Gradient of n colors ranging from color 1 and color 2

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)

https://i.stack.imgur.com/dGQU0.png

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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:

https://i.stack.imgur.com/28xXQ.png

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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)

https://i.stack.imgur.com/9HJJN.png

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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

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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:

https://i.stack.imgur.com/HnREA.jpg

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:

https://i.stack.imgur.com/md2Lv.jpg

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

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