The document is an introduction to a course on the plyr package in R. It includes: - An overview of the instructor, Hadley Wickham, who develops tools to make data analysis easier in R. - A note that the course will include interactive activities to break up the lecture format. - Information on resources for the course materials and getting help. - An outline of topics to be covered: exploring US baby name trends, modeling Texas house sales, and how it all fits together using the split-apply-combine approach. - A list of code files that will be used in examples during the course.

1. plyr
Introduction
Hadley Wickham
Tuesday, 7 July 2009

2. 1. About me
2. Active learning
3. Resources
4. Outline of the course
Tuesday, 7 July 2009

3. About me
New assistant professor at Rice
University.
Interested in developing tools
that make analysis easier -
focussing more on data
cleaning, organisation and
exploration than traditional
statistics.
Have written 17 R packages at
last count.
Tuesday, 7 July 2009

4. Active learning
Hard to absorb much information from
pure lecture format (especially after
lunch!), so we’ll be breaking things up
with some short interactive activities.
Please take 30 seconds now to introduce
yourself to your neighbours. You’ll be
working with them in a bit.
Tuesday, 7 July 2009

5. Resources
All code, data & slides available at
http://had.co.nz/plyr/09-user
Tutorial covers most of the material in
http://had.co.nz/plyr/plyr-intro.pdf, but
with more examples, and at a higher level.
Problems or questions? Use the
manipulatr mailing list:
http://groups.google.com/group/manipulatr
Tuesday, 7 July 2009

6. Outline
Basic strategy: split-apply-combine.
Exploring US baby name trends
Modelling Texas house sales
Wrap up: how it all fits together
Can’t go into much detail in 3 hours, but hopefully
this tutorial will get you off to a good start.
Tuesday, 7 July 2009

7. Code
bnames-explore.r—what we’ll work through next
bnames-cluster.r—expanded example showing how
to find cluster of similar names
tx-explore-houston.r—introduction to housing data
tx-explore-all.r—process of model building for
large data
nnet.r—fitting a neural network with many random
starts and varying parameters
Tuesday, 7 July 2009

8. Split-apply-combine
Split up a big dataset
Apply a function to each piece
Combine all the pieces back together
Keep this theme in mind as we work
through the examples.
Tuesday, 7 July 2009