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by Graham Williams
Duck Duck Go


Tooling For R Programming

20210103 We will first need to have available to us the computer software that interprets our programs that we write as sentences in the R language. Usually we will install this software, called the R Interpreter, on our own computer and instructions for doing so are readily available on the Internet. The https://www.r-project.orgR Project is a good place to start. Most GNU/Linux distributions freely provide R packages from their application stores and a commercial offering is available from Microsoft who also provide direct access within SQL Server and through Cortana Analytics on the Azure cloud. Now is a good time to install R on your own computer if that is required. The open source and freely available https://www.rstudio.com software is recommended as a modern integrated development environment (IDE) for writing R programs. It can be installed on common desktop computers or servers running GNU/Linux, Microsoft/Windows, or Apple/MacOS. A browser version of also allows a desktop to access R running on a back-end cloud server. This version of runs on a remote computer (e.g., a server in the cloud) and provides a graphical interface presented within a web browser on our own desktop. The interface is much the same as the desktop version but all of the commands are run on the server rather than on our own desktop. Such a setup is useful when we require a powerful server on which to analyse very large datasets. We can then control the analyses from our own desktop with the results sent from the server back to our desktop whilst all the computation is performed on the server itself which may be a considerably more powerful computer than our usually less capable personal computers. An alternative cloud server setup is to connect to a remote cloud server using X2Go as a remote desktop protocol.
Support further development by purchasing the PDF version of the book.
Other online resources include the GNU/Linux Desktop Survival Guide.
Books available on Amazon include Data Mining with Rattle and Essentials of Data Science.
Popular open source software includes rattle and wajig.
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