R (programming language)

R.
	; Logo since 2015
Basic data
Paradigms :	functional , dynamic , object-oriented
Publishing year:	1993
Designer:	Ross Ihaka , Robert Gentleman
Developer:	R core team
Current version	4.0.2 (June 22, 2020)
Typing :	dynamic, implicit, weak
Influenced by:	S , Scheme
Affected:	Julia
Operating system :	Unixoide , Mac OS , Windows
License :	GNU GPL
	www.R-project.org

R is a free programming language for statistical calculations and graphics. It was newly developed in 1992 by statisticians for users with statistical tasks. The syntax is based on the S programming language , with which R is largely compatible, and the semantics on Scheme . As a standard distribution, R is offered with an interpreter as a command line environment with rudimentary graphic buttons. R is currently available on the most important platforms ; the environment is also explicitly designated as R by the developers . R is part of the GNU project . RStudio is also offered as an integrated development environment and to increase the user-friendliness of R.

Numerous packages that can be accessed online contain additional functions for analyzing data with regard to issues from different departments; further functions of your own can be created. The language offers interfaces to other programming languages and options for integration into various software. R is different from other known statistical environments in several respects and is not to be referred to exclusively as statistical software . Although other statistical environments nowadays provided with graphical user interfaces such as SPSS also began as specialized programming languages (and have retained this ability to this day), R focuses on its strength as a statistics-oriented programming language. R is distinguished from other programming languages by the data structures and functions designed for statistics as well as the special options for generating graphics.

R is considered a standard language for statistical problems in both business and science. In TIOBE index R is ranked 13 in the ranking of RedMonk 12th place in PYPL 7th place and the Institute of Electrical and Electronics Engineers course. 6

history

Origins (1992)

Ross Ihaka (2010)

R was developed in 1992 by statisticians Ross Ihaka and Robert Gentleman at the University of Auckland . They were closely based on the S language developed by Bell Laboratories (now part of Alcatel-Lucent and thus Nokia ), which is used to process statistical data. R can be seen as a free implementation of S. Therefore, the majority of programs written for S can be run under R. The name of the language can be traced back to the first letter of the developer's first name and was also based on S.

S took a different approach than previous statistics software. S data could quickly explorative be investigated and appropriate graphics are created during analysis functions were not implemented extensively in the early 1990s. The company Statistical Sciences, Inc. acquired an exclusive license for the distribution of S software from 1993. Ihaka and Gentleman liked the approach and opportunities for S statistical questions bot (former version: S3). They criticized the scoping capabilities of S, which clearly differentiated between local and global variables, and in particular the memory management , which led to a rapid increase in dynamic memory without garbage collection . They wanted to use these properties in their research projects and also implement new analytical methods faster and more flexibly, without having to rely on the S developers. So they created R. Another advantage was the available source code , so that they could clearly use R for teaching purposes at the university.

The developers first wrote an interpreter for Scheme and modified the language so that it resembled S. The programming of R took place in C (so-called primitive functions), Fortran (for example BLAS and LAPACK for numerical linear algebra ) and, based on this, in R itself. A few years ago, 22% of the source code of the standard library consisted of R code, while 52% and 26% of all program lines were written in C and Fortran.

Language and Community Growth (1993–2000)

first logo

The language was first publicized in 1993: the designers uploaded binary files from their previous work to the StatLib section of Carnegie Mellon University , which was a collection point and distribution channel for statistical software, and were hoping for feedback. There were also separate notices to people who were more closely involved with S. Martin Mächler from ETH Zurich was one of the people who gave feedback. He also encouraged Ihaka and Gentleman to distribute R freely, so that the language has been under the GNU General Public License since June 1995 . By 1996 or 1997 there were between 50 and 100 people on a mailing list who helped improve the language together. Some used Linux , which was spreading at the same time , for which R was the only statistics environment. In 1997 the R Development Core Team was formed (today R Core Team ), which takes care of the further development of R and can change the source code. There are now twenty people in this closest development team, led by Ross Ihaka and Robert Gentleman. The Comprehensive R Archive Network (CRAN) as a platform for packages was launched on April 23, 1997 to give users the opportunity to more easily share functions they have written with others. The oldest publicly available source code for Unixoids falls on this date; the alpha versions for Microsoft Windows and Mac appeared a short time later. On February 29, 2000, version 1.0, considered stable by the R Development Core Team, was released.

Performance optimization (since 2001)

R for macOS has been available since April 2001. In September 2002 the members of the R Development Core Team founded the non-profit association The R Foundation for Statistical Computing in Vienna , which takes care of the external presentation.

The R version 2.0 was released on October 4, 2004. Since then, R has been using lazy loading to be able to load data quickly with little memory usage. From version 2.1 (April 18, 2005), R supports different language versions ( internationalization ) and character encodings , especially UTF-8 .

As a result there was some improvement in performance . This includes the introduction of version 2.11 in April 2010, which makes R usable on 64-bit systems and can address up to eight terabytes of RAM. In addition, with version 2.13 the package compiler appeared , which can compile functions written in R into bytecode and contains a just-in-time compiler ; meanwhile, the functions that already exist and were not written by the user are already available as bytecode. In addition, coarse-grain concurrency was introduced in October 2011 (version 2.14) for parallel execution of functions. From version 3.0 (April 2013) index values of 2 ³¹ and larger are possible on 64-bit systems.

Versions

The version numbers of R consist of three numbers separated by periods. Fundamental changes are indicated by increasing the first number, normal changes by increasing the second number. The third number is increased with new versions that are mainly used to fix bugs . Since R version 2.14.0, each version has an additional code name . The naming is unsystematic. Some names have been chosen to reflect seasonal events, for example version 3.3.1 is called Bug in Your Hair .

Version number	Publication date	Code name	comment
0.49	April 23, 1997		Launch of CRAN, first public Unix version of R (package base )
0.60	December 5, 1997
0.61	December 21, 1997
0.61.1	11.1.1998
0.61.2	3/17/1998
0.61.3	2.5.1998
0.62	June 14, 1998
0.62.1	June 15, 1998
0.62.2	7/10/1998
0.62.3	8/28/1998
0.62.4	10/23/1998
0.63	11/13/1998
0.63.1	December 4, 1998
0.63.2	11.1.1999
0.63.3	5.3.1999
0.64.0	7.4.1999
0.64.1	7.5.1999
0.64.2	2.7.1999
0.65.0	August 27, 1999
0.65.1	10/6/1999		Possibility to install packages from R from CRAN
0.90.0	11/22/1999		The splines package has been added for smoothing regression splines and interpolating splines
0.90.1	12/15/1999
0.99.0	7.2.2000
1.0.0	29.2.2000		first version considered stable by the developers
1.0.1	April 14, 2000
1.1.0	June 15, 2000		Addition of the tcltk package in order to be able to access the Tk-Toolkit for the creation of graphical user interfaces
1.1.1	August 15, 2000
1.2.0	12/15/2000		Revised memory management system with generational garbage collector
1.2.1	January 15, 2001
1.2.2	02/26/2001
1.2.3	April 26, 2001		first version for macOS
1.3.0	June 22, 2001
1.3.1	August 31, 2001
1.4.0	12/19/2001		Addition of the packages methods , which provides S4 methods, and tools for package development and management
1.4.1	January 30, 2002
1.5.0	April 29, 2002
1.5.1	June 17, 2002
1.6.0	October 1, 2002
1.6.1	1.11.2002
1.6.2	10.1.2003
1.7.0	April 16, 2003
1.7.1	June 16, 2003
1.8.0	October 8, 2003		The grid package has been added for better control of the position of graphics in output files
1.8.1	11/21/2003
1.9.0	April 12, 2004		the packages graphics (graphics), stats (statistics functions) and utils (programming and developer tools) are separated from the package base (most important R functions); the previous packages ctest , eda , modreg , mva , nls , stepfun and ts will be moved to the newly created stats package , mle to the newly created stats4 package (statistical functions for S4 classes); lqs is only a recommended part of the standard library
1.9.1	June 21, 2004
2.0.0	October 4, 2004		Lazy loading support; the package grDevices is released from graphics to separate the data visualization step from the output file; Addition of the datasets package , which contains sample data sets
2.0.1	11/15/2004
2.1.0	April 18, 2005		Support of different character encodings, start of several language versions
2.1.1	June 20, 2005
2.2.0	10/6/2005
2.2.1	12/20/2005
2.3.0	April 24, 2006
2.3.1	1.6.2006
2.4.0	10/3/2006
2.4.1	12/18/2006
2.5.0	April 24, 2007
2.5.1	June 28, 2007
2.6.0	10/3/2007
2.6.1	11/26/2007
2.6.2	8.2.2008
2.7.0	April 22, 2008
2.7.1	June 23, 2008
2.7.2	August 25, 2008
2.8.0	10/20/2008
2.8.1	12/22/2008
2.9.0	April 17, 2009
2.9.1	6/26/2009
2.9.2	8/24/2009
2.10.0	10/26/2009
2.10.1	December 14, 2009
2.11.0	April 22, 2010		64-bit R.
2.11.1	May 31, 2010
2.12.0	10/15/2010		Addition of reference classes
2.12.1	12/16/2010
2.12.2	February 25, 2011
2.13.0	April 13, 2011		Addition of the compiler package , which provides a bytecode compiler
2.13.1	8.7.2011
2.13.2	30.9.2011
2.14.0	October 31, 2011	Great pumpkin	Adding the package in parallel , which allows coarse-grain concurrency
2.14.1	12/22/2011	December snowflakes
2.14.2	29.2.2012	Gift-Getting Season
2.15.0	March 30, 2012	Easter Beagle
2.15.1	June 22, 2012	Roasted marshmallows
2.15.2	10/26/2012	Trick or Treat
2.15.3	1.3.2013	Security blanket
3.0.0	3.4.2013	Masked Marvel	Support of index values greater than 2 ³¹
3.0.1	May 16, 2013	Good sport
3.0.2	25.9.2013	Frisbee sailing
3.0.3	6.3.2014	Warm Puppy
3.1.0	April 10, 2014	Spring dance
3.1.1	July 10, 2014	Sock it to Me
3.1.2	10/31/2014	Pumpkin helmet
3.1.3	9.3.2015	Smooth sidewalk
3.2.0	April 16, 2015	Full of ingredients
3.2.1	June 18, 2015	World-Famous Astronaut
3.2.2	August 14, 2015	Fire safety
3.2.3	12/10/2015	Wooden Christmas tree
3.2.4	3/10/2016	Very Secure Dishes
3.2.5	April 14, 2016	Very, Very Secure Dishes
3.3.0	3.5.2016	Supposedly Educational
3.3.1	June 21, 2016	Bug in Your Hair
3.3.2	10/31/2016	Sincere Pumpkin Patch
3.3.3	6.3.2017	Another canoe
3.4.0	April 21, 2017	You stupid darkness	The just-in-time byte code compiler is enabled by default
3.4.1	June 30, 2017	Single candle
3.4.2	28.9.2017	Short summer
3.4.3	11/30/2017	Kite-Eating Tree
3.4.4	03/15/2018	Someone to Lean On
3.5.0	04/23/2018	Joy in Playing	ALTREP Framework activated by default (alternative representation of R objects)
3.5.1	07/02/2018	Feather spray
3.5.2	December 20, 2018	Eggshell Igloo
3.5.3	March 26, 2019	Great Truth
3.6.0	04/26/2019	Planting of a Tree
4.0.0	April 24, 2020	Arbor Day

properties

R does not have to be compiled and executes user inputs in the command line console immediately after pressing the Enter key. Programs can also be run in scripts. In the following, the programming paradigms, syntax and data types are discussed as well as the file formats and functionalities of the standard version:

Programming paradigms

R is a fourth generation multi- paradigm language . The Canadian statistician John M. Chambers , who helped develop S, sums up how R works as follows:

“To understand computations in R, two slogans are helpful: Everything that exists is an object. Everything that happens is a function call. "

“To understand calculations in R, two sentences are helpful: Everything that exists is an object. Everything that happens is a function call. "

- John M. Chambers

The functional heart is inspired by Scheme and Haskell . Functions can be newly created as first-class objects , saved under a different name, overwritten (they are therefore not keywords ) and, in turn, passed to other functions ( closures ). It is possible to name functions or to declare them anonymously ( Lambda functions ). Some functions are designed to be executed with vector values; for recursion was not optimized. Many functions work differently depending on the input ( reflection ). Often there are necessary arguments for functions (e.g. data), usually further, optional arguments. Arguments can be defined depending on other arguments. Arguments are passed via deep copy . The names of the respective function parameters can be abbreviated when the function is called so that they are unique or they can be omitted, provided the order of the arguments corresponds to that of the function parameters. It is also possible to set standard values for function parameters when creating new functions . Also currying is possible. Unless otherwise specified, the last assigned object within a function is its return value . Overall, functions consist of the components arguments, bodies and surroundings. The environment in which it was created is critical for a function to function, not the one from which it is called. This lexical scoping is one of the characteristics of Scheme that was adopted in R and does not exist in the other S implementations. Newly created objects are in the environment in which they were created and each expression to be evaluated can be replaced by its result ( referential transparency ). R uses lazy evaluation , i.e. code is only evaluated when it is needed (call by need). R can therefore deal with futures , which also makes it possible to nest functions and create unlimited data structures. By default, calculations at the symbolic level are not part of R.

R also has properties that are typical of dynamic programming. Variables can flexibly change the structure. It is possible to get code for expressions that have not yet been evaluated. Text can be evaluated dynamically as code. Furthermore, the futures can be evaluated multiple times.

R implements the classes and multimethods for ad hoc polymorphism added in the fourth version of S , making object-oriented programming possible. As a class system with a different approach, the reference classes were added later ( see subsection Classes ).

R stores read data in main memory . The data storage is column-oriented. R uses garbage collection and lazy loading to reduce memory consumption. There is no aliasing .

syntax

R is case sensitive , so it distinguishes between upper and lower case. Objects can be created or overridden using the assignment operator ( <-and in many cases =). For object names are alphanumeric characters , dot and underscore allowed, but as the first character only letters or a point (in the latter case the object is hidden). The super-assignment operator ( <<-) has variables to the next higher ambient values, and the only way effects to achieve. Functions are used to change variables, relate them to one another, perform statistical analyzes and more. When the function name is called, there is an opening round bracket ; the closing parenthesis completes the function. Arguments, separated by commas , can be passed to the function within the brackets . The logical , mathematical, and assignment operators, as well as operators of the kind %...%(including all user-defined ones) are usually placed between two arguments instead of a prefix. A double colon such as in Paket::Funktion()accesses Funktionfrom Paketwithout the package being loaded or added to the search path . Functions that should be executed one after the other can be separated by semicolons or written on separate lines of code. Square brackets are used to index elements from data structures. The dollar sign ( $) facilitates - if available - indexing by name, so that content can be addressed associatively . Curly brackets define programming blocks. Text after double cross ( #) provides to end of line is a comment. Indentation and repeated space will not affect the execution of R code.

When control structures there are if, else if, elseand the element-wise ifelseas well switchas conditional statements and loops for (loop count and amount), while(head-driven) and repeat ... if (Bedingung) break(foot-controlled). Goto jump instructions are not part of R.

In the field of statistical models , dependencies are specified by formulas with the help of a tilde ( ~) in many procedures . The dependent variables are on the left and explanatory variables on the right of the tilde. Arithmetic operators have a different meaning in this context and stand for the inclusion and removal of further variables as well as the inclusion of interaction effects .

Data structures

The atomic data types in R are the empty set ( NULL), logical ( TRUEand FALSE), numeric (double) , integer and complex values as well as character strings (character) and raw for byte representation.

There are no scalars . The simplest data structure that occurs is the vector. A vector is defined by the three properties type, length and attributes. The elements of vectors (one-dimensional), matrices (one or two-dimensional) and arrays (any dimensional) must be elements of the same data type. Arithmetic operations are applied to all elements of these data structures. In assigning different data types into a vector, the data in the most flexible type of data to be converted . The first element of a vector has the index 1.

In addition to these homogeneous data structures, data frames are often used to represent data as a data set. Data frames are matrix-shaped, but can consist of columns of different data types. There are also lists . Lists contain data of any R structures and data types. Objects of different data structures can exist together in the working environment and can be used in analyzes at the same time. When data structures of different dimensions are linked with one another element by element, so-called recycling is usually used, whereby the shorter object is strung together until it reaches the length of the longer object.

Missing values are indicated in R as NA (Not Available) ; custom missing values cannot be specified. Undefined values are shown as NaN (Not a Number) .

Attributes provide metadata on R objects. Name, class (S3) and dimension are the most important attributes. In many cases they are retained when you modify objects; most of the other attributes are omitted. Custom attributes can be added.

Classes

R has four class systems. The base type was created in C and forms the basis for the other class systems. S3 classes have existed in R since the beginning, S4 classes were based on the classes of the fourth version of S, which was later developed, and reference classes were added last.

In the S3 class system, methods do not belong to objects or classes, but to functions; they therefore correspond to generic functions . The generic function decides which method is called. There is no formal description of a class. To assign an object to a class, it is sufficient to set the attribute of the class. A class that is often used is factor, whereby an integer vector is used for categorical variables by assigning values to the numbers.

The S4 class system is an extension of the S3 classes. The way it works is similar, so methods are part of functions. However, classes have formal definitions that describe the fields and inheritance structures ( base class ). An S4 class consists of three properties: the name to identify the class, a list of fields to define names of the fields and permitted classes, and a character string with the class from which it is derived . Multiple inheritance is possible with S4 classes . There are auxiliary functions to define methods and generic functions. The S4 class system implements multi-methods so that generic functions can select methods based on the classes of multiple arguments. The at symbol ( @) can be used to extract fields from an S4 object. Object and method are separated by a period in the function call. New classes can be created or existing ones can be redefined.

In the system of reference classes, methods belong to classes and not to functions. Methods are sent to objects ( message exchange ) and the object decides which function is to be called. Objects are mutable and behave similarly to objects in the object-oriented languages Python, Ruby, Java and C #. Object and method are separated by the dollar sign in the function call.

File formats

R code is stored in text files, which usually have the file extension .R . The .RData format (or .Rda for short ) is used to save an R object, for example a data record, or a collection of R objects, i.e. data and functions, in the R-internal binary serialized format, these files also being standard - are compressed . The entire working environment can also be saved as an .RData file. The commands last used in the command line are also saved as .Rhistory . Individual objects can be saved as .Rds .

In R, data is often imported and exported through CSV files and text files . With the help of packages ( see section Packages ) numerous other file formats (e.g. from Microsoft Excel and other statistical software) can be imported and exported. Data can also be pasted from the clipboard .

For creating graphics can devices be opened by which output is written instead of the console in files. The graphic formats JPEG , PNG , SVG , TIFF , Windows Bitmap and Metafile as well as Cairo graphics are supported. In addition, the formats PDF , PostScript and Encapsulated PostScript can be created and the Quartz graphics layer common in macOS as well as the Unix-typical X Window System and Xfig can be addressed.

Basic functions

The range of functions of an R installation includes the management of files including downloading , unpacking and importing. It also includes functions for creating, checking and converting data structures. There are numerous functions for data management , using regular expressions or printf to manipulate and format strings. Loops, conditional statements and branches are included as well as functions of the apply and MapReduce families ( higher-order functions ) that can be used alternatively.

Numerous functions of descriptive statistics are implemented and some analysis functions. This includes linear and generalized linear models as well as analysis of variance . In the area of time series analysis , this includes ARMA models , interpolation and smoothing methods (such as exponential smoothing , Kalman filters and Fourier transformation ) and decomposition. Also, principal component and factor analysis , the multidimensional scaling and methods of cluster analysis ( hierarchical and K-means ) are enumerated. Some statistical tests and probability distributions with density , distribution function , quantile function and random numbers are part of the standard scope . Several mathematical functions are also included, such as special functions , trigonometric functions , set operations , matrix operations and optimization algorithms .

In addition, there are sample data sets in R. There are numerous functions for creating graphics and additional graphic elements including LaTeX- like expressions for mathematical labeling symbols . R uses the hexadecimal system to represent colors and contains 657 colors already specified in words.

There are also functions for calling up metadata, for package management , for debugging and profiling, and for changing settings.

To use R as a scripting language for automated analysis, R code can be written to a text file and executed on Windows either with Rscript <Dateiname>or R CMD BATCH <Dateiname>as an instruction in a BAT file . On Unix systems, R-code can be made executable with chmod , and if Rscript is installed, the code can be executed like any other script. Thus, recurring tasks can be created with the help of the Windows task scheduler or with Cron . The program below writes on the command line ( see also Hello World program ); the first line of the program is the so-called shebang line: "Hello World!"

#! /usr/bin/Rscript

# mein erstes R-Skript:
writeLines("Hello World!")

In R there is a bytecode compiler and the basic installation of R contains the command system(), with which commands are passed to the operating system . This means that any existing programs and scripts can be started with the transfer of command line parameters and the return value can be saved in a variable. The commands .C()and .Fortran()are also available to integrate programs that have already been compiled in C and Fortran . In this way, many computationally intensive subroutines are outsourced to more suitable programming languages, while the statistical methods are implemented in R. Statisticians who evaluate their data in R can quickly develop new methods, while programmers optimize them later if necessary.

Packages

The standard library of R consists of 29 packages ( program libraries ) in which functions on similar topics are bundled. These packages are included in R's downloadable distributions. The 14 most important packages with the above functions are loaded each time the program is started; they are updated along with R itself. The other 15 packages are recommended.

The range of functions can be expanded with a large number of additional packages and adapted to specific statistical problems from various application areas. Many packages can be selected directly from a list that can be called up via the R console and installed automatically. The central archive for these packages is the Comprehensive R Archive Network (CRAN) with the main server at the Vienna University of Economics and Business and numerous mirror servers . Bioconductor is another collection of R-packages with extensions from bioinformatics , in particular the analysis of gene expression data . There are over 10,000 packages on CRAN and 1294 packages on Bioconductor.

Under the heading Task Views , CRAN contains a list of 33 subject areas for which a commented description of the packages relevant to the subject area is available. These are Bayesian statistics , chemometrics and computer physics , clinical studies , cluster analysis , differential equations , probability distributions , econometrics , mathematical description in the environmental field, statistical experimental planning , finance , genetics , graphics, high-performance computing and parallel computing , machine learning , medical imaging , meta-analysis , multivariate methods , computational linguistics , numerical analysis , official statistics and survey , optimization , pharmacokinetics , phylogeny , psychometrics , reproducible research, robust estimation , social science , geostatistics , geostatistics, taking into account the time survival analysis , time series analysis , web services and technologies and probabilistic graphical models . The following is an overview of important packages that link R to other software or that have been downloaded frequently.

Interfaces

... to other software and their file formats

The foreign package allows data sets from the other statistical programs SPSS , SAS (partially), Stata , SYSTAT , Minitab , Epi Info , GNU Octave and Weka to be read in, analyzed and saved in the respective formats. translateSPSS2R supports the translation of SPSS code according to R. sas7bdat enables reading of SAS files, R.matlab that of Matlab files. In addition, this allows Matlab to be controlled via R. RcppOctave also offers something similar for GNU Octave. Machine learning software such as Weka and H2O can be integrated by RWeka and h2o . The TensorFlow ( tensorflow ) and Caffe ( caffeR ) libraries for deep learning can be used with R. The programs OpenBUGS ( R2OpenBUGS ), Stan ( rstan ) and JAGS ( rjags ) for Bayesian statistics and Gurobi ( gurobi ) for mathematical optimization can be integrated via packages. With hexView , EViews files can be read in. With readxl to Microsoft Excel files are read, with gnumeric OpenDocuments . In the field of markup languages for data serialization are available for XML package files XML and yaml for YAML files. The Chemistry Development Kit ( chemoinformatics ), which uses the Chemical Markup Language , can be accessed with rcdk . For scientific data, the formats NetCDF and the hierarchical data format with RNetCDF and rhdf5 can be imported. Astronomical data from the Flexible Image Transport System can also be loaded into R - with FITSio . R-ArcGIS , geosapi , RSAGA , RQGIS and rgrass7 offer interfaces to the geographic information systems ArcGIS , GeoServer , SAGA , QGIS and GRASS GIS , aRT to TerraLib and rgdal to the Geospatial Data Abstraction Library for raster data . Shapefiles and files of the Keyhole Markup Language from Google Earth can be imported with maptools . With tuneR MP3 files and wave sounds can be read into R, audio enables the acoustic playback of these audio files with the help of the media player .

... to databases

Several packages provide interfaces to access databases. This generally includes the front-end DBI together with RODBC ( ODBC ) or Java-based RJDBC ( JDBC ). For relational databases there are also RMySQL ( MySQL and MariaDB ), RSQLite ( SQLite ) and teradataR ( Teradata Aster ). With regard to object-relational databases, there are the packages RSQLServer (for Microsoft SQL Server ), ibmdbr ( Db2 ), RPostgreSQL ( PostgreSQL ) and ROracle for Oracle databases . The r-exasol package enables the connection to the relational in-memory database from EXASOL , MonetDB.R to the column-oriented database MonetDB . For NoSQL and key value databases there are RCassandra ( Apache Cassandra ) and rredis ( Redis ), RNeo4j for the graph database Neo4j and RCouchDB , rfml and mongolite for the document-oriented databases CouchDB , MarkLogic and MongoDB .

SparkR integrates R into the big data framework Apache Spark , which is based on in-memory processing, and rkafka obtains message protocols from Apache Kafka . RImpala uses Apache Impala for fast interactive SQL queries and elastic uses Elasticsearch . SQL -like queries can be used within R with sqldf .

... to other programming languages

Various packages offer interfaces to other programming languages that are used to optimize performance and expand the range of functions. The Rcpp package is mainly used for this , which enables, for example, the use of C ++ functions in external source code files or in R itself, with the functions being recompiled each time the program is run. rJava provides an interface to Java , rscala for Scala and rPython for Python . The command line interpreter IPython ( Jupyter ) can be used with IRkernel . With rocker , R can be isolated in virtual containers as part of Docker .

... to web services

With rvest , websites can be scraped to make their HTML content usable in R. The way it works is modeled on Python's Beautiful Soup . Various other packages are tailored directly to specific websites and offer more convenience there. The twitteR package allows access to posts on Twitter , Rfacebook accesses the Facebook API . With googleVis can Google Charts are used with RGoogleAnalytics Google Analytics . Osmar offers an interface to OpenStreetMap . This Wikipedia article and other pages of related projects can be loaded into R as HTML or Wikitext with WikipediR .

With RSelenium can Selenium -WebDriver be integrated and thus by R of a web browser can be controlled. The mailR package allows you to send e-mails from R.

Reporting

For reporting purposes, R-Code can be integrated in LaTeX ( knitr , Sweave ) or HTML or Markdown ( knitr , rmarkdown ). knitr also prepares R-Code for the other markup languages AsciiDoc and reStructuredText and offers a connection to Pandoc . xtable allows tables to be designed with R data and supplies their LaTeX and HTML code. texreg and stargazer present the results of different models in a table and support different output formats (text, HTML, LaTeX). The ReporteRs package can be used to create vector graphics, among other things, which can also be subsequently edited in Microsoft Word and PowerPoint . tikzDevice creates the code for the PGF / Ti k Z graphics that are often used in LaTeX . jsonlite enables data frames to be saved as JSON objects.

Graphic creation

The lattice package implements the idea of trellis graphics for the visualization of multivariate data. ggplot2 also makes it possible to create complex graphics more quickly through greater abstraction. This package is an implementation of Leland Wilkinson's Grammar of Graphics . With ggvis and plotly (based on ggplot2 ) and shiny you can create interactive, web-based graphics. rgl is suitable for interactive three-dimensional graphics. When creating graphics, the packages also support scales (assignment of data to aesthetic elements) and labeling (further axis labeling options). The Graph Modeling Language and its application in graphics for network analysis is implemented by igraph . The Graphviz can also be accessed for graph visualization with Rgraphviz . Turtle graphics can be implemented with TurtleGraphics . Animated concepts from statistics in the animation package , which also provides functions to implement your own animations in R , are also used for illustration .

With the munsell package , the Munsell color system can be used; with the help of colorspace , color assignments can be implemented within a large number of color systems . The RColorBrewer can produce a range of colors according to user requirements, for example for coloring of maps .

Data management

In the area of data management , plyr simplifies the processing of lists, dplyr that of data frames, tidyr the transformation of data frames ( wide format and long format ), stringi and stringr the processing of character strings, lubridate the editing of dates and times and zoo dealing with time series. data.table is a more efficient version with an extended range of functions instead of data frames. A Message Passing Interface for the exchange of messages in parallel calculations on distributed computer systems can be implemented either by master / slave ( Rmpi ) or by SPMD ( pbdMPI ). The CUDA technology from Nvidia , with the aid of gputools be realized. With digest , various cryptological hash functions can be applied to R objects.

Developer tools

With devtools you can create, install and check your own packages. roxygen2 supports your documentation. RUnit ( xUnit ) and testthat enable automated software tests . The sos package allows you to search R and R packages for functions. installr updates R and other software (on Windows). With the pipe operator ( %>%) from the package magrittr , R functions can be executed sequentially instead of nested inside each other in order to achieve better code readability.

user interface

The R installation includes RGui , an interface in which R runs in a kind of command line environment . A few menu commands provide access to help, package management, operations related to the work environment, and the ability to create and run script files.

External user interfaces

Several graphical user interfaces and integrated development environments offer further possibilities when working with R. These include RStudio (also available as a version for Linux servers), Visual Studio Code from Microsoft, the Java-based user interface JGR ( Jaguar , Java GUI for R ), RKWard , R AnalyticFlow , the mathematics software Cantor , the cloud-based Number Analytics for beginners as well as StatET ( Eclipse ) and the architect based on it . They are mainly characterized by autocompletion , automatic indentation, syntax highlighting , code folding , integrated help, information about objects in the working environment and data viewers or editors. Extended development options such as version management with Git or graphical debugging are partially included.

User interfaces in packages

Two extensive graphical user interfaces that are provided as packages in R are the R-Commander (package name: Rcmdr ) and relax . For both, some important procedures of exploratory and analytical statistics can be called up via a menu system. Standard graphics can also be generated via the menu. The R-Commander is written independently of the operating system and facilitates data management and the writing of scripts. relax is specially designed to integrate the data analysis and documentation of the results in a document in the style of literate programming (compare Sweave).

There is also the rattle package , which is a graphical user interface that provides an introduction to data mining projects. RQDA is a graphical user interface for qualitative data analysis , statnet for network analysis. The Deducer is particularly suitable for processing data frames. Another package is pmg.

Graphical user interfaces like these can be created with Tk , GTK + (with the help of the RGtk2 package ) or Qt ( qtbase ).

Editors

The editors Notepad ++ , Bluefish , CodeMirror , Emacs / Aquamacs , Geany , gedit , jEdit , Kate , SciTE , Smultron , Sublime Text , TextMate , TextPad , Tinn , Vim , WinEdt and TextWrangler as well as SubEthaEdit support R either natively or with the help of appropriate extensions.

Word processing tools

The word processing systems GNU TeXmacs , LyX (with Sweave or knitr ) and ShareLaTeX (knitr) integrate R, as do the software documentation tool Natural Docs and Travis CI for continuous integration . The note software Org-mode and Zim use R for graphics and enable interactive editing.

Integrations

Alternative open source interpreter

Several alternative interpreters were developed, for example to make R more powerful and to integrate it better into existing software. pqR is a faster R interpreter and a spin-off from GNU R. pqr is suitable for parallel programming , as several processor cores can be used automatically . The interpreter is written in C and can only be run under Linux; many R (CRAN) packets work with pqR, but not all.

The Renjin interpreter is based on the Java Virtual Machine and is characterized by implicit concurrency, just-in-time compilation of bytecode and a better implementation of Java. Garbage collection takes place in parallel. It is possible to store code with platform-as-a-service providers such as Google App Engine , Amazon Beanstalk or Salesforce Heroku . Renjin is supported by BeDataDriven .

FastR is a Java-based interpreter that was built on top of the Truffle interpreter and the Graal -byte compiler. It was created in collaboration between Oracle Labs, Purdue University and the University of Linz and enables concurrency.

Riposte is a faster interpreter rewritten in C ++, supported by Tableau for Linux, which also uses just-in-time compilation of Bitecode. The lazy evaluation of R has been revised so that fewer internal variables are created in intermediate steps. Riposte enables implicit concurrency with multiple cores and uses streaming SIMD extensions and advanced vector extensions from processors.

Another interpreter is CXXR from the University of Kent with the support of Google, which makes modifications to the R interpreter as a fork written in C ++. The reason for the development was the lack of an S-PLUS function in R, which makes it possible to look at the code that led to the creation of a certain object. The documentation has also been improved.

Integration in business platforms

Revolution Analytics created the Revolution R analysis platform , which offers R functions together with components developed in-house. In April 2015, Microsoft completed the purchase of Revolution Analytics . In addition to the Windows version, Revolution R Enterprise now runs as a Microsoft R Server ; This applies to ported versions for Hadoop ( Hortonworks , Cloudera , MapR ) for the Teradata database, for Red Hat Linux , SUSE Linux Enterprise Server , Apache Spark, the cloud computing platform Microsoft Azure and the SQL server. The Microsoft R server consists of the free component Microsoft R Open as well as Distributed R (normalization, porting), ScaleR (interpreter with big data R functions), ConnectR (interfaces), DevelopR (development environment) and DeployR for web services. Microsoft R Open introduces first performance improvements by replacing BLAS and LAPACK with the Intel Math Kernel Library . ScaleR is an optimized interpreter and contains numerous R functions that are particularly suitable for big data analyzes and Rxbegin with the prefix . This includes reading data in data blocks , executing scripts in the computer network instead of just local, and statistical analysis functions that were rewritten in C ++ and can be used in parallel. ConnectR provides interfaces to other file formats and databases (text files, SQL servers, ODBC databases, SAS, SPSS, Teradata) for its own .xdf data format. .xdf does not need a parser , is about five times smaller than a .csv file and data is only read in when it is needed. When the data is saved on the hard drive, the limitation by the internal memory no longer applies. DevelopR provides a fast, interactive development environment based on Visual Studio or RStudio for Linux users. DeployR brings an interface for web applications to R-code, embedded with tools for authentication , information security , monitoring , resource management , a session manager and a REST API for JSON and XML. Web applications can be written as clients in Java, JavaScript / Node.js and .NET . The web services are administered via an Apache Tomcat server, the database with the help of MongoDB . R has been integrated into the Microsoft SQL server since 2016. The analysis software Predixion Insight from Predixion Software as part of Microsoft Business Intelligence previously connected R to the SQL server and other big data technologies.

With TIBCO Enterprise Runtime for R (TERR), TIBCO Spotfire Analytics has an analysis platform that includes an R interpreter newly written in C ++. Each data type is represented as an abstract C ++ class; a native C ++ interface is also a part. TIBCO also provides the commercial dialect of S (S-PLUS) and StreamBase, a platform for complex event processing with R integration.

In October 2011 Oracle announced the Big Data Appliance , which links R, Apache Hadoop , Oracle Linux , and a NoSQL database with hardware from Exadata . The most important R component is Oracle R Enterprise (ORE) , whereby R objects are analyzed directly in the Oracle database, which increases efficiency. There is an implicit translation from R to SQL. ORE and Oracle Data Mining (ODM) , which contains its own R functions for data mining, form the Oracle Advanced Analytics Option .

IBM offers an integration of R into its own InfoSphere BigInsights , which includes Hadoop as a Service including HBase and Hive and can be connected to some databases and web services. The R component is called Big R . Analyzes are also possible with the Spectrum Symphony using R. The subsidiary Netezza integrates R into the main product, the in-memory database TwinFin for fast analysis of large amounts of data.

SAP enables an R connection via the in-memory database HANA . In addition, the analysis options for stock market data in the RAP software have been improved at the subsidiary Sybase with the help of R.

Hewlett-Packard developed Distributed R , which implements R functions for analyzes on large amounts of data on the basis of distributed computing . Distributed R , together with the Vertica database, is part of the HP Haven Predictive Analytics software .

MicroStrategy enables integration of R with the R Integration Pack , Information Builders with WebFOCUS . In the Dundas BI from Dundas Data Visualization R can be integrated well in Tableau and QlikView . Zementis models data mining questions uniformly with the Predictive Model Markup Language , executes them in R and transfers them to its own products ADAPA and UPPI . Techila integrates R for application programming with distributed computing. Within the icCube server, R can be used for online analytical processing . With R integration , MonetDB enables the integration of R.

Integration in software

Most major software packages with a focus on statistics or mathematics provide interfaces to R or offer integration. These are SAS and JMP , SPSS, MATLAB, Maple , Sage , Mathematica , Statistica , gretl , Showgun , RapidMiner , KNIME as well as Mondrian , ASReml and the WPS . The R plug-in RExcel is available for Excel .

In addition, the geographic information system ArcGIS from ESRI , AFNI ( neurosciences ), Bioclipse ( life sciences ), GenGIS (bioinformatics), Bio7 ( ecological modeling and image analysis ), INVEP (insolvency administration) and Compass from Cytel (for clinical studies) offer the integration of R.

Integration into other programming languages and program libraries

Numerous scripting languages can access the functionality of R. These include Python ( rpy2 ), Julia (including RCall ), Perl ( Statistics :: R ), Ruby ( rsruby ) and F # ( RProvider ).

With PL / R , R can be used within a PostgreSQL database for server-side programming, which is what the Rasdaman database does for array or raster data , for example .

SWIG makes modules written in C and C ++ available to R. MicroAPL integrates with APLX R in APL . The Python tools for package management ( Conda from Continuum Analytics ) and automation ( Dexy , with the R filter ) use R.

Integration as a script language in server environments

rApache enables the development of web applications for R based on the Apache HTTP Server ( Server Side Scripting ). Other web offers include Rserve as a binary server and Rwui for Java web servers . R can be used as a scripting language in the LabKey server (for biomedicine ).

Support from foundations

R Foundation

The nonprofit R Foundation for Statistical Computing owns and manages R's copyright and documentation. One goal is to promote the spread of R as an open source language. In addition, their role serves to communicate with the press and organizations interested in R. The R Foundation is financed through membership fees and donations.

The magazine The Journal R is twice a year in June and December by the R Foundation accessible issued as a PDF file. It provides information about news in the R world, changes in new R versions, new packages as well as user tips and tutorials. Before the first issue in June 2009 there was the R News .

Every year in the conference useR! instead, which is aimed at R users. The first of these events was useR! 2004 in May 2004 in Vienna. After skipping 2005, the conference was held annually in different locations:

year	city	country	Attendees	Website
2004	Vienna	Austria Austria	194	useR! 2004
2006	Vienna	Austria Austria	334	useR! 2006
2007	Ames , Iowa	United States United States	unb.	useR! 2007
2008	Dortmund	Germany Germany	387	useR! 2008
2009	Rennes	France France	463	useR! 2009
2010	Gaithersburg , Maryland	United States United States	465	useR! 2010
2011	Coventry	United Kingdom United Kingdom	430	useR! 2011
2012	Nashville , Tennessee	United States United States	469	useR! 2012
2013	Albacete	Spain Spain	328	useR! 2013
2014	Los Angeles , California	United States United States	604	useR! 2014
2015	Aalborg	Denmark Denmark	660	useR! 2015
2016	Stanford , California	United States United States	900	useR! 2016
2017	Brussels	Belgium Belgium	1,200	useR! 2017
2018	Brisbane	Australia Australia	almost 600	useR! 2018
2019	Toulouse	France France	1,178	useR! 2019

The next conference is scheduled for July 2020 in St. Louis .

In addition to organizing these and other conferences, the R Foundation introduces R on appropriate occasions and supports research projects related to R.

Mailing lists have been set up and are open to users with questions about R. Answers to questions are often swift, sometimes by members of the R core team .

R Consortium

Several larger companies that use R or base their business model on it joined forces in 2015 to form the R Consortium as part of the Linux Foundation . The aim is in particular to improve the business infrastructure so that R can be used more comfortably in the corporate environment. In addition to the R Foundation, the founding members of the R Consortium include Microsoft, RStudio, Tibco, alteryx , Google, Hewlett-Packard, Ketchum Trading , Mango Solutions and Oracle. The Gordon and Betty Moore Foundation and other companies such as IBM and ESRI later joined. In order to be able to better integrate R into company processes, specific projects should be promoted. The first funded project is R-Hub , which aims to simplify the process of creating and testing R packages.

reception

R is the most comprehensive tool for statistical analysis, both in terms of the methods already implemented and in terms of the potential that the language offers for further statistical questions. R was designed by statisticians for statistical questions and thus directly addresses the needs that are required for such purposes (a few lines of code are necessary for complex statistical problems). The code of the statistical methods is openly visible and has already been viewed and improved by many studied statisticians with experience in the application; in addition, R is validated for medical purposes by the Food and Drug Administration . R is under a free license and is open source and can therefore be easily adapted to individual preferences and expanded using your own methods. In addition, R does not cost a license fee and can be used on different operating systems. The graphics can be adapted very flexibly to user-defined requirements (for example using mathematical symbols). The data structures also allow flexibility. The range of functions of R is constantly being expanded through numerous packages; because of the uncomplicated procedure, many new statistical methods are implemented in R first. Numerous functions and packages link R with other software and thus enable the import and export of many file formats . Other programming languages and databases can also be integrated. R there are now extensive literature and documentation . R has active user groups to help each other with problems and a large presence on portals such as Stack Overflow and GitHub . Employees with good R skills who participated in the Dice Tech Salary Survey (2013) had a higher average income than employees with other IT skills.

A complete graphical user interface as it exists in other statistics programs is not available in R. The language therefore requires some programming skills in order to be able to use it, which means that initial results are slower to emerge. In addition, R requires a greater degree of statistical understanding to be meaningful. The documentation of the R commands is sometimes short and / or inconsistent. The names of functions and arguments in the language itself (especially in packages) only follow a few conventions. There is no extensive quality assurance of the content of new packages. In the event of malfunctions, no one can be held accountable or is responsible for rapid improvement. Since R aims at the needs of statisticians and many methods are implemented by them (and not by programmers), performance optimization plays only a subordinate role in R, which is why other programming languages are often faster and are sometimes used for optimization. R is based on programming languages and concepts dating back several decades. The developers at Julia have set themselves the goal of being able to use the language for data analysis just as easily and as easily as R and want to equip the language with high speed.

example

As a simple example, the correlation coefficient of two data series is calculated:

# Groesse wird als numerischer Vektor
# durch den Zuweisungsoperator "<-" definiert:
Groesse <- c(176, 166, 172, 184, 179, 170, 176)

# Gewicht wird als numerischer Vektor definiert:
Gewicht <- c(65, 55, 67, 82, 75, 65, 75)

# Berechnung des Korrelationskoeffizienten nach Pearson mit der Funktion "cor":
cor(Gewicht, Groesse, method = "pearson")

The result is 0.9295038.

Graphic output of the example

A linear regression can be carried out as a further analysis . This can be done in R by the function lm , where the dependent variable is separated from the independent variables by the tilde. The summary function outputs the coefficients of the regression and other statistics:

# Lineare Regression mit Gewicht als Zielvariable
# Ergebnis wird als reg gespeichert:
reg <- lm(Gewicht~Groesse)

# Ausgabe der Ergebnisse der obigen linearen Regression:
summary(reg)

Diagrams are easy to create:

# Streudiagramm der Daten:
plot(Gewicht~Groesse)

# Regressionsgerade hinzufügen:
abline(reg)

literature

Ross Ihaka, Robert Gentleman: R: A Language for Data Analysis and Graphics . In: Journal of Computational and Graphical Statistics . tape 5 , no. 3 . American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America, Alexandria 1996, pp. 299-314 . Presentation of the programming language R as a scientific paper (online: R: A Language for Data Analysis and Graphics. (PDF, 1.7 MB) Retrieved on July 29, 2015 . )
Uwe Ligges: Programming with R . 4th edition. Springer, Heidelberg 2016, ISBN 978-3-642-37602-3 , doi : 10.1007 / 978-3-540-79998-6 ( material - explanation of the most important part of how R works).
Lothar Sachs , Jürgen Hedderich: Applied statistics. Toolkit with R . 16th edition. Springer, Berlin 2018, ISBN 978-3-662-56656-5 , doi : 10.1007 / 978-3-662-56657-2 (Comprehensive textbook on statistical methods with R).
Hadley Wickham: R Packages . O'Reilly, Sebastopol 2015, ISBN 978-1-4919-1059-7 ( online - package creation with R).
Hadley Wickham: Advanced R . Chapman & Hall / CRC, Boca Raton 2014, ISBN 978-1-4665-8696-3 ( online - detailed explanation of how R works).
Michael J. Crawley: The R Book . 2nd Edition. John Wiley & Sons, Chichester 2012, ISBN 978-0-470-97392-9 ( Material - Comprehensive work that introduces the implementation of numerous statistical methods with R).

Further literature

Many works can be classified into different categories. As a rule, a book was classified in a category of the application area, provided a target group was clearly highlighted, in the next step the category with a specific statistical method, otherwise general categories.

introduction

Tilman M. Davies: The Book of R. A First Course in Programming and Statistics . No Starch Press, San Francisco 2016, ISBN 978-1-59327-651-5 .
Emilio López Cano, Javier Martínez Moguerza: R desde el principio . Curso de cero R . Ediciones del Orto, Madrid 2015, ISBN 978-84-7923-526-0 ( material ).
Andrie de Vries, Joris Meys: R for Dummies . 2nd Edition. John Wiley & Sons, Chichester 2015, ISBN 978-1-119-05580-8 .
Gundula Wagner, Christa Monika Reisinger: Every beginning is easy. Data analysis with the R Commander . Facultas, Vienna 2015, ISBN 978-3-7089-1276-9 .
Michael J. Crawley: Statistics. An Introduction Using R . 2nd Edition. John Wiley & Sons, Chichester 2014, ISBN 978-1-118-94109-6 ( material ).
Kurt Taylor Gaubatz: A Survivor's Guide to R. An Introduction for the Uninitiated and the Unnerved . Sage, Thousand Oaks 2014, ISBN 978-1-4833-4673-1 ( materials ).
Reinhold Hatzinger, Kurt Hornik, Herbert Nagel, Marco Maier: R. Introduction through applied statistics . 2nd Edition. Pearson, Munich 2014, ISBN 978-3-86894-250-7 .
Manas A. Pathak: Beginning with data science R . Springer, 2014, ISBN 978-3-319-12065-2 .
Margot Tollefson: R Quick Syntax Reference . A Quick, Handy Guide to Using R . Apress, New York 2014, ISBN 978-1-4302-6640-2 .
Sarah Stowell: Using R for Statistics . Apress, New York 2014, ISBN 978-1-4842-0140-4 .
John Verzani: Using R for Introductory Statistics . 2nd Edition. Chapman & Hall / CRC, Boca Raton 2014, ISBN 978-1-4665-9073-1 ( material ).
Richard Cotton: Learning R . O'Reilly, Sebastopol 2013, ISBN 978-1-4493-5710-8 .
Mark Gardener: Beginning R . The Statistical Programming Language . Wrox, Birmingham 2012, ISBN 978-1-118-16430-3 ( material ).
Mark Gardener: The Essential R Reference . John Wiley & Sons, Chichester 2012, ISBN 978-1-118-39141-9 ( material ).
Sarah Stowell: Instant R . An Introduction to R for Statistical Analysis . Jotunheim Publishing, 2013, ISBN 978-0-9574649-0-2 ( material ).
Daniel Wollschläger: Fundamentals of Data Analysis with R . An application-oriented introduction . 3. Edition. Springer, Berlin 2014, ISBN 978-3-662-45506-7 ( material ).
Daniel Wollschläger: R compact . The quick introduction to data analysis . Springer, Berlin 2013, ISBN 978-3-642-40310-1 ( material ).
Jim Albert, Maria Rizzo: R by Example . Springer, New York 2012, ISBN 978-1-4614-1364-6 ( material ).
Brian Dennis: The R Student Companion . Chapman & Hall / CRC, Boca Raton 2012, ISBN 978-1-4398-7540-7 .
Claus Thorn Ekstrom: The R Primer . Chapman & Hall / CRC, Boca Raton 2011, ISBN 978-1-4398-6206-3 ( material ).
Brian Everitt, Torsten Hothorn: An Introduction to Applied Multivariate Analysis with R . Springer, New York 2011, ISBN 978-1-4419-9649-7 .
Paul Teetor: 25 Recipes for Getting Started with R . O'Reilly, Sebastopol 2011, ISBN 978-1-4493-0323-5 .
Andreas Behr, Ulrich Pötter: Introduction to Statistics with R . 2nd Edition. Vahlen, Munich 2010, ISBN 978-3-8006-3599-3 .
Gunter Faes: Introduction to R . A Cookbook for statistical data analysis with R . Books on Demand, Hamburg 2010, ISBN 978-3-8334-9184-9 .
Jürgen Large: Basic Statistics with R . An application-oriented introduction to the use of statistical software R . Vieweg + Teubner Verlag, Wiesbaden 2010, ISBN 978-3-8348-1039-7 .
John M. Quick: Statistical Analysis with R . Beginner's Guide . Packt Publishing, Birmingham 2010, ISBN 978-1-84951-208-4 .
Thomas P. Hogan: bare-bones R. A Brief Introduction Guide . Sage, Thousand Oaks 2009, ISBN 978-1-4129-8041-8 .
Günther Sawitzki: Computational Statistics. An Introduction to R . Chapman & Hall / CRC, Boca Raton 2009, ISBN 978-1-4200-8678-2 ( material ).
Alain F. Zuur, Elena N. Ieno, Erik Meesters: A Beginner's Guide to R . Springer, New York 2009, ISBN 978-0-387-93836-3 ( material ).
Peter Dalgaard: Introductory Statistics with R . 2nd Edition. Springer, New York 2008, ISBN 978-0-387-79053-4 ( material ).

Cross-topic statistics

Andy Nicholis, Richard Pugh, Aimee Gott: R in 24 Hours . Sams, Carmel 2016, ISBN 978-0-672-33848-9 .
Gergely Daróczi: Mastering Data Analysis with R . Packt Publishing, Birmingham 2015, ISBN 978-1-78398-202-8 .
Elena N. Ieno, Alain F. Zuur: A Beginner's Guide to Data Exploration and Visualization with R . Highland Statistics, Newburgh 2015, ISBN 978-0-9571741-7-7 ( material ).
Robert I. Kabacoff: R in Action. Data Analysis and Graphics with R . 2nd Edition. Manning, Shelter Island 2015, ISBN 978-1-61729-138-8 ( material ).
Nicholas J. Horton, Ken Kleinman: Using R and R Studio for Data Management, Statistical Analysis and Graphics . 2nd Edition. Chapman & Hall / CRC, Boca Raton 2015, ISBN 978-1-4822-3736-8 ( material ).
Deborah Nolan, Duncan Temple Lang: Data Science in R. A Case Studies Approach to Computational Reasoning and Problem Solving . Chapman & Hall / CRC, Boca Raton 2015, ISBN 978-1-4822-3481-7 .
Nicole M. Radziwill: Statistics (The Easier Way) with R. An Informal Text on Applied Statistics . Lapis Lucera, Harrisonburg 2015, ISBN 978-0-692-33942-8 .
Randall Ernest Schumacker: Using R with Multivariate Statistics . Sage, Thousand Oaks 2015, ISBN 978-1-4833-7796-4 .
María Dolores Ugarte, Ana F. Militino, Alan T. Arnholt: Probability and Statistics with R . 2nd Edition. Chapman & Hall / CRC, Boca Raton 2015, ISBN 978-1-4665-0439-4 ( material ).
Viswa Viswanathan: R Data Analysis Cookbook . Packt Publishing, Birmingham 2015, ISBN 978-1-78398-906-5 .
Lise Bellanger, Richard Tomassone: Exploration de données et méthodes statistiques. Data analysis and data mining Avec le logiciel R . Éditions Ellipses, Paris 2014, ISBN 978-2-7298-8486-4 ( material ).
Michael Falk et al. a .: Statistics in theory and practice. With Applications in R . Springer, Berlin 2014, ISBN 978-3-642-55252-6 .
Torsten Hothorn, Brian S. Everitt: A Handbook of Statistical Analyzes Using R . 3. Edition. Chapman & Hall / CRC, Boca Raton 2014, ISBN 978-1-4822-0458-2 .
Jay Jacobs, Bob Rudis: Data Driven Security. Analysis, visualization and dashboards . John Wiley & Sons, Chichester 2014, ISBN 978-1-118-79372-5 .
Pierre Lafaye de Micheaux, Rémy Drouilhet, Benoit Liquet: The R Software. Fundamentals of Programming and Statistical Analysis . Springer, New York 2014, ISBN 978-1-4614-9019-7 .
Kandethody M. Ramachandran, Chris P. Tsokos: Mathematical Statistics with Applications in R . 2nd Edition. Academic Press, Waltham 2014, ISBN 978-0-12-417113-8 .
Dan Toomey: R for Data Science . Packt Publishing, Birmingham 2014, ISBN 978-1-78439-086-0 .
Nina Zumel, John Mount: Practical Data Science with R . Manning, Shelter Island 2014, ISBN 978-1-61729-156-2 ( material ).
Rainer W. Alexandrowicz: R in 10 steps . UTB, Vienna 2013, ISBN 978-3-8252-8484-8 .
Dennis D. Boos, Len A. Stefanski: Essential Statistical Inference. Theory and Methods . Springer, New York 2013, ISBN 978-1-4614-4817-4 ( material ).
Carsten Dormann: Parametric Statistics. Distributions, maximum likelihood and GLM in R . Springer, Berlin 2013, ISBN 978-3-642-34785-6 .
Joseph Hilbe, Andrew P. Robinson: Methods of Statistical Model Estimation . Chapman & Hall / CRC, Boca Raton 2013, ISBN 978-1-4398-5802-8 .
Heinz Holling, Günther Gediga: Statistics. Probability Theory and Estimation Methods . Hogrefe, Göttingen 2013, ISBN 978-3-8017-2135-0 .
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani: An Introduction to Statistical Learning with Applications in R . Springer, New York 2013, ISBN 978-1-4614-7137-0 ( material ).
Jared Lander: R for Everyone. Advanced Analytics and Graphics . Addison-Wesley Professional, Boston 2013, ISBN 978-0-321-88803-7 ( Materials ).
ND Lewis: 100 Statistical Tests in R . With over 300 Illustrations and Examples . CreateSpace, Charleston 2013, ISBN 978-1-4840-5299-0 .
Luiz Alexandre Peter Martinelli, Marcio Mello Pupin: conhecendo o R . Uma visão mais que Estatística . 3. Edition. Editora UFV, Viçosa 2013, ISBN 978-85-7269-495-7 ( material ).
K. Gerald van den Boogaart, Raimon Tolosana-Delgado: Analyzing Compositional Data with R . Springer, Berlin 2013, ISBN 978-3-642-36808-0 .
Joseph Adler: R in a Nutshell . 2nd Edition. O'Reilly, Sebastopol 2012, ISBN 978-1-4493-1208-4 .
Pierre-Andre Cornillon et al. a .: R for Statistics . Chapman & Hall / CRC, Boca Raton 2012, ISBN 978-1-4398-8145-3 .
Pierre-André Cornillon et al. A .: Statistiques avec R . 3. Edition. Presses Universitaires de Rennes, Rennes 2012, ISBN 978-2-7535-1992-3 ( material ).
Andy Field , Jeremy Miles, Zoe Field: Discovering Statistics Using R . Sage, Thousand Oaks 2012, ISBN 978-1-4462-0046-9 .
Søren Højsgaard, David Edwards, Steffen Lauritzen: Graphical Models with R . Springer, New York 2012, ISBN 978-1-4614-2298-3 .
Laura Chihara, Tim Hester Berg: Mathematical Statistics with resampling and R . John Wiley & Sons, Chichester 2011, ISBN 978-1-118-02985-5 ( material ).
Jeremy Leipzig, Xiao-Yi Li: Data Mashups in R. A Case Study in Real-World Data Analysis . O'Reilly, Sebastopol 2011, ISBN 978-1-4493-0353-2 .
Norman Matloff: The Art of R Programming. A Tour of Statistical Software Design . No Starch Press, San Francisco 2011, ISBN 978-1-59327-384-2 ( materials ).
Russell B. Millar: Maximum Likelihood Estimation and Inference. With examples in R, SAS and ADMB . John Wiley & Sons, Chichester 2011, ISBN 978-0-470-09482-2 ( material ).
Randall Pruim: Foundations and Applications of Statistics. An Introduction Using R . American Mathematical Society, Providence 2011, ISBN 978-0-8218-5233-0 .
Paul Teetor: R Cookbook . O'Reilly, Sebastopol 2011, ISBN 978-0-596-80915-7 ( online ).
Shravan Vasishth, Michael Broe: The Foundations of Statistics. A simulation-based approach . Springer, Berlin 2011, ISBN 978-3-642-16312-8 ( material ).
Joseph Adler: R in a Nutshell . O'Reilly, Cologne 2010, ISBN 978-3-89721-649-5 (en).
Frank Bretz, Torsten Hothorn, Peter Westfall: Multiple Comparisons Using R . Chapman and Hall / CRC, Boca Raton 2010, ISBN 978-1-58488-574-0 .
Heinz Holling, Günther Gediga: Statistics. Descriptive procedure . Hogrefe, Göttingen 2010, ISBN 978-3-8017-2134-3 .
François Husson, Sebastien Lê, Jérôme Pagès: Exploratory Multivariate Analysis by Example Using R . Chapman & Hall / CRC, Boca Raton 2010, ISBN 978-1-4398-3580-7 ( material ).
John Main Donald, W. John Brown: Data Analysis and Graphics Using R . An example-based approach . 3. Edition. Cambridge University Press, Cambridge 2010, ISBN 978-0-521-76293-9 ( materials ).
François Husson, Sebastien Lê, Jérôme Pagès: Analyze de données avec R . Presses Universitaires de Rennes, Rennes 2009, ISBN 978-2-7535-0938-2 ( material ).
Michael W. Trosset: An Introduction to Statistical Inference and its Applications with R . Chapman & Hall / CRC, Boca Raton 2009, ISBN 978-1-58488-947-2 .
Kenneth Baclawski: Introduction to Probability with R . Chapman & Hall / CRC, Boca Raton 2008, ISBN 978-1-4200-6521-3 ( material ).
Christine Duller: Introduction to nonparametric statistics with SAS and R . An application-oriented text and workbook . Physica-Verlag, Heidelberg 2008, ISBN 978-3-7908-2059-1 ( material ).
Jane M. Horgan: Probability with R . An Introduction with Computer Science Applications . John Wiley & Sons, Chichester 2008, ISBN 978-0-470-28073-7 .
Kai Velten: Mathematical Modeling and Simulation . Introduction for Scientists and Engineers . Wiley-VCH, Chichester 2008, ISBN 978-3-527-40758-3 .
Michael Greenacre: Correspondence Analysis in Practice . 2nd Edition. Chapman & Hall / CRC, Boca Raton 2007, ISBN 978-1-58488-616-7 .
Stefano Maria Iacus, Guido Masarotto: Laboratorio di Statistica con R . 2nd Edition. McGraw-Hill, Milan 2007, ISBN 978-88-386-6369-7 .
Maria L. Rizzo: Statistical Computing with R . Chapman & Hall / CRC, Boca Raton 2007, ISBN 978-1-58488-545-0 ( material ).
Brian S. Everitt: An R and S-Plus Companion to Multivariate Analysis . Springer, New York 2005, ISBN 978-1-85233-882-4 ( material ).
Jana Jurečková, Jan Picek: Robust Statistical Methods with R . Chapman & Hall / CRC, Boca Raton 2005, ISBN 978-1-58488-454-5 .
Fionn Murtagh: Correspondence Analysis and Data Coding with Java and R . Chapman & Hall / CRC, Boca Raton 2005, ISBN 978-1-4200-3494-3 ( material ).
Richard M. Heiberger, Burt Holland: Statistical Analysis and Data Display. An Intermediate Course with Examples in S-Plus, R and SAS . Springer, New York 2004, ISBN 978-0-387-40270-3 .
Rainer Schlittgen: Statistical analysis with R . Oldenbourg, Munich 2004, ISBN 978-3-486-57616-0 .
Deborah Nolan, Terry Speed: Stat Labs. Mathematical Statistics through Applications . Springer, New York 2000, ISBN 978-0-387-98974-7 ( material ).

Data management

Garrett Grolemund, Hadley Wickham: R for Data Science. Visualize, Model, Transform, Tidy and Import Data . O'Reilly, Sebastopol 2016, ISBN 978-1-4919-1039-9 ( online ).
Jaynal Abedin, Kishore Kumar Das: Data Manipulation with R . 2nd Edition. Packt Publishing, Birmingham 2015, ISBN 978-1-78528-881-4 .
Stef van Buuren: Flexible Imputation of Missing Data . Chapman & Hall / CRC, Boca Raton 2012, ISBN 978-1-4398-6824-9 ( material ).
Phil Spector: Data Manipulation with R . Springer, New York 2008, ISBN 978-0-387-74730-9 .

Programming and performance optimization

Dan Zhang: R for Programmers . Mastering the tools . Chapman & Hall / CRC, Boca Raton 2016, ISBN 978-1-4987-3681-7 .
Aloysius Lim, William Tjhi: R High Performance Programming . Packt Publishing, Birmingham 2015, ISBN 978-1-78398-926-3 ( material ).
Norman Matloff: Parallel Computing for Data Science. With Examples in R, C ++ and CUDA . Chapman & Hall / CRC, Boca Raton 2015, ISBN 978-1-4665-8701-4 .
Kelly Black: R Object-oriented Programming . Packt Publishing, Birmingham 2014, ISBN 978-1-78398-668-2 .
Paulo Cortez: Modern Optimization with R . Springer, 2014, ISBN 978-3-319-08262-2 ( material ).
Garrett Grolemund, Hadley Wickham: Hands-On Programming with R . Write Your Own Functions and Simulations . O'Reilly, Sebastopol 2014, ISBN 978-1-4493-5901-0 .
Ajay Ohri: R for Cloud Computing . An Approach for Data Scientists . Springer, New York 2014, ISBN 978-1-4939-1701-3 .
Owen Jones, Robert Maillardet, Andrew Robinson: Introduction to Scientific Programming and Simulation Using R . 2nd Edition. Chapman & Hall / CRC, Boca Raton 2014, ISBN 978-1-4665-6999-7 .
Dirk Eddelbuettel: Seamless R and C ++ Integration with Rcpp . Springer, New York 2013, ISBN 978-1-4614-6867-7 ( material ).
Özgür Ergül: Guide to Programming and Algorithms Using R . Springer, London 2013, ISBN 978-1-4471-5327-6 .
Mark Hornick, Tom Plunkett: Using R to Unlock the Value of Big Data. Big Data Analytics with Oracle R Enterprise and Oracle R Connector for Hadoop . Oracle Press, 2013, ISBN 978-0-07-182438-5 .
Claus Weihs, Olaf Mersmann, Uwe Ligges: Foundations of Statistical Algorithms. With Reference to R Packages . Chapman & Hall / CRC, Boca Raton 2013, ISBN 978-1-4398-7885-9 ( material ).
Michael F. Lawrence, John Verzani: Programming Graphical User Interfaces in R . Chapman & Hall / CRC, Boca Raton 2012, ISBN 978-1-4398-5682-6 .
Michael R. Chernick, Robert A. LaBudde: An Introduction to Bootstrap Methods with Applications to R . John Wiley & Sons, Chichester 2011, ISBN 978-0-470-46704-6 .
Randall L. Eubank, Ana Kupresanin: Statistical Computing in C ++ and R . Chapman & Hall / CRC, Boca Raton 2011, ISBN 978-1-4200-6650-0 ( material ).
Q. Ethan McCallum, Stephen Weston: Parallel R . O'Reilly, Sebastopol 2011, ISBN 978-1-4493-0992-3 ( material ).
James O. Ramsay, Giles Hooker, Spencer Graves: Functional Data Analysis with R and Matlab . Springer, New York 2009, ISBN 978-0-387-98184-0 .
John M. Chambers: Software for Data Analysis. Programming with R . Springer, New York 2008, ISBN 978-0-387-75935-7 .
W. John Brown, Duncan J. Murdoch: A First Course in Statistical Programming with R . Cambridge University Press, Cambridge 2007, ISBN 978-0-521-87265-2 ( materials ).

Mathematical optimization

Nick Fieller: Basics of Matrix Algebra for Statistics with R . Chapman & Hall / CRC, Boca Raton 2015, ISBN 978-1-4987-1236-1 .
Victor A. Bloomfield: Using R for Numerical Analysis in Science and Engineering . Chapman & Hall / CRC, Boca Raton 2014, ISBN 978-1-4398-8448-5 .
John C. Nash: Nonlinear Parameter Optimization Using R Tools . John Wiley & Sons, Chichester 2014, ISBN 978-1-118-88396-9 .
Karline Soetaert, Jeff Cash, Francesca Mazzia: Solving Differential Equations in R . Springer, Berlin 2012, ISBN 978-3-642-28069-6 ( material ).
Hrishikesh D. Vinod: Hands-on matrix algebra Using R . Active and Motivated Learning with Applications . World Scientific, Hackensack 2011, ISBN 978-981-4313-69-8 ( material ).
Stefano Maria Iacus: Simulation and Inference for Stochastic Differential Equations. With R Examples . Springer, New York 2008, ISBN 978-0-387-75838-1 .
Guy P. Nason: Wavelet Methods in Statistics with R . Springer, New York 2008, ISBN 978-0-387-75960-9 .

Compared to other statistics software

David E. Hiebeler: R and Matlab . Chapman & Hall / CRC, Boca Raton 2015, ISBN 978-1-4665-6838-9 ( material ).
Ken Kleinman, Nicholas J. Horton: SAS and R. Data Management, Statistical Analysis and Graphics . Chapman & Hall / CRC, Boca Raton 2014, ISBN 978-1-4665-8449-5 ( material ).
Robert A. Muenchen: R for SAS and SPSS Users . 2nd Edition. Springer, New York 2011, ISBN 978-1-4614-0684-6 ( material ).
Robert A. Muenchen, Joseph M. Hilbe: R for Stata Users . Springer, New York 2010, ISBN 978-1-4419-1317-3 ( material ).
Richard M. Heiberger, Erich Neuwirth: R Through Excel. A Spreadsheet Interface for Statistics, Data Analysis and Graphics . Springer, New York 2009, ISBN 978-1-4419-0051-7 .

Reporting and reproducible research

Christopher Gandrud: Reproducible Research with R and RStudio . 2nd Edition. Chapman & Hall / CRC, Boca Raton 2015, ISBN 978-1-4987-1537-9 ( material ).
Paul Gerrard, Radia M. Johnson: Mastering Scientific Computing with R . Packt Publishing, Birmingham 2015, ISBN 978-1-78355-525-3 ( material ).
Yihui Xie: Dynamic Documents with R and knitr . 2nd Edition. Chapman & Hall / CRC, Boca Raton 2015, ISBN 978-1-4987-1696-3 ( material ).
Victoria Stodden, Friedrich Leisch, Roger D. Peng: Implementing Reproducible Research . Chapman & Hall / CRC, Boca Raton 2014, ISBN 978-1-4665-6159-5 ( material ).

Graphic creation

Atmajitsinh Gohil: R Data Visualization Cookbook . Packt Publishing, Birmingham 2015, ISBN 978-1-78398-950-8 .
John Jay Hilfiger: Graphing Data with R . O'Reilly, Sebastopol 2015, ISBN 978-1-4919-2261-3 .
Donato Teutonico: ggplot2 Essentials . Packt Publishing, Birmingham 2015, ISBN 978-1-78528-352-9 .
Antony Unwin: Graphical Data Analysis with R . Chapman & Hall / CRC, Boca Raton 2015, ISBN 978-1-4987-1523-2 ( material ).
Hadley Wickham, Carson Sievert: ggplot2. Elegant Graphics for Data Analysis . 2nd Edition. Springer, 2015, ISBN 978-3-319-24275-0 ( material ).
Jaynal Abedin, Hrishi V. Mittal: R Graphs Cookbook . 2nd Edition. Packt Publishing, Birmingham 2014, ISBN 978-1-78398-878-5 .
ND Lewis: Visualizing Data Using Complex R . CreateSpace, Charleston 2014, ISBN 978-1-5030-2826-5 .
David Alexander Lillis: R Graph Essentials . Packt Publishing, Birmingham 2014, ISBN 978-1-78355-455-3 .
Oscar Perpinan Lamigueiro: Displaying Time Series, Spatial and Space-Time Data with R . Chapman & Hall / CRC, Boca Raton 2014, ISBN 978-1-4665-6520-3 ( material ).
Thomas Rahlf: Data design with R. 100 visualization examples . Open Source Press, Munich 2014, ISBN 978-3-95539-094-5 ( material ).
Chris Beeley: Web Application Development with R Using Shiny . Packt Publishing, Birmingham 2013, ISBN 978-1-78328-447-4 .
Winston Chang: R Graphics Cookbook . O'Reilly, Sebastopol 2013, ISBN 978-1-4493-1695-2 ( online ).
Paul Murrell: R Graphics . 2nd Edition. Chapman & Hall / CRC, Boca Raton 2011, ISBN 978-1-4398-3176-2 ( material ).
Nathan Yau: Visualize this. The FlowingData Guide to Design, Visualization, and Statistics . John Wiley & Sons, Chichester 2011, ISBN 978-0-470-94488-2 ( material ).
Kevin J. Keen: Graphics for Statistics and Data Analysis with R . Chapman & Hall / CRC, Boca Raton 2010, ISBN 978-1-58488-087-5 ( material ).
Deepayan Sarkar: Lattice. Multivariate Data Visualization with R . Springer, New York 2008, ISBN 978-0-387-75968-5 ( material ).
Dianne Cook, Deborah F. Swayne: Interactive and Dynamic Graphics for Data Analysis. With R and GGbobi . Springer, New York 2007, ISBN 978-0-387-71761-6 ( material ).

Time series analysis

Robert H. Shumway, David S. Stoffer: Time Series Analysis and its Applications with R Examples . 4th edition. Springer, New York 2017, ISBN 978-3-319-52452-8 ( material ).
Rainer Schlittgen: Applied time series analysis with R . 3. Edition. De Gruyter Oldenbourg, Berlin 2015, ISBN 978-3-11-041398-4 .
Galit Shmueli, Kenneth C. Light Dahl Jr .: Practical Time Series Forecasting with R . A hands-on guide . Axelrod Schnall, Mumbai 2015, ISBN 978-0-9915766-3-0 ( material ).
Randal Douc, Eric Moulines, David Stoffer: Nonlinear Time Series. Theory, Methods and Applications with R Examples . Chapman & Hall / CRC, Boca Raton 2014, ISBN 978-1-4665-0225-3 ( material ).
Daniel Mirman: Growth Curve Analysis and Visualization Using R . Chapman & Hall / CRC, Boca Raton 2014, ISBN 978-1-4665-8432-7 ( material ).
Frans Willekens: Multistate Analysis of Life Histories with R . Springer, 2014, ISBN 978-3-319-08382-7 .
Rob J. Hyndman, George Athanasopoulos: Forecasting. Principles and Practice . OTexts, 2013, ISBN 978-0-9875071-0-5 ( online ).
Ruey S. Tsay: Multivariate Time Series Analysis. With R and Financial Applications . John Wiley & Sons, Chichester 2013, ISBN 978-1-118-61790-8 ( material ).
January Beyer man, Arthur Allignol, Martin Schumacher: Competing Risks and multistate models with R . Springer, New York 2012, ISBN 978-1-4614-2034-7 ( material ).
Göran Broström: Event History Analysis with R . Chapman & Hall / CRC, Boca Raton 2012, ISBN 978-1-4398-3164-9 .
Dimitris Rizopoulos: Joint Models for Longitudinal and Time-to-Event Data. With Applications in R . Chapman & Hall / CRC, Boca Raton 2012, ISBN 978-1-4398-7286-4 ( material ).
Yves Aragon: Séries Temporelles avec R . Methodes et cas . Springer, Paris 2011, ISBN 978-2-8178-0207-7 ( material ).
Raquel Prado, Mike West: Time Series. Modeling, Computation and Inference . Chapman & Hall / CRC, Boca Raton 2010, ISBN 978-1-4200-9336-0 .
Paul SP Cowpertwait, Andrew Metcalfe: Introductory Time Series with R . Springer, New York 2009, ISBN 978-0-387-88697-8 ( material ).
Walter Zucchini, Iain L. MacDonald: Hidden Markov Models for Time Series. An Introduction Using R . Chapman & Hall / CRC, Boca Raton 2009, ISBN 978-1-58488-573-3 .
Jonathan D. Cryer, Kung-Sik Chan: Time Series Analysis. With Applications in R . Springer, New York 2008, ISBN 978-0-387-75958-6 ( material ).
Bernhard Pfaff: Analysis of Integrated and Cointegrated Time Series with R . 2nd Edition. Springer, New York 2008, ISBN 978-0-387-75966-1 ( material ).

Regression analysis

Frank E. Harrell: Regression Modeling Strategies. With Applications to Linear Models, Logistic and Ordinal Regression and Survival Analysis . 2nd Edition. Springer, New York 2015, ISBN 978-3-319-19424-0 ( material ).
Joseph M. Hilbe: Practical Guide to Logistic Regression . Chapman & Hall / CRC, Boca Raton 2015, ISBN 978-1-4987-0957-6 .
Christopher R. Pictures, Thomas M. Loughin: Analysis of Categorical Data with R . Chapman & Hall / CRC, Boca Raton 2014, ISBN 978-1-4398-5567-6 ( material ).
W. Holmes Finch, Jocelyn E. Bolin, Ken Kelley: Multilevel Modeling Using R . Chapman & Hall / CRC, Boca Raton 2014, ISBN 978-1-4665-1585-7 .
Joseph M. Hilbe: Modeling Count Data . Cambridge University Press, Cambridge 2014, ISBN 978-1-107-61125-2 ( materials ).
Jussi Klemelä: Multivariate Nonparametric Regression and Visualization. With R and Applications to Finance . John Wiley & Sons, Chichester 2014, ISBN 978-0-470-38442-8 .
John Kloke, Joseph W. McKean: Nonparametric Statistical Methods Using R . Chapman & Hall / CRC, Boca Raton 2014, ISBN 978-1-4398-7343-4 ( material ).
Brady T. West, Kathleen B. Welch, Andrzey T. Galecki: Linear Mixed Models. A Practical Guide Using Statistical Software . 2nd Edition. Chapman & Hall / CRC, Boca Raton 2014, ISBN 978-1-4665-6099-4 ( material ).
Alain F. Zuur, Anatoly A. Saveliev, Elena N. Ieno: A Beginner's Guide to Generalized Additive Models Mixed with R . Highland Statistics, Newburgh 2014, ISBN 978-0-9571741-5-3 ( material ).
Eugene Demidenko: Mixed Models. Theory and Applications with R . 2nd Edition. John Wiley & Sons, Chichester 2013, ISBN 978-1-118-09157-9 ( material ).
Andrzej Galecki, Tomasz Burzykowski: Linear Mixed Effects Models Using R . A step-by-step approach . Springer, New York 2013, ISBN 978-1-4614-3899-1 .
Rainer Schlittgen: regression analysis with R . Oldenbourg, Munich 2013, ISBN 978-3-486-71701-3 .
Alain F. Zuur, Joseph M. Hilbe, Elena N. Leno: A Beginner's Guide to GLM and GLMM with R. A Frequentist and Bayesian Perspective for Ecologists . Highland Statistics, Newburgh 2013, ISBN 978-0-9571741-3-9 ( material ).
Alain F. Zuur: A Beginner's Guide to Generalized Additive Models with R . Highland Statistics, Newburgh 2012, ISBN 978-0-9571741-2-2 ( material ).
Alain F. Zuur, Anatoly A. Savaliev, Elena N. Ieno: Zero Inflated Models and Generalized Linear Mixed Models with R . Highland Statistics, Newburgh 2012, ISBN 978-0-9571741-0-8 ( material ).
Damon Mark Berridge, Robert Crouchley: Multivariate Generalized Linear Mixed Models Using R . Chapman & Hall / CRC, Boca Raton 2011, ISBN 978-1-4398-1326-3 .
Pierre André Cornillon, Eric Matzner Lober: regression avec R . Springer, Paris 2011, ISBN 978-2-8178-0183-4 ( material ).
Christopher Hay-Jahans: R Companion to Linear Models . Chapman & Hall / CRC, Boca Raton 2011, ISBN 978-1-4398-7365-6 .
Joseph M. Hilbe: Negative Binomial Regression . 2nd Edition. Cambridge University Press, Cambridge 2011, ISBN 978-0-521-19815-8 .
John Fox, Sanford Weisberg: An R Companion to Applied Regression . 2nd Edition. Sage, Thousand Oaks 2010, ISBN 978-1-4129-7514-8 ( materials ).
Joseph M. Hilbe: Logistic Regression Models . Chapman & Hall / CRC, Boca Raton 2009, ISBN 978-1-4200-7575-5 .
Giovanni Petris, Sonia Petrone, Patriza Campagnoli: Dynamic Linear Models with R . Springer, New York 2009, ISBN 978-0-387-77237-0 ( material ).
Simon Sheather: A Modern Approach to Regression with R . Springer, New York 2009, ISBN 978-0-387-09607-0 .
Daniel B. Wright, Kamala London: Modern Regression Techniques Using R . A Practical Guide for Students and Researchers . Sage, London 2009, ISBN 978-1-84787-903-5 .
Alain F. Zuur et al. A .: Mixed Effects Models and Extensions in Ecology with R . Springer, New York 2009, ISBN 978-0-387-87457-9 ( material ).
Luke Keele: Semiparametric Regression for the Social Sciences . John Wiley & Sons, Chichester 2008, ISBN 978-0-470-31991-8 ( material ).
Christian Ritz, Jens C. Streibig: Nonlinear regression with R . Springer, New York 2008, ISBN 978-0-387-09615-5 .
Simon N. Wood: Generalized Additive Models. An Introduction with R . Chapman & Hall / CRC, Boca Raton 2006, ISBN 978-1-58488-474-3 .
Julian J. Faraway: Extending Linear Models with R. Generalized Linear Mixed Effects and Nonparametric Regression Models . Chapman & Hall / CRC, Boca Raton 2005, ISBN 978-1-58488-424-8 ( material ).
Julian J. Faraway: Linear Models with R . Chapman & Hall / CRC, Boca Raton 2004, ISBN 978-1-58488-425-5 ( material ).
Sylvie Huet, Annie Bouvier, Marie-Anne Gruet, Emmanuel Jolivet: Statistical Tools for Nonlinear Regression. A Practical Guide with S-PLUS and R Examples . Springer, New York 2003, ISBN 978-0-387-40081-5 .

Factor analysis

A. Alexander Beaujean: Latent Variable Modeling Using R . A step-by-step guide . Routledge, New York 2014, ISBN 978-1-84872-699-4 ( materials ).
Jérôme Pagès: Multiple Factor Analysis by Example Using R . Chapman & Hall / CRC, Boca Raton 2014, ISBN 978-1-4822-0547-3 ( material ).
Jérôme Pagès: Analysis factorielle multiple avec R . EDP Sciences, Les Ulis 2013, ISBN 978-2-7598-0963-9 .
James W. Hardin, Joseph M. Hilbe: Generalized Estimating Equations . 2nd Edition. Chapman & Hall / CRC, Boca Raton 2012, ISBN 978-1-4398-8113-2 .

Network analysis

Douglas Luke: A User's Guide to Network Analysis in R . Springer, 2015, ISBN 978-3-319-23882-1 .
Eric D. Kolaczyk, Gábor Csárdi: Statistical Analysis of Network Data with R . Springer, New York 2014, ISBN 978-1-4939-0982-7 ( material ).
Nagiza F. Samatova et al. A .: Practical Graph Mining with R . Chapman & Hall / CRC, Boca Raton 2013, ISBN 978-1-4398-6084-7 .

Data mining and machine learning

Pawel Cichosz: Data Mining Algorithms. Explained Using R . John Wiley & Sons, Chichester 2015, ISBN 978-1-118-33258-0 .
Rui Miguel Forte: Mastering Predictive Analytics with R . Packt Publishing, Birmingham 2015, ISBN 978-1-78398-280-6 ( material ).
Daniel D. Gutierrez: Machine Learning and Data Science. An Introduction to Statistical Learning Methods with R . Technics Publications, Denville 2015, ISBN 978-1-63462-096-3 .
Brett Lantz: Machine Learning with R . 2nd Edition. Packt Publishing, Birmingham 2015, ISBN 978-1-78439-390-8 ( material ).
Cory Lesmeister: Mastering Machine Learning with R . Packt Publishing, Birmingham 2015, ISBN 978-1-78398-452-7 .
ND Lewis: 92 Applied Predictive Modeling Techniques in R. With step by step instructions on how to build them FAST . CreateSpace, Charleston 2015, ISBN 978-1-5175-1679-6 .
Eric Mayor: Learning Predictive Analytics with R . Packt Publishing, Birmingham 2015, ISBN 978-1-78216-935-2 .
Chiu Yu-Wei: Machine Learning with R Cookbook . Packt Publishing, Birmingham 2015, ISBN 978-1-78398-204-2 .
Bater Makhabel: Learning Data Mining with R . Packt Publishing, Birmingham 2014, ISBN 978-1-78398-210-3 .
Michele Usuelli: R Machine Learning Essentials . Packt Publishing, Birmingham 2014, ISBN 978-1-78398-774-0 .
Max Kuhn, Kjell Johnson: Applied Predictive Modeling . Springer, New York 2013, ISBN 978-1-4614-6848-6 ( material ).
John Ledolter: Data Mining and Business Analytics with R . John Wiley & Sons, Chichester 2013, ISBN 978-1-118-44714-7 .
Vignesh Prajapati: Big Data Analytics with R and Hadoop . Packt Publishing, Birmingham 2013, ISBN 978-1-78216-328-2 .
Yanchang Zhao Yonghua Cen: Data Mining Applications with R . Academic Press, Waltham 2013, ISBN 978-0-12-411511-8 ( Materials ).
Yanchang Zhao: R and Data Mining. Examples and case studies . Academic Press, Waltham 2012, ISBN 978-0-12-396963-7 ( materials ).
Graham Williams: Data Mining with Rattle and R. The Art of Excavating Data for Knowledge Discovery . Springer, New York 2011, ISBN 978-1-4419-9889-7 ( material ).
Luís Torgo: Data Mining with R . Learning with case studies . Chapman & Gall / CRC, Boca Raton 2010, ISBN 978-1-4398-1018-7 ( material ).

Web scraping

Simon Munzert: Automated Data Collection with R. A Practical Guide to Web Scraping and Text Mining . John Wiley & Sons, Boca Raton 2015, ISBN 978-1-118-83481-7 ( materials ).
Sharan Kumar Ravindran, Vikram Garg: Mastering Social Media Mining with R . Packt Publishing, Birmingham 2015, ISBN 978-1-78439-631-2 .
Nathan Danneman: Social Media Mining with R . Packt Publishing, Birmingham 2014, ISBN 978-1-78328-177-0 ( material ).
Deborah Nolan, Duncan Temple Lang: XML and Web Technologies for Data Sciences with R . Springer, New York 2014, ISBN 978-1-4614-7899-7 .

Sample design and surveys

Guido Black, James R Carpenter, Gerta Rücker: Meta-Analysis with R . Springer, 2015, ISBN 978-3-319-21415-3 .
John Lawson: Design and Analysis of Experiments with R . Chapman & Hall / CRC, Boca Raton 2014, ISBN 978-1-4398-6813-3 .
Ding-Geng Chen, Karl E. Peace: Applied Meta-Analysis with R . Chapman & Hall / CRC, Boca Raton 2013, ISBN 978-1-4665-0599-5 .
Richard Valliant, Jill A. Dever, Frauke Kreuter: Practical Tools for Designing and Weighting Survey Samples . Springer, New York 2013, ISBN 978-1-4614-6448-8 ( material ).
Bruno Falissard: Analysis of Questionnaire Data with R . Chapman & Hall / CRC, Boca Raton 2011, ISBN 978-1-4398-1766-7 .
Göran Kauermann, Helmut Küchenhoff: Samples. Methods and practice with R . Springer, Berlin 2011, ISBN 978-3-642-12317-7 .
Dieter Rasch, Jürgen Pilz, Rob Verdooren, Albrecht Gebhardt: Optimal Experimental Design with R . Chapman & Hall / CRC, Boca Raton 2011, ISBN 978-1-4398-1697-4 .
Thomas Lumley: Complex Surveys . A Guide to Analysis Using R . John Wiley & Sons, Chichester 2010, ISBN 978-0-470-28430-8 ( material ).
Paul R. Rosenbaum: Design of Observational Studies . Springer, New York 2010, ISBN 978-1-4419-1212-1 .

Bayesian statistics

Hari M. Koduvely: Learning Bayesian models with R . Packt Publishing, Birmingham 2015, ISBN 978-1-78398-760-3 .
Peter D. Congdon: Applied Bayesian Modeling . John Wiley & Sons, Chichester 2014, ISBN 978-1-119-95151-3 ( material ).
Mary Kathryn Cowles: Applied Bayesian Statistics . With R and OpenBUGS Examples . Springer, New York 2013, ISBN 978-1-4614-5695-7 .
Jean-Baptiste Denis, Marco Scutari: Réseaux bayésiens avec R . EDP Sciences, Les Ulis 2014, ISBN 978-2-7598-1198-4 .
John K. Kruschke: Doing Bayesian Data Analysis . A tutorial with R, JAGS and Stan . 2nd Edition. Academic Press, Waltham 2014, ISBN 978-0-12-405888-0 ( materials ).
Jean-Michel Marin, Christian P. Robert: Bayesian Essentials with R . 2nd Edition. Springer, New York 2014, ISBN 978-1-4614-8686-2 ( material ).
Marco Scutari, Jean-Baptiste Denis: Bayesian Networks . With Examples in R . Chapman & Hall / CRC, Boca Raton 2014, ISBN 978-1-4822-2558-7 ( material ).
Radhakrishnan Nagarajan, Marco Scutari, Sophie Lèbre: Bayesian Networks in R with Applications in Systems Biology . Springer, New York 2013, ISBN 978-1-4614-6445-7 ( material ).
Ronald Christensen, Wesley Johnson, Adam Branscum, Timothy E. Hanson: Bayesian Ideas and Data Analysis . An Introduction for Scientists and Statisticians . Chapman & Hall / CRC, Boca Raton 2010, ISBN 978-1-4398-0354-7 ( material ).
Peter D. Congdon: Applied Bayesian Hierarchical Methods . Chapman & Hall / CRC, Boca Raton 2010, ISBN 978-1-58488-720-1 ( material ).
John K. Kruschke: Doing Bayesian Data Analysis . A tutorial with R and BUGS . Academic Press, Waltham 2010, ISBN 978-0-12-381485-2 .
Christian P. Robert, George Casella: Introducing Monte Carlo Methods with R . Springer, New York 2010, ISBN 978-1-4419-1575-7 ( material ).
Eric A. Suess, Bruce E. Trumbo: Introduction to Probability Simulation and Gibbs sampling with R . Springer, New York 2010, ISBN 978-0-387-40273-4 ( Materia ).
Jim Albert: Bayesian Computation with R . 2nd Edition. Springer, New York 2009, ISBN 978-0-387-92297-3 .
Peter D. Hoff: A First Course in Bayesian Statistical Methods . Springer, New York 2009, ISBN 978-0-387-92299-7 ( material ).

Applications in genetics

Cedric Gondro: primer to Analysis of Genomic Data Using R . Springer, 2015, ISBN 978-3-319-14474-0 .
Conrad Bessant, Darren Oakley, Ian Shadforth: Building Bioinformatics Solutions with Perl, R and SQL . 2nd Edition. Oxford University Press, Oxford 2014, ISBN 978-0-19-965856-5 ( materials ).
Eija Korpelainen u. a .: RNA-seq Data Analysis . A practical approach . Chapman & Hall / CRC, Boca Raton 2014, ISBN 978-1-4665-9500-2 .
Paurush Praveen Sinha: Bioinformatics with R Cookbook . Packt Publishing, Birmingham 2014, ISBN 978-1-78328-313-2 .
Emmanuel Paradis: Analysis of Phylogenetics and Evolution with R . 2nd Edition. Springer, New York 2012, ISBN 978-1-4614-1742-2 ( material ).
Sorin Drăghici: Statistics and Data Analysis for Microarrays Using R and Bioconductor . 2nd Edition. Chapman & Hall / CRC, Boca Raton 2011, ISBN 978-1-4398-0975-4 .
Sunil K. Mathur: Statistical Bioinformatics with R . Academic Press, Waltham 2010, ISBN 978-0-12-375104-1 .
Victor Bloomfield: Computer Simulation and Data Analysis in Molecular Biology and Biophysics . An Introduction Using R . Springer, New York 2009, ISBN 978-1-4419-0084-5 .
Karl W. Broman, Śaunak Sen: A Guide to QTL Mapping with R / qtl . Springer, New York 2009, ISBN 978-0-387-92124-2 ( material ).
Andrea S. Foulkes: Applied Statistical Genetics with R . For Population-based Association Studies . Springer, New York 2009, ISBN 978-0-387-89553-6 .
Sandrine Dudoit, Mark J. van der Laan: Multiple Testing Procedures with Applications to Genomics . Springer, New York 2008, ISBN 978-0-387-49316-9 .
Robert Gentleman: R Programming for Bioinformatics . Chapman & Hall / CRC, Boca Raton 2008, ISBN 978-1-4200-6367-7 ( material ).
Florian Hahne, Wolfgang Huber, Robert Gentleman, Seth Falcon: Bioconductor Case Studies . Springer, New York 2008, ISBN 978-0-387-77239-4 ( material ).
David Siegmund, Benjamin Yakir: The Statistics of Gene Mapping . Springer, New York 2007, ISBN 978-0-387-49684-9 .
Richard C. Deonier, Simon Tavaré, Michael S. Waterman: Computational Genome Analysis . An Introduction . Springer, New York 2005, ISBN 978-0-387-98785-9 .
Robert Gentleman u. a .: Bioinformatics and Computational Biology Solutions Using R and Bioconductor . Springer, New York 2005, ISBN 978-0-387-25146-2 ( material ).
Giovanni Parmigiani, Elizabeth S. Garrett, Rafael A. Irizarry, Scott L. Zeger: The Analysis of Gene Expression Data. Methods and Software . Springer, New York 2003, ISBN 978-0-387-95577-3 .

Applications in the environment and ecology

Martina Bremer, Rebecca W. Doerge: Using R at the Bench . Step-by-step data analytics for biologists . Cold Spring Harbor Laboratory Press, New York 2015, ISBN 978-1-62182-112-0 .
Franzi Korner-Nievergelt a. a .: Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS and Stan . Academic Press, Waltham 2015, ISBN 978-0-12-801370-0 .
Derek H. Ogle: Introductory Fisheries Analyzes with R . Chapman & Hall / CRC, Boca Raton 2015, ISBN 978-1-4822-3520-3 ( material ).
Hideo Aizaki, Tomoaki Nakatani, Kazuo Sato: Stated Preference Methods Using R . Chapman & Hall / CRC, Boca Raton 2014, ISBN 978-1-4398-9047-9 ( material ).
Pierre Lafaye de Micheaux, Rémy Drouilhet, Benoît liquet: Le logiciel R . Maîtriser le langage - Effectuer des analyzes (bio) statistiques . 2nd Edition. Springer, Paris 2014, ISBN 978-2-8178-0534-4 ( material ).
Andrew B. Lawson: Bayesian Disease Mapping. Hierarchical Modeling in Spatial Epidemiology . 2nd Edition. Chapman & Hall / CRC, Boca Raton 2013, ISBN 978-1-4665-0481-3 ( material ).
Gael Millot: Comprendre et réaliser les tests statistiques à l'aide de R . Manuel de biostatistique . 3. Edition. De Boeck, Louvain-la-Neuve 2014, ISBN 978-2-8041-8498-8 .
Nathan G. Swenson: Functional and Phylogenetic Ecology in R . Springer, New York 2014, ISBN 978-1-4614-9541-3 .
Andrew P. Beckerman, Owen L. Petchey: Getting Started with R . An Introduction for Biologists . Oxford University Press, Oxford 2012, ISBN 978-0-19-960162-2 ( material ).
Stanislav Pekár, Marek Brabec: modernization Analýza dat biologickych second Linearni modely s korelacemi v prostředí R . Masaryk University Press, Brno 2012, ISBN 978-80-210-5812-5 .
Richard E. Plant: Spatial Data Analysis in Ecology and Agriculture Using R . Taylor & Francis / CRC, Boca Raton 2012, ISBN 978-1-4398-1913-5 ( material ).
Babak Shahbaba: Biostatistics with R . An Introduction to Statistics through Biological Data . Springer, New York 2012, ISBN 978-1-4614-1301-1 ( material ).
Daniel Borcard, François Gillet, Pierre Legendre: Numerical Ecology with R . Springer, New York 2011, ISBN 978-1-4419-7975-9 ( material ).
Andrew P. Robinson, Jeff D. Hamann: Forest Analytics with R . An Introduction . Springer, New York 2011, ISBN 978-1-4419-7761-8 ( material ).
Stanislav Pekár, Marek Brabec: modernization Analýza dat biologickych first Zobecnene linearni modely v prostředí R . Scientia, Prague 2009, ISBN 978-80-86960-44-9 .
Karline Soetaert, Peter MJ Herman: A Practical Guide to Ecological Modeling. Using R as a Simulation Platform . Springer, 2009, ISBN 978-1-4020-8623-6 .
M. Henry H. Stevens: A Primer of Ecology with R . Springer, New York 2009, ISBN 978-0-387-89881-0 ( material ).
Benjamin M. Bolker: Ecological Models and Data in R . Princeton University Press, Princeton 2008, ISBN 978-0-691-12522-0 ( Materials ).
Julien Claude: Morphometrics with R . Springer, New York 2008, ISBN 978-0-387-77789-4 ( material ).
Clemens Reimann, Peter Filzmoser, Robert Garrett, Rudolf Dutter: Statistical Data Analysis Explained. Applied Environmental Statistics with R . John Wiley & Sons, Chichester 2008, ISBN 978-0-470-98581-6 ( material ).
James S. Clark: Statistical Computation for Environmental Sciences in R . Lab Manual for Models for Ecological Data . Princeton University Press, Princeton 2007, ISBN 978-0-691-12262-5 .
Nhu D. Le, James V. Zidek: Statistical Analysis of Environmental Space-Time Processes . Springer, New York 2006, ISBN 978-0-387-26209-3 .
Alain F. Zuur, Elena N. Ieno, Graham M. Smith: Analyzing Ecological Data . Springer, New York 2007, ISBN 978-0-387-45967-7 ( material ).

Applications in chemistry, medicine, epidemiology and health

Ludwig A. Hothorn: Statistics in Toxicology Using R . Chapman & Hall / CRC, Boca Raton 2016, ISBN 978-1-4987-0127-3 .
Bertram KC Chan: Biostatistics for Epidemiology and Public Health Using R . Springer, 2015, ISBN 978-0-8261-1025-1 .
Dan Lin et al. A .: Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R . Order-Restricted Analysis of Microarray Data . Springer, Berlin 2012, ISBN 978-3-642-24006-5 .
Rainer Muche, Stefanie Lanzinger, Michael Rau: Medical statistics with R and Excel . Introduction to the RExcel and R-Commander interfaces for statistical analysis . Springer, Berlin 2011, ISBN 978-3-642-19483-2 .
Ron Wehrens: Chemometrics with R . Multivariate Data Analysis in the Natural Sciences and Life Sciences . Springer, Berlin 2011, ISBN 978-3-642-17840-5 .
Ding-Geng Chen, Karl E. Peace: Clinical Trial Data Analysis with R . Chapman & Hall / CRC, Boca Raton 2010, ISBN 978-1-4398-4020-7 .
James Michael Curran: Introduction to Data Analysis with R for Forensic Scientists . CRC, Boca Raton 2010, ISBN 978-1-4200-8826-7 .
Ewout W. Steyerberg: Clinical Prediction Models . A Practical Approach to Development, Validation and Updating . Springer, New York 2009, ISBN 978-0-387-77243-1 ( material ).
Kurt Varmuza, Peter Filzmoser: Introduction to Multivariate Statistical Analysis in Chemometrics . Taylor & Francis / CRC, Boca Raton 2009, ISBN 978-1-4200-5947-2 ( material ).
Roger D. Peng, Francesca Dominici: Statistical Methods for Environmental Epidemiology with R . A Case Study in Air Pollution and Health . Springer, New York 2008, ISBN 978-0-387-78166-2 ( material ).

Applications in psychology

Yvonnick Noël: Psychology statistique avec R . EDP Sciences, Les Ulis 2015, ISBN 978-2-7598-1756-6 .
Björn Rasch, Malte Friese, Wilhelm Hofmann, Ewald Naumann: Quantitative Methods 1 . Introduction to statistics for psychologists and social scientists . 4th edition. Springer, Berlin 2014, ISBN 978-3-662-43523-6 (and Volume 2, ISBN 978-3-662-43547-2 ).
Carolin Strobl: The Rasch model . A comprehensible introduction to study and practice . 3. Edition. Rainer Hampp Verlag, Munich 2015, ISBN 978-3-95710-050-4 ( material ).
Sebastien Le, Thierry Worch: Analyzing Sensory Data with R . Chapman & Hall / CRC, Boca Raton 2014, ISBN 978-1-4665-6572-2 ( material ).
Kenneth garlic, Laurence T. Maloney: Modeling Psycho Physical Data in R . Springer, New York 2012, ISBN 978-1-4614-4474-9 .
Ingrid Koller, Rainer W. Alexandrowicz, Reinhold Hatzinger: The Rasch model in practice . An introduction to eRm . UTB Facultas, Vienna 2012, ISBN 978-3-8252-3786-8 .
Yuelin Li, Jonathan Baron: Behavioral Research Data Analysis with R . Springer, New York 2012, ISBN 978-1-4614-1237-3 .
Jeffrey D. Long: Longitudinal Data Analysis for the Behavioral Sciences Using R . Sage, Thousand Oaks 2011, ISBN 978-1-4129-8268-9 .

Applications in linguistics and literature

Dirk Speelman: Mastering Corpus Linguistics Methods . A Practical Introduction with AntConc and R . John Wiley & Sons, Chichester 2016, ISBN 978-1-118-53445-8 .
Natalia Levshina: How to do Linguistics with R . Data Exploration and Statistical Analysis . John Benjamin, Amsterdam 2015, ISBN 978-90-272-1225-2 .
Matthew L. Jockers: Text Analysis with R for Students of Literature . Springer, 2014, ISBN 978-3-319-03163-7 ( material ).
Stefan Thomas Gries: Statistics for Linguistics with R . A Practical Introduction . 2nd Edition. De Gruyter Mouton, Berlin 2013, ISBN 978-3-11-030728-3 ( material ).
Stefan Thomas Gries: Quantitative Corpus Linguistics with R . A Practical Introduction . Routledge, New York 2009, ISBN 978-0-415-96270-4 ( materials ).
R. Harald Baayen: Analyzing Linguistic Data . A Practical Introduction to Statistics Using R . Cambridge University Press, Cambridge 2008, ISBN 978-0-521-70918-7 .
Stefan Thomas Gries: Statistics for Linguists . Vandenhoeck & Ruprecht, Göttingen 2008, ISBN 978-3-525-26551-2 ( material ).
Keith Johnson: Quantitative Methods in Linguistics . Wiley-Blackwell, Chichester 2008, ISBN 978-1-4051-4424-7 ( Materials ).

Applications in social sciences

Taylor Arnold, Lauren Tilton: Humanities Data in R . Exploring Networks, Geospatial Data, Images and Text . Springer, New York 2015, ISBN 978-3-319-20701-8 ( material ).
Maike Luhmann: R for beginners . Introduction to statistical software for the social sciences . 4th edition. Beltz, Weinheim 2015, ISBN 978-3-621-28249-9 ( material ).
James E. Monogan III: Political Analysis Using R . Springer, 2015, ISBN 978-3-319-23445-8 .
David Kaplan: Bayesian Statistics for the Social Sciences . The Guilford Press, New York 2014, ISBN 978-1-4625-1651-3 ( Materials ).
Thomas M. Carsey, Jeffrey J. Harden: Monte Carlo Simulation and Resampling Methods for Social Science . Sage, Thousand Oaks 2013, ISBN 978-1-4522-8890-1 .
Katharina Manderscheid: Social Science Data Analysis with R . An introduction . 2nd Edition. VS Verlag für Sozialwissenschaften, Wiesbaden 2012, ISBN 978-3-531-17642-0 .
Simon Jackman: Bayesian Analysis for the Social Sciences . John Wiley & Sons, Chichester 2009, ISBN 978-0-470-01154-6 .
Hrishikesh D. Vinod: Advances in Social Science Research Using R . Springer, New York 2010, ISBN 978-1-4419-1763-8 .
Scott M. Lynch: Introduction to Applied Bayesian Statistics and Estimation for Social Scientists . Springer, New York 2007, ISBN 978-0-387-71264-2 .

Applications in business

Emilio López Cano, Javier Martínez Moguerza, Mariano Prieto Corcoba: Quality Control with R . An ISO Standards Approach . Springer, 2015, ISBN 978-3-319-24044-2 .
Christopher N. Chapman, Elea McDonnell Feit: R for Marketing Research and Analytics . Springer, 2015, ISBN 978-3-319-14435-1 ( material ).
Vikram Dayal: An Introduction to R for Quantitative Economics . Graphing, simulating, computing . Springer, 2015, ISBN 978-81-322-2339-9 .
Thomas W. Miller: Marketing Data Science . Modeling Techniques in Predictive Analytics with R and Python . Pearson FT Prentice Hall, Upper Saddle River 2015, ISBN 978-0-13-388655-9 .
Changyou Sun: Empirical Research in Economics . Growing up with R . Pine Square, Starkville 2015, ISBN 978-0-9965854-0-8 ( materials ).
Arthur Charpentier: Computational Actuarial Science with R . Chapman & Hall / CRC, Boca Raton 2014, ISBN 978-1-4665-9259-9 ( material ).
Wolfgang Kohn, Riza Öztürk: Statistics for Economists . Data analysis with R and SPSS . 2nd Edition. Springer, Berlin 2013, ISBN 978-3-642-37351-0 ( material ).
Thomas W. Miller: Modeling Techniques in Predictive Analytics . Business Problems and Solutions with R . Pearson FT Prentice Hall, Upper Saddle River 2013, ISBN 978-0-13-341293-2 .
Emilio López Cano, Javier Martínez Moguerza, Andrés Redchuk: Six Sigma with R . Statistical Engineering for Process Improvement . Springer, New York 2012, ISBN 978-1-4614-3651-5 ( material ).
Daniel S. Putler, Robert E. Krider: Customer and Business Analytics . Applied Data Mining for Business Decision Making Using R . Chapman & Hall / CRC, Boca Raton 2012, ISBN 978-1-4665-0396-0 ( material ).
Reiner Hellbrück: Applied Statistics with R . An introduction for economists and social scientists . 2nd Edition. Gabler Verlag, Wiesbaden 2011, ISBN 978-3-8349-2826-9 .
Wolfgang Jank: Business Analytics for Managers . Springer, New York 2011, ISBN 978-1-4614-0405-7 .
Neeraj R. Hatekar: Principles of Econometrics . An Introduction (Using R) . Sage, Thousand Oaks 2010, ISBN 978-81-321-0469-8 .
Detlev Reymann: Competitive analysis for small and medium-sized enterprises (SMEs) . Theoretical basics and practical application using the example of horticultural businesses . Verlag Detlev Reymann, Geisenheim 2009, ISBN 978-3-00-027013-0 ( material ).
Philip J. Boland: Statistical and Probabilistic Methods in Actuarial Science . Chapman & Hall / CRC, Boca Raton 2007, ISBN 978-1-58488-695-2 .
Christian Kleiber, Achim Zeileis: Applied Econometrics with R . Springer, New York 2008, ISBN 978-0-387-77316-2 .
Hrishikesh D. Vinod: Hands-on Intermediate Econometrics Using R . Templates for Extending Dozens of Practical Examples . World Scientific, Hackensack 2008, ISBN 978-981-4350-41-9 ( material ).
Hans Peter Wolf, Peter Naeve, Veith Tiemann: Business Administration Crash Course Statistics . active with R . UTB, Stuttgart 2006, ISBN 978-3-8252-2780-7 ( material ).
Dubravko Dolić: Statistics with R . Introduction for economists and social scientists . R. Oldenbourg, Munich 2003, ISBN 978-3-486-27537-7 .
Manuel Castejón Limas, Joaquín Ordieres Meré, Francisco Javier de Cos Juez, Francisco Javier Martínez de Pisón Ascacibar: Control de Calidad . Metodologia para el analisis previo a la modelización de datos en procesos industriales. Fundamentos teóricos y aplicaciones con R . Servicio de Publicaciones de la Universidad de La Rioja, 2001, ISBN 978-84-95301-48-2 .

Applications with financial data

Clifford S. Ang: Analyzing Financial Data and Implementing Financial Models Using R . Springer, New York 2015, ISBN 978-3-319-14074-2 ( material ).
Edina Berlinger u. a .: Mastering R for Quantitative Finance . Packt Publishing, Birmingham 2015, ISBN 978-1-78355-207-8 .
Harry Georgakopoulos: Quantitative Trading with R . Understanding Mathematical and Computational Tools from a Quant's Perspective . Palgrave Macmillan, London 2015, ISBN 978-1-137-35407-5 .
David Ruppert, David S. Matteson: Statistics and Data Analysis for Financial Engineering . with R examples . 2nd Edition. Springer, New York 2015, ISBN 978-1-4939-2613-8 .
Argimiro Arratia: Computational Finance . An Introductory Course with R . Atlantis Press, Amsterdam 2014, ISBN 978-94-6239-069-0 ( material ).
René Carmona: Statistical Analysis of Financial Data in R . 2nd Edition. Springer, New York 2014, ISBN 978-1-4614-8787-6 ( material ).
Gergely Daróczi et al. a .: Introduction to R for Quantitative Finance . Packt Publishing, Birmingham 2013, ISBN 978-1-78328-093-3 .
Bernhard Pfaff: Financial Risk Modeling and Portfolio Optimization with R . John Wiley & Sons, Chichester 2012, ISBN 978-0-470-97870-2 ( material ).
Ruey S. Tsay: An Introduction to Analysis of Financial Data with R . John Wiley & Sons, Chichester 2012, ISBN 978-0-470-89081-3 ( material ).
Manfred Gilli, Dietmar Maringer, Enrico Schumann: Numerical Methods and Optimization in Finance . Academic Press, Waltham 2011, ISBN 978-0-12-375662-6 ( materials ).
Ruey S. Tsay: Analysis of Financial Time Series . 3. Edition. John Wiley & Sons, Chichester 2010, ISBN 978-0-470-41435-4 ( material ).

Applications with geospatial data

Robin Lovelace, Morgane Dumont: Spatial Microsimulation with R . Chapman & Hall / CRC, Boca Raton 2016, ISBN 978-1-4987-1154-8 ( material ).
Marta Blangiardo, Michela Cameletti: Spatial and Spatio-temporal Bayesian Models with R - INLA . John Wiley & Sons, Chichester 2015, ISBN 978-1-118-32655-8 ( material ).
Chris Brunsdon, Lex Comber: An Introduction to R for Spatial Analysis and Mapping . Sage, Thousand Oaks 2015, ISBN 978-1-4462-7295-4 .
Giuseppe Arbia: A Primer for Spatial Econometrics . With Applications in R . Polgrave Macmillan, London 2014, ISBN 978-0-230-36038-9 .
David A. Armstrong II et al. A .: Analyzing Spatial Models of Choice and Judgment with R . Chapman & Hall / CRC, Boca Raton 2014, ISBN 978-1-4665-1715-8 ( material ).
Sudipto Banerjee, Bradley P. Carlin, Alan E. Gelfand: Hierarchical Modeling and Analysis for Spatial Data . 2nd Edition. Chapman & Hall / CRC, Boca Raton 2014, ISBN 978-1-4398-1917-3 .
Michael Dorman: Learning R for Geospatial Analysis . Packt Publishing, Birmingham 2014, ISBN 978-1-78398-436-7 .
Roger S. Bivand, Edzer J. Pebesma, Virgilio Gómez-Rubio: Applied Spatial Data Analysis with R . 2nd Edition. Springer, New York 2013, ISBN 978-1-4614-7617-7 ( material ).
Yongwan Chun, Daniel A. Griffith: Spatial Statistics & Geostatistics . Sage, Thousand Oaks 2013, ISBN 978-1-4462-0174-9 .
Carlo Gaetan, Xavier Guyon: Spatial Statistics and Modeling . Springer, New York 2010, ISBN 978-0-387-92256-0 ( material ).
Michael D. Ward, Kristian Skrede Gleditsch: Spatial Regression Models . Sage, Thousand Oaks 2008, ISBN 978-1-4129-5415-0 ( materials ).
Peter J. Diggle, Paulo Justiniano Ribeiro: Model-based Geostatistics . Springer, New York 2007, ISBN 978-0-387-32907-9 .

Sports data applications

Max Marchi, Jim Albert: Analyzing Data with R Baseball . Chapman & Hall / CRC, Boca Raton 2013, ISBN 978-1-4665-7022-1 ( material ).

Web links

Commons : R - collection of pictures, videos and audio files

Wikibooks: R - learning and teaching materials

R-project.org Official website for R with information, download options, documentation and the like
Rweb offer to use R online
Rdocumentation.org Advanced search and overview on R functions and packages
R-bloggers Collection of over 10,000 blog posts on R topics, contributed by more than 500 bloggers
25 years: How R became the most important programming language for statisticians . heise news, August 3, 2018

Individual evidence

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R.
Logo since 2015
Basic data
Paradigms :	functional , dynamic , object-oriented
Publishing year:	1993
Designer:	Ross Ihaka , Robert Gentleman
Developer:	R core team
Current version	4.0.2 (June 22, 2020)
Typing :	dynamic, implicit, weak
Influenced by:	S , Scheme
Affected:	Julia
Operating system :	Unixoide , Mac OS , Windows
License :	GNU GPL
www.R-project.org