misspecified categories and fix them with the click of a button.

Fuzzy matching

In order to prepare for record linkage on book titles, we normalized the strings by performing a few operations on them. An alternative is to use what is referred to as fuzzy matching.

An exact match between two strings requires that the strings be... well... exactly the same. For example, Finnegans...