Is there a built-in way to do a logarithmic color

2020-01-29 04:43发布

Here's an example of a binned density plot:

require(ggplot2)
n <- 1e5
df <- data.frame(x = rexp(n), y = rexp(n))
p <- ggplot(df, aes(x = x, y = y)) + stat_binhex()
print(p)

enter image description here

It would be nice to adjust the color scale so that the breaks are log-spaced, but a try

my_breaks <- round_any(exp(seq(log(10), log(5000), length = 5)), 10)
p + scale_fill_hue(breaks = as.factor(my_breaks), labels = as.character(my_breaks))

Results in an Error: Continuous variable () supplied to discrete scale_hue. It seems breaks is expecting a factor (maybe?) and designed with categorical variables in mind?

There's a not built-in work-around I'll post as an answer, but I think I might just be lost in my use of scale_fill_hue, and I'd like to know if there's anything obvious I'm missing.

标签: r ggplot2
2条回答
Viruses.
2楼-- · 2020-01-29 05:06

Another way, using a custom function in stat_summary_hex:

ggplot(cbind(df, z = 1), aes(x = x, y = y, z = z)) + 
  stat_summary_hex(function(z){log(sum(z))})

This is now part of ggplot, but was originally inspired by the wonderful code by by @kohske in this answer, which provided a custom stat_aggrhex. In versions of ggplot > 2.0, use the above code (or the other answer)

ggplot(cbind(df, z = 1), aes(x = x, y = y, z = z)) +
    stat_aggrhex(fun = function(z) log(sum(z))) +
    labs(fill = "Log counts")

To generate this plot.

enter image description here

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爱情/是我丢掉的垃圾
3楼-- · 2020-01-29 05:09

Yes! There is a trans argument to scale_fill_gradient, which I had missed before. With that we can get a solution with appropriate legend and color scale, and nice concise syntax. Using p from the question and my_breaks = c(2, 10, 50, 250, 1250, 6000):

p + scale_fill_gradient(name = "count", trans = "log",
                        breaks = my_breaks, labels = my_breaks)

enter image description here

My other answer is best used for more complicated functions of the data. Hadley's comment encouraged me to find this answer in the examples at the bottom of ?scale_gradient.

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