You can bin/coarse-grain the continuous values to give them a discrete-ordered value to map to the discrete 8 to 12 colors included in your Brewer palette of choice.

But I think the solution you are looking for is to "distill" them with the scale_color_distiller() or scale_fill_distiller() functions already included in ggplot2, effectively converting them to a continuous color scale. Documentation and examples are provided in some canonical locations:

• https://ggplot2.tidyverse.org/reference/scale_brewer.html
• https://ggplot2-book.org/scale-colour.html#colour-continuous

Discrete

If you wish to specify a default, discrete colour scale such as that produced by scale_colour_brewer() use the ggplot2.discrete.colour option. Similarly, use the option ggplot2.discrete.fill for a discrete fill scale.

Default Discrete Scales

library(ggplot2)

ggplot(mtcars, aes(hp, mpg, color = factor(cyl), fill = factor(cyl))) + geom_point()

^{Created on 2021-07-01 by the reprex package (v1.0.0)}

Custom Discrete Scales

library(ggplot2)

scale_colour_brewer_d <- function(..., palette = "Dark2") {
  scale_colour_brewer(..., palette = palette )
}

scale_fill_brewer_d <- function(..., palette = "Dark2") {
  scale_fill_brewer(..., palette = palette)
}

options(
  ggplot2.discrete.colour = scale_colour_brewer_d,
  ggplot2.discrete.fill = scale_fill_brewer_d
)

ggplot(mtcars, aes(hp, mpg, color = factor(cyl), fill = factor(cyl))) + geom_point()

^{Created on 2021-07-01 by the reprex package (v1.0.0)}

Continuous

If you wish to specify a default, continuous color scale, you will need to use scale_colour_distiller() instead of scale_colour_brewer(). Similarly, use scale_fill_distiller() as opposed to scale_fill_brewer() for a continuous fill scale. You will also use the options ggplot2.continuous.colour and ggplot2.continuous.fill respectively.

Default Continuous Scales

library(ggplot2)

ggplot(mtcars, aes(hp, mpg, color = cyl, fill = cyl)) + geom_point()

^{Created on 2021-07-01 by the reprex package (v1.0.0)}

Custom Continuous Scales

library(ggplot2)

scale_colour_brewer_c <- function(..., palette = "Dark2") {
  scale_colour_distiller(..., palette = palette )
}

scale_fill_brewer_c <- function(..., palette = "Dark2") {
  scale_fill_distiller(..., palette = palette)
}

options(
  ggplot2.continuous.colour = scale_colour_brewer_c,
  ggplot2.continuous.fill = scale_fill_brewer_c
)

ggplot(mtcars, aes(hp, mpg, color = cyl, fill = cyl)) + geom_point()

^{Created on 2021-07-01 by the reprex package (v1.0.0)}

Using Cynthia Brewer's color brewer website might provide some insight: http://colorbrewer2.org/#type=sequential&scheme=YlOrRd&n=6

You'll notice when you change the number of classes, it spreads out the distribution of the color ramp to be more gradual so each class still has a unique hex/RGB etc for the ramp.

ColorBrewer was primarily developed for GIS and cartography so I know when I use color brewer ramps for my maps in ArcGIS, I'm supposed to use the 5-class version of the palette for maps w/ 5 classes and so on. If I change my map classification to have 4 classes but leave the 5-class ramp on, it won't display correctly. Think about your data classification vs your palette choice. It might have to do with that.