This post shares the video from the talk presented on 15th May 2013 by Dr Kendra Vant on ProjectTemplate, github and Rstudio at Melbourne R Users. Overview: Want to minimise the drudge work of data prep? Get started with test … Continue reading →
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)
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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