top of page

E-Nertia Global Ente Group

Public·58 members

R Project Challenges: Test Your Skills and Knowledge with Fun and Interactive Exercises

This directory contains binaries for the base distribution and of R and packages to run on macOS. R and package binaries for R versions older than 4.0.0 are only available from the CRAN archive so users of such versions should adjust the CRAN mirror setting ( accordingly.Note: Although we take precautions when assembling binaries, please use the normal precautions with downloaded executables.

This release uses Xcode 14.2/14.3 and GNU Fortran 12.2. If you wish to compile R packages which contain Fortran code, you may need to download the corresponding GNU Fortran compiler from Any external libraries and tools are expected to live in /opt/R/arm64 (Apple silicon) or /opt/R/x86_64 (Intel). NEWS (for Mac GUI)News features and changes in the Mac GUIMac-GUI-1.79.tar.gz SHA1-hash: 26d6f142ca5130329a30e2c6b3ec8d4245265183Sources for the GUI 1.79 for macOS. This file is only needed if you want to join the development of the GUI (see also Mac-GUI repository), it is not intended for regular users. Read the INSTALL file for further instructions.Subdirectories: tools Additional tools necessary for building R for Mac OS X:Universal GNU Fortran compiler for Mac OS X (see R for Mac tools page for details). big-sur-arm64 Binaries for macOS 11 or higher (Big Sur) for arm64-based Macs (aka Apple silicon such as the M1 chip) big-sur-x86_64 Binaries for macOS 11 or higher (Big Sur) for Intel-based MacsBinaries for legacy macOS/OS X systems:R-4.2.3.pkg (notarized and signed)SHA1-hash: 82a1871bad7cd90c15117d46134183c4d38f8e14(ca. 89MB) for Intel Macs R 4.2.3 binary for macOS 10.13 (High Sierra) and higher, signed and notarized package. Contains R 4.2.3 framework, GUI 1.79 in 64-bit for Intel Macs, Tcl/Tk 8.6.6 X11 libraries and Texinfo 6.7. The latter two components are optional and can be ommitted when choosing "custom install", they are only needed if you want to use the tcltk R package or build package documentation from sources.Note: the use of X11 (including tcltk) requires XQuartz to be installed (version 2.7.11 or later) since it is no longer part of macOS. Always re-install XQuartz when upgrading your macOS to a new major version.This release uses Xcode 12.4 and GNU Fortran 8.2. If you wish to compile R packages from sources, you may need to download GNU Fortran 8.2 - see the tools directory. NOTE: package binaries for the following releases are no longer present on CRAN. Please use as the CRAN mirror if you want to install the corresponding package binaries. R-3.6.3.nn.pkg (signed) SHA1-hash: c462c9b1f9b45d778f05b8d9aa25a9123b3557c4 (ca. 77MB) R 3.6.3 binary for OS X 10.11 (El Capitan) and higher, signed package. Contains R 3.6.3 framework, GUI 1.70 in 64-bit for Intel Macs, Tcl/Tk 8.6.6 X11 libraries and Texinfo 5.2. The latter two components are optional and can be ommitted when choosing "custom install", they are only needed if you want to use the tcltk R package or build package documentation from sources. R-3.3.3.pkgMD5-hash: 893ba010f303e666e19f86e4800f1fbfSHA1-hash: 5ae71b000b15805f95f38c08c45972d51ce3d027(ca. 71MB)R 3.3.3 binary for Mac OS X 10.9 (Mavericks) and higher, signed package. Contains R 3.3.3 framework, GUI 1.69 in 64-bit for Intel Macs, Tcl/Tk 8.6.0 X11 libraries and Texinfo 5.2. The latter two components are optional and can be ommitted when choosing "custom install", it is only needed if you want to use the tcltk R package or build package documentation from sources.Note: the use of X11 (including tcltk) requires XQuartz to be installed since it is no longer part of OS X. Always re-install XQuartz when upgrading your OS X to a new major version. R-3.2.1-snowleopard.pkgMD5-hash: 58fe9d01314d9cb75ff80ccfb914fd65SHA1-hash: be6e91db12bac22a324f0cb51c7efa9063ece0d0(ca. 68MB)R 3.2.1 legacy binary for Mac OS X 10.6 (Snow Leopard) - 10.8 (Mountain Lion), signed package. Contains R 3.2.1 framework, GUI 1.66 in 64-bit for Intel Macs.This package contains the R framework, 64-bit GUI (, Tcl/Tk 8.6.0 X11 libraries and Texinfop 5.2. GNU Fortran is NOT included (needed if you want to compile packages from sources that contain FORTRAN code) please see the tools directory.NOTE: the binary support for OS X before Mavericks is being phased out, we do not expect further releases! The following directories contain older binaries. For even older versions, please see the CRAN archive. base Binaries of R builds for macOS 10.13 or higher (High Sierra), Intel build contrib Binaries of package builds for macOS 10.13 or higher (High Sierra), Intel build The new Cocoa GUI has been written by Simon Urbanek and Stefano Iacus with contributions from many developers and translators world-wide, see "About R" in the GUI.You may also want to read the R FAQ and R for Mac OS X FAQ. For discussion of Mac-related topics and reporting Mac-specific bugs, please use the R-SIG-Mac mailing list.Information, tools and most recent daily builds of the R GUI, R-patched and R-devel can be found at Please visit that page especially during beta stages to help us test the macOS binaries before final release!Package maintainers should visit CRAN check summary page to see whether their package is compatible with the current build of R for macOS.Binary libraries for dependencies not present here are available from and corresponding sources at modified: 2023/06/16, by Simon Urbanek

r project


R was started by professors Ross Ihaka and Robert Gentleman as a programming language to teach introductory statistics at the University of Auckland.[13] The language took heavy inspiration from the S programming language with most S programs able to run unaltered in R[6] as well as from Scheme's lexical scoping allowing for local variables.[1] The name of the language comes from being an S language successor and the shared first letter of the authors, Ross and Robert.[14] Ihaka and Gentleman first shared binaries of R on the data archive StatLib and the s-news mailing list in August 1993.[15] In June 1995, statistician Martin Mächler convinced Ihaka and Gentleman to make R free and open-source under the GNU General Public License.[15][16] Mailing lists for the R project began on 1 April 1997 preceding the release of version 0.50.[17] R officially became a GNU project on 5 December 1997 when version 0.60 released.[18] The first official 1.0 version was released on 29 February 2000.[13]

The R Core Team was formed in 1997 to further develop the language.[23][24] As of January 2022[update], it consists of Chambers, Gentleman, Ihaka, and Mächler, plus statisticians Douglas Bates, Peter Dalgaard, Kurt Hornik, Michael Lawrence, 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.[15][25] In April 2003,[26] the R Foundation was founded as a non-profit organization to provide further support for the R project.[27]

r project for statistical computing

r project download windows

r project tutorial

r project cran

r project documentation

r project packages

r project data analysis

r project machine learning

r project graphics

r project shiny

r project book

r project blog

r project online

r project linux

r project mac

r project ide

r project vs python

r project help

r project examples

r project code

r project github

r project courses

r project certification

r project jobs

r project salary

r project interview questions

r project resume

r project cheat sheet

r project dashboard

r project web scraping

r project text mining

r project sentiment analysis

r project natural language processing

r project image processing

r project deep learning

r project neural network

r project regression

r project classification

r project clustering

r project time series analysis

r project forecasting

r project optimization

r project simulation

r project testing

Other R package resources include R-Forge,[59][50] a platform for the collaborative development of R packages. The Bioconductor project provides packages for genomic data analysis, including object-oriented data handling and analysis tools for data from Affymetrix, cDNA microarray, and next-generation high-throughput sequencing methods.[60]

The R Journal is an open access, refereed journal of the R project. It features short to medium-length articles on the use and development of R, including packages, programming tips, CRAN news, and foundation news.

R is a popular and widely used software for data science. Like any large open source software project, it takes a lot of work to maintain and develop. Much of the work is done by a small number of volunteers and there is a need to grow the contributor community.

Further sponsors welcome to support participant travel, accommodation and/or social events. Sponsors will be acknowledged on this website, on the R Contributors Twitter and Mastodon accounts, and in reports of the sprint. Please contact to discuss!

Every new project likely fills you with enthusiasm and excitement. And it should. You are about to find answers to your research questions, and you hopefully come out more knowledgeable due to it. However, there are likely certain aspects of data analysis that you find less enjoyable. I can think of two:

While we cover data wrangling in great detail in the next Chapter (Chapter 7), I would like to share some insights from my work that helped me stay organised and, consequently, less frustrated. The following applies to small and large research projects, which makes it very convenient no matter the situation and the scale of the project. Of course, feel free to tweak my approach to whatever suits you. However, consistency is king.

All the files that you generate are in the same place. Your data, your coding, your exported plots, your reports, etc., all are in one place together without you having to manage the files manually. This is due to the fact that RStudio sets the root directory to whichever folder your project is saved to.

This section is not directly related to RStudio, R or data analysis in general. Instead, I want to convey to you that a good folder structure can go a long way. It is an excellent habit to start thinking about folder structures before you start working on your project. Placing your files into dedicated folders, rather than keeping them loosely in one container, will speed up your work and save you from the frustration of not finding the files you need. I have a template that I use regularly. You can either create it from scratch in RStudio or open your file browser and create the folders there. RStudio does not mind which way you do it. If you want to spend less time setting this up, you might want to use the function create_project_folder() from the r4np package. It creates all the folders as shown in Figure 6.1.


Welcome to the group! You can connect with other members, ge...
bottom of page