When I first saw a graphic made from Yihui’s animation package (Xie, 2012) I was amazed at the magic and thought “I could never do that”. Passage of time… One night I found myself bored and as usual avoiding work. … Continue reading →
As noted on paragraph 18.4.1 of the book Veterinary Epidemiologic Research, logistic regression is widely used for binary data, with the estimates reported as odds ratios (OR). If it’s appropriate for case-control studies, risk ratios (RR) are preferred for cohort studies as RR provides estimates of probabilities directly. Moreover, it is often forgotten the assumption
If you’re not using version control, you should be. Learn git. If you’re not on github, you should be. That’s real open source. To help some colleagues get started with git and github, I wrote a minimal tutorial. There are lots of git and github resources available, but I thought I’d give just the bare
I've recently managed to reproduce my first charts using the nice package rNVD3 from Ramnath Vaidyanathan:
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 »
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)
Encouraged by this post I had another look at quandl for collecting datasets from different agencies. Right now I need to get data for four countries on a couple of dozen indicators. This graphic is just a quick example with only two indicators of what I am aiming to be able to do. The process
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...