Garrett Krolimund from RStudio introduces dplyr, a package that uses five core verbs to filter, summarize, and transform data fast. The piping operator and C-based functions let you work with datasets larger than memory, and you'll hear from dplyr's author Hadley Wickham after the exercises.
Chapters
Have you ever had a data set that you were sure contains simple insights that you just could not access? I'm not talking about the sorts of information that you can discover with machine learning or modeling algorithms, just simple things like new variables, summary statistics, group differences, and so on. Most data sets contain more information than they display, and dplyr is a package that can help you access that information. Dplyr introduces a grammar, data manipulation, five simple functions that you can use to reveal new variables, new observations, and new ways to describe your data. You can also use these functions to subset your data and do group wise operations and dplyr is fast, very fast. The key pieces of dplyr are written in C, which means you get the speed of C with the ease of R. This course will show you how to use dplyr like an expert. You'll learn to use dplyr's grammar and data manipulation to solve any data related task you can think of. Along the way, you'll learn to think about and manipulate the structure of data. You'll also learn to use dplyr's table structure in the piping operator, two features that can save you tons of time. You'll even learn how to use dplyr to access data stored in a database, which provides an easy way to work with data that's too big to fit in R all at once. With dplyr, R is literally faster, bigger and better. I'll be your guide through the dplyr package. My name is Garrett Krolimund and I'm a data scientist at RStudio. I work closely with Hadley Wickham, the author of DPLYR, and I spend much of my time teaching people how to use Hadley's tools as well as the tools that my colleagues at RStudio Create. I've asked Hadley to join us at the end of the course, so if you work your way through all of the exercises, you'll have a chance to hear Hadley's own thoughts on the dplyr package. But before we can do any of that, you'll need to set up your R session to use dplyr. If you're ready to begin, head over to the exercises where we'll load the dplyr package and our dataset.
