Logarithmic scaling with ggplot2 in R

Question

I am trying to create a diagram using ggplot2. There are several very small values to be displayed and a few larger ones. I'd like to display all of them in an appropriate way using logarithmic scaling. This is what I do:

plotPointsPre <- ggplot(data = solverEntries, aes(x = val, y = instance, 
                                                  color = solver, group = solver))

...

finalPlot <- plotPointsPre + coord_trans(x = 'log10') + geom_point() +
xlab("costs") + ylab("instance")

This is the result:

It is just the same as without coord_trans(x = 'log10').

However, if I use it with the y-axis:

How do I achieve the logarithmic scaling on the x-axis? Besides, it is not about the x-axis, if I switch the values of x and y, then it works on the x-axis and no longer on the y-axis. So there seems to be some problem with the displayed values. Does anybody have an idea how to fix this?

Edit - Here's the used data contained in solverEntries:

solverEntries <- data.frame(instance = c(1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8, 9, 9, 9, 9, 10, 10, 10, 10, 11, 11, 11, 11, 12, 12, 12, 12, 13, 13, 13, 13, 14, 14, 14, 14, 15, 15, 15, 15, 16, 16, 16, 16, 17, 17, 17, 17, 18, 18, 18, 18, 19, 19, 19, 19, 20, 20, 20, 20),
                 solver = c(4, 3, 2, 1, 4, 3, 2, 1, 4, 3, 2, 1, 4, 3, 2, 1, 4, 3, 2, 1, 4, 3, 2, 1, 4, 3, 2, 1, 4, 3, 2, 1, 4, 3, 2, 1, 4, 3, 2, 1, 4, 3, 2, 1, 4, 3, 2, 1, 4, 3, 2, 1, 4, 3, 2, 1, 4, 3, 2, 1, 4, 3, 2, 1, 4, 3, 2, 1, 4, 3, 2, 1, 4, 3, 2, 1, 4, 3, 2, 1),
                 time = c(1, 24, 13, 6, 1, 41, 15, 5, 1, 26, 16, 5, 1, 39, 7, 4, 1, 28, 11, 3, 1, 31, 12, 3, 1, 38, 20, 3, 1, 37, 10, 4, 1, 25, 11, 3, 1, 32, 18, 4, 1, 27, 21, 3, 1, 23, 22, 3, 1, 30, 17, 2, 1, 36, 8, 3, 1, 37, 19, 4, 1, 40, 21, 3, 1, 29, 11, 4, 1, 33, 10, 3, 1, 34, 9, 3, 1, 35, 14, 3),
                 val = c(6553.48, 6565.6, 6565.6, 6577.72, 6568.04, 7117.14, 6578.98, 6609.28, 6559.54, 6561.98, 6561.98, 6592.28, 6547.42, 7537.64, 6549.86, 6555.92, 6546.24, 6557.18, 6557.18, 6589.92, 6586.22, 6588.66, 6588.66, 6631.08, 6547.42, 7172.86, 6569.3, 6582.6, 6547.42, 6583.78, 6547.42, 6575.28, 6555.92, 6565.68, 6565.68, 6575.36, 6551.04, 6551.04, 6551.04, 6563.16, 6549.86, 6549.86, 6549.86, 6555.92, 6544.98, 6549.86, 6549.86, 6561.98, 6558.36, 6563.24, 6563.24, 6578.98, 6566.86, 7080.78, 6570.48, 6572.92, 6565.6, 7073.46, 6580.16, 6612.9, 6557.18, 7351.04, 6562.06, 6593.54, 6547.42, 6552.3, 6552.3, 6558.36, 6553.48, 6576.54, 6576.54, 6612.9, 6555.92, 6560.8, 6560.8, 6570.48, 6566.86, 6617.78, 6572.92, 6578.98))

Answer 1

Your data in current form is not log distributed -- most val around 6500 and some 10% higher. If you want to stretch the data, you could use a custom transformation using the scales::trans_new(), or here's a simpler version that just subtracts a baseline value to make a log transform useful. After subtracting 6500, the small values will be mapped to around 50, with the large values around 1000, which is a more appropriate range for a log scale. Then we apply the same transformation to the breaks so that the labels will appear in the right spots. (i.e. the label 6550 is mapped to the data that is mapped to 6550 - 6500 = 50)

This method helps if you want to make the underlying values more distinguishable, but at the cost of distorting the underlying proportions between values. You might be able to help with this by picking useful breaks and labeling them with scaling stats, e.g.

7000 +7% over min

my_breaks <- c(6550, 6600, 6750, 7000, 7500)
baseline = 6500

library(ggplot2)
ggplot(data = solverEntries, 
       aes(x = val - baseline, y = instance, 
           color = solver, group = solver)) +
  geom_point() +
  scale_x_log10(breaks = my_breaks - baseline,
                labels = my_breaks, name = "val")

Answer 2

Is this what you're looking for?

x_data <- seq(from=1,to=50)
y_data <- 2*x_data+rnorm(n=50,mean=0,sd=5)

#non log y
ggplot()+
  aes(x=x_data,y=y_data)+
  geom_point()

#log y scale
ggplot()+
  aes(x=x_data,y=y_data)+
  geom_point()+
  scale_y_log10()

#log x scale
ggplot()+
  aes(x=x_data,y=y_data)+
  geom_point()+
  scale_x_log10()