This post is a quick reminder that the next Cologne R user group meeting is only one week away. We will meet on 6 July 2012. The meeting will kick off at 18:00 with three short talks at the Institute of Sociology and will continue, even more informal, from 20:00 in a pub (LUX) nearby.
One of the great research papers of the 20th century celebrates its 60th anniversary in a few weeks time: A quantitative description of membrane current and its application to conduction and excitation in nerve by Alan Hodgkin and Andrew Huxley. Only a shortly after Andrew Huxley died, 30th May 2012, aged 94.
In 1952 Hodgkin and Huxley published a series of papers, describing the basic processes underlying the nervous mechanisms of control and the communication between nerve cells, for which they received the Nobel prize in physiology and medicine, together with John Eccles in 1963.
Their research was based on electrophysiological experiments carried out in the late 1940s and early 1950 on a giant squid axon to understand how action potentials in neurons are initiated and propagated.
Thanks to the work by Thomas Petzoldt, Karsten Rinke, Karline Soetaert and R. Woodrow Setzer it is really straight forward to model and analyse dynamical systems in R with their
I will give a brief overview of the functionality using a predator-prey model as an example.
For a further example of a dynamical system with
simecol see my post about the Hodgkin-Huxley model, which describes the action potential of a giant squid axon.
I shouldn't forget to mention the other talks tonight as well:
- Writing R for Dummies - Andrie De Vries
- News from data.table 1.6, 1.7 and 1.8 - Matthew Dowle
- Converting S Plus Applications into R - Andy Nicholls (postponed to 18 September 2012)
Transforming data sets with R is usually the starting point of my data analysis work. Here is a scenario which comes up from time to time: transform subsets of a data frame, based on context given in one or a combination of columns.
As an example I use a data set which shows sales figures by product for a number of years:
df <- data.frame(Product=gl(3,10,labels=c("A","B", "C")),
## Product Year Sales
## 1 A 2002 1
## 2 A 2003 2
## 3 A 2004 3
## 4 A 2005 4
## 5 A 2006 5
## 6 A 2007 6
I am interested in absolute and relative sales developments by product over time. Hence, I would like to add a column to my data frame that shows the sales figures divided by the total sum of sales in each year, so I can create a chart which looks like this:
To illustrate the new feature I looked for a quarterly data set and stumbled across the quarterly UK house price data published by Nationwide, a building society. The data is available in a spread sheet format and presents the average house prices and indexed to 100 in Q1 1993 by region in the UK from Q4 1973 to Q1 2012. Unfortunately the data is formated for human eyes rather than for computers, see the screen shot below.
|Screen shot of Nationwide's UK house price data in Excel|