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R meets the Workplace - Embedding R into Excel and making it more accessible Erich Neuwirth University of Vienna erich.neuwirth@univie.ac.at. R meets the Workplace - Embedding R into Excel and making it more accessible Erich Neuwirth University of Vienna erich.neuwirth@univie.ac.at.

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  1. R meets the Workplace - Embedding R into Excel and making it more accessibleErich NeuwirthUniversity of Viennaerich.neuwirth@univie.ac.at

  2. R meets the Workplace - Embedding R into Excel and making it more accessibleErich NeuwirthUniversity of Viennaerich.neuwirth@univie.ac.at

  3. Connecting Spreadsheets and RAdvanced Statistics in a Familiar Office Environment Erich NeuwirthUniversity of Viennaerich.neuwirth@univie.ac.at

  4. Topics covered Spreadsheet examples Conceptual issues Implementation issues User profiles

  5. Why spreadsheets as client? Well known user interface Easy data manipulation Integration into daily working environment Widely accessible environment for numerical computations Simple (simplistic?) toolkit for static and animated graphics

  6. User profiles Methods developer Sophisticated methods user Naïve user

  7. User profiles Methods developer Sophisticated methods user and small scale application developers Naïve user knowledgeable about the spreadsheet model

  8. Design issues for user interface Closed application Some “end user programming” Open development environment Enhancement of core spreadsheet functionality

  9. Design issues Processing paradigm Spreadsheet • menu driven • Automatic recalculation (spreadsheet model!) R • language controlled • sequential processing model

  10. Current Implementation DCOM in Win32 Server • In software (multilayered) Client • “talks to” server via VBA Architecture is currently being extended

  11. Project subtasks Client-Server task assignment: who does what? • Exploratory methods • (Powerful) analytical methods • (Visual) Presentation

  12. Tools for subtasks Spreadsheets for • Data preparation and manipulation • Exploratory methods (possibly) • Presentation Statistical Number crunching software for • Powerful analytical methods • Exploratory methods (numerically intensive) • Preparing data for visual presentation • Advanced statistical graphics(additional libraries)

  13. Why spreadsheets? Users are familiar with it Readily available numerical environment Easy to use graphics toolkit Animated graphics

  14. Why R Powerful statistical package Advanced methods library Advanced graphics

  15. Conclusions • Excel • embedding R solves almost all problems • Excel is vastly improved as a tool for serious statistics • response to bashing by statisticians • R • acquiring a more accessible user interface • bringing it closer to many people • Adding new programming paradigms, • automatic dependency tracking and recalculation • point and click creation of formulas

  16. More information R:http://www.R-project.org CRAN repositories Spreadsheets:http://sunsite.univie.ac.at/Spreadsite RExcel and R(D)COMhttp://rcom.univie.ac.at (check the Wiki) /download/RAndFriends.distro/RAndFriendsSetup…exe /download/RAndFriends.distro/RAndFriendsLightSetup…exe

  17. How to learn more through ExcelR. M. Heiberger, E. NeuwirthSpringer Verlag (to appear soon)

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