![R Commander Default Menu Tree [current as of version 2.0-0]
File - Change working directory
|- Open script file
|- Save script
|- Save script as |- Open R Markdown file
|- Save R Markdown file
|- Save R Markdown file as
|- Save output
|- Save output as
|- Save R workspace
|- Save R workspace as
|- Exit - from Commander
|- from Commander and R
Edit - Cut
|- Copy
|- Paste
|- Delete
|- Find
|- Select all
|- Undo
|- Redo
|- Clear Window](https://image.slidesharecdn.com/zposterrpresentation-140205143517-phpapp02/85/R-and-Rcmdr-Statistical-Software-24-320.jpg)
![Data - New data set
|- Load data set
|- Merge data sets
|- Import data - from text file, clipboard, or URL
| |- from SPSS data set | |- from SAS xport file
| |- from Minitab data set
| |- from STATA data set
| |- from Excel, Access, or dBase data set [32-bit Windows only]
| |- from Excel file [currently 64-bit Windows only]
|- Data in packages - List data sets in packages
| |- Read data set from attached package
|- Active data set - Select active data set
| |- Refresh active data set
| |- Help on active data set (if available)
| |- Variables in active data set
| |- Set case names
| |- Subset active data set
| |- Aggregate variables in active data set
| |- Remove row(s) from active data set
| |- Stack variables in active data set
| |- Remove cases with missing data
| |- Save active data set
| |- Export active data set
|- Manage variables in active data set - Recode variable
|- Compute new variable
|- Add observation numbers to data set
|- Standardize variables
|- Convert numeric variables to factors
|- Bin numeric variable
|- Reorder factor levels
|- Define contrasts for a factor
|- Rename variables
|- Delete variables from data set](https://image.slidesharecdn.com/zposterrpresentation-140205143517-phpapp02/85/R-and-Rcmdr-Statistical-Software-25-320.jpg)
R and Rcmdr Statistical Software
- 1. Introducing R and Rcmdr Statistical Software FutureVideo HealthVideo November 24, 2013 (Sunday) 12:20 PM Jabria-2 Auditorium By: Dr. Kang Mun Arturo Tan Management Sciences Department Yanbu University College
- 2. R is the 18th letter of the alphabet. R is data analysis software. R is a programming language. R is an environment for statistical analysis.
- 3. A Bit of History (and Credits) The R Project The Department of Statistics of The University of Auckland, New Zealand is well known for being the birthplace of the R Project.
- 4. Founders of the R Project are, at the time senior lecturers Robert Gentleman and Ross Ihaka, now Associate Professors. Starting to work in 1991, the R codes were first released in 1996. The R Project is a language and environment for statistical computing and graphics.
- 5. John Hopkins University University of Washington Princeton University Stanford University Google Pfizer Merck Bank of America Intercontinental Hotels Shell … It is widely taught around the world and is being used by Ivy League Universities, Google, students, and even by school children. second-year Statistics
- 6. “R is the most powerful statistical computing language on the planet.”
- 7. www.r-project.org R runs on Windows, Linux and MacOS.
- 9. 44 countries serve as a mirror site.
- 16. Total of 44 countries Total of 88 mirror sites More than 3000 packages ..Stanford University
- 17. Books related to R A total of 141 books and increasing…..
- 18. Rcmdr R Commander > library (Rcmdr)
- 24. R Commander Default Menu Tree [current as of version 2.0-0] File - Change working directory |- Open script file |- Save script |- Save script as |- Open R Markdown file |- Save R Markdown file |- Save R Markdown file as |- Save output |- Save output as |- Save R workspace |- Save R workspace as |- Exit - from Commander |- from Commander and R Edit - Cut |- Copy |- Paste |- Delete |- Find |- Select all |- Undo |- Redo |- Clear Window
- 25. Data - New data set |- Load data set |- Merge data sets |- Import data - from text file, clipboard, or URL | |- from SPSS data set | |- from SAS xport file | |- from Minitab data set | |- from STATA data set | |- from Excel, Access, or dBase data set [32-bit Windows only] | |- from Excel file [currently 64-bit Windows only] |- Data in packages - List data sets in packages | |- Read data set from attached package |- Active data set - Select active data set | |- Refresh active data set | |- Help on active data set (if available) | |- Variables in active data set | |- Set case names | |- Subset active data set | |- Aggregate variables in active data set | |- Remove row(s) from active data set | |- Stack variables in active data set | |- Remove cases with missing data | |- Save active data set | |- Export active data set |- Manage variables in active data set - Recode variable |- Compute new variable |- Add observation numbers to data set |- Standardize variables |- Convert numeric variables to factors |- Bin numeric variable |- Reorder factor levels |- Define contrasts for a factor |- Rename variables |- Delete variables from data set
- 26. Popularity of R
- 30. Why use R? 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
- 31. 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.
- 32. 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 cutand-paste from a static report.
- 33. 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 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.
- 34. 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 realtime GIS analysis toolkit. That's just one big idea -- what's yours? “The great beauty of R is that you can modify it to do all sorts of things,” said Hal Varian, chief economist at Google. “And you have a lot of prepackaged stuff that’s already available, so you’re standing on the shoulders of giants.”
- 35. Here are our suggestions for the best on-line resources for information about R. The R Project homepage. Look here for official news from the R Project, plus links to documentation, mailing lists, the official R FAQs, and more. StackOverflow. Got a question about R? Search for questions tagged with "r" and you'll probably find your question already answered. If not, ask away. R bloggers. For a steady stream of news, tips and articles related to R follow this blog aggregator for posts from dozens of R bloggers, including the team from Revolution Analytics.
- 36. The Video Rchive. Watch recordings of speakers at R user group meetings and conferences talk about various aspects of using R. #rstats on Twitter. To listen in on (or contribute to) an information-rich conversation about R 140 characters at a time, search for the #rstats hastag. CRAN Task Views. The list of 2000+ add-on packages for R can be daunting, but these Task Views list the most important ones in domain-specific areas as diverse as Finance, Clinical Trials, and Machine Learning. Crantastic.org.On the other hand, if you're looking for a specific page, you can search by keyword at this interactive directory of all R packages. You can also log in and rate and comment on packages.