My last post discussed a technique for integrating functions in R using a Monte Carlo or randomization approach. The mc.int function (available here) estimated the area underneath a curve by multiplying the proportion of random points below the curve by the total area covered by points within the interval: The estimated integration (bottom plot) is 
Over the past few days, I have been introduced to a few new-to-me R packages, via some comments from the Shiny guys and the R-bloggers site. This seems a rather haphazard way of acquiring knowledge and I cannot be alone in thinking that this is not the most productive way to become aware of new/better
I've recently managed to reproduce my first charts using the nice package rNVD3 from Ramnath Vaidyanathan:
https://github.com/ramnathv/rNVD3
This rNVD3 package uses NVD3, which provides re-usable charts with d3.js, without taking away the power that d3.js brings with itself. But let's make a "Hello world" type of example, with the minimum complexity in it.
For this, I...
The initial version of the timeline package has been released to CRAN. This package provides creates timeline plots using ggplot2 in a style similar to Preceden. I would considered this beta quality as there are more features I would like to add but has enough functionality to possibly be useful to others.
install.packages('timeline',repos='http://cran.r-project.org')
require(timeline)
data(ww2)
timeline(ww2, ww2.events, event.spots=2, event.label='', event.above=FALSE)Read more »
Today the front page of the Huffington Post featured the new data available from the CMS that shows the cost of many popular procedures broken down by hospital. We here at Simply Statistics think you should be able to explore … Continue reading →
I finally had an opportunity to play with Shiny, and I am very impressed. I have created a Github Project so head over there for the source code. There are a number of ways to distribute Shiny apps. If you are running R (and mostly likely you are if you are reading this), you can download and...
A few days ago there was an interesting R based article by diffuseprior on the decline and fall in the quality of The Simpsons The author scraped results from GEOS, an online survey of TV programs, and applied the R package changepoint to offer an analysis of the show over time This seemed a candidate aaaa
I am not sure I have ever done a post like this, but I was so blown away I had to do this post simply to embed this amazing Youtube video from the author of the R packages rCharts and slidify. Watch this screencast as he creates d3/raphael charts...
Our 5th Cologne R user group meeting was the best attended meeting so far, with 20 members finding their way to the Institute of Sociology for two talks by Diego de Castillo on shiny and Stephan Holtmeier on cluster analysis, followed by beer and schnitzel at the Lux, a gastropub nearby.
Welcome presentation Nicola Sturaro, Consultant at Quantide (download PDF, 0.5 MB) Machine learning A case of digit recognition based on a shallow neural network implemented in R. Michele Vitali, Statistical consultant (download PDF, 0.2 MB) Chess betting odds How to develop … Continue reading →
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
