Abstract
A review of a recent book on data science is framed within the context of open science. I propose that R is a natural bridge between data and open science and a powerful ally in promoting transparent, reproducible science.
Highlights
Data science is a critical component of many domains of research, including the domain within which I primarily function, ecology
Failure to appreciate the differences between data science and statistics is common, and it is an easy distinction to blur because the disciplines support one another
There are many books to this effect that both appreciate and communicate the value of data science thinking and improve the skill set that one can apply to teaching, research, and collaboration in general
Summary
Data science is a critical component of many domains of research, including the domain within which I primarily function, ecology. There are many books to this effect that both appreciate and communicate the value of data science thinking and improve the skill set that one can apply to teaching, research, and collaboration in general (kaggle provides an excellent list of resources here: https://www.kaggle.com/wiki/DataSci enceBooksAndCourses). Reading a book written using one of the most powerful open science/data science tools, i.e. R (language and environment) implemented through RStudio, and being able to access it online with code, you immediately appreciate the trickle effects of ‘open data science’ thinking on writing, collaboration, and even professional publishing.
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