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R 3.0.0 Released
DaBombDotCom writes "R, a popular software environment for statistical computing and graphics, version 3.0.0 codename "Masked Marvel" was released. From the announcement: 'Major R releases have not previously marked great landslides in terms of new features. Rather, they represent that the codebase has developed to a new level of maturity. This is not going to be an exception to the rule. Version 3.0.0, as of this writing, contains only [one] really major new feature: The inclusion of long vectors (containing more than 2^31-1 elements!). More changes are likely to make it into the final release, but the main reason for having it as a new major release is that R over the last 8.5 years has reached a new level: we now have 64 bit support on all platforms, support for parallel processing, the Matrix package, and much more.'"
Hard to know where to start, especially as you give no information on your target audience...Do they know stats already?
Also, if your target audience is used to GUIs rather than CL, then...
http://answers.oreilly.com/topic/954-introducing-the-r-graphical-user-interface/
Alternative, you use Web front-end here, (disclaimer, I've not tried it)
http://www.squirelove.net/r-node/doku.php
Writing a tutorial from nothing is hard. You can do this to get some good ideas:
1. Download a free evaluation copy of 'Minitab'. :) Obviously, don't just rip off their stuff; not cool
(I'm not connected with Minitab, but I've used it a lot, and it's great 'basic' stats analysis software)
2. Install, and then open help
3. Consult 'tutorials' section
As a suggested flow, I've found that, as a start, you can introduce basic stats, then demonstrate how the software works.
Using the same data-set for the first few, (say ten), lessons is better. Minitab tutorials keep changing the data, which confuses students.
You'll only need 5 columns or so, and remember to include some discrete variables to enable stratification of your continuous variables.
Use a real-world example, such as household expenses for different families, whatever.
For tutorial flow, what works for me as a 'basic' intro to a stats package:
1. What is data? What are statistics?
2. Types of data, how they look as raw data, (in the database) and then once we start to analyse them with stats and graphs (to start, just 'common' stuff like continuous variables, normal & lognormal, and discrete, binomial & poisson).
3. Basic stats & graphical analysis for single variables. Normality tests. Include time series plots as well as histograms / dotplots / boxplots.
4. Multivariate analysis; x/y charts, matrix plots, interaction plots.
5. Hypo tests (for both cont & disc variables)
6. Regression, (simple, then multiple if you're feeling brave)
7. Control charts (for both cont & disc variables)
If you work out how to do this in 'R', by actually using it, your tutorial will pretty much write itself, (keep saving your screens - Irfanview is a great, free, tool I use for this. Install, open, hit 'C' for manual or automatic screen save options.)
The best-known one is called 'R commander' (package name = Rcmdr ). It gives you a point-and-click interface and (like SPSS) drops the R code to repeat what you did using the menu (so that your work is reproducable).
Functionality includes: data summaries, contingency tables, means tests, proportions tests, variance tests, ANOVA, cluster analysis, model fitting (linear, generalised linear, logit), various graphs, tests for comparison between fitted models, plus draws and lookup tables for lots of continuous and discrete distributions. Rcmdr allows for plugins, and a number of them are also downloadable as R packages (e.g. experimental design).
The second one I know about is called 'Deducer' (package name Deducer), which provides a GUI loosely resembling that of SPSS.
Both GUIs are workable and allow you to do simple things simply.
There's also a rather nice IDE, called RStudio (which is a separate download).