Kam Eley

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Posts: 4

Robert Kabacoff

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posted 6 years ago

Hi Kam,

I'm not sure what you mean by "what technologies R is dependent on". R is an open source case-senstive interpreted language and platform for data analysis and graphics. It is similar to the S Language originally developed at Bell Labs.

Here is the description from the R project homepage:

The description is a bit dry and out of date. For example, R is object oriented, you can imbed R functionality in programs written in other languages (e.g., Java), and the number of add-on packages now number in the thousands. Everything in R is done through function calls, and you can write your own functions to modify or build upon existing functions. This lets you carry out complex and involved analyses and data manipulations with a few lines of code. And of course, most people are originally attracted to R for its extensive graphics capabilites.

I'm not sure what you mean by "what technologies R is dependent on". R is an open source case-senstive interpreted language and platform for data analysis and graphics. It is similar to the S Language originally developed at Bell Labs.

Here is the description from the R project homepage:

R is a system for statistical computation and graphics. It consists of a language plus a run-time environment with graphics, a debugger, access to certain system functions, and the ability to run programs stored in script files.

The design of R has been heavily influenced by two existing languages: Becker, Chambers & Wilks' S and Sussman's Scheme. Whereas the resulting language is very similar in appearance to S, the underlying implementation and semantics are derived from Scheme.

The core of R is an interpreted computer language which allows branching and looping as well as modular programming using functions. Most of the user-visible functions in R are written in R. It is possible for the user to interface to procedures written in the C, C++, or FORTRAN languages for efficiency. The R distribution contains functionality for a large number of statistical procedures. Among these are: linear and generalized linear models, nonlinear regression models, time series analysis, classical parametric and nonparametric tests, clustering and smoothing. There is also a large set of functions which provide a flexible graphical environment for creating various kinds of data presentations. Additional modules are available for a variety of specific purposes.

The description is a bit dry and out of date. For example, R is object oriented, you can imbed R functionality in programs written in other languages (e.g., Java), and the number of add-on packages now number in the thousands. Everything in R is done through function calls, and you can write your own functions to modify or build upon existing functions. This lets you carry out complex and involved analyses and data manipulations with a few lines of code. And of course, most people are originally attracted to R for its extensive graphics capabilites.

Charles Caplan

Greenhorn

Posts: 2

Robert Kabacoff

author

Ranch Hand

Ranch Hand

Posts: 33

5

posted 6 years ago

R is actually used quite heavily in finance. See for example, the CRAN Task view on Empirical Finance, Statistics and Finance: An Introduction by David Ruppert, or Statistical Analysis in Financial Data in S-Plus (and now R) by Rene Carmona. The Rmetrics website is also interesting. Finally, there is an annual an R/Finance conference every year.

Kam Eley

Greenhorn

Posts: 4