Great innovation on R by Radford Neal
pqR — a “pretty quick” version of R — is now available to be downloaded, built, and installed on Linux/Unix systems. This version of R is based on R-2.15.0, but with many performance improvements, as well as some bug fixes and new features. Notable improvements in pqR include:
- Multiple processor cores can automatically be used to perform some numerical computations in parallel with other numerical computations, and with the thread performing interpretive operations. No changes to R code are required to take advantage of such computation in “helper threads”.
- pqR makes a better attempt at avoiding unnecessary copying of objects, by maintaining a real count of “name” references, that can decrease when the object bound to a name changes. Further improvements in this scheme are expected in future versions of pqR.
- Some operations are avoided completely in pqR — for example, in pqR, the statement
for (i in 1:10000000) ...does not actually create a…
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R is an open source software package for performing the various data operations. R has its own programming language used by data scientist statisticians and others who need to make sense of data. R is registered under GNU General Public License. R was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R.
R provides a wide variety of statistical, machine learning (linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering,) and graphical techniques, and is highly extensible. R has various inbuilt as well as extended functions for statistical, machine learning and visualization tasks are like.
It’s one of the most popular statistical tools because of freely available, cross platform support, community support and huge numbers of R packages for data operations. With the large numbers of packages, R has easily communicate with other data storage like MySQL, SQLite, MongoDB and Hadoop and etc.
- Effective programming language
- Various database support
- Data Analytics
- Data Visualization
- Functional extensions by R packages
Based on the graph provided from KDnuggests, R is the most used data mining language.
This graph provides the detail about total numbers of R packages released from the year 2005 to 20013. There is a very high peak value for the year 2012. And still counting for the year 2013.
R provides Data analytics by various statistical and machine learning operations as follows
Popular R Communities:
1.first download jdk.tar.gz(http://dc392.4shared.com/download/ZvD6aTLn/jdktar.gz?tsid=20110728-090825-a859e8c8)
2.unpack in to perticular folder
3.tar -xvzf jad.tar.gz
4.chmod a+x jdk1.5.0_17-linux-i586.bin
6.set PATH of bin directory using export command
7.set JAVA_HOME of jdk
OR try this video..