R

4.3.2 is out

4.3.0 is out https://cran.r-project.org/doc/manuals/r-release/NEWS.html

The latest version is 4.2.3

Version 4.0.0 released today. Release notes here https://stat.ethz.ch/pipermail/r-announce/2020/000653.html

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

Today's download is for the SOURCE code, not the R.app application. You will need the developer tools to build the app.

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?

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.

cross platform capability, extremely powerful, well supported. Learning curve is a bit steep but well worth it.

Great software, but take care with "Snow Leopard" download link !! It exists a "Mavericks" version too ;-)

Package installer fails on my 2009 MacBook Pro 13" running Snow Leopard 10.6.8 with all available Software Updates installed

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.

GREAT!

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.

Top-notch statistical analysis software for an unbeatable price. You do have to invest some time to learn how to work it, but that's well worth the price of admission. Many statistical tools become available on this platform way before others (much more costly ones). It is supported by a wide, global user and programmer base. Get yourself a book to learn how to use it if you are not the adventurous or "I'd rather do this with a command-line" type.

I downloaded the package, ran the installer and after starting R, the app remains in 'loading R' status interminably. I have to 'force quit' R to get out of it. I'm running a 20" G5 iMac with OSX10.4.6 Any idea of what the problem is?

A significant upgrade. The interface is now a lot less baffling. The most immediately-visible changes affect the ease with which you can see what is in your workspace and how easily you can access help files. R is powerful, but has hitherto been daunting in its minimalist interface. This is still a package for the professional, but is now going to be a lot easier to get to grips with.

One of the most robust and powerful statistical package that just happens to be free, as well. Most of its power comes from command line interface, but the object browsers can't hurt. Is it just me or it tends to run a lot faster when run directly from terminal app?