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R (programming language)
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- Snippet from Wikipedia: R (programming language)
R is a programming language for statistical computing and data visualization. It has been adopted in the fields of data mining, bioinformatics, and data analysis.
The core R language is augmented by a large number of extension packages, containing reusable code, documentation, and sample data.
R software is open-source and free software. It is licensed by the GNU Project and available under the GNU General Public License. It is written primarily in C, Fortran, and R itself. Precompiled executables are provided for various operating systems.
As an interpreted language, R has a native command line interface. Moreover, multiple third-party graphical user interfaces are available, such as RStudio—an integrated development environment—and Jupyter—a notebook interface.
Introduction to R
“R is a language and environment for R statistical computing and R graphics. It is a R GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by R John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R.”
“R provides a wide variety of R statistical (R linear modelling and R nonlinear modelling, R classical statistical tests, R time-series analysis, R classification, R clustering, …) and R graphical techniques, and is highly R extensible. The S language is often the vehicle of choice for R research in R statistical methodology, and R provides an R Open Source route to participation in that activity.”
“One of R’s strengths is the ease with which well-designed publication-quality R plots can be produced, including R mathematical symbols and R mathematical formulae where needed. Great care has been taken over the R defaults for the minor R design choices in R graphics, but the user retains full control.”
“R is available as R Free Software under the terms of the Free Software Foundation’s GNU General Public License in R source code form. It R compiles and runs on a wide variety of R UNIX platforms and similar R systems (including R FreeBSD and R Linux), R Windows and R macOS.”
The R environment
R environment: “R is an integrated suite of R software facilities for R data manipulation, R calculation] and R graphical display. It includes:
- an effective R data handling and R storage facility,
- a large, R coherent, R integrated R collection of intermediate R tools for R data analysis, R graphical facilities for R data analysis and R display either on-screen or on hardcopy, and
- a well-developed, R simple and R effective programming language which includes R conditionals, R loops, R user-defined recursive functions and R input and R output facilities.
The R term “R environment” is intended to characterize it as a fully planned and coherent system, rather than an incremental accretion of very specific and inflexible tools, as is frequently the case with other data analysis software.”
“R, like S, is R designed around a true computer language, and it allows R users to add additional R functionality by defining new R functions. Much of the R system is itself written in the R dialect of S, which makes it easy for users to follow the R algorithmic choices made. For computationally-intensive R tasks, C, C++ and Fortran code can be R linked and R called at R run time. Advanced R users can write C code to manipulate R objects directly.”
“Many R users think of R as a statistics system. We prefer to think of it as an environment within which R statistical techniques are R implemented. R can be R extended (easily) via R packages. There are about eight packages supplied with the R distribution and many more are available through the R CRAN family of R Internet sites covering a very wide range of R modern statistics.”
R has its own R LaTeX-like documentation format, which is used to supply comprehensive R documentation, both R on-line in a number of formats and in hardcopy.“
Fair Use Source: https://www.r-project.org/about.html (RProjAbout
Why use R?
“R is a language and environment for statistical computing and graphics, similar to the S language originally developed at Bell Labs. It’s an open source solution to data analysis that’s supported by a large and active worldwide research community. But there are many popular statistical and graphing packages available (such as Microsoft Excel, SAS, IBM SPSS, Stata, and Minitab). Why turn to R?” (RinAct 2022)
R has many features to recommend it:” (RinAct 2022)
- Most commercial statistical software platforms cost thousands, if not tens of thousands, of dollars. R is free! If you’re a teacher or a student, the benefits are obvious.
- R is a comprehensive statistical platform, offering all manner of data-analytic techniques. Just about any type of data analysis can be done in R.
- R has state-of-the-art graphics capabilities. If you want to visualize complex data, R has the most comprehensive and powerful feature set available.
- R is a powerful platform for interactive data analysis and data exploration. From its inception, it was designed to support the approach outlined in figure 1.1. For example, the results of any analytic step can easily be saved, manipulated, and used as input for additional analyses.
- Getting data into a usable form from multiple sources can be a challenging proposition. R can easily import data from a wide variety of sources, including text files, database-management systems, statistical packages, and specialized data stores. It can write data out to these systems as well. R can also access data directly from web pages, social media sites, and a wide range of online data services.
- R provides an unparalleled platform for programming new statistical methods in an easy, straightforward manner. It’s easily extensible and provides a natural language for quickly programming recently published methods.
- R functionality can be integrated into applications written in other languages, including C++, Java, Python, PHP, Pentaho, SAS, and SPSS. This allows you to continue working in a language that you may be familiar with, while adding R’s capabilities to your applications.
- R runs on a wide array of R platforms, including R on Windows, R on Unix-R on Linux, and R on MacOS. It’s likely to run on any computer you may have. (I’ve even come across guides for installing R on an iPhone, which is impressive but probably not a good idea.)
- If you don’t want to learn a new language, a variety of R graphic user interfaces (R GUIs]]) are available, offering the power of R through menus and dialogs.“ (RinAct 2022)
Installing R
Windows
Chocolatey v0.11.2
Installing the following packages:
By installing, you accept licenses for the packages.
Progress: Downloading R.Project 4.1.1… 100%
R.Project v4.1.1 [Approved]
r.project package files install completed. Performing other installation steps.
Installing r.project…
r.project has been installed.
r.project can be automatically uninstalled.
The install of r.project was successful.
Software installed to 'C:\Program Files\R\R-4.1.1\'
Chocolatey installed 1/1 packages.
See the log for details (C:\ProgramData\chocolatey\logs\chocolatey.log).
macOS
With Homebrew using brew install
Linux
REPL
R version 4.1.1 (2021-08-10) – “Kick Things”
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
Natural language support but running in an English locale
R is a collaborative project with many contributors.
Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
Rscript
C:\Program Files\R\R-4.1.1\bin>Rscript.exe
Usage: /path/to/Rscript [–options] [-e expr [-e expr2 …] | file] [args]
–options accepted are
--help Print usage and exit --version Print version and exit --verbose Print information on progress --default-packages=list Where 'list' is a comma-separated set of package names, or 'NULL'or options to R, in addition to –no-echo –no-restore, such as
--save Do save workspace at the end of the session --no-environ Don't read the site and user environment files --no-site-file Don't read the site-wide Rprofile --no-init-file Don't read the user R profile --restore Do restore previously saved objects at startup --vanilla Combine --no-save, --no-restore, --no-site-file --no-init-file and --no-environ
'file' may contain spaces but not shell metacharacters
Expressions (one or more '-e <expr>') may be used *instead* of 'file' See also ?Rscript from within R
C:\Program Files\R\R-4.1.1\bin>Rscript.exe –version
R scripting front-end version 4.1.1 (2021-08-10)
RStudio
RStudio Installation
Windows
https://community.chocolatey.org/packages/R.Studio
Chocolatey v0.11.2
Installing the following packages:
r.studio
By installing, you accept licenses for the packages.
Progress: Downloading R.Studio 1.4.1717… 100%
R.Studio v1.4.1717 [Approved]
r.studio package files install completed. Performing other installation steps.
Downloading R.Studio
from 'https://download1.[[rstudio.org]]/[[desktop]]/[[windows]]/RStudio-1.4.1717.exe'
Progress: 100% - Completed download of
C:\Users\USERNAME\AppData\Local\Temp\chocolatey\R.Studio\1.4.1717\RStudio-1.4.1717.exe (148.95 MB).
Download of RStudio-1.4.1717.exe (148.95 MB) completed.
Installing R.Studio…
R.Studio has been installed.
r.studio may be able to be automatically uninstalled.
The install of r.studio was successful.
Software installed as 'exe', install location is likely default.
Chocolatey installed 1/1 packages.
See the log for details (C:\ProgramData\chocolatey\logs\chocolatey.log).
macOS
Linux
Fair Use Sources
R: R Fundamentals, R Inventor - R Language Designer: Ross Ihaka and Robert Gentleman in August 1993; R Core Team, R Language Definition on R-Project.org, R reserved words (R keywords), R data structures - R algorithms, R syntax, R input and Output, R data transformations, R probability, R statistics, R linear regression (ANOVA), R time series analysis, R graphics, R markdown, R OOP, R on Linux, R on macOS, R on Windows, R installation, R containerization, R configuration, R compiler - R interpreter (R REPL), R IDEs (RStudio, Jupyter Notebook), R development tools, R DevOps - R SRE, R data science - R DataOps, R machine learning, R deep learning, Functional R, R concurrency, R history, R bibliography, R glossary, R topics, R courses, R Standard Library, R libraries, R packages (tidyverse package), R frameworks, RDocumentation.org / CRAN, R research, R GitHub, Written in R, R popularity, R Awesome list, R Versions, Python. (navbar_r)
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