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Training Course: What's All The Fuss About R? An Introduction to R Using Real-world Examples
REvolution Computing’s one-day, hands-on basic R training provides an introduction to R through the quantitative exploration of real-world problems. We’ll begin by using R for exploratory data analysis and statistical inference; specific topics include graphical methods (both base and grid graphics) and hypothesis testing via parametric and non-parametric methods. This introduction motivates a more formal presentation of the R language, including types of objects, reading/writing data, functions, loops, and control statements. Other strengths of R will be addressed, including statistical modeling, classes and methods, the package management system, and the interface to C/C++ and Fortran.
This course will be conducted hands-on, classroom style. Computers will not be provided. Registrants are required to bring their own laptops.
| Cost: |
Commercial/Government: $600
Academic/Student: $250 |
Course Agenda:
| 8:30 am - 9:00 am |
Registration & Continental Breakfast |
| 9:00 am - Noon |
Morning Session |
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Getting started with data
- Importing data: from files on disk or on the web.
- Example: the 2000 Sydney Olympic Diving Competition.
- Example: Bookies and Brokers.
- Data types in R: numeric, character, and logical data; factors.
- Data structures in R: data frames, matrices, vectors, and lists.
Working with data
- Exploratory data analysis in R.
- Use and manipulation of R objects.
- Summarizing, poking, and prodding continuous and categorical data.
- Traditional (base) R graphics: from quick and dirty to quick and professional.
- Finding help
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| Noon - 1:00 pm |
Lunch |
| 1:00 pm |
Afternoon Session Starts |
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The power and flexibility of R
- Towards statistical inference:
- Functions, loops and control statements.
- Working with missing values (two perspectives).
- Example: parametric and non-parametric inference.
Statistical analysis in R
- The linear model: basics.
- Diagnostics, prediction, and plots.
- The linear model: at the cutting edge.
- Contrasts and customized graphics.
Publication-quality graphics in R
- An introduction: working on the screen.
- Customizing traditional (base) graphics.
- Trellis (lattice) and grid graphics.
- Graphical output formats:
- Postscript, pdf, jpeg, and more.
- Multiple pages of output.
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| 3:00 pm - 3:30 pm |
Break |
| 3:30 pm - 4::30ish |
Concluding Session |
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The future is now: high-performance computing with R
- The package management system.
- Tools for parallel computing.
- Managing massive data sets:
- In RAM (if they aren’t too massive)
- In shared memory
- With file-backing (if they are truly massive)
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To register choose from the available course dates listed in the left column, or view the complete course schedule.
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