Hadley Wickham - R for Data Science

by Hadley Wickham , Mine etinkaya-Rundel , and Garrett Grolemund

Copyright 2023 Hadley Wickham, Mine etinkaya-Rundel, and Garrett Grolemund. All rights reserved.

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Data science is an exciting discipline that allows you to transform raw data into understanding, insight, and knowledge. The goal of R for Data Science is to help you learn the most important tools in R that will allow you to do data science efficiently and reproducibly, and to have some fun along the way! After reading this book, youll have the tools to tackle a wide variety of data science challenges using the best parts of R.

First, you must import your data into R. This typically means that you take data stored in a file, database, or web application programming interface (API) and load it into a data frame in R. If you cant get your data into R, you cant do data science on it!

Once youve imported your data, it is a good idea to tidy it. Tidying your data means storing it in a consistent form that matches the semantics of the dataset with how it is stored. In brief, when your data is tidy, each column is a variable, and each row is an observation. Tidy data is important because the consistent structure lets you focus your efforts on answering questions about the data, not fighting to get the data into the right form for different functions.

Once you have tidy data, a common next step is to transform it. Transformation includes narrowing in on observations of interest (like all people in one city or all data from the last year), creating new variables that are functions of existing variables (like computing speed from distance and time), and calculating a set of summary statistics (like counts or means). Together, tidying and transforming are called wrangling because getting your data in a form thats natural to work with often feels like a fight!

Once you have tidy data with the variables you need, there are two main engines of knowledge generation: visualization and modeling. These have complementary strengths and weaknesses, so any real data analysis will iterate between them many times.

Visualization