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Mention the tidyverse in the intro

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hadley 2016-07-14 14:41:51 -05:00
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@ -32,6 +32,12 @@ There's one important toolset that's not shown in the diagram: programming. Prog
You'll use these six tools in every data science project, but for most projects they're not enough. There's a rough 80-20 rule at play: you can probably tackle 80% of every project using the tools that you'll learn in this book, but you'll need more to tackle the remaining 20%. Throughout this book we'll point you to resources where you can learn more.
## The tidyverse
The majority of the packages that you will learn in this book are part of the so-called tidyverse. All packages in the tidyverse share a common philosophy of data and R programming, which makes them fit together naturally. Because they are designed with a unifying vision you should experience fewer problems combining multiple packages to solve real problems. The packages in the tidyverse are not perfect, but over time they will continue to evolve towards greater consistency.
There are many other excellent packages that are not part of the tidyverse, because they are designed with a different set of underlying principles. This doesn't make them better or worse, just different. In other words, the complement to the tidyverse is not the messyverse, but many other universes of interrelated packages. As you tackle more data science projects with R you'll learn new packages, and new ways of thinking about data. But we hope that the tidyverse will continue to provide a solid foundation no matter how far you go in R.
## How you will learn
The above description of the tools of data science is organised roughly around the order in which you use them in analysis (although of course you'll iterate through them multiple times). In our experience, however, this is not the best way to learn them: