Introduction to R Programming
When you see powerful analytics, statistics, and visualizations used by data scientists and business leaders, chances are that the R language is behind them. Open-source R is the statistical programming language that data experts the world over use for everything from mapping broad social and marketing trends online to developing financial and climate models that help drive our economies and communities.
R was first implemented in the early 1990’s by Robert Gentleman and Ross Ihaka, both faculty members at the University of Auckland. The R language was closely modeled on the S Language for Statistical Computing conceived by John Chambers, Rick Becker, Trevor Hastie, Allan Walks and others at Bell Labs in the mid 1970s, and made publicly available in the early 1980’s.
Robert and Ross established R as an open source project in 1995.Since 1997; the R project has been managed by the R Core Group. And, in February 2000, came the first release of R. See also Ross Ihaka brief account of how R got started highlights some of the connections between R and S.
They, along with many others, kept working on and using R. They continue to create new tools for R and find new applications for R every day. There are over 10,000 user-created libraries that were built to enhance R functionality. These packages have crowded sourced quality-validation and support from recognized leaders in every field. All of this is great because R is the best at what it does: The R programming language includes functions that support linear modeling, non-linear modeling, classical statistics, classifications, clustering and more.
It has remained popular in academic settings due to its robust features and the fact that it is free to download in source code form under the terms of the Free Software Foundation's GNU general public license. It compiles and runs on UNIX platforms and other systems including Linux, Windows and MacOS.
The appeal of the R language has gradually spread out of academia into business settings, as many data analysts who trained on R in college prefer to continue using it rather than pick up a new tool with which they are inexperienced.
The R software environment
The R language programming environment is built around a standard command-line interface. Users leverage this to read data and load it to the workspace, specify commands and receive results. Commands can be anything from simple mathematical operators, including +, -, * and /, to more complicated functions that perform linear regressions and other advanced calculations.
Users can also write their own functions. The environment allows users to combine individual operations, such as joining separate data files into a single document, pulling out a single variable and running a regression on the resulting data set, into a single function that can be used over and over.
Looping functions are also popular in the R Programming Training in Gurgaon, environment. These functions allow users to repeatedly perform some action, such as pulling out samples from a larger data set, as many times as the user wants to specify.
We Provides Other Courses Graphics Designing Training in Delhi, Android Training in Delhi, Web Designing Training in Delhi, VMware Training in Delhi, Google SketchUp Training in Delhi etc.
Comments