My question is how to properly implement logarithmic bins for histograms in R? The following test case highlights my issue. Given some lognormally distributed data that has been left-truncated, we try and plot the histogram. Depending on if you plot the histogram directly of the log-transformed data, versus you use a log-axis, you can have 2 drastically different interpretations of the data. Method 1 seems to be wrong, as you actually have a linear axis from ~3 to ~ 8, but Method 2 seems to erase the lognormal that is obviously there. Without truncation, Method 1 also seems to be preferrable.
sln <- rlnorm(5e3, 10.12, 1.93)
sln <- sln[sln>5e3]
brks<- hist(log10(sln), breaks = "FD", prob = T, plot = T, main = "method 1")$breaks
hist(sln, breaks = 10^brks, prob = T, log = "x", main = 'Method 2')
EDIT I'll add that stepping away from histograms and using the kernel density estimates has a similar problem:
plot(density(log(sln)), main = 'Method 1')
plot(density(sln), log = "x", main = 'Method 2')
