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README fix something misleading fix something misleading initial import
calendarHeat.R add
cellphone_chart.png add
cellphone_subscriptions_timeseries.R minor tweak
crayola.R initial import
crimeRatesByState2008.csv add
diamonds.R initial import
murder.R add
unemployment.R refactored and added base graphics version
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van_housing_orig.png add

Useful Links

Introductions And A Quick Survey

Name and background

Experience with R?

Statistical training/experience?

Programming experience?

If no R experience, what have you heard about R?

If no R experience, what do you want to learn?

Thanks to our Sponsors!

Pulse Energy, our host

Revolution Analytics



stoke your interest!

have some fun playing with it

line up speakers for next meeting in May

For the Group

Not just about R!

Data management, analysis and visualization in general

meetings every 2 months

mix of interactive sessions and talks

R as an Environment

long history (S, S-PLUS)

huge, rapidly growing community

thousands of libraries Lots of interesting blogs:

optimized for fast, INTERACTIVE data exploration

awesome help system

very easy to learn the basics, while doing useful things

A Few Quick Examples

employment data


US crimes by state


timeseries lattice graphics of sales data


Crayola colours over the years


calendar heat map of blog posts


R as a Programming Language

assignment with <- instead of =

not zero indexed, starts at 1..

vector-based rather than scalar based (very important!)

functional, in a loose sense

higher order functions

lexical scoping

an “expression language”

looping in R syntax is inefficient

… but there are higher order functions that make it very efficient and easy.

not side-effect free, like Haskell or Erlang!

Very useful built-in data types (similar to Python)

with a very useful vector/array/matrix slicing syntax

functions take keyword arguments, with optional defaults

“object-oriented”, but with “generic functions”

obj$attr NOT obj.attr this_is_a_valid_varname for modern versions of R, but wasn’t in the past

a bit inconsistent and idiosyncratic, but awesome nonetheless


import a small tabular / numeric dataset into R

Preferably your own data from real life projects, but there is plenty of interesting CSV data on the web if you can’t use your own.

print summary descriptive statistics of the data

examine the data structures that R stores it in

create subsets of it

create same basic charts of the data

if applicable, explore relationships in the data (correlation, etc.)


What’s next???

A source of meeting / talk ideas:

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