R Programming Language - Asia B Technology

R Programming Language

R is a programming language and software for statistical and graph analysis. Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is now developed by the R Development Core Team, of which Chambers is a member. R is named partly after the names of its two makers (Robert Gentleman and Ross Ihaka), and partly after the name S.

The R language is now the de facto standard among statisticians for the development of statistical applications, and is widely used for the development of statistical software and data analysis.

R is part of the GNU project. The source code is freely available under the GNU General Public License, and binary versions are available for a variety of operating systems. R uses a commandbar interface, although some graphical user interfaces are also available.

R provides a wide range of techniques (linear and nonlinear modeling, classical statistical tests, current analysis, structured division of regards, clustering, etc.) as well as graphs. R, like S, is made into the computer’s actual personal language, and allows its users to add additional functions using defining new functions. Another great strength of R is its graphics facilities, which produces publication-quality graphics that can contain mathematical symbols. Rme has documentation formats such as LaTeX, which is used to provide complete documentation, both online (in various formats) and in print. http://id.wikipedia.org/wiki/R_persen28bahasa_pemrogramanpersen29

Why use R? http://www.unej.ac.id/files/pdf2/sigit-mengenal-aplikasi-statistik-R.pdf

Data analysis cannot be done without doing the right techniques. That is, reliable analysis should utilize accuracy & speed of calculation using statistical computing packages. So many choices of statistical tool packages that we use such as SPSS, EVIEWS, STATA, MINITAB, SAS and so on. Each tool is designed for users who have bhineka characteristics and certainly have fatigue and shortcomings. According to Ihaka and Gentleman (1996), there are several reasons why using R, including:

R is a programming language, so there are no limits for users to use

a mechanism that only remains in the standard package. Even R programming is object-oriented and has a very useful poly library developed by programming r contributors. Users are free to add & decrease libraries depending on their needs. R also has a programming interface C, python, even java which of course thanks to the hard work of active contributors to the R project. So in addition to this R language is relatively smart, its users can also become smarter and creative. Some analyses that require advanced functions are not yet available on R. Does not mean R does not provide the facility, but rather due to the current factor. So just waiting for the time, the advanced package was available.

At this time data analysis requires interactive operation. Especially if the data analyzed is moving data. R is equipped with connectivity to database servers, olap, as well as web service data formats such as XML, spreadsheets and so on. So that if the data set changes the analysis output can immediately change (real time).

three. S-based is a derivative based on the commercial statitistic tool S-Plus.

R is almost entirely compatible with the S-Plus. This means that most of the program code created by the S can be run on S-plus except for functions that are add-on packages or additionally formed by R project contributors.

Generically SAS is a well-known commercial statistical application, but nevertheless R or S is the most popular language used by researchers in the field of statistics. Some writings in the form of statistical journals confirm the truth of this. R is also well known for quantitative applications in the field of finance.

Fairly Ihaka & Gentlemen reveal the shortcomings according to R is not easy to learn. Some initial requirements are expected before benefiting from R obtained for example an understanding of the basics of programming. But in the author’s opinion, linux users should have an understanding of the basics of programming so that it will be easier and productive to expolitize this great statistical tool.

If the installation process has been completed then it is time to try R. Type the following command:

if the display below appears, R is successfully installed properly (version R depends on your ubuntu version).

Copyright (C) 2009 The R Foundation for Statistical Computing

R is free software and comes with ABSOLUTELY NO WARRANTY.

You are welcome to redistribute it under certain conditions.

Type ‘license()’ or ‘licence()’ for distribution details.

Natural language support but running in an English locale

R is a collaborative project with many contributors.

Type ‘contributors()’ for more information and

‘citation()’ on how to cite R or R packages in publications.

Type ‘demo()’ for some demos, ‘help()’ for on-line help, or

‘help.start()’ for an HTML browser interface to help.

[Previously saved workspace restored]

Try a demo on a built-in package/plugin

The installation results contain packages or plugins that have been able to try. Some

package provides demonstrations in the form of archives & commands that can be directly on the execution.

Let’s try the demo that is already available. Just type the commands below.

The result is as follows.

is.things Explore some properties of R objects and

is. FOO() functions. Not for newbies!

recursion Using recursion for adaptive integration

scoping An illustration of lexical scoping.

Demos in package ‘graphics’:

Hershey Tables of the characters in the Hershey vector

Japanese Tables of the Japanese characters in the

graphics A show of some of R’s graphics capabilities

image The image-like graphics builtins of R

persp Extended persp() examples

plotmath Examples of the use of mathematics annotation

glm.vr Some glm() examples from Vdanamp; R with several

lm.glm Some linear and generalized linear modelling

examples from ‘An Introduction to Statistical

nlm Nonlinear least-squares using nlm()

smooth ‘Visualize’ steps in Tukey’s smoothers

Use ‘demo(package = .packages(all.available = TRUE))’

to list the demos in all *available* packages.

We can select through the demo() function by typing the format command

“demo(nama_demo)”. For example type the following command:

The result is to be the following:

Advantages and Disadvantages of Open Source R

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