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R Programming Basics and History - helpcodes.me

 R Programming



Our real goal is not only to teach you R, but also to teach you the basic concepts on which programming depends. In each new tutorial we build on what we have already learned and go one step further.

    

  1. The R programming language and development environment is open source and has gained popularity since its conception in the early 1990s by Ross Ihaka and Robert Gentlemen at the University of Auckland in New Zealand. Learn R with a variety of online courses and lessons from Harvard, MIT and other leading universities in the world.
  2. The R programming language, the free software environment and graphics are supported by the R Foundation for Statistical Computing for statistical computation. The R programming environment runs on a variety of Unix platforms, Linux, Windows and Mac OS. The command-line interface has several third-party graphical user interfaces, such as the integrated development environment RStudio and the interface Jupyter Notebook.
  3. R was designed by Ross Ihaka and Robert Gentlemen at the University of Auckland in New Zealand and developed by the R Development Team. \. R is not the only language you can use for statistical calculations and graphs.   
  4. The best machine learning algorithms are implemented in R packages such as Keras and TensorFlow, which enable high-quality machine learning techniques to be developed. R packages work very well, like XGBoost, which is one of the best algorithms in the Kaggle competition. R is the first choice for statisticians, mathematicians and professional programmers who prefer to implement new algorithms in a programming language they know.    
  5. R has functions for many plot types, including graphics, maps, mosaic plots, biplots and the list goes on. It is possible to run Python, Java and C + + from R. The world of big data is accessible from R and can be linked to various databases such as Spark and Hadoop. R has CRAN, a comprehensive archive and network repository that contains more than 10,000,000 packages.    
  6. R packages An R package is a collection of R functions that compile code into sample data. The R programming language welcomes and offers bug fixes, code enhancements and new packages. It is also a comprehensive statistical analysis package and new technologies and ideas appear in R. R is open source so you can run it at any time and sell it without conditions or licenses.    
  7. In recent years, the open source project R has become the leading statistical computing platform. With its unsurpassed libraries and available packages, R is able to solve statistical derivative problems. The R programming language is one of the most sought after programming languages on the data science employment market, making it a hot trend.    
  8. In this course you will learn how to use over 100 functions and operators from R-based packages that serve as basic tools to access data from multiple sources, manipulating different types of R objects, manipulating characters and creating reports.    
  9. Typically used by statisticians and other practitioners who require a statistical and software development environment that works in a general matrix toolbox with performance benchmarks comparable to GNU Octave and Matlab. R supports procedural programming, function by function, object-oriented programming and generic functions.    
  10. These R functions have been enhanced with custom [39] packages that enable special statistical techniques, import and export functions of graphical devices and reporting tools (e.g. Rmarkdown, strickr and Sweave). R also includes a library, ggplot2, which provides its users with aesthetic graphics.
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  12. With the r-shiny package, you can embed your visualizations to enhance the story of your data analysis with aesthetic visualizations. With this package you can develop interactive dashboards and consoles in your R IDE.    
  13. There are a great deal of documentation for the standard R programming language, packages such as the comprehensive R Archive Network (CRAN), [2] many resources for reference books, forums such as StackOverflow, [3] and personal blogs [4], but these resources are scattered and can be difficult to find and compare. There is an extensive catalogue of statistical and graphical methods including machine learning algorithms, linear regression, time series and statistical conclusions, to name just a few. Most R libraries are written in R, but C, C + + and Fortran code are preferred for heavy arithmetic tasks.    
  14. Before proceeding with this tutorial, you should have a basic understanding of computer programming terminology. You do not have time to learn how these two tools compete with each other, especially if you are just beginning to learn data science. It is a bad way to point out the virtues of a difficult task, and if you feel that way before you can tackle it alone, you end up feeling defeated and wasting your time.    
  15. The presented material is not logical and introduces functions with which we are not familiar, without very little explanation. For example, he uses the rnorm function from the beginning to generate arbitrary numbers and works backwards, so last week I devoted a random number simulation to the function he used all the time.    
  16. At the end of the course, you should have a solid foundation to start analyzing the data. Now that we have control over the course of our program, we can create our first function.

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