Gradient of n colors ranging from color 1 and color 2

I often work with ggplot2 that makes gradients nice (click here for an example). I have a need to work in base and I think scales can be used there to create color gradients as well but I'm severely off the mark on how. The basic goal is generate a palette of n colors that ranges from x color to y color. The solution needs to work in base though. This was a starting point but there's no place to input an n. 

 scale_colour_gradientn(colours=c("red", "blue"))

I am well aware of:

brewer.pal(8, "Spectral")

from RColorBrewer. I'm looking more for the approach similar to how ggplot2 handles gradients that says I have these two colors and I want 15 colors along the way. How can I do that?

165533 次浏览
小开
最佳答案

colorRampPalette could be your friend here:

colfunc <- colorRampPalette(c("black", "white"))
colfunc(10)
# [1] "#000000" "#1C1C1C" "#383838" "#555555" "#717171" "#8D8D8D" "#AAAAAA"
# [8] "#C6C6C6" "#E2E2E2" "#FFFFFF"

And just to show it works:

plot(rep(1,10),col=colfunc(10),pch=19,cex=3)

enter image description here

小开

The above answer is useful but in graphs, it is difficult to distinguish between darker gradients of black. One alternative I found is to use gradients of gray colors as follows 

palette(gray.colors(10, 0.9, 0.4))
plot(rep(1,10),col=1:10,pch=19,cex=3))

More info on gray scale here.

Added

When I used the code above for different colours like blue and black, the gradients were not that clear. heat.colors() seems more useful.

This document has more detailed information and options. pdf

小开

Just to expand on the previous answer colorRampPalettecan handle more than two colors.

So for a more expanded "heat map" type look you can....

colfunc<-colorRampPalette(c("red","yellow","springgreen","royalblue"))
plot(rep(1,50),col=(colfunc(50)), pch=19,cex=2)

The resulting image:

enter image description here

小开

Try the following:

color.gradient <- function(x, colors=c("red","yellow","green"), colsteps=100) {
return( colorRampPalette(colors) (colsteps) [ findInterval(x, seq(min(x),max(x), length.out=colsteps)) ] )
}
x <- c((1:100)^2, (100:1)^2)
plot(x,col=color.gradient(x), pch=19,cex=2)

Colored plot using color.gradient

Edit
Let me try to explain why I think this function is superior to the other suggested solutions.

Let's apply the function suggested by jsol for the exponential data I used for my plot. I try two variations using range and length in the call to colfunc.
Result: It simply does not work as intended.

colfunc <- colorRampPalette(c("red","yellow","springgreen","royalblue"))
x <- c((1:100)^2, (100:1)^2)
plot(x, col=colfunc(range(x)), pch=19,cex=2)
plot(x, col=colfunc(length(x)), pch=19,cex=2)

Colored plot using confunc

小开

An alternative approach (not necessarily better than the previous answers!) is to use the viridis package. As explained here, it allows for a variety of color gradients that are based on more than two colors.

The package is pretty easy to use - you just need to replace the ggplot2 scale fill function (e.g., scale_fill_gradient(low = "skyblue", high = "dodgerblue4")) with the equivalent viridis function.

So, change the code for this plot:

ggplot(mtcars, aes(wt*1000, mpg)) +
geom_point(size = 4, aes(colour = hp)) +
xlab("Weight (pounds)") + ylab("Miles per gallon (MPG)") + labs(color='Horse power') +
scale_x_continuous(limits = c(1000, 6000),
breaks = c(seq(1000,6000,1000)),
labels = c("1,000", "2,000", "3,000", "4,000", "5,000", "6,000")) +
scale_fill_gradient(low = "skyblue", high = "dodgerblue4") +
theme_classic()

Which produces:

enter image description here

To this, which uses viridis:

ggplot(mtcars, aes(wt*1000, mpg)) +
geom_point(size = 4, aes(colour = factor(cyl))) +
xlab("Weight (pounds)") + ylab("Miles per gallon (MPG)") + labs(color='Number\nof cylinders') +
scale_x_continuous(limits = c(1000, 6000),
breaks = c(seq(1000,6000,1000)),
labels = c("1,000", "2,000", "3,000", "4,000", "5,000", "6,000")) +
scale_color_viridis(discrete = TRUE) +
theme_classic()

The only difference is in the second to last line: scale_color_viridis(discrete = TRUE).

This is the plot that is produced using viridis:

enter image description here

Hoping someone finds this useful, as its the solution I ended up using after coming to this question.


提交答案