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Sas Business Analytics And Business Intelligence Software Wiki

Sas Business Analytics And Business Intelligence Software Wiki

Sas Business Analytics And Business Intelligence Software Wiki – R is a statistical computing and graphics programming language supported by the R Core Team and the R Foundation for Statistical Computing. Created by statisticians Ross Ihaka and Robert Gtleman, R is used among data miners, bioinformaticians, and statisticians for data analysis and statistical software development.
According to user surveys and research of scientific literature databases, R is one of the most widely used programming languages ​​used in data mining.
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R is ranked 11th in the TIOBE index, a measure of popularity of programming languages, where the language peaked at 8th in August 2020.
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The official R vironmt software is free op-source software in a GNU package, available under the GNU Geral Public Licse. It is written mostly in C, Fortran and just R (partially self-hosted). Pre-compiled executables for various operating systems are available. R has a command line interface.
Many third-party graphical user interfaces are also available, such as RStudio, an integrated development environment, and Jupyter, a notebook interface.
R was started by Professors Ross Ihaka and Robert Gtleman as a programming language to teach introductory statistics at the University of Auckland.
The language took a lot of inspiration from the S programming language, with most S programs being able to run unchanged in R
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The name of the language comes from being the successor of the S language and the common first letter of the authors, Ross and Robert.
Ihaka and Gtleman first released the R binaries on the StatLib data archive and s-news mailing list in August 1993.
In June 1995, statistician Martin Mächler convinced Ihaka and Gtleman to make R free and op-source under the GNU Geral Public Licse.
The Comprehsive R Archive Network (CRAN) was founded in 1997 by Kurt Hornik and Fritz Leisch to host R source code, executables, documentation, and user-created packages. Its name and scope mimic the TeX comprehensive archives network and the Perl comprehensive archives network.
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, consists of Chambers, Gtleman, Ihaka and Mächler, as well as statisticians Douglas Bates, Peter Dalgaard, Kurt Hornik, Michael Lawrce, Friedrich Leisch, Uwe Ligges, Thomas Lumley, Sebastian Meyer, Paul Murrell, Martyn Plummer, Brian Ripley, Deepayan Sarkar , Duncan Temple Lang, Luke Tierney and Simon Urbanek, as well as computer scientist Tomas Kalibera. Stefano Iacus, Guido Masarotto, Heiner Schwarte, Seth Falcon, Martin Morgan and Duncan Murdoch were members.
Vectors are ordered collections of values ​​that can be mapped to arrays of one or more dimensions in the order of the main column. That is, giving an ordered set of dimensions, you first fill the values ​​along the first dimension, th fills the one-dimensional arrays in the second dimension, and so on.
A special case of an array with two dimensions is called a matrix. Lists serve as collections of objects that do not necessarily have the same data type. Data frames contain a list of vectors of the same length and a unique set of row names.
R and its libraries implement various statistical techniques, including linear, geralized linear and non-linear modeling, classical statistical tests, spatial and time series analysis, classification, clustering, and more. For computationally intensive tasks, C, C++, and Fortran code can be combined and called at runtime. Another strength of R is the static graphics; can create publication-quality charts that contain mathematical symbols.
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The extensible R object system includes objects for (among others): regression models, time series, and geospatial coordinates. Advanced users can write in C, C++,
Functions are first-class objects and can be manipulated in the same way as data objects, facilitating metaprogramming that allows multiple dispatches. Function arguments are passed by value and are lazy – that is, they are evaluated only when they are used, not when the function is called.
The geric function behaves differently depending on the classes of arguments passed to it. In other words, the geric function calls a method implementation specific to the class of that object. For example, R has geric
Packages that offer statistical techniques, graphics devices, import/export, reporting (RMarkdown, knitr, Sweave), etc. These packages and their ease of installation and use are cited as contributing to the widespread adoption of the language in data science.
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The packaging system is also used by researchers to organize research data, coding, and report files in a systematic way for sharing and archiving.
Co-development platform R. The Bioconductor project provides packages for gomic data analysis, including object-oriented data handling and data analysis tools from Affymetrix, cDNA microarrays and next-generation high-throughput sequencing methods.
A group of packages called Tidyverse, which can be considered a “dialect” of the R language, is gaining popularity among programmers.
It tries to provide a consistent set of features to handle common data science tasks, including data import, cleaning, transformation, and visualization (especially with the ggplot2 package). Dynamic and interactive graphics are available as additional packages.
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R is one of the 5 languages ​​with the Apache Spark API, along with Scala, Java, Python, and SQL.
This is the last alpha version developed mainly by Ihaka and Gtleman. Many basic functions from the “White Book” have been implemented (see story S). Mailing lists started on April 1, 1997.
Packages are byte-compiled by default during installation. A compact internal representation of integer strings. A new serialization format has been added to support compact internal mappings.
. Reference counting is used to track object sharing, which reduces the need to copy objects. New syntax for raw string constants.
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R uses UTF-8 as native encoding on Windows rect systems. Removed support for 32-bit builds of Windows. Vocation
IDEs for R include (in alphabetical order) (OSX/macOS only), Rattle GUI, R Commander, RKWard, RStudio, and Tinn-R.
Several other implementations to improve speed or increase extensibility. A closely related implementation is pqR (quite fast R) by Radford M. Neal with improved memory management and support for automatic multithreading. Rjin and FastR are Java implementations of R for use in the Java Virtual Machine. CXXR, rho and riposte
Are implementations of R in C++. Rjin, Riposte and pqR try to improve performance by using multiple cores and deferred evaluation.
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Most of these alternative implementations are experimental and incomplete, with relatively few users compared to the main implementation maintained by the R Development Core Team.
TIBCO, which previously sold a commercial implementation of S-PLUS, built a runtime gine called TERR which is part of Spotfire.
The R Foundation supports two conferences, useR! and Directions in Statistical Computing (DSC) and several others such as [email protected], ConectaR, LatinR and R Day.
R Journal is the op-access, peer-reviewed journal of the R project. It contains short and medium articles on using and developing R, including packages, development tips, CRAN news, and foundation news.
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In January 2009, the New York Times published an article outlining the development of R, noting its extensibility with user-created packages, as well as the op-source nature of R as opposed to SAS.
R has precompiled binaries for Windows, macOS, and Linux with the option to compile and install R from source code.
SAS can only store data in rectangular datasets, while R’s more versatile data structures allow more flexibility to perform difficult analyses. Full feature integration in SAS requires a development kit, but in R user-defined features are already on par with supplied features.
In a technical report by Patrick Burns, respondents felt that R was more appropriate for periodic reporting, but preferred SAS for big data issues.
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Stata and R are designed to be easily expandable. The output data in both programs are structured in such a way that they become input data for further analysis. They store data in main memory, which increases performance but limits the amount of data they can handle. R is free software while Stata is not.
Python and R are interpreted, dynamically typed duck programming languages ​​that can be extended by importing packages. Python is a general purpose programming language while R is specifically designed to perform statistical analysis. Python has a BSD-like license unlike GNU Geral Public Licse R, but still allows modification of the language implementation and tools.
In 2007, Richard Schultz, Martin Schultz, Steve Weston, and Kirk Mettler founded Revolution Analytics to provide commercial support for Revolution R, their R distribution that includes components developed by the company. Major additional components include ParallelR, the R Productivity vironmt IDE, RevoScaleR (for big data analytics), RevoDeployR, a web services platform, and the ability to read and write data to SAS file format.
Revolution Analytics offers an R distribution designed according to established IQ/OQ/PQ criteria that allows clitoris in the pharmaceutical sector to verify their REvolution R installation.
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And integrated the R programming language with SQL Server, Power BI, Azure SQL Managed Instance, Azure Cortana Intelligce, Microsoft ML Server and Visual Studio 2017.
In October 2011, Oracle announced Big Data Appliance which integrates R, Apache Hadoop, Oracle Linux and NoSQL database with Exadata hardware.
To comply with drug approval agencies such as the FDA. These agencies required the use of proven software, confirmed by vdor or sponsor.
The following examples illustrate basic language syntax and command-line interface usage. (For an extended list of standard language features, see the R manual “Introduction to R”.
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One of the strengths of R is how easy it is to create new features. Objects in the function body remain local to the function and any type of data can be returned.

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