**Probability and statistics blog » r**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Computer creations are perfect by design. We put in numbers, and if all goes well we get out an exact result. If we want a line, we want it perfectly straight. If we want a circle, it should conform to the platonic ideal of a circle. From a mathematical standpoint, these perfect shapes and precisely computed numbers are ideal.

Someday, perhaps, we will have true fuzzy computation built right into our hardware. For now, it takes considerable effort to achieve just the right level of imperfection needed for simulating mistakes, or any organic processes.

I sent each of the circles shown above on a random walk. That part was easy, getting each circle to end up where it started (and close the loop) took a bit more effort. To vary the “wigglyness” of the lines, adjust the “sd” parameter in “rnorm”. To change how quickly randomness tapers off, change the “4″ in “i/4″. Here is my code:

# Circle lengths j = seq(0.1,1.9,.08) par(bg = "black") plot(-2,-2,pch=".",xlim=c(-2,2),ylim=c(-2,2),col="white") # How many dots around the circle? dots = 1000 # Create an offkilter circle rads = seq(0,2*pi,2*pi/dots) for(aLength in j) { # Pick a random color myCol = paste("#",paste(sample(c(1:9,"A","B","C","D","E","F"),6,replace=T),collapse=""),collapse="",sep="") # Start at length = 1, then walk. myLength = rep(aLength,dots) for(i in 2:dots) { myLength[i] = myLength[(i-1)] + rnorm(1,0,sd=.005) # Closer we are to end, faster we return to where started so circle closes dist = aLength - myLength[i] myLength[i] = aLength - (dist*((dots-(i/4))/(dots))) } for(i in 1:dots) { cat(myLength[i]*cos(rads[i]),myLength[i]*sin(rads[i]),"n") points(myLength[i]*cos(rads[i]),myLength[i]*sin(rads[i]),col=myCol,pch=20,cex=2) } }

What do your circles look like?

**leave a comment**for the author, please follow the link and comment on their blog:

**Probability and statistics blog » r**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.