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
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 →
Everyone loves to aggregate data. Everyone loves to create new columns based on other columns. Everyone hates to do the same thing twice. In my continuing work on multilevel view of loss reserving, I reached a point where I realized that I needed a robust mechanism to aggregate computed columns. SQL server and (I’m assuming) 
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...
I love the syntax of calls to lm and ggplot, wherein the dataframe is specified as a variable and specific columns are referenced as though they were separate variables. While developing some of my functions, I’d wanted to introduce something similar. I often find that I have a single large dataframe and want to execute 
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
In my last post I mentioned that I had improved on R’s summaryRprof() function with a custom function called proftable(). I’ve updated proftable() to take advantage of R 3.0.0’s ability to record line numbers while profiling. I’ve put it on github – you can get it there or below.
proftable reads in a file generated by...
I already mentioned R for dummies a while ago on the ‘Og and never got around to read it from cover to back. Now that I am reduced to a dummy state with too much free time!, I can produce a full review of the book. R for dummies was written by two Belgian statistics 
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
