We can add them to our plot, and they'll get transformed nicely: ggplot(ame(x = d$x, y = d$y), aes(x, y)) + For example, compare an untransformed density: d x y This binning can affect the resulting density shape. To put my comments into a fuller context, ggplot is taking the log before doing the density estimation, which is causing the difference in shape because the binning covers different parts of the domain. How do I find the intercepts for the 2 new maximum points?įinally, I attempt to convert the y-axis to count instead: ggplot(myData, aes(x=attr)) + Why does it have 2 maximum points now? Why is my x-intercept no longer at the maximum point(s)?Ģ.
Since the data is skewed, I then attempted to draw x-axis in log scale by adding scale_x_log10() to the ggplot, here is the new graph:ġ. Here is the plot I have got with the x-intercept at maximum point:
Geom_vline(xintercept = density(myData$attr)$x)+ Geom_density(color="darkblue", fill="lightblue")+
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I am not sure how to make a simplified or reproducible data.Īnyway, I am plotting the density of attr like below, and I also find the attr value where density is maximum: library(ggplot) I have data with around 25,000 rows myData with column attr having values from 0 -> 45,600.
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