Data Manipulation in R: Black and White edition

by Stephanie Locke

Discover how to systematically process and analyse data - a vital skill for a data scientist. This series of books takes you through everything you need to know and starts off with the very basics. The second book gives you a thorough grounding in analysing data. From preparing it so you can go on to applying machine learning algorithms, to producing high-level analysis, this book gives you what you need.
You'll be equipped to work with common data sources like spreadsheets and databases, process data, and pass it on to others. "Great stuff. I learned lots of new things, including some advanced wrangling that I had not seen covered elsewhere. This book packs a lot in - covering all the essential requirements for day to day working with R. This book will definitely help those transitioning from spreadsheets to become proficient with R." - John MacKintosh Whether you're looking to become more productive with data analysis, or you'd like to learn machine learning and statistics, this book gives you a rock-solid foundation in wrangling data that will enable you to grow and achieve your goals.

158 pages, Paperback

Published December 14, 2017

Reviews

[Peter Baumgartner · Rating 4 out of 5]

The book explains with many examples of the functionality of the tidyverse collection of R packages. At first, I thought that this subject is already thoroughly covered in R for Data Science: Import, Tidy, Transform, Visualize, and Model Data by Hadley Wickham. But Locke goes much more into details and brings examples and commands of these packages I had not known previously. Especially the syntax and use cases for the different _all, _if, and _at functions, the point as placeholder symbol in pipes and advanced conditional commands were new for me.

The only thing why I have not rated "Data Manipulation in R" with five stars: There are many typos like "the the" and sometimes the example commands and their listings could have better chosen.