Diverging colour gradient

Usage

scale_colour_gradient2(..., low = muted("red"), mid = "white", high = muted("blue"), 
  midpoint = 0, space = "rgb", na.value = "grey50", guide = "colourbar")

scale_fill_gradient2(..., low = muted("red"), mid = "white", high = muted("blue"), 
      midpoint = 0, space = "rgb", na.value = "grey50", guide = "colourbar")

scale_color_gradient2(..., low = muted("red"), mid = "white", high = muted("blue"), 
      midpoint = 0, space = "rgb", na.value = "grey50", guide = "colourbar")

Arguments

midpoint

The midpoint (in data value) of the diverging scale. Defaults to 0.

guide

Type of legend. Use "colourbar" for continuous colour bar, or "legend" for discrete colour legend.

...

Other arguments passed on to discrete_scale to control name, limits, breaks, labels and so forth.

na.value

Colour to use for missing values

low

colour for low end of gradient.

mid

colour for mid point

high

colour for high end of gradient.

space

colour space in which to calculate gradient. "Lab" usually best unless gradient goes through white.

Examples

dsub <- subset(diamonds, x > 5 & x < 6 & y > 5 & y < 6)
dsub$diff <- with(dsub, sqrt(abs(x-y))* sign(x-y))
(d <- qplot(x, y, data=dsub, colour=diff))



d + scale_colour_gradient2()


# Change scale name
d + scale_colour_gradient2(expression(sqrt(abs(x - y))))


d + scale_colour_gradient2("Difference\nbetween\nwidth and\nheight")



# Change limits and colours
d + scale_colour_gradient2(limits=c(-0.2, 0.2))



# Using "muted" colours makes for pleasant graphics
# (and they have better perceptual properties too)
library(scales) # for muted
d + scale_colour_gradient2(low="red", high="blue")


d + scale_colour_gradient2(low=muted("red"), high=muted("blue"))



# Using the Lab colour space also improves perceptual properties
# at the price of slightly slower operation
d + scale_colour_gradient2(space="Lab")



# About 5% of males are red-green colour blind, so it's a good
# idea to avoid that combination
d + scale_colour_gradient2(high=muted("green"))



# We can also make the middle stand out
d + scale_colour_gradient2(mid=muted("green"), high="white", low="white")



# or use a non zero mid point
(d <- qplot(carat, price, data=diamonds, colour=price/carat))


d + scale_colour_gradient2(midpoint=mean(diamonds$price / diamonds$carat))



# Fill gradients work much the same way
p <- qplot(letters[1:5], 1:5, fill= c(-3, 3, 5, 2, -2), geom="bar")
p + scale_fill_gradient2("fill")

Mapping a variable to y and also using stat="bin".
  With stat="bin", it will attempt to set the y value to the count of cases in each group.
  This can result in unexpected behavior and will not be allowed in a future version of ggplot2.
  If you want y to represent counts of cases, use stat="bin" and don't map a variable to y.
  If you want y to represent values in the data, use stat="identity".
  See ?geom_bar for examples. (Deprecated; last used in version 0.9.2)

# Note how positive and negative values of the same magnitude
# have similar intensity

See also