Make sure to click on the image to see the large version. Code for this graph:
moxbuller = function(n) {
u = runif(n)
v = runif(n)
x = cos(2*pi*u)*sqrt(-2*log(v))
y = sin(2*pi*v)*sqrt(-2*log(u))
r = list(x=x, y=y)
return(r)
}
r = moxbuller(50000)
par(bg="black")
par(mar=c(0,0,0,0))
plot(r$x,r$y, pch=".", col="blue", cex=1.2)
I’ve started using this one quite often. Over time your environment fills up with objects, then when you run a script you don’t know if an error or unexpected result is related to an existing object in your environment.
Use with caution since it will remove all of your working data.
rm(list = ls())
datafilename <- file.choose()
myData <- read.table(datafilename,header=TRUE)
Not easy to produce cool looking graphs in R, but it can be done. The results of some messing around are above. Here is the code I used:
x = runif(1000)
y = x/runif(1000)
cexes = 10*y/max(y) # For circle size
par(bg="black") # I see a white background and I want it painted black.
par(mar=c(0,0,0,0)) # Margins? We don't kneed no stinkin' margins.
plot(x,log(y), pch=20, col="white", cex=cexes)
If you have a plot open and want to add a vertical line to it:
abline(v=20) #Add vertical line at x=20
gm(x)
But this requires package heR.Misc so you might as well just use:
exp(mean(log(x)))
Had to look this one up, so here it is:
length(unique(x))
Counts the number of distinct items in a vector in R.