Plot Negative Values in Logarithmic Scale with Ggplot 2

Plot negative on logarithmic axis in ggplot

You can pmax(0, .) it:

library(ggplot2)
library(scales)

ggplot(df, aes(x = Sample, y = Fe, ymin = pmax(0, Fe - sd), ymax = Fe + sd)) +
  theme_bw() +
  geom_point(size=3, stat = "identity") + 
  geom_errorbar(aes(width = 0.1), stat = "identity") +
  scale_y_continuous(trans = log10_trans(),
                     breaks = trans_breaks("log10", function(x) 10^x),
                     labels = trans_format("log10", math_format(10^.x))) +
  annotation_logticks(sides = "l")

Or you can use a smaller number and see the lower end of that error bar, though that might suggest a present value that isn't ...

ggplot(df, aes(x = Sample, y = Fe, ymin = pmax(1, Fe - sd), ymax = Fe + sd)) +
  theme_bw() +
  geom_point(size=3, stat = "identity") + 
  geom_errorbar(aes(width = 0.1), stat = "identity") +
  scale_y_continuous(trans = log10_trans(),
                     breaks = trans_breaks("log10", function(x) 10^x),
                     labels = trans_format("log10", math_format(10^.x))) +
  annotation_logticks(sides = "l")

Using much smaller values will continue to squish the other errors a little tighter as the y-axis tries to adjust.

plot with logarithmic y scale and negative values

Thank you all for your comments. I've solved the problem by myself: instead of using simple plot() for generating the graph, I've used ggplot2 package with scale_y_log() function to present linear units at logarithmic scale (including negative values).

However, I have one more - similar - question, which can be found here.

R ggplot2 logarithmic cut with negative and positive values on x-axis and mean per bin of y-axis

First version of an answer, using data.table for faster speeds and better readability:

The code reproduces the question with shorter and faster code

library(data.table)

# function that returns the lower bound of a cut
lower.bound <- function(x, n) {
  c <- cut(x, n)
  tmp <- substr(x = c, start = 2, stop = regexpr(",", c) - 1)
  return(as.numeric(tmp))
}

nbin <- 10
set.seed(123)
dat <- data.table(value = rnorm(1000,0, 20),
                  dist = c(rep(0, 15), sample(1:490), sample(-1:-495)))

dat[, log := log2(abs(dist) + 1)]
dat[, labels := 2^(abs(log))]
dat[, sign := ifelse(dist == 0, 
                     0,
                     ifelse(dist > 0, "+", "-"))]

dat[, bin := ifelse(sign == 0, 
                    0,
                    ifelse(sign == "+", 
                           lower.bound(log, nbin),
                           -lower.bound(log, nbin)))]

sumdat <- dat[, .(mvalue = mean(value),
                  nvalue = .N,
                  ylab = mean(dist)), 
                 by = .(bin, sign)][order(bin)]

ggplot(sumdat, aes(x = ylab, y = mvalue)) + geom_line()

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