R Programming Succinctly:
The R programming language on its own is a powerful tool. That can perform thousands of statistical tasks. But by writing programs in R, you gain tremendous power and flexibility to extend its base functionality. Senior Succinctly series author and editor James McCaffrey shows you how in R Programming Succinctly. The R programing language is designe to perform statistical analyses. Surveys of programming languages show that the use of R is increasing rapidly. And apparently in conjunction with the increasing collection of data. R can be use in two distinct ways. Most commonly, R is use as an interactive tool.
It’s no secret that the best way to learn a programming language or technology is to use it. Although you can probably learn quite a bit about R. Simply by reading this e-book, you’ll learn a lot more if you install R. And run the demo programs that accompany each section. Installing R is relatively quick and easy. If you’re using a Linux system, the installation process varies quite a bit.Also Depending on which flavor of Linux you’re running. But there are many step-by-step installation guides available on the Internet.
In conclusion Notice the webpage title reads “(32/64 bit)”. By default, on a 64-bit system (which you’re almost certainly using), the R installer will give you a 32-bit version of R. And also a 64-bit version. The 32-bit version is for old machines and for backward compatibility with older R add-on packages.Therefore That can only use the 32-bit version of R.
Advanced R Programming:
The R programming language contains all the features needed to create very sophisticated programs. This chapter presents three topics random number generation, neural networks, and combinatorial optimization. That illustrate the power of R, demonstrate useful R programming techniques. And give you starting points for a personal code library. Most programming languages allow you to create multiple instances of random number generators. The R language uses a single, global-system RNG. That is somewhat limiting. However, you can write your own RNG class in R.
In addition There are many different RNG algorithms. Among the most common are the linear congruential algorithm, the Lehmer algorithm, the Wichmann-Hill algorithm, and the Lagg Fibonacci algorithm. Neural network as a complicated math function that accepts numeric input values. Does some processing in a way that loosely models biological neurons. And produces numeric output values that can be interpret as predictions. A combinatorial-optimization problem occurs. When the goal is to arrange a set of items in the best way.
The traveling salesman problem (TSP) is arguably the most well-known combinatorial-optimization problem. And bee colony optimization (BCO), an algorithm that loosely models the behavior of honey bees. It is use to find a solution for TSP. BCO is a metaheuristic rather than a prescriptive algorithm. This means BCO is a set of general design guidelines. That can be implement in many ways. The demo program models a collection of synthetic bees. Each bee has a path to a food source. And an associated measure of that path’s quality. There are two kinds of bees—worker bees try different paths in a controll fashion. And scout bees try random paths.
Table of Contents:
2:Vectors and Functions
4:Permutations and Combinations
5:Advanced R Programming