**Gosset's student » R**, and kindly contributed to R-bloggers)

If you’ve read any of my previous posts, you’ll notice that they’re rather scanty on colour. There’s a reason for this. Mainly, that to get a good colour output takes some time. I recently read a commentary in Nature methods (sorry if you don’t have access to it, but this looks like it may be the first part of an interesting series of articles), which discusses colour in graphics. The author suggests a colour wheel, and I thought I’d have a go in R:

You have to click on it to read the text, sorry. There’s probably much easier ways to do it, and it takes a silly amount of time to render (several seconds! – all those nested loops), but this code below makes the colour wheel. If you set the variables t.hue, t.sat and t.val, the bottom right box is the resulting colour (the box just to the bottom right of the colour wheel is the hue with sat and val set to 1.0). Then on the right is the plot of val, and below is the plot of sat. As you go anti-clockwise from the x axis round your hue increases from 0.0 to 1.0.

So you can play around with colour, see what works and what doesn’t. This uses the HSV approach, which seemed okay for my purposes. rgb2hsv() converts rgb into hsv (obviously), if you are more familiar with the RGB approach. There are lots of other resources for colour in R, one of my favourites is here, and of course you can always search R-bloggers.

## colour plot require(graphics) t.hue <- 0.65 ## this is the user entered hue, sat and value t.sat <- 0.5 t.val <- 0.9 def.par <- par(no.readonly = TRUE) layout( matrix(c(1,1,2,1,1,2,3,3,4), 3, 3, byrow = TRUE)) ## prepare the plot for the wheel x <- (-100:100)*0.01 y <- (-100:100)*0.01 ## blank plot to prepare the axis plot(x,y, pch = 20, col = 0, bty = "n", xaxt = "n", yaxt = "n", ann = F) ## make the wheel for (x in (-100:100)*0.01){ for (y in (-100:100)*0.01){ theta <- atan2(y,x) # theta is the angle hue <- Mod(theta/(pi)) # make the hue dependent upon the angle sat <- (x^2 + y^2) # make the saturation depend upon distance from origin if (x^2 + y^2 <= 1){ if (y > 0) {points(x,y, pch = 19, col = hsv(h = hue/2, s = sat, v = 1))} if (y < 0) {points(-x,y, pch = 19, col = hsv(h = hue/2 + 0.5, s = sat, v = 1))} } } } legend("center", "hue", bty = "n") text(0.9,0, labels = "0.0") text(0,0.9, labels = "0.25") text(-0.9,0, labels = "0.5") text(0,-0.9, labels = "0.75") ## bottom right colour box inset into wheel for (x in (80:100)*0.01){ for (y in (-80:-100)*0.01){ points (x,y, pch = 19, col = hsv(t.hue, s = 1, v = 1)) } } ## right sided v scale x <- (0:100)*0.01 y <- (0:100)*0.01 plot(x,y, pch = 20, col = 0, xaxt = "n", yaxt = "n", bty = "n", ann = F) for (x in (50:100)*0.01){ for (y in (0:100)*0.01){ hue <- t.hue sat <- 1 points(x,y, pch = 19, col = hsv(h = hue, s = sat, v = y)) } } legend("topleft", "value", bty = "n") arrows(0.0, t.val, 0.5, t.val,length = 0.01, angle = 20) ## bottom saturation scale x <- (0:100)*0.01 y <- (0:100)*0.01 plot(x,y, pch = 20, col = 0, xaxt = "n", yaxt = "n", bty = "n", ann = F) for (x in (0:100)*0.01){ for (y in (0:50)*0.01){ hue <- t.hue points(x,y, pch = 19, col = hsv(h = hue, s = x, v = 1)) } } legend("topleft", "saturation", bty = "n") arrows(t.sat,1.0, t.sat, 0.5, length = 0.01, angle = 20) ## bottom right plot x <- (0:100)*0.01 y <- (0:100)*0.01 plot(x,y, pch = 20, col = 0, xaxt = "n", yaxt = "n", bty = "n", ann = F) for (x in (0:25)*0.01){ for (y in (0:100)*0.01){ points(x,y, pch = 19, col = hsv(h = t.hue, s = t.sat, v = t.val)) } } legtr <- paste( "hue=", t.hue, sep = "") legr <- paste( "sat=", t.sat, sep = "") legbr <- paste("val=", t.val, sep = "") legend("topright", legtr, bty = "n") legend("right", legr, bty = "n") legend("bottomright", legbr, bty = "n") ## reset the graphics display to default par(def.par)

Tagged: colour wheel, R

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

**Gosset's student » R**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...