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• Rstudio Shortcut List For Mac

Chapter 1. Overview, Installation This book introduces users to the RStudio™ Integrated Development Environment (IDE) for using and programming R, the widely used open-source statistical computing environment. RStudio is a separate open-source project that brings many powerful coding tools together into an intuitive, easy-to-learn interface. RStudio runs in all major platforms (Windows, Mac, Linux) and through a web browser (using the server installation). This book should appeal to newer R users, students who want to explore the interface to get the most out of R, and long-time R users looking for a more modern development environment. R is an open-source software environment for statistical computing and graphics.

R compiles and runs on Windows, Mac OS X, and numerous UNIX platforms (such as Linux). For most platforms, R is distributed in binary format for ease of installation. The R software project was first started by Robert Gentleman and Ross Ihaka. The language was very much influenced by the S language, which was originally developed at Bell Laboratories by John Chambers and colleagues. Since then, with the direction and talents of R’s core development team, R has evolved into the lingua franca for statistical computations in many disciplines of academia and various industries. R is much more than just its core language. It has a worldwide repository system, the Comprehensive R Archive Network (CRAN)——for user-contributed add-on packages to supplement the base distribution.

As of 2011, there were more than 3,000 such packages hosted on CRAN and numerous more on other sites. In total, R currently has functionality to address an enormous range of problems and still has room to grow. R is designed around its core scripting language but also allows integration with compiled code written in C, C, Fortran, Java, etc., for computationally intensive tasks or for leveraging tools provided for other languages. What is an IDE? R, like other programming languages, is extended (or developed) through user-written functions. An integrated development environment (IDE), such as RStudio, is designed to facilitate such work.

In addition, unlike many other statistical software packages in which a graphical user interface is employed, a typical user interacts with R primarily through the command line. An IDE for R then must also include a means for issuing commands interactively.

R is not unique in this respect, and IDEs for interactive scientific programming languages have matured to include features such as. A rich set of keyboard shortcuts. Automatic source-code formatting, assistance with parentheses, keyword highlighting.

Code folding and easy navigation through a file and among files. Context-sensitive assistance. Interfaces for compiling or running of software. Project-management features. Debugging assistance. Integration with report-writing tools. Object browsers; in interactive use, a user’s workspace includes variables that have been defined.

An object browser allows the user to identify quickly the type and values for each such variable. Object editors; from an object browser, a means to inspect or edit objects is typically provided. Name Platforms Description ESS All ESS is a powerful and commonly used interface for R that integrates the venerable Emacs editor with R. There are numerous conveniences, but some find that it is difficult to learn and has an old-school feel, which precludes adoption. Eclipse All The open-source StatET plugin turns Eclipse, a Java-based multipurpose IDE, into a full-featured IDE for R. SciViews All An R API and extension for the Komodo code editor. JGR All Java-based editor that interfaces with R through the rJava and JRI packages.

The Deducer package adds a suite of data analysis tools. Tinn-R Windows An extension for the Tinn editor that allows integration with an underlying R process. Notepad Windows With the NpptoR extension allows the Notepad editor to interact with an R process.

RGui Windows The Windows GUI for R (the default interface) has some of the features of an IDE. R.app Mac OS X Like the Windows GUI, provides the basic features of an IDE. The main components of an IDE are all nicely integrated into a four-pane layout that includes a console for interactive R sessions, a tabbed source-code editor to organize a project’s files, and tabbed panes within notebooks to organize less central components. The source-code editor is easy to use, feature-rich, has excellent code-navigation features, and is well-integrated into the built-in console. The console and source-code editor are tightly linked to R’s internal help system through tab completion and the help page viewer component.

The project feature make it easy to organize different workflows. Setting up different projects is a snap, and switching between them is even easier. RStudio provides many convenient and easy-to-use administrative tools for managing packages, the workspace, files, and more. The IDE is available for the three main operating systems and can be run through a web browser for remote access.

RStudio is much easier to learn than Emacs/ESS, easier to configure and install than Eclipse/StatET, has a much better editor than JGR, is better organized than Sciviews, and unlike Notepad and RGui, is available on more platforms than just Windows. The RStudio program can be run on the desktop or through a web browser. The desktop version is available for Windows, Mac OS X, and Linux platforms and behaves similarly across all platforms, with minor differences for keyboard shortcuts. To support so many platforms, RStudio leverages numerous existing web technologies in its design. For the desktop applications, it cleverly displays them within an industry standard HTML widget provided by Qt (a cross-platform application and UI framework) to create a desktop application. Consequently, R users can have a feature-rich and consistent programming environment for R their way—desktop- or web-based.

Web-based usage is done through a trusted server within a department or organization (though a “cloud” service may be forthcoming). RStudio is the brainchild of J. Allaire, who, with his brother, previously had tremendous success developing the influential ColdFusion IDE and scripting language for web development. Allaire is currently joined by the very able Joseph Cheng, Joshua Paulson, and Paul DiCristina. In the short time that their initial beta has been available, they have proven to be very responsive to user input.

RStudio is under active development. As such, elements discussed in this book may be changed by the time you are reading it. Sorrybut you’ll likely be better off with the new feature than my description of the old one. Like R, RStudio is an open-source project. Its stated goal—which it is already meeting—is “.to develop a powerful tool that supports the practices and techniques required for creating trustworthy, high-quality analysis.” The codebase is released under the AGPLv3 license and is available from GitHub. RStudio is built on top of many other open-source projects.

Most visible of these are GWT, Google’s Web Toolkit; Qt, the graphical toolkit of Nokia; and Ace, the JavaScript code editor. Other leveraged projects are listed in RStudio’s About dialog. The bulk of the code is written in C and Java, the language for working with GWT. Figure 1-1. RStudio on initial startup; the main interface has four panes (one hidden in this screenshot), an application toolbar, and in some cases, a menu bar In we see three main panes: the Console, which should look familiar to any R user; a tabbed Workspace pane (with no items, as the initial workspace is empty) and the History interface.

The latter two are part of notebooks that can contain multiple panes. The Source pane, or code editor, is not open in the screenshot, as no files are open for editing or viewing. Server Version Starting the server version requires one to know the appropriate URL for the resource. We used a local URL for this book, but the real value comes from using RStudio as a resource on the wider internet. When accessing RStudio, one must first authenticate. The basic screen to do so looks like.

Authentication depends on the server, but the default is to authenticate against the user accounts on the machine, so the web adminstrator should have provided a secure means to access RStudio. R is started in the working directory.

If present, the.Rprofile file’s commands are executed. If present, the.RData file is loaded. Other actions described in?Startup are followed. When R quits, a user is queried to “Save workspace image?” When the workspace is saved it writes the contents to an.RData file, so that when R is restarted the workspace can persist between sessions. (One can also initiate this with save.image.) This process allows R users to place commands they desire to run in every session in an.Rprofile file, and to have per directory.RData files, so that different global workspaces can be used for different projects.

RStudio does not require a special version of R to run, as long as it is a fairly modern one (R 2.11.1 or later). It will work with binary versions from CRAN or user-compiled versions. As such, when RStudio starts up, it must be able to locate a version of R, which could possibly reside in many different places. Usually RStudio just finds the right one, but one can bypass the search process.

The online document at details how to specify which R installation to use. In short, it depends on the underlying operating system. For Windows desktop users, it can be specified in the Options dialog (see ), or chosen if the Ctrl key is held on startup.

For Linux and Mac OS X users, one can set an environment variable, as seen here: $ export RSTUDIOWHICHR=/usr/local/bin/R Web-based users really don’t have a choice, as this is determined by who configures the server. In the upper left is a Source browser pane for editing files (see ) or viewing some data sets. In this is not visible, as that session had no files open. In the lower left is a Console for interacting with an R process (see ).

In the upper right are tabs for a Workspace browser (see the section ) and a History browser (see the section ). In the lower right are tabbed panes for interacting with the Files , Plots , Packages , and Help system components. If the facilities are present, an additional tab for version control is presented. The Console pane is somewhat privileged: it is always visible, and it has a title bar. For the other components, their tab serves as a title bar. These panes have page-specific toolbars (perhaps more than one)—which in the case of the Source pane are also context-specific. The user may change the default dimensions for each of the panes, as follows.

There is an adjustable divider appearing in the middle of the interface between the left and right sides that allows the user to adjust the horizontal allocation of space. Furthermore, each side then has another divider to adjust the vertical space between its two panes. As well, the title bar of each pane has icons to shade a component, maximize a component vertically, or share the space.

The Options Dialog RStudio preferences are adjusted through the Options dialog. There are five panels for this dialog to adjust: general properties, editing properties , appearance properties, pane layout , and version control (requires additional support tools to be installed).

The pane layout allows the user to determine which panes go in which corners, and, for the supplemental panes (not the Console or Source editor), where those pane’s tabs appear. One modifies a placement simply by adjusting a combobox, or by checking one of the checkboxes.

In, the choices put the code editor on the right, the console in the upper right, and the file browser on the upper left. There are many examples of pane placement on The appearance panel of the options dialog allows one to set the default font size and modify the theme for the editing in the console or source-code editor.

This book uses the default TextMate theme for its screenshots. Installing RStudio Installing RStudio is usually a straightforward process.

First, RStudio requires a working, relatively modern R installation. If that is not already present, then one should consult to learn how to install R for the given operating system. For Windows and Mac OS X, one can simply download a self-installing binary; for Linux, installation varies.

For the Debian distribution (including Ubuntu), the R system can be installed using the regular package-management tools. Of course, as R is open source, one can also compile and install it using the source code. The RStudio package is available for download from There is a choice between a Desktop version and a Server version.

The Desktop version is appropriate for single-user use. The files come in a common format for binary installation (e.g., exe, dmg, deb, or rpm). One downloads the file and installs it as any other program.

For those searching out the latest features, follow the link on to get the binaries for the most recent (but not necessarily stable) build. Installing a server version requires more work and care. Some directions are given at One can also install RStudio from its source code. A link for the source “tarball” for the current stable version appears on the appropriate download page.

For the adventurous, the latest development build files are available from Installation details are in the INSTALL file accompanying the source code. The same source is used to compile both the Desktop and Server version. As RStudio depends on some of the latest features of many moving parts, such as GWT, there can be issues with compiling from the source. The support forums ( are an excellent place to find specific answers to any issues. Logging RStudio creates hidden files for itself to store information, including logging information.

When there are issues at startup, the log can be consulted for direction as to what is going wrong. For desktop users, the log directory is either /.rstudio-desktop/log for Mac and Linux users; or for Windows users,%localappdata% RStudio-Desktop log (Windows Vista and 7) or%USERPROFILE% Local Settings Application Data RStudio-Desktop log for XP. In the application’s menu bar, the Help Diagnostics item can be used to find the log files. Updating RStudio Updating RStudio is also straightforward. To see if an update is available, the Help Check for Updates menu item will open a dialog with update information.

If an update is available, one can stop RStudio, install the new version, then restart. RStudio writes session information to the user’s home directory (e.g., to the file /.rstudio-desktop). This will persist between upgrades.

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. Launch RStudio (and, therefore, R).

Notice the default panes:. Console (entire left). Workspace/History (tabbed in upper right).

Files/Plots/Packages/Help (tabbed in lower right) FYI: you can change the default location of the panes: Go into the Console, where we interact with the live R process. Make an assignment and then inspect the object you just created. X Set Working Directory - To Files Pane Location. (You'll see even more options there). Or within the Files pane, choose More and Set As Working Directory.

But there's a better way. A way that also puts you on the path to managing your R work like an expert. Keeping all the files associated with a project organized together - input data, R scripts, analytical results, figures - is such a wise and common practice that RStudio has built-in support for this via it's projects. Let's make one to use for the rest of this tutorial.

Do this: Projects menu - Create project. The directory name you choose here will be the project name. Call it whatever you want (but bear in mind that good names are short and informative). Now verify that the directory associated with your project is also the working directory of our current R process: getwd I won't print my output here because this document itself does not reside in the RStudio Project we just created and it will be confusing. Let's enter a few commands in the Console, as if we are just beginning an analytical project. I'm going to set the intercept (a ) and slope (b ) of a line, generate some (x ) values uniformly on the interval (0, 1 ), and finally generate (y ) values as (a + bx ) plus some noise from a Gaussian distribution.

To emulate a real analysis, let's write a numerical result to file for later use - the average of the (x )'s - and let's save a scatterplot to PDF - a scatterplot of (y ) versus (x ) with the true data-generating line superimposed. Dev.print(pdf, 'toylinePlot.pdf') ## pdf ## 2 Let's say this is a good start of an analysis and you're ready to preserve the logic and code.

Visit the History tab of the upper right pane. Select these commands, skipping any that didn't work or contained typos.

Rstudio Shortcut List For Mac

Click 'To Source'. Now you have a new pane containing a nascent R script. Click on the floppy disk to save. Give it a name ending in.R, I used toyline.R and note that, by default, it will go in the directory associated with your project.

Quit RStudio. Inspect the folder associated with your project if you wish. Understand why certain files are or are not there. View the PDF in an external viewer, view the plain text files (the script and the average of the (x )'s) any way you wish.

Restart RStudio. Notice that things, by default, restore to where we were earlier, e.g. Objects in the workspace, the command history, which files are open for editing, where we are in the file system browser, the working directory for the R process, etc. These are all Good Things.

Change some things about your code. Top priority would be to set a sample size n at the top, e.g. N Options-General). For the record, when loading data into R and/or writing outputs to file, you can always specify the absolute path and thereby insulate yourself from the current working directory. This is rarely useful when using RStudio.

My older workflow, based on Emacs + ESS, did use this approach, bu with personal helper functions to ease the pain. Links that may be relevant - but may not be!