Data science is an exciting discipline that allows you to turn raw data into understanding, insight, and knowledge. The goal of "R for Data Science" is to introduce you to the most important tools that you need to do data science with in R. After reading this book, you'll have the tools to tackle a wide variety of data science challenges, using the best parts of R.
Data science is a huge field, and there's no way you can master by after reading a single book. The goal of this book is to give you a solid foundation into the most important tools. These are the tools that in our experience, people use everyday. There's definitely an 80-20 rule at play: you'll do 80% of every project using this handful of tools, but the remaining 20% will is much more variable. Our goal is to teach you that 80% and to point you to where you can learn more.
First you must __import__ your data in R. This typically means that you take data stored in file, in a database, or in an web API, and load it into a data frame in R. If you can't get your data into R, you can't do data science on it!
Once you've imported your data, it's a good idea to __tidy__ it. Tidying your data means storing it in a standard form that matches the semantics of the dataset with the way its storage. In brief, when your data is tidy, each column is a variable, and each row is an observation. Working with tidy data is important because the consistency lets you spend your time struggling with your questions, not fighting to get data into the right form for different functions.
Once you have tidy data, a common first step is to __transform__ it to add new variables that are functions of existing variables (like computing velocity from speed and distance), to rename the variables to be easier to understand, to sort your data, or summarise it.
There are two main engines of knowledge generation: visualisation and modelling. These have complementary strengths and weaknesses so any real analysis will iterate between them many times. For example, you might see a scatterplot that inspires you to fit a linear model, then you transform the data to add a column of residuals from the model, and look at another scatterplot.
__Visualisation__ is a fundamentally human activity. A good visualisation will show you things that you did not expect, or raise new questions of the data. A good visualisation might also hint that you're asking the wrong question and you need to refine your thinking. In short, visualisations can surprise you, but don't scale particularly well.
__Models__ are the complementary tools to visualisation. Models are a fundamentally mathematical or computation tool, so generally scale well. Even when they don't, it's usually cheaper to buy more computers than it is to buy more brains. But every model makes assumptions, and by its very nature a model can not question its own assumptions. That means a model can not fundamentally surprise you.
It doesn't matter how well models and visualisation have led you to understand the data, unless you can __commmunicate__ your results to other people. Communication is an absolutely critical part of any data analysis project.
## How you will learn
Above, I've listed the components of the data science process in roughly the order you'll encounter them in an analysis (although of course you'll iterate multiple times). In our experience, however, this is not the best way to learn them:
We've honed this order based on our experience teaching live classes, and it's been carefully designed to keep you motivated. We try and stick to a similar pattern within each chapter: give some bigger motivating examples so you can see the bigger picture, and then dive into the details.
Each section of the book also comes with exercises to help you practice what you've learned. It's tempting to skip these, but there's no better way to learn than practicing. If you were taking a class with either of us, we'd force you to do them by making them homework. (Sometimes I feel like teaching is the art of tricking people to do what's in their own best interests.)
Throughout the book, we will discuss the principles of data that will help you become a better scientist. That begins here. We will refer to the terms below throughout the book because they are so useful.
* A _variable_ is a quantity, quality, or property that you can measure.
This book focuses exclusively on structured data sets: collections of values that are each associated with a variable and an observation. There are lots of data that doesn't naturally fit in this paradigm: images, sounds, trees, text. But data frames are extremely common in science and in industry and we believe that they're a great place to start your data analysis journey.
In this book, you won't learn anything about Python, or any other programming language. This isn't because we think Python is bad! It's a great tool, and most data science teams use a mix of (at least!) R and Python.
However, we strongly believe that it's best to master one tool at a time. You will get better faster if you dive deep, rather than spreading yourself thinly over many topics. This doesn't mean you should be only know one thing, just that you'll generally learn faster if you stick to one thing at a time.
We've made few assumptions about what you already know in order to get the most out of this book. You should be generally numerically literate, and it's helpful if you have some programming experience already. If you've never programmed before, you might find [Hands on Programming with R](http://amzn.com/1449359019) by Garrett to be a useful adjunct to this book.
To run the code in this book, you will need to install both R and the RStudio IDE, an application that makes R easier to use. Both are open source, free and easy to install:
You'll also need to install some R packages. An R _package_ is a collection of functions, data, and documentation that extends the capabilities of base R. Using packages is key to the successful use of R. To install all the packages used in this book open RStudio and run:
R will download the packages from CRAN and install them in your system library. If you have problems installing, make that you are connected to the internet, and that you haven't blocked <http://cran.r-project.org> in your firewall or proxy.
You will not be able to use the functions, objects, and help files in a package until you load it with `library()`. You will need to reload the package if you start a new R session.
