Announcing pqR: A faster version of R

Great innovation on R by Radford Neal

Radford Neal's blog

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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What an R is!!

R introduction:

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.

  • Data extraction
  • Data cleaning
  • Data extraction
  • Data loading
  • Data transformation
  • Statistical analysis
  • Predictive modeling
  • Data visualization

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.

R features:

  1. Effective programming language
  2. Various database support
  3. Data Analytics
  4. Data Visualization
  5. Functional extensions by R packages

R popularity:    

Based on the graph provided from KDnuggests, R is the most used data mining language.



R users:

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:


How to Install JAVA on Ubuntu OPerating System

procedures for installing java on ubuntu

1.first download jdk.tar.gz(
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..