Make y-axis logarithmic in histogram using R [duplicate]

Answer 1

A histogram with the y-axis on the log scale will be a rather odd histogram. Technically it will still fit the definition, but it could look rather misleading: the peaks will be flattened relative to the rest of the distribution.

Instead of using a log transformation, have you considered:

• Dividing the counts by 1 million:

  h <- hist(hplot$V1, plot=FALSE)

  h$counts <- h$counts/1e6

  plot(h)

• Plotting the histogram as a density estimate:

  hist(hplot$V1, freq=FALSE)

Answer 2

You can log your y-values for the plot and add a custom log y-axis afterwards.

Here is an example for a table object of random normal distribution numbers:

# data
count = table(round(rnorm(10000)*2))
# plot
plot(log(count) ,type="h",  yaxt="n", xlab="position", ylab="log(count)")
# axis labels
yAxis = c(0,1,10,100,1000)
# draw axis labels
axis(2, at=log(yAxis),labels=yAxis, las=2)

Answer 3

You can save the histogram data to tweak it before plotting:

set.seed(12345)
x = rnorm(1000)

hist.data = hist(x, plot=F)
hist.data$counts = log10(hist.data$counts)

dev.new(width=4, height=4)
hist(x)

dev.new(width=4, height=4)
plot(hist.data, ylab='log10(Frequency)')

Answer 4

Another option would be to use plot(density(hplot$V1), log="y").

It's not a histogram, but it shows just about the same information, and it avoids the illogical part where a bin with zero counts is not well-defined in log-space.

Of course, this is only relevant when your data is continuous and not when it's really categorical or ordinal.