Bahasa pemrograman R

Dari Wikipedia bahasa Indonesia, ensiklopedia bebas
R
Dirancang olehRoss Ihaka and Robert Gentleman
pengembangR Development Core Team
Rilis perdana1993[1]
Rilis stabil
2.13.1 / 8 Juli 2011; 12 tahun lalu (2011-07-08)
Rilis pratayang
Through Subversion
Tipe SistemDynamic
OSCross-platform
LisensiGNU General Public License
Websitewww.r-project.org
Terpengaruh oleh
S, Scheme

GNU R[2] adalah suatu bahasa pemrograman dan lingkungan perangkat lunak (juga dikenal sebagai GNU S[3]) untuk komputasi dan grafis statistik. Bahasa pemrograman R sudah menjadi standar de facto bagi para statistikawan untuk mengembangkan perangkat lunak statistik, [4][5] dan digunakan secara luas untuk pengembangan software statistik dan analisis data.[5]

R adalah implementasi dari bahasa pemrograman S yang dikombinasikan dengan pelingkupan leksikal semantik yang terinspirasi oleh Scheme. S diciptakan oleh John Chambers ketika masih di Bell Labs. R diciptakan oleh Ross Ihaka dan Robert Gentleman[6] at the University of Auckland, New Zealand, dan sekarang dikembangkan oleh Tim Inti Pengembangan R, dengan Chambers sebagai anggota. Nama R sebagian berasal dari nama depan dari dua pengembang R pertama (Robert Gentleman and Ross Ihaka), dan sebagian sebagai permainan dari nama S.[7]

R merupakan bagian dari GNU project.[8][9] Kode Sumbernya tersedia secara bebas dibawah lisensi GNU General Public License, dan versi pre-compiled binary tersedia untuk berbagai sistem operasi. R menggunakan command line interface; namun, beberapa graphical user interface juga tersedia untuk digunakan bersama R.

Statistical features

R provides a wide variety of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and others. R is easily extensible through functions and extensions, and the R community is noted for its active contributions in terms of packages. There are some important differences, but much code written for S runs unaltered. Many of R's standard functions are written in R itself, which makes it easy for users to follow the algorithmic choices made. For computationally intensive tasks, C, C++, and Fortran code can be linked and called at run time. Advanced users can write C or Java[10] code to manipulate R objects directly.

R is highly extensible through the use of user-submitted packages for specific functions or specific areas of study. Due to its S heritage, R has stronger object-oriented programming facilities than most statistical computing languages. Extending R is also eased by its permissive lexical scoping rules.[11]

According to Rexer's Annual Data Miner Survey in 2010, R has become the data mining tool used by more data miners (43%) than any other.[12]

Another strength of R is static graphics, which can produce publication-quality graphs, including mathematical symbols. Dynamic and interactive graphics are available through additional packages such as RGL.[13]

R has its own LaTeX-like documentation format, which is used to supply comprehensive documentation, both on-line in a number of formats and in hard copy.

Programming features

R is an interpreted language typically used through a command line interpreter. If one types "2+2" at the command prompt and presses enter, the computer replies with "4".

  > 2+2
 [1] 4

Like many other languages, R supports matrix arithmetic. R's data structures include scalars, vectors, matrices, data frames (similar to tables in a relational database) and lists.[14] The R object system has been extended by package authors to define objects for regression models, time-series and geo-spatial coordinates.

R supports procedural programming with functions and, for some functions, object-oriented programming with generic functions. A generic function acts differently depending on the type of arguments it is passed. In other words the generic function dispatches the function (method) specific to that type of object. For example, R has a generic print() function that can print almost every type of object in R with a simple "print(objectname)" syntax.

Although R is mostly used by statisticians and other practitioners requiring an environment for statistical computation and software development, it can also be used as a general matrix calculation toolbox with performance benchmarks comparable to GNU Octave or MATLAB.[15] An R interface has been added to the popular data mining software Weka,[16] which allows for the use of the data mining capabilities in Weka and statistical analysis in R. Similarly, an R interface[17] to the widely used open source data mining software RapidMiner,[18] allows the integration of the statistical analysis available in R into RapidMiner data mining processes. Some proprietary analysis software vendors like SAS have introduced connectors to R.[19]

Examples

Example 1

The following examples illustrate the basic syntax of the language and use of the command-line interface.

In R and S, the assignment operator is an arrow made from two characters "<-".

> x <- c(1,2,3,4,5,6)   # Create ordered collection (vector)
> y <- x^2              # Square the elements of x
> print(y)              # print (vector) y
[1]  1  4  9 16 25 36
> mean(y)               # Calculate average (arithmetic mean) of (vector) y; result is scalar
[1] 15.16667
> var(y)                # Calculate sample variance
[1] 178.9667
> lm_1 <- lm(y ~ x)     # Fit a linear regression model "y = f(x)" or "y = B0 + (B1 * x)" 
                        # store the results as lm_1
> print(lm_1)           # Print the model from the (linear model object) lm_1

Call:
lm(formula = y ~ x)

Coefficients:
(Intercept)            x  
     -9.333        7.000 

> summary(lm_1)         # Compute and print statistics for the fit of the (linear model object) lm_1

Call:
lm(formula = y ~ x)

Residuals:
1       2       3       4       5       6
3.3333 -0.6667 -2.6667 -2.6667 -0.6667  3.3333

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept)  -9.3333     2.8441  -3.282 0.030453 *
x             7.0000     0.7303   9.585 0.000662 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.055 on 4 degrees of freedom
Multiple R-squared: 0.9583,	Adjusted R-squared: 0.9478
F-statistic: 91.88 on 1 and 4 DF,  p-value: 0.000662

> par(mfrow=c(2, 2))    # Request 2x2 plot layout
> plot(lm_1)       # Diagnostic plot of regression model


Diagnostic graphs produced by plot.lm() function. Features include mathematical notation in axis labels, as at lower left.

Example 2

Short R code calculating Mandelbrot set through the first 20 iterations of equation z = z² + c plotted for different complex constants c. This example demonstrates:

  • use of community developed external libraries (called packages), in this case caTools package
  • handling of complex numbers
  • multidimensional arrays of numbers used as basic data type, see variables C, Z and X
library(caTools)         # external package providing write.gif function
jet.colors <- colorRampPalette(c("#00007F", "blue", "#007FFF", "cyan", "#7FFF7F", 
                                 "yellow", "#FF7F00", "red", "#7F0000")) 
m <- 1200                # define size
C <- complex( real=rep(seq(-1.8,0.6, length.out=m), each=m ), 
              imag=rep(seq(-1.2,1.2, length.out=m), m ) ) 
C <- matrix(C,m,m)       # reshape as square matrix of complex numbers
Z <- 0                   # initialize Z to zero
X <- array(0, c(m,m,20)) # initialize output 3D array
for (k in 1:20) {        # loop with 20 iterations
  Z <- Z^2+C             # the central difference equation  
  X[,,k] <- exp(-abs(Z)) # capture results
} 
write.gif(X, "Mandelbrot.gif", col=jet.colors, delay=100)


"Mandelbrot.gif" - Graphics created in R with 14 lines of code in Example 2

Packages

The capabilities of R are extended through user-created packages, which allow specialized statistical techniques, graphical devices, import/export capabilities, reporting tools, etc. These packages are developed primarily in R, and sometimes in Java, C and Fortran. A core set of packages are included with the installation of R, with more than 4300 (hingga Maret 2011) available at the Comprehensive R Archive Network (CRAN), Bioconductor, and other repositories. [20]

The "Task Views" page (subject list) on the CRAN website lists the wide range of applications (Finance, Genetics, Machine Learning, Medical Imaging, Social Sciences and Spatial statistics) to which R has been applied and for which packages are available.

Other R package resources include Crantastic, a community site for rating and reviewing all CRAN packages. And also R-Forge, a central platform for the collaborative development of R packages, R-related software and projects. It hosts many unpublished, beta packages and development versions of CRAN packages.

The Bioconductor project provides R packages for the analysis of genomic data, such as Affymetrix and cDNA microarray object-oriented data handling and analysis tools, and has started to provide tools for analysis of data from next-generation high-throughput sequencing methods.

Reproducible research and automated report generation can be accomplished with packages that support execution of R code embedded within LaTeX, OpenDocument format and other markups[21].

Milestones

The full list of changes is maintained in the NEWS file. Some highlights are listed below.

  • Version 0.16 – This is the last alpha version developed primarily by Ihaka and Gentleman. Much of the basic functionality from the "White Book" (see S history) was implemented. The mailing lists commenced on April 1, 1997.
  • Version 0.49 – April 23, 1997 – This is the oldest available source release, and compiles on a limited number of Unix-like platforms. CRAN is started on this date, with 3 mirrors that initially hosted 12 packages. Alpha versions of R for Microsoft Windows and Mac OS are made available shortly after this version.
  • Version 0.60 – December 5, 1997 – R becomes an official part of the GNU Project. The code is hosted and maintained on CVS.
  • Version 1.0.0 – February 29, 2000 – Considered by its developers stable enough for production use.[22]
  • Version 1.4.0 – S4 methods are introduced and the first version for Mac OS X is made available soon after.
  • Version 2.0.0 – October 4, 2004 – Introduced lazy loading, which enables fast loading of data with minimal expense of system memory.
  • Version 2.1.0 – Support for UTF-8 encoding, and the beginnings of internationalization and localization for different languages.
  • Version 2.11.0 – April 22, 2010 – Support for Windows 64 bit systems.
  • Version 2.13.0 – April 14, 2011 – Adding a new compiler function that allows speeding up functions by converting them to byte-code.

Interfaces

Graphical user interfaces

  • RGUI – comes with the pre-compiled version of R
  • Java Gui for R – cross-platform stand-alone R terminal and editor based on Java (also known as JGR)
  • Deducer - GUI for menu driven data analysis (similar to SPSS/JMP/Minitab). It has a to do common data manipulation and analysis tasks, and an excel- designed to be used with JGR but also RGUI.
  • Rattle GUI – cross-platform GUI based on RGtk2 and specifically designed for data mining
  • R Commander – cross-platform menu-driven GUI based on tcltk (several plug-ins to Rcmdr are also available)
  • RExcel – using R and Rcmdr from within Microsoft Excel
  • Sage – web browser interface as well as rpy support
  • Sim.DiffProcGUI – Graphical User Interface for Simulation of Diffusion Processes based on tcltk
  • Cantor (software) – KDE worksheet interface to several mathematical applications, including R
  • Red-R – visual analysis interface that uses R for statistics
  • Tinn-R – GUI for R Language and Environment
  • RKWard – extensible GUI and IDE for R
  • R AnalyticFlow - analysis flowcharts with R (freeware)
  • RStudio - cross-platform open source IDE (which can also be run on a remote linux server)
  • RapidMiner and the RapidMiner R extension - extensible open source GUI and IDE for R, Weka, and RapidMiner data mining processes and their seamless integration

Editors and IDEs

Text editors and Integrated development environments (IDEs) with some support for R include: Bluefish,[23] Crimson Editor, ConTEXT, Eclipse,[24] Emacs (Emacs Speaks Statistics), Vim, Tinn-R,[25] Geany, jEdit,[26] Kate,[27] Syn, TextMate, gedit, SciTE, WinEdt (R Package RWinEdt), RPE, notepad++[28] and SciViews.

Scripting languages

R functionality has been made accessible from several scripting languages such as Python (by the RPy[29] interface package), Perl (by the Statistics::R[30] module) and Ruby (with the rsruby[31] rubygem). Scripting in R itself is possible via littler[32] as well as via Rscript.

Relationship with SAS

R is often compared to other statistical packages, with its main competitor being SAS.[33] One popular consensus is that R is at par with SAS except in the realm of very large data sets.[34]

In January 2009, the New York Times ran an article about R gaining acceptance among data analysts.[35] The paper quoted Anne Milley of SAS saying that the company has "customers who build engines for aircraft. I am happy they are not using freeware when I get on a jet," which resulted in criticism of Milley from both R and SAS users alike. In a blog comment, Milley later apologized for her remark.[36] In March of the same year, SAS announced plans to offer R integration in its software.[19]

Commercial support for R

In 2007, Revolution Analytics was founded to provide commercial support for a version of R that it developed for clusters of workstations called ParallelR. In 2011, the ability for reading and writing data in the SAS File Format was first added in Revolution's Enterprise R.[37].

Other commercial software systems supporting connections to R include Oracle[38], Spotfire[39], SPSS[40], STATISTICA[41] and Platform Symphony. [42]

See also

References

  1. ^ A Brief History R: Past and Future History, Ross Ihaka, Statistics Department, The University of Auckland, Auckland, New Zealand, available from the CRAN website
  2. ^ "The R Project for Statistical Computing". Diakses tanggal 2011-06-06.  "Free Software Directory - GNU R". Diakses tanggal 2011-06-06. 
  3. ^ "2.1 Frequently Asked Questions on R - What is R?". Diakses tanggal 2011-06-06. 
  4. ^ Fox, John and Andersen, Robert (January 2005). "Using the R Statistical Computing Environment to Teach Social Statistics Courses" (PDF). Department of Sociology, McMaster University. Diakses tanggal 2006-08-03. 
  5. ^ a b Vance, Ashlee (2009-01-06). "Data Analysts Captivated by R's Power". New York Times. Diakses tanggal 2009-04-28. R is also the name of a popular programming language used by a growing number of data analysts inside corporations and academia. It is becoming their lingua franca... 
  6. ^ "Robert Gentleman's home page". Diakses tanggal 2009-07-20. 
  7. ^ Kurt Hornik. The R FAQ: Why is R named R?. ISBN 3-900051-08-9. Diakses tanggal 2008-01-29. 
  8. ^ "Free Software Foundation (FSF) Free Software Directory: GNU R". Diakses tanggal 2010-07-05. 
  9. ^ "What is R?". Diakses tanggal 2009-04-28. 
  10. ^ Duncan Temple Lang, Calling R from Java (PDF), diakses tanggal 2010-07-05 
  11. ^ Jackman, Simon (Spring 2003). "R For the Political Methodologist" (PDF). The Political Methodologist. Political Methodology Section, American Political Science Association. 11 (1): 20–22. Diarsipkan dari versi asli (PDF) tanggal 2006-07-21. Diakses tanggal 2006-08-03. 
  12. ^ http://www.rexeranalytics.com/Data-Miner-Survey-Results-2010.html
  13. ^ http://cran.r-project.org/web/views/Graphics.html
  14. ^ Dalgaard, Peter (2002). Introductory Statistics with R. New York, Berlin, Heidelberg: Springer-Verlag. ISBN 0387954759X pages=10–18, 34 Periksa nilai: invalid character |isbn= (bantuan). 
  15. ^ "Speed comparison of various number crunching packages (version 2)". SciView. Diakses tanggal 2007-11-03. 
  16. ^ "RWeka: An R Interface to Weka. R package version 0.3-17". Kurt Hornik, Achim Zeileis, Torsten Hothorn and Christian Buchta. Diakses tanggal 2009. 
  17. ^ R Extension Presented on RCOMM 2010
  18. ^ "Data Mining / Analytic Tools Used Poll (May 2010)". 
  19. ^ a b http://www.sas.com/news/preleases/RintegrationSGF09.html
  20. ^ Robert A. Muenchen. "The Popularity of Data Analysis Software". 
  21. ^ http://cran.r-project.org/web/views/ReproducibleResearch.html
  22. ^ Peter Dalgaard. "R-1.0.0 is released". Diakses tanggal 2009-06-06. 
  23. ^ Customizable syntax highlighting based on Perl Compatible regular expressions, with subpattern support and default patterns for..R, tenth bullet point, Bluefish Features, Bluefish website, retrieved 9 July 2008.
  24. ^ Stephan Wahlbrink. "StatET: Eclipse based IDE for R". Diakses tanggal 2009-09-26. 
  25. ^ "Tinn-R Editor - GUI for R Language and Environment". Tinn-R Team. Diakses tanggal 2010-11-07. 
  26. ^ Jose Claudio Faria. "R syntax". Diakses tanggal 2007-11-03. 
  27. ^ "Syntax Highlighting". Kate Development Team. Diarsipkan dari versi asli tanggal 2008-07-07. Diakses tanggal 2008-07-09. 
  28. ^ "NppToR: R in Notepad++". sourceforge.net. Diakses tanggal 2010-07-11. 
  29. ^ RPy home page
  30. ^ Statistics::R page on CPAN
  31. ^ RSRuby rubyforge project
  32. ^ littler web site
  33. ^ Robert A. Muenchen. "The Popularity of Data Analysis Software". 
  34. ^ Comparison of R to SAS, Stat and SPSS
  35. ^ Vance, Ashlee (2009-01-07). "Data Analysts Captivated by R's Power". The New York Times. 
  36. ^ http://blogs.sas.com/sascom/index.php?/archives/434-This-post-is-rated-R.html#c1801
  37. ^ 'Red Hat for stats' goes toe-to-toe with SAS
  38. ^ http://cran.fhcrc.org/web/packages/RODM/index.html
  39. ^ http://spotfire.tibco.com/community/blogs/stn/archive/2010/03/08/spotfire-integration-with-s-and-r.aspx
  40. ^ http://www.unt.edu/benchmarks/archives/2007/october07/rss.htm
  41. ^ http://www.statsoft.com/solutions/r-language-platform/
  42. ^ R” integrated with Symphony

External links

  • Situs web resmi of the R project
  • The R wiki, a community wiki for R
  • R books, has extensive list (with brief comments) of R-related books
  • The R Graphical Manual, a collection of R graphics from all R packages, and an index to all functions in all R packages
  • R seek, a custom frontend to Google search engine, to assist in finding results related to the R language

Templat:Numerical analysis software Templat:Statistical software id:R (bahasa pemrograman)