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7 Ways to Increase Your R Productivity


Presenters:

David Smith, Vice President of Marketing, Revolution Computing
  1. Organize, view, add, remove, rearrange, and deploy R scripts with the visual Solution Explorer
    • Organize scripts: Use the Solution Explorer to view, add, remove, and rearrange R scripts within one or more Projects contained in the Solution
    • Start a new R session: From the File menu, select one of your existing R Solutions to start a new R session with easy access to all of the relevant scripts and data
    • Create Project Templates: Save a Project as a template, and then automatically create a set of customized scripts for a new project. Easily Distribution Solutions to Others: Just zip up your solution directory and give it to colleagues to load and use
       
  2. Use the enhanced R script editor and get new and occasional R users up and going quickly and greatly improve productivity of experienced users
    • Hover-over help, word completion, find-across-files capability, automatic syntax checking, bookmarks, and navigation buttons
       
  3. View all the data and function objects that are available, including those in loaded and installed R packages in the Object Browser
    • Browse all available objects
    • Get object-specific information
    • Plot and edit data
       
  4. Debug R scripts, with step-in, step-over, and step-out capability, allowing users to inspect and modify R objects as they are debugging
    • Debug mode for scripts : step through progress line-by-line
    • Set visual breakpoints directly in R source code
    • Step into and out of functions while debugging
    • Inspect variables while debugging
       
  5. Automatically generate fill-in-the-blank sections of R code for a variety of analyses
    • Write your own code snippet and share with others
       
  6. Search the text of all of installed R package help files or all of the included electronic manuals with enhanced help features
    • Hover-over tooltips for functions and data objects
       
  7. Analyze larger data sets with 64-bit Windows support

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