R
R
3.6.1

4.7

R free download for Mac

R

3.6.1
12 December 2019

Statistical computing and graphics.

Overview

R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by 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 statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity.

One of R's strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control.

What's new in R

Version 3.6.1:
Installation on a Unix-Alike:
  • The default detection of the shell variable libNN is overridden for derivatives of Debian Linux, some of which have started to have a /usr/lib64 directory. (E.g. Ubuntu 19.04.) As before, it can be specified in config.site.
Utilities:
  • R CMD config knows the values of AR and RANLIB, often set for LTO builds.
  • DEPRECATED AND DEFUNCT:
  • The use of a character vector with .Fortran() is formally deprecated and gives a non portability warning. (It has long been strongly discouraged in 'Writing R Extensions'.)
Bug fixes:
  • On Windows, GUI package installation via menuInstallPkgs() works again.
  • R CMD check on data() fixing.
  • quasi(*, variance = list(..)) now works more efficiently, and should work in all cases fixing PR#17560. Further, quasi(var = mu(1-mu)) and quasi(var = "mu ^ 3") now work, and quasi(variance = "log(mu)") now gives a correct error message.
  • Creation of lazy loading database during package installation is again robust to Rprofile changing the current working directory.
  • boxplot(y ~ f, horizontal=TRUE) now produces correct x- and y-labels.
  • rbind.data.frame() allows to keep levels from factor columns (PR#17562) via new option factor.exclude.
  • Additionally, it works in one more case with matrix-columns which had been reported on 2017-01-16 by Krzysztof Banas.
  • Correct messaging in C++ pragma checks in tools code for R CMD check, fixing PR#17566 thanks to Xavier Robin.
  • print()ing and auto-printing no longer differs for functions with a user defined print.function, thanks to Bill Dunlap's report.
  • as.data.frame() treats 1D arrays the same as vectors.
  • Improvements in smoothEnds(x, *) working with NAs (towards runmed() working in that case, in the next version of R).
  • vcov(glm(), dispersion = *) works correctly again.
  • R CMD INSTALL and install.packages() on Windows are now more robust against a locked file in an earlier installation of the package to be installed. The default value of option install.lock on Windows has been changed to TRUE.
  • On Unix alikes (when readline is active), only expand tilde (~) file names starting with a tilde, instead of almost all tildes.
  • In R documentation (*.Rd) files, item [..] is no longer treated specially when rendered in LaTeX and hence pdf, but rather shows the brackets in all cases.

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14 R Reviews

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You need to invest time in learning R, but then once you're really into it there's no way you can go back to SPSS etc. An IDE (e.g. RStudio) is highly recommended though.
Like (2)
Version 3.1.3
Hachepunto
31 July 2015
There's lots of software available for data analysis today: spreadsheets like Excel, batch-oriented procedure-based systems like SAS; point-and-click GUI-based systems like SPSS; data mining systems, and so on. What makes R different? R is free. As an open-source project, you can use R free of charge: no worries about subscription fees, license managers, or user limits. But just as importantly, R is open: you can inspect the code and tinker with it as much as you like (provided you respect the terms of the GNU General Public License version 2 under which it is distributed). Thousands of experts around the world have done just that, and their contributions benefit the millions of people who use R today. R is a language. In R, you do data analysis by writing functions and scripts, not by pointing and clicking. That may sound daunting, but it's an easy language to learn, and a very natural and expressive one for data analysis. But once you learn the language, there are many benefits. As an interactive language (as opposed to a data-in-data-out black-box procedures), R promotes experimentation and exploration, which improves data analysis and often leads to discoveries that wouldn't be made otherwise. A script documents all your work, from data access to reporting, and can instantly be re-run at any time. (This makes it much easier to update results when the data change.) Scripts also make it easy to automate a sequence of tasks that can be integrated into other processes. Many R users who have used other software report that they can do their data analyses in a fraction of the time. Graphics and data visualization. One of the design principles of R was that visualization of data through charts and graphs is an essential part of the data analysis process. As a result, it has excellent tools for creating graphics, from staples like bar charts and scatterplots to multi-panel Lattice charts to brand new graphics of your own devising. R's graphical system is heavily influenced by thought leaders in data visualization like Bill Cleveland and Edward Tufte, and as a result graphics based on R appear regularly in venues like the New York Times, the Economist, and the FlowingData blog. A flexible statistical analysis toolkit. All of the standard data analysis tools are built right into the R language: from accessing data in various formats, to data manipulation (transforms, merges, aggregations, etc.), to traditional and modern statistical models (regression, ANOVA, GLM, tree models, etc). All are included in an object-oriented framework that makes it easy to programatically extract out and combine just the information you need from the results, rather than having to cut-and-paste from a static report. Access to powerful, cutting-edge analytics. Leading academics and researches from around the world use R to develop the latest methods in statistics, machine learning, and predictive modeling. There are expansive, cutting-edge edge extensions to R in finance, genomics, and dozens of other fields. To date, more than 2000 packages extending the R language in every domain are available for free download, with more added every day. A robust, vibrant community. With thousands of contributors and more than two million users around the world, if you've got a question about R chances are, someone's answered it (or can). There's a wealth of community resources for R available on the Web, for help in just about every domain. Unlimited possibilities. With R, you're not restricted to choosing a pre-defined set of routines. You can use code contributed by others in the open-source community, or extend R with your own functions. And R is excellent for "mash-ups" with other applications: combine R with a MySQL database, an Apache web-server, and the Google Maps API and you've got yourself a real-time GIS analysis toolkit. That's just one big idea -- what's yours? source: http://www.inside-r.org/why-use-r
Like (1)
Version 3.2.1
1 answer(s)
Markus-Winter
Markus-Winter
08 November 2016
Excel is NOT a data processing app, even though many use it as one.

The biggest problem is "Intellisense" (a misnomer if ever I saw one)

Example: Genes named Jun16, Oct4 etc become dates on import. When you define the cells as TEXT then they become something like 2856198267 (which I think is the number of seconds since a certain date which is how Excel stores dates internally). In other words: Excel changes your data without you telling it to. That is an absolute no-no for data analysis apps.
Like
WooDMco
22 June 2015
Today's download is for the SOURCE code, not the R.app application. You will need the developer tools to build the app.
Like
Version 3.2.1
buffonm1
05 May 2015
Hey everyone, i have a problem. Just downloaded R, but this came up: You're using a non-UTF8 locale, therefore only ASCII characters will work. Does anyone know what i can do?
Like
Version 3.2.0
You need to invest time in learning R, but then once you're really into it there's no way you can go back to SPSS etc. An IDE (e.g. RStudio) is highly recommended though.
Like (2)
Version 3.1.3
Chuckk
08 February 2015
cross platform capability, extremely powerful, well supported. Learning curve is a bit steep but well worth it.
Like (1)
Version 3.1.2
Tobit
12 April 2014
Great software, but take care with "Snow Leopard" download link !! It exists a "Mavericks" version too ;-)
Like (2)
Version 3.1.0
Dorkypants
12 April 2014
Package installer fails on my 2009 MacBook Pro 13" running Snow Leopard 10.6.8 with all available Software Updates installed
Like
Version 3.1.0
Danlfsmith
03 August 2012
R has revolutionized statistical computing over the last 10 years. Every student of statistics or science today probably needs to learn R. It can be used for amazingly complex analysis, as well as the simple stuff. It has a reputation for being hard to learn, but that's mainly because it's so powerful and flexible. Fortunately there are many good books available to teach R. I like "Introductory Statistics with R," by Peter Dalgaard.
Like (2)
Version 2.15.1
biop090
05 January 2012
GREAT!
Like
Version 2.14.1
Pedroj
26 September 2010
R is the tool for choice for serious statistical analysis. It's not an easy platform, however, and learning takes some time. The good side is how powerful it is for *any* type of analysis, data, or problem. The help support is very good and user forums are very active and helpful. This is not the package of choice if you are doing sporadic data analysis, but I'd recommend it to anyone seriously involved in statistical analysis. If you are just starting with statistics and plan to keep doing data analysis- go for it. If you are using other packages and statistical analysis is a major part of your study, go for it. No other package offers the versatility and support R has. If the command line mode is really intimidating to you, you can use the R-Commander GUI (just install the Rcmdr package), but the real power of R lies in its command-line. You can run R with the binary cocoa application, from the Terminal, within emacs, or within TextMate.
Like (3)
Version 2.11.1
1 answer(s)
myschizobuddy-1
myschizobuddy-1
05 January 2012
and rStudio http://rstudio.org/
Like