R4DS teaches 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.
This website is [and will always be] free to use, and is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License. If you’d like a physical copy of the book, you can order it from amazon. If you’d like to give back please make a donation to Kākāpō Recovery: the kākāpō [which appears on the cover of R4DS] is a critically endangered native NZ parrot; there are only 213 left.
Please note that R4DS uses a Contributor Code of Conduct. By contributing to this book, you agree to abide by its terms.
I felt like I learned months in a week. I love how Codecademy uses learning by practice and gives great challenges to help the learner to understand a new concept and subject.Rodrigo Codecademy Learner @ UKR is a programming language for statistical computing and graphics supported by the R Core Team and the R Foundation for Statistical Computing. Created by statisticians Ross Ihaka and Robert Gentleman, R is used among data miners, bioinformaticians and statisticians for data analysis and developing statistical software.[6] Users have created packages to augment the functions of the R language.
According to user surveys and studies of scholarly literature databases, R is one of the most commonly used programming languages used in data mining.[7] As of October 2022, R ranks 12th in the TIOBE index, a measure of programming language popularity, in which the language peaked in 8th place in August 2020.[8][9]
The official R software environment is an open-source free software environment within the GNU package, available under the GNU General Public License. It is written primarily in C, Fortran, and R itself [partially self-hosting]. Precompiled executables are provided for various operating systems. R has a command line interface.[10] Multiple third-party graphical user interfaces are also available, such as RStudio, an integrated development environment, and Jupyter, a notebook interface.
History[edit]
R is an open-source implementation of the S programming language combined with lexical scoping semantics from Scheme, which allow objects to be defined in predetermined blocks rather than the entirety of the code.[1] S was created by Rick Becker, John Chambers, Doug Dunn, Jean McRae, and Judy Schilling at Bell Labs around 1976. Designed for statistical analysis, the language is an interpreted language whose code could be directly run without a compiler.[11] Many programs written for S run unaltered in R.[10] As a dialect of the Lisp language, Scheme was created by Gerald J. Sussman and Guy L. Steele Jr. at MIT around 1975.[12]
In 1991, statisticians Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, embarked on an S implementation.[13] It was named partly after the first names of the first two R authors and partly as a play on the name of S.[10] They began publicizing it on the data archive StatLib and the s-news mailing list in August 1993.[14] In 1995, statistician Martin Mächler convinced Ihaka and Gentleman to make R free and open-source software under the GNU General Public License.[14][15][16] The first official release came in June 1995.[14] The first official "stable beta" version [v1.0] was released on 29 February 2000.[17][18]
The Comprehensive R Archive Network [CRAN] was officially announced on 23 April 1997. CRAN stores R's executable files, source code, documentations, as well as packages contributed by users. CRAN originally had 3 mirrors and 12 contributed packages.[19] As of January 2022, it has 101 mirrors[20] and 18,728 contributed packages.[21] In addition to hosting packages CRAN hosts binaries for major distributions of Linux, MacOS and Windows.[22]
The R Core Team was formed in 1997 to further develop the language.[10][23] As of January 2022, it consists of Chambers, Gentleman, Ihaka, and Mächler, plus statisticians Douglas Bates, Peter Dalgaard, Kurt Hornik, Michael Lawrence, Friedrich Leisch, Uwe Ligges, Thomas Lumley, Sebastian Meyer, Paul Murrell, Martyn Plummer, Brian Ripley, Deepayan Sarkar, Duncan Temple Lang, Luke Tierney, and Simon Urbanek, as well as computer scientist Tomas Kalibera. Stefano Iacus, Guido Masarotto, Heiner Schwarte, Seth Falcon, Martin Morgan, and Duncan Murdoch were members.[14][24] In April 2003,[25] the R Foundation was founded as a non-profit organization to provide further support for the R project.[10]
Features[edit]
Data processing[edit]
R's data structures include vectors, arrays, lists, and data frames.[26] Vectors are ordered collections of values and can be mapped to arrays of one or more dimensions in a column major order. That is, given an ordered collection of dimensions, one fills in values along the first dimension first, then fill in one-dimensional arrays across the second dimension, and so on.[27] R supports array arithmetics and in this regard is like languages such as APL and MATLAB.[26][28] The special case of an array with two dimensions is called a matrix. Lists serve as collections of objects that do not necessarily have the same data type. Data frames contain a list of vectors of the same length, plus a unique set of row names.[26] R has no scalar data type.[29] Instead, a scalar is represented as a length-one vector.[30]
R and its libraries implement various statistical techniques, including linear, generalized linear and nonlinear modeling, classical statistical tests, spatial and time-series analysis, classification, clustering, and others. For computationally intensive tasks, C, C++, and Fortran code can be linked and called at run time. Another of R's strengths is static graphics; it can produce publication-quality graphs that include mathematical symbols.[31]
Programming[edit]
R is an interpreted language; users can access it through a command-line interpreter. If a user types 2+2 at the R command prompt and presses enter, the computer replies with 4.
R supports procedural programming with functions and, for some functions, object-oriented programming with generic functions.[32] Due to its S heritage, R has stronger object-oriented programming facilities than most statistical computing languages.[citation needed] Extending it is facilitated by its lexical scoping rules, which are derived from Scheme.[33] R uses S-expressions to represent both data and code.[citation needed] R's extensible object system includes objects for [among others]: regression models, time-series and geo-spatial coordinates. Advanced users can write C, C++,[34] Java,[35] .NET[36] or Python code to manipulate R objects directly.[37]
Functions are first-class objects and can be manipulated in the same way as data objects, facilitating meta-programming that allows multiple dispatch. Function arguments are passed by value, and are lazy—that is to say, they are only evaluated when they are used, not when the function is called.[38] A generic function acts differently depending on the classes of the arguments passed to it. In other words, the generic function dispatches the method implementation specific to that object's class. For example, R has a generic print function that can print almost every class of object in R with print[objectname].[39] Many of R's standard functions are written in R,[citation needed] which makes it easy for users to follow the algorithmic choices made. R is highly extensible through the use of packages for specific functions and specific applications.
Packages[edit]
R's capabilities are extended through user-created[40] packages, which offer statistical techniques, graphical devices, import/export, reporting [RMarkdown, knitr, Sweave], etc. These packages and their easy installation and use has been cited as driving the language's widespread adoption in data science.[41][42][43][44][45] The packaging system is also used by researchers to organize research data, code and report files in a systematic way for sharing and archiving.[46]
Multiple packages are included with the basic installation. Additional packages are available on CRAN,[20] Bioconductor, R-Forge,[47] Omegahat,[48] GitHub, and other repositories.[49][50][51]
The "Task Views" on the CRAN website[52] lists packages in fields including Finance, Genetics, High Performance Computing, Machine Learning, Medical Imaging, Meta-Analysis,[53] Social Sciences and Spatial Statistics.[53] R has been identified by the FDA as suitable for interpreting data from clinical research.[54] Microsoft maintains a daily snapshot of CRAN that dates back to Sept. 17, 2014.[55]
Other R package resources include R-Forge,[56][47] a platform for the collaborative development of R packages. The Bioconductor project provides packages for genomic data analysis, including object-oriented data-handling and analysis tools for data from Affymetrix, cDNA microarray, and next-generation high-throughput sequencing methods.[57]
A group of packages called the Tidyverse, which can be considered a "dialect" of the R language, is increasingly popular among developers.[note 1] It strives to provide a cohesive collection of functions to deal with common data science tasks, including data import, cleaning, transformation and visualisation [notably with the ggplot2 package]. Dynamic and interactive graphics are available through additional packages.[58]
R is one of 5 languages with an Apache Spark API, along with Scala, Java, Python, and SQL.[59][60]
Milestones[edit]
A list of changes in R releases is maintained in various "news" files at CRAN.[61] Some highlights are listed below for several major releases.
ReleaseDateDescription0.16This is the last alpha version developed primarily by Ihaka and Gentleman. Much of the basic functionality from the "White Book" [see S history] was implemented. The mailing lists commenced on 1 April 1997.0.491997-04-23This is the oldest source release which is currently available on CRAN.[62] CRAN is started on this date, with 3 mirrors that initially hosted 12 packages.[63] Alpha versions of R for Microsoft Windows and the classic Mac OS are made available shortly after this version.[citation needed]0.601997-12-05R becomes an official part of the GNU Project. The code is hosted and maintained on CVS.0.65.11999-10-07First versions of update.packages and install.packages functions for downloading and installing packages from CRAN.[64]1.02000-02-29Considered by its developers stable enough for production use.[65]1.42001-12-19S4 methods are introduced and the first version for Mac OS X is made available soon after.1.82003-10-08Introduced a flexible condition handling mechanism for signalling and handling condition objects.2.02004-10-04Introduced lazy loading, which enables fast loading of data with minimal expense of system memory.2.12005-04-18Support for UTF-8 encoding, and the beginnings of internationalization and localization for different languages.2.6.22008-02-08Last version to support Windows 95, 98, Me and NT 4.0[66]2.112010-04-22Support for Windows 64-bit systems.2.12.22011-02-25Last version to support Windows 2000[67]2.132011-04-14Adding a new compiler function that allows speeding up functions by converting them to bytecode.2.142011-10-31Added mandatory namespaces for packages. Added a new parallel package.2.152012-03-30New load balancing functions. Improved serialisation speed for long vectors.3.0.02013-04-03Support for numeric index values 231 and larger on 64-bit systems.3.3.32017-03-06Last version to support Microsoft Windows XP.3.4.02017-04-21Just-in-time compilation [JIT] of functions and loops to byte-code enabled by default.3.5.02018-04-23Packages byte-compiled on installation by default. Compact internal representation of integer sequences. Added a new serialisation format to support compact internal representations.3.6.02019-04-26Improved sampling from a discrete uniform distribution, which was noticeably non-uniform on large populations.[68] New serialisation format supported since 3.5.0 becomes the default.4.0.02020-04-24R now uses astringsAsFactors = FALSE default, and hence by default no longer converts strings to factors in calls to data.frame[] and # Declare function “f” with parameters “x”, “y“ # that returns a linear combination of x and y. f