R_users_group_1 is a project mainly written in R and SHELL, it's free.
first meeting of the Vancouver R Users Group
README (autogenerated from README.org)
======Author: Tavis Rudd Date: 2011-03-23 11:39:17 PDT
1 Useful Links 2 Introductions And A Quick Survey 2.1 Name and background 2.2 Experience with R? 2.3 Statistical training/experience? 2.4 Programming experience? 2.5 If no R experience, what have you heard about R? 2.6 If no R experience, what do you want to learn? 3 Thanks to our Sponsors! 3.1 Pulse Energy, our host 3.2 Revolution Analytics 4 Goals 4.1 Today 4.1.1 stoke your interest! 4.1.2 have some fun playing with it 4.1.3 line up speakers for next meeting in May 4.2 For the Group 4.2.1 Not just about R! 4.2.2 meetings every 2 months 4.2.3 mix of interactive sessions and talks 5 R as an Environment 5.1 long history (S, S-PLUS) 5.2 huge, rapidly growing community 5.3 optimized for fast, INTERACTIVE data exploration 5.4 awesome help system 5.5 very easy to learn the basics, while doing useful things 6 A Few Quick Examples 6.1 employment data 6.2 US crimes by state 6.3 timeseries lattice graphics of sales data 6.4 Crayola colours over the years 6.5 calendar heat map of blog posts 7 R as a Programming Language 7.1 assignment with <- instead of = 7.2 not zero indexed, starts at 1.. 7.3 vector-based rather than scalar based (very important!) 7.4 functional, in a loose sense 7.4.1 higher order functions 7.4.2 lexical scoping 7.4.3 an "expression language" 7.4.4 looping in R syntax is inefficient 7.4.5 not side-effect free, like Haskell or Erlang! 7.5 Very useful built-in data types (similar to Python) 7.5.1 with a very useful vector/array/matrix slicing syntax 7.6 functions take keyword arguments, with optional defaults 7.7 "object-oriented", but with "generic functions" 7.8 obj$attr NOT obj.attr 7.9 a bit inconsistent and idiosyncratic, but awesome nonetheless 8 Exercises 8.1 import a small tabular / numeric dataset into R 8.2 print summary descriptive statistics of the data 8.3 examine the data structures that R stores it in 8.4 create subsets of it 8.5 create same basic charts of the data 8.6 if applicable, explore relationships in the data (correlation, etc.) 9 Resources 9.1 http://www.statmethods.net/index.html 9.2 http://www.r-tutor.com/ 10 What's next??? 10.1 A source of meeting / talk ideas:
https://bitbucket.org/tavisrudd/r_users_group_1/src https://bitbucket.org/tavisrudd/r_users_group_1/get/tip.tar.gz
4.1.1 stoke your interest!
4.1.2 have some fun playing with it 4.1.3 line up speakers for next meeting in May
4.2 For the Group
==================
4.2.1 Not just about R!
~~~~~~~~~~~~~~~~~~~~~~~~
Data management, analysis and visualization in general
4.2.2 meetings every 2 months
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
4.2.3 mix of interactive sessions and talks
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
5 R as an Environment
----------------------
5.1 long history (S, S-PLUS)
=============================
5.2 huge, rapidly growing community
====================================
thousands of libraries
[http://crantastic.org/]
[http://cran.r-project.org/]
Lots of interesting blogs:
[http://www.r-bloggers.com/]
[http://learnr.wordpress.com/]
5.3 optimized for fast, INTERACTIVE data exploration
=====================================================
5.4 awesome help system
========================
5.5 very easy to learn the basics, while doing useful things
=============================================================
6 A Few Quick Examples
-----------------------
6.1 employment data
====================
[unemployment]
[unemployment]: file:unemployment.R::library(ggplot2)
6.2 US crimes by state
=======================
[ murder_vs_burglary]
[ murder_vs_burglary]: file:murder.R::crime%20<-%20read.csv("http:/datasets.flowingdata.com/crimeRatesByState2008.csv",
6.3 timeseries lattice graphics of sales data
==============================================
[lattice_example]
[lattice_example]: file:lattice_timeseries_plots.R::require(zoo)
6.4 Crayola colours over the years
===================================
[crayola]
[crayola]: file:crayola.R::library(Cairo)
6.5 calendar heat map of blog posts
====================================
[heatmap]
[heatmap]: file:life_sciences_heatmap.R::library(ggplot2)%20#%20also%20loads%20library%20plyr
7 R as a Programming Language
------------------------------
7.1 assignment with <- instead of =
====================================
7.2 not zero indexed, starts at 1..
====================================
7.3 vector-based rather than scalar based (very important!)
============================================================
7.4 functional, in a loose sense
=================================
7.4.1 higher order functions
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
7.4.2 lexical scoping
~~~~~~~~~~~~~~~~~~~~~~
7.4.3 an "expression language"
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
7.4.4 looping in R syntax is inefficient
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
... but there are higher order functions that make it very efficient and easy.
7.4.5 not side-effect free, like Haskell or Erlang! 7.5.1 with a very useful vector/array/matrix slicing syntax
7.6 functions take keyword arguments, with optional defaults
=============================================================
7.7 "object-oriented", but with "generic functions"
====================================================
7.8 obj$attr NOT obj.attr
==========================
this.is.just.a.variable.name
this_is_a_valid_varname for modern versions of R, but wasn't in the past
7.9 a bit inconsistent and idiosyncratic, but awesome nonetheless
==================================================================
8 Exercises
------------
8.1 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.
8.2 print summary descriptive statistics of the data
=====================================================
8.3 examine the data structures that R stores it in
====================================================
8.4 create subsets of it
=========================
8.5 create same basic charts of the data
=========================================
8.6 if applicable, explore relationships in the data (correlation, etc.)
=========================================================================
9 Resources
------------
9.1 [http://www.statmethods.net/index.html]
============================================
9.2 [http://www.r-tutor.com/]
==============================
10 What's next???
------------------
10.1 A source of meeting / talk ideas:
=======================================
[http://www.meetup.com/R-Users/#past]