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## Modifying basic plots in R

Below is a walk-through of some of the basics of customizing plots in R. These are all based on the graphics package that comes in the base installation of R.

Let’s start by making a basic plot in R. In the code snippets below, green text behind a # sign is considered comments by R, so everything after a # sign on a line will be ignored by R. We’ll call the plot command and supply it with two vectors of numbers representing the x and y values:

plot(c(1,2,3,4,5,6),c(4,3,6,2,1,1)) #x and y data for our example plot

which produces this plot:

## Eclipse and StatET – a working environment for R

In the endless search to find an interface for the R statistics package that recreates the features of my favorite Matlab development environment, I finally ran across the Eclipse and StatET combination. The Eclipse project produces the program that acts as the integrated development environment (also useful for Java development, php development, perl scripting etc.). To integrate your R installation into this development environment, you use the StatET plug-in for Eclipse, available at http://www.walware.de/?page=/it/statet/. The result looks like the picture below:

[caption id=”attachment_195″ align=”aligncenter” width=”300″ caption=”The Eclipse/StatET development environment for R. There aren't normally crudely drawn … Continue Reading

## Calculating LT50 (median lethal temperature, aka LD50) quickly in R

Say you’ve got a bunch of survival/mortality data from an experiment. Maybe you exposed batches of snails to various high temperatures for a few hours, and recorded the number alive and dead in each batch at the end. Now you’d like to report the median lethal temperature (or perhaps a lethal dosage if you were injecting stuff into critters). We can do this fairly quickly using R statistical software to perform a logistic regression and back-calculate the LT50.

This assumes that you already have R installed on your computer and you know how to fire it up.