R is a comprehensive statistical programming language that iscooperatively developed on the Internet as an open source project. Itis often referred to as the “GNU S,” because it almostcompletely emulates the S programming language. It has packages to doregression, ANOVA, general linear models, hazard models andstructural equations.Graphical output can be created using a TeX plug-in to convert the standard ASCII-based output.
R has a massive range of tests, PDF and PostScript output, a function to expand zip archives, and numerous other unexpected features. R programs and algorithms are distributed by the Comprehensive R Archive Network (CRAN). A simple graphic user interface is included for Mac users; R Commander can be installed using the built-in package installer, which can also install file import features (which aren't installed by default). R Commander is an X11 program, which means it uses an alien interface and has odd open/save dialogues, but if you get past that it offers menu driven commands not dissimilar from, say, SPSS, just a lot more awkward to use, and without an output or data window.
Take control of your R code. RStudio is an integrated development environment (IDE) for R. It includes a console, syntax-highlighting editor that supports direct code execution, as well as tools for plotting, history, debugging and workspace management. Alice 3 is the newest installment of the Alice programming language. It has all of the features that have made Alice an exciting and creative first programming experience with an added emphasis on object-oriented concepts. The first of its kind, Q# is a new high-level quantum-focused programming language. Q# features rich integration with Visual Studio and Visual Studio Code and interoperability with the Python programming language. Enterprise-grade development tools provide the fastest path to quantum programming on Windows, macOS, or Linux. Xcode includes everything developers need to create great applications for Mac, iPhone, iPad, Apple TV, and Apple Watch. Xcode provides developers a unified workflow for user interface design, coding, testing, and debugging. The Xcode IDE combined with the Swift programming language make developing. And you need to go to the Comprehensive R Archive Network, or CRAN, so let's type that in here. And you'll see that at the top there's three options. There's Linux, a Mac and Windows. So you can go to the Windows version here. And you want to click on the base link here. So you can just click on this link and the download will start.
Like many open source projects, R is exceedingly capable but has a steep learning curve. Some believe this is for the best because people will get a deeper understanding of the statistics they generate with a program such as R, versus one which allows the rapid creation of scads of irrelevant statistics leading to incorrect conclusions. Those who expect even a basic graphical interface (e.g. SPSS 4) may be disappointed by the R community’s definition of a GUI.
Most of this page is rather out of date. See our free software page for more current but less detailed information.
Ashish Ranpura wrote:
Last week I finally put R through its paces on two recent experiments from our lab. It performed spectacularly. It's pretty easy to learn using online tutorials, in particular John Verzani's tutorial which is a course in introductory statistics using R.
The highlight: figuring out the 15 or so commands to import, parse, slice and graph a 3-way comparison of control subjects using a scatterplot and a violin plot. Then using BBEdit to search and replace the word 'control' with my two experimental conditions, pasting that back into R, and generating a report with all 6 graphs in about 3 keystrokes! Now that's how a program ought to work.
But the major advantages of R are that it is absolutely cross-platform (Linux, MacOS, Windows) and that it's open source. You've a good chance of accessing your data 10 years from now, which I wouldn't say with the commercial packages. The user base is large, active, and productive. The S language on which it's based is a well-accepted standard in statistics. R has stood the test of time and is likely to continue to do so.
There is one significant caveat: R is relentlessly command-line driven, and even the graphs cannot be edited with mouse clicks. It's trivial to take the PDF graphs into Illustrator, though, so this limitation hasn't been a problem for me.
Some resources include:
- The R project home page (with download links)
- This web page on R, S and S/Plus statistics systems, which provides a background on the software and summarizes available packages
- Using R for structural equation modeling
R has a massive range of tests and now has Matrix as a recommended package, a useKerning argument for PDF and PostScript output, a recursive argument for file.copy(), an unzip function to expand or list zip archives, and other changes.
There is a R for Mac Special Interest Group, called R-Sig-Mac. Thegroup is implemented as an e-mail list. You can subscribe to the list or see the archives going to its official web page:http://www.stat.math.ethz.ch/mailman/listinfo/r-sig-mac
S and R Programming Languages
Beginning in 1976, the Sprogramming language was developed at Bell Labs (whose statisticsdepartment employed John Tukey and Joseph Kruskal) by John Chambersand others. Version 1 required Honeywell mainframes, Version 2 (1980)added Unix support, Version 3 (1988) added functions and objects, andVersion 4 (1998) added full support for object-oriented design. In 1993, Bell Labs issued an exclusive license toStatSci (later MathSoft).S-Plus is Mathsoft’s commercial implementation of S, and the only waythe language is available outside Lucent.
R was begun by Robert Gentleman and Ross Ihaka of the Universityof Auckland. It is now an opensource project staffed by volunteers from around the world whose development is coordinated through the Comprehensive R Archivenetwork. Source code, binaries, and documentation areat the CRAN website.
Documentation that compares R and S include:
- The R and S discussion in CRAN’s FAQ.
- The online supplement to Venables and Ripley (1999).
- The published text of Venables and Ripley (2000), and its online errata.
Adapted from an August 2000 Academy of Management workshop on stat packages, we are showing how to use R for analyses common in management research:
Base package commands:
- anova: analysis of variance
- glm: general linear model, including logit, probit and poisson models
- ls/lsfit: fit an OLS or WLS regression model
Built-in packages
- ts package:
- arima: ARIMA time series models
Contributed R packages and their capabilities:
- boot: bootstrapping and jacknifing
- coda: analysis and diagnostics for Markov Chain Monte Carlo simulation
- fracdiff: ARIMA time series models
- matrix: matrix math
- cmdscale: multi-dimensional scaling
- multiv: cluster analysis, correspondance analysis, principal component factor analysis
- pls: Partial Least Squares structural equation modeling
- survival5: survival analysis (hazard models)
Books by MacStats maintainer David Zatz • MacStats created in 1996 by Dr. Joel West; edited since 2005 by Dr. David Zatz of Toolpack Consulting. Copyright © 2005-2020 Zatz LLC. All rights reserved. Contact us.
C Language Free Download
With the R plugin installed in PyCharm, you can perform various statistical computing using R language and use coding assistance, visual debugging, smart running and preview tools, and other popular IDE features. PyCharm supports R 3.4 and later.
With the R plugin, you can also get native support for .R files. Such files are marked with the icon.
R plugin support in PyCharm includes:
- Coding assistance:
- Error and syntax highlighting.
- Code completion.
- Code refactoring.
- Ability to create line commentsCtrl+/.
- Intention actions and quick fixes, including quick fixes for the missing import statements.
- Auto-saving changes that you make in your files. Saving is triggered by various events, for example, closing a file or a project, or quitting the IDE.
- Ability to preview data in the graphic and tabular forms.
- Ability to run and debug R scripts with live variables view.
Quick start with the R plugin in PyCharm
To start working the R files in in PyCharm:
- Download and install the R language.
- Install the R plugin for PyCharm.
- Create a new R project.
- Configure an R interpreter.
- Inspect the set of the installed R packages and install additional packages required for your project.
- Open or create an .R file.
- Run the R script.
- Analyze, export, and save the results.
Get familiar with the user interface
When you edit and execute R files with the R plugin in PyCharm, you should notice the following changes in PyCharm user interface:
The R Tools window contains tabs to analyze plots, preview R documentation, and configure R packages. With the R Console, you can monitor R code execution as well as preview variable values.
At any time you can open R Tools and R Console windows by selecting the corresponding option in the View | Tool Windows menu.
R Console
The R Console tab appears in the group of the PyCharm tool windows. It enables executing R commands line by line similar to the console provided with the R installation.
Code completion (Ctrl+Space) is available as you type commands in the R Console. You can preview the values of the declared variables and the execution results in the Variables area.
Item | Description |
---|---|
Execute the current statement in the one-line console (Enter). | |
Open the R Console History to preview the list of the executed commands. | |
Open any file in the editor, then click this button. The location of the opened file will be set as the current directory for the current console tab. | |
Restart the console session. | |
Softly wraps lines in the R Console. | |
Open a new R Console tab. | |
Help on executing R scripts. |
You can open several tabs in the R console for different tasks. To distinguish between the opened consoles, right-click any tab and enter a specific name.
In the R console, you can use call the magrittr pipe function,
%>%
. Press the Ctrl + Shift + M (for Windows and Linux) or Command + Shift + M (for macOS) to insert it. The Jobs tab shows the execution of the jobs initiated for R files. You can preview the job status (succeeded or failed), duration of the execution, and the time you launched the job.
Use the following icons of the Jobs toolbar:
Item | Description |
---|---|
Add a new job. | |
Clear the list of jobs. | |
Rerun the job. |
Plots
In the Plots tab of the R Tools window, you can preview various graphs built with the R-specific data plotting libraries. The window displays all the graphs built within a single execution session that lasts until you close the R Console or explicitly terminate the process. The graphs are arranged one on a page.
Item | Description |
---|---|
Go to the previous or next graphics page. | |
Save the graphics in a .png file. | |
Copy the graphics in the clipboard. | |
Zoom in the graphics. | |
Close the currently selected page. | |
Close all graphics pages. | |
Open the Graphics device settings dialog to set up the size of the image and its resolution. |
Tables
When you analyze tabular data in the Variables view, you can click the View Table link and preview the table in a separate tab the editor. To order values in a particular column, click its header.
Item | Description |
---|---|
Save the table in a .csv file. | |
Filter data in the table columns. Hover over the button to preview the available filters and type the filter criteria under the column header. | |
Organize table in pages. Toggle this button and specify the number of table rows to display on a page: 10, 15, 30, or 100. |
Viewer
With the Viewer tab of the R Tools window you can preview R graphics built with the JavaScript visualization libraries.
R Programming Language Download Mac Version
R packages
Install, uninstall, and update R packages in the Packages tab of the R Tools window. It lists all the installed R packages.
Item | Description |
---|---|
Install an R package from the list of the available packages. | |
Upgrade all packages to the latest available versions. This button is enabled if at least for one of the installed packages there is a newer version. | |
Update the Latest version column by fetching the latest available versions for the installed packages. |
R files toolbar
When you open an R file in the editor, the following toolbar appears. Use it to run and debug R code.
Item | Description |
---|---|
Execute the R file. | |
Debug the R file | |
Run a job for an R file | |
Execute the selected code fragment | |
Debug the selected code fragment | |
Open the Documentation tab. |
Markdown toolbar
The toolbar appears the editor window when you open an .rmd file.
Item | Description |
---|---|
Output directory | Select the directory for the generated HTML output. By default, the project directory is selected. To alter the location, select Custom from the list and specify any directory in your file system. |
Renders the document in the HTML format. The filename corresponds to name of the R Markdown file. The location of the generated file is defined by the option selection in the Output Directory list. | |
Opens the generated HTML document. Note that you should build the output first (). | |
Runs all the executable R chunks in the file. During the execution the icon changes its state to . | |
Inserts a new chunk for R code below the current chunk. | |
Softly wraps lines in the editor. |
Refer to the following topics for more information about R plugin support in PyCharm: