Python Programming Language - Run Code & Learn Quickly with Fundamentals Tutorial
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The classic R programming language for iPad, iPhone and iPod touch. Programming language is a perfect tool for studying, complex mathematical calculation, entertainment and many other useful tasks. The application is especially useful for learning the R programming language. You have to buy compilations inside the application. Internet connection is required.
- The great programming tool on the AppStore.
- Your programming language for iOS is amazing!
* FEATURES *
- Compile and run your program.
- Text input before program run and text output.
- Enhanced source code editor with syntax highlighting, line numbers, color themes and additional keyboard.
- Online language reference and several program samples.
* LIMITATIONS *
- Internet connection is required to compile and run a program.
- Graphics, network, file system and real-time input are not supported.
- Maximum running time of a program is 15 seconds.
- Plotting is not supported at this moment.
Thanks for using the application!
R is a programming language and software environment for statistical computing and graphics. The R language is widely used among statisticians for developing statistical software and data analysis.
R is an implementation of the S programming language combined with lexical scoping semantics inspired by Scheme. S was created by John Chambers while at Bell Labs. R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and now, R is developed by the R Development Core Team, of which Chambers is a member. R is named partly after the first names of the first two R authors (Robert Gentleman and Ross Ihaka), and partly as a play on the name of S.
R is part of the GNU project. The source code for the R software environment, which is written primarily in C, Fortran, and R. R is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems. R uses a command line interface; however, several graphical user interfaces are available for use with R.
R provides a wide variety of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and others. R is easily extensible through functions and extensions, and the R community is noted for its active contributions in terms of packages. There are some important differences, but much code written for S runs unaltered. Many of R's standard functions are written in R itself, which makes it easy for users to follow the algorithmic choices made. For computationally intensive tasks, C, C++, and Fortran code can be linked and called at run time. Advanced users can write C or Java code to manipulate R objects directly.
R is highly extensible through the use of user-submitted packages for specific functions or specific areas of study. Due to its S heritage, R has stronger object-oriented programming facilities than most statistical computing languages. Extending R is also eased by its permissive lexical scoping rules.
According to Rexer's Annual Data Miner Survey in 2010, R has become the data mining tool used by more data miners (43%) than any other.
Another strength of R is static graphics, which can produce publication-quality graphs, including mathematical symbols. Dynamic and interactive graphics are available through additional packages.
R has its own LaTeX-like documentation format, which is used to supply comprehensive documentation, both on-line in a number of formats and in hard copy.
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