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knit: "bookdown::render_book"
title: "R for Data Science"
author: ["Garrett Grolemund", "Hadley Wickham"]
description: "This book will teach you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you'll learn how to clean data and draw plots---and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You'll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You'll also learn how to manage cognitive resources to facilitate discoveries when wrangling, visualizing, and exploring data."
description: "This book will teach you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you'll learn how to clean data and draw plots---and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You'll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You'll also learn how to manage cognitive resources to facilitate discoveries when wrangling, visualising, and exploring data."
url: 'http\://r4ds.had.co.nz/'
github-repo: hadley/r4ds
twitter-handle: hadley
@ -13,7 +13,7 @@ documentclass: book
# Welcome {-}
This is the website for __"R for Data Science"__. This book will teach you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you'll learn how to clean data and draw plots---and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You'll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You'll also learn how to manage cognitive resources to facilitate discoveries when wrangling, visualizing, and exploring data.
This is the website for __"R for Data Science"__. This book will teach you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you'll learn how to clean data and draw plots---and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You'll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You'll also learn how to manage cognitive resources to facilitate discoveries when wrangling, visualising, and exploring data.
To be published by O'Reilly in late 2016. Pre-order from [amazon](http://amzn.to/2aHLAQ1).

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@ -20,7 +20,7 @@ Once you have tidy data with the variables you need, there are two main engines
__Visualisation__ is a fundamentally human activity. A good visualisation will show you things that you did not expect, or raise new questions about the data. A good visualisation might also hint that you're asking the wrong question, or you need to collect different data. Visualisations can surprise you, but don't scale particularly well because they require a human to interpret them.
__Models__ are complementary tools to visualisation. Once you have made your questions sufficiently precise, you can use a model to answer them. Models are a fundamentally mathematical or computational tool, so they 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 cannot fundamentally surprise you.
__Models__ are complementary tools to visualisation. Once you have made your questions sufficiently precise, you can use a model to answer them. Models are a fundamentally mathematical or computational tool, so they 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 cannot question its own assumptions. That means a model cannot fundamentally surprise you.
The last step of data science is __communication__, an absolutely critical part of any data analysis project. It doesn't matter how well your models and visualisation have led you to understand the data unless you can also communicate your results to others.
@ -32,7 +32,7 @@ You'll use these six tools in every data science project, but for most projects
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 when you combine multiple packages to solve real problems. The packages in the tidyverse are not perfect, but they fit together well, and over time that fit will continue to improve.
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.
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
@ -41,7 +41,7 @@ The previous description of the tools of data science is organised roughly accor
* Starting with data ingest and tidying is sub-optimal because 80% of the time
it's routine and boring, and the other 20% of the time it's weird and
frustrating. That's a bad place to start learning a new subject! Instead,
we'll start with visualisation and transformation on data that's already been
we'll start with visualisation and transformation of data that's already been
imported and tidied. That way, when you ingest and tidy your own data, your
motivation will stay high because you know the pain is worth it.
@ -52,7 +52,7 @@ The previous description of the tools of data science is organised roughly accor
* Programming tools are not necessarily interesting in their own right,
but do allow you to tackle considerably more challenging problems. We'll
give you a selection of programming tools in the middle of the book, and
then you'll see they combine with the data science tools to tackle interesting
then you'll see they can combine with the data science tools to tackle interesting
modelling problems.
Within each chapter, we try and stick to a similar pattern: start with some motivating examples so you can see the bigger picture, and then dive into the details. Each section of the book is paired with exercises to help you practice what you've learned. While it's tempting to skip the exercises, there's no better way to learn than practicing on real problems.
@ -63,7 +63,7 @@ There are some important topics that this book doesn't cover. We believe it's im
### Big data
This book proudly focuses on small, in-memory datasets. This is the right place to start because you can't tackle big data unless you have experience with small data. The tools you learn in this book will easily handle hundreds of megabytes of data, and with a little care you can typically use them to work with 1-2 Gb of data. If you're routinely working with larger data (10-100 Gb, say), you should learn more about [data.table](https://github.com/Rdatatable/data.table). This book doesn't teach data.table because it has a very concise interface which makes it harder to learn because it offers fewer linguistic cues. But if you're working with large data, the performance payoff is worth the extra effort required to learn it.
This book proudly focuses on small, in-memory datasets. This is the right place to start because you can't tackle big data unless you have experience with small data. The tools you learn in this book will easily handle hundreds of megabytes of data, and with a little care you can typically use them to work with 1-2 Gb of data. If you're routinely working with larger data (10-100 Gb, say), you should learn more about [data.table](https://github.com/Rdatatable/data.table). This book doesn't teach data.table because it has a very concise interface which makes it harder to learn since it offers fewer linguistic cues. But if you're working with large data, the performance payoff is worth the extra effort required to learn it.
If your data is bigger than this, carefully consider if your big data problem might actually be a small data problem in disguise. While the complete data might be big, often the data needed to answer a specific question is small. You might be able to find a subset, subsample, or summary that fits in memory and still allows you to answer the question that you're interested in. The challenge here is finding the right small data, which often requires a lot of iteration.
@ -81,7 +81,7 @@ This book focuses exclusively on rectangular data: collections of values that ar
### Hypothesis confirmation
It's possible to divide data analysis into two camps: hypothesis generation and hypothesis confirmation (sometimes called confirmatory analysis). The focus of this book is unabashedly on hypothesis generation, or data exploration. Here you'll look deeply at the data and in combination with your subject knowledge generate many interesting hypotheses to help explain why the data behaves the way it does. You evaluate the hypotheses informally, using your scepticism to challenge the data in multiple ways.
It's possible to divide data analysis into two camps: hypothesis generation and hypothesis confirmation (sometimes called confirmatory analysis). The focus of this book is unabashedly on hypothesis generation, or data exploration. Here you'll look deeply at the data and, in combination with your subject knowledge, generate many interesting hypotheses to help explain why the data behaves the way it does. You evaluate the hypotheses informally, using your scepticism to challenge the data in multiple ways.
The complement of hypothesis generation is hypothesis confirmation. Hypothesis confirmation is hard for two reasons:
@ -95,7 +95,7 @@ The complement of hypothesis generation is hypothesis confirmation. Hypothesis c
even when you have seen the data. We'll talk a little about some
strategies you can use to make this easier in [modelling](#model-intro).
It's common to think about modelling as a tool for hypothesis confirmation, and visualisation for a tool for hypothesis generation. But that's a false dichotomy: models are often used for exploration, and with a little care you can use visualisation for confirmation. The key difference is how often do you look at each observation: if you look only once, it's confirmation; if you look more than once, it's exploration.
It's common to think about modelling as a tool for hypothesis confirmation, and visualisation as a tool for hypothesis generation. But that's a false dichotomy: models are often used for exploration, and with a little care you can use visualisation for confirmation. The key difference is how often do you look at each observation: if you look only once, it's confirmation; if you look more than once, it's exploration.
## Prerequisites
@ -105,11 +105,11 @@ To run the code in this book, you will need to install both R and the RStudio ID
1. Download and install R, <https://www.r-project.org/>.
1. Download and install RStudio, <http://www.rstudio.com/download>.
1. Install needed packages (see below).
1. Install required packages (see below).
### RStudio
RStudio is an integrated development environment, or IDE, for R programming. There are three key regions in interface:
RStudio is an integrated development environment, or IDE, for R programming. There are three key regions in the interface:
```{r echo = FALSE, out.width = "75%"}
knitr::include_graphics("diagrams/intro-rstudio.png")
@ -135,7 +135,7 @@ We strongly recommend making two changes to the default RStudio options:
knitr::include_graphics("screenshots/rstudio-workspace.png")
```
This ensures that every time you restart RStudio you get a completely clean slate. That's good practice because it encourages you to capture all important interactions in your code. There's nothing worse than discovering three months after the fact that you've only stored the results of an important calculation in your workspace, not the calculation itself in your code. During a project, it's good practice to regularly restart R either using the menu Session | Restart R or the keyboard shortcut Cmd + Shift + F10.
This ensures that every time you restart RStudio you get a completely clean slate. That's good practice because it encourages you to capture all important interactions in your code. There's nothing worse than discovering three months after the fact that you've only stored the results of an important calculation in your workspace, not the calculation itself in your code. During a project, it's good practice to regularly restart R either using the menu option Session | Restart R or the keyboard shortcut Cmd + Shift + F10.
### R packages
@ -180,16 +180,16 @@ Throughout the book we use a consistent set of conventions to refer to code:
This book is not an island: there is no single resource that will allow you to master R. As you start to apply the techniques described in this book to your own data you will soon find questions that I do not answer. This section describes a few tips to help you get help, and to help you keep learning.
If you get stuck, start with google. Typically adding "R" to a query is enough to restrict it to relevant results: if the search isn't useful, it often means that there aren't any R specific results available. Google is particularly useful for error messages. If you get an error message and you have no idea what it means, try googling it! Chances are that someone else has been confused by it in the past, and there will be help somewhere on the web. (If the error message isn't in English, run `Sys.setenv(LANGUAGE = "en")` and re-run the code; you're more likely to find help for English error messages.)
If you get stuck, start with Google. Typically adding "R" to a query is enough to restrict it to relevant results: if the search isn't useful, it often means that there aren't any R-specific results available. Google is particularly useful for error messages. If you get an error message and you have no idea what it means, try googling it! Chances are that someone else has been confused by it in the past, and there will be help somewhere on the web. (If the error message isn't in English, run `Sys.setenv(LANGUAGE = "en")` and re-run the code; you're more likely to find help for English error messages.)
If google doesn't help, try [stackoverflow](http://stackoverflow.com). Start by spending a little time searching for an existing answer (including `[R]` to restrict your search to questions about R). If you don't find anything useful, prepare a minimal reproducible example or __reprex__. A good reprex makes it easier for other people to help you, and often you'll figure out the problem yourself in the course of making it.
If Google doesn't help, try [stackoverflow](http://stackoverflow.com). Start by spending a little time searching for an existing answer (including `[R]` to restrict your search to questions about R). If you don't find anything useful, prepare a minimal reproducible example or __reprex__. A good reprex makes it easier for other people to help you, and often you'll figure out the problem yourself in the course of making it.
There are three things you need to include to make your example reproducible: required packages, data, and code.
1. **Packages** should be loaded at the top of the script, so it's easy to
see which ones the example needs. This is a good time to check that you're
using the latest version of each package: it's possible you've discovered
a bug that been fixed since you installed the package.
a bug that's been fixed since you installed the package.
1. The easiest way to include **data** in a question is to use `dput()` to
generate the R code to recreate it. For example, to recreate the `mtcars`
@ -216,13 +216,13 @@ There are three things you need to include to make your example reproducible: re
Finish by checking that you have actually made a reproducible example by starting a fresh R session and copying and pasting your script in.
You should also spend some time preparing yourself to solve problems before they occur. Investing a little time in learning R each day will pay off handsomely in the long run. One way to is follow what Hadley, Garrett, and everyone else at RStudio are doing on the [RStudio blog](https://blog.rstudio.org). This is where we post announcements about new packages, new IDE features, and in-person courses. You might also want to follow Hadley ([@hadleywickham](https://twitter.com/hadleywickham)) or Garrett ([@statgarrett](https://twitter.com/statgarrett)) on twitter, or follow [@rstudiotips](https://twitter.com/rstudiotips) to keep up with new features in the IDE.
You should also spend some time preparing yourself to solve problems before they occur. Investing a little time in learning R each day will pay off handsomely in the long run. One way to is follow what Hadley, Garrett, and everyone else at RStudio are doing on the [RStudio blog](https://blog.rstudio.org). This is where we post announcements about new packages, new IDE features, and in-person courses. You might also want to follow Hadley ([@hadleywickham](https://twitter.com/hadleywickham)) or Garrett ([@statgarrett](https://twitter.com/statgarrett)) on Twitter, or follow [@rstudiotips](https://twitter.com/rstudiotips) to keep up with new features in the IDE.
To keep up with the R community more broadly, we recommend reading <http://www.r-bloggers.com>: it aggregates over 500 blogs about R from around the world. If you're an active twitter user, follow the `#rstats` hashtag. Twitter is one the key tools that Hadley uses to keep up with new developments in the community.
To keep up with the R community more broadly, we recommend reading <http://www.r-bloggers.com>: it aggregates over 500 blogs about R from around the world. If you're an active Twitter user, follow the `#rstats` hashtag. Twitter is one of the key tools that Hadley uses to keep up with new developments in the community.
## Acknowledgements
This book isn't just the product of Hadley and Garrett, but is the result of many conversations (in person and online) that we've had with the many people in the R community. There are few people we'd like to thank in particular, because they have spent many hours answering our dumb questions and helping us to better think about data science:
This book isn't just the product of Hadley and Garrett, but is the result of many conversations (in person and online) that we've had with the many people in the R community. There are a few people we'd like to thank in particular, because they have spent many hours answering our dumb questions and helping us to better think about data science:
* Jenny Bryan and Lionel Henry for many helpful discussions around working
with lists and list-columns.
@ -256,7 +256,7 @@ cat(".\n")
## Colophon
An online version of this book is available at <http://r4ds.had.co.nz>. It will continue to evolve in between reprints of the physical book. The source of the book is available at <https://github.com/hadley/r4ds>. The book is powered by <https://bookdown.org> which makes it easy to turn R markdown files into html, pdf, and epub.
An online version of this book is available at <http://r4ds.had.co.nz>. It will continue to evolve in between reprints of the physical book. The source of the book is available at <https://github.com/hadley/r4ds>. The book is powered by <https://bookdown.org> which makes it easy to turn R markdown files into HTML, PDF, and EPUB.
This book was built with: