Force bars to start from a lower value than 0 in ggplot geom_bar in R

Question

I want to plot bar plots using ggplot. I used scale_y_log10 to rescale the y axis. When use the script below, I get bars that are in both directions (upward and downward) and the bars start from 1. I understand that it is because, log10(1)=0.

ggplot (data=dat, aes(x=Col2,y=Col4,fill=Col3,group=Col1))+
geom_bar(stat="identity")+ facet_grid(.~Col1,scales="free_x",space="fixed")+
ylab("")+xlab("")+scale_fill_discrete("")+
scale_y_log10("#Counts",breaks=c(.1,1,10,100,1000),expand=c(0,0))+
theme_bw()+
theme(axis.text.x=element_text(angle=45,hjust=1,vjust=1))

Now, I want to force the bars to start from a lower value (say 0.1 or 0.01) and make all the bars in upward direction. How can I do that? Tried to find related posts and info but had no luck. Can someone direct me if there are such info already?

Edit: I want to use scale_y_log10 because most of the bars are low and some are extremely high. If I use scale_y_continuous, the shorter bars are not visible properly.

Answer 1

You can define your own scale, instead of using scale_y_continuous(trans="log10")). In the example below, you will have to change the argument from=-2 to you specific example.

# defining example data (since I don't have your data)
data(mtcars)
mtcars <- rbind(mtcars, mtcars)
mtcars <- rbind(mtcars, mtcars)
mtcars <- rbind(mtcars, mtcars)
mtcars <- rbind(mtcars, mtcars)
mtcars[1, "cyl"] <- 2
# sample plot
c <- ggplot(mtcars, aes(factor(cyl))) + geom_bar()
c + scale_y_log10() # this starts from 1
# defining the scale change
require(scales)
mylog_trans <- function(base=exp(1), from=0) 
{
  trans <- function(x) log(x, base)-from
  inv <- function(x) base^(x+from)
  trans_new("mylog", trans, inv, log_breaks(base=base), 
            domain = c(base^from, Inf))
}
# 
c + scale_y_continuous(trans = mylog_trans(base=10, from=-2)) # starts from 1e-2 
c + scale_y_continuous(trans = mylog_trans(base=10, from=-5)) # starts from 1e-5

As you can see in the above example, this plot can be very misleading. The two plots display the same data, but look very different, so be careful when using this scale-change.