R: Bar plot on a continuous x-axis (time-scaled)

Question

I'm fairly new to R so please comment on anything you see.

I have data taken at different timepoints, under two conditions (for one timpoint) and I want to plot this as a bar plot with errorbars and with the bars at the appropriate timepoint.

I currently have this (stolen from another question on this site):

library(ggplot2)
example <- data.frame(tp = factor(c(0, "14a", "14b", 24, 48, 72)), means  = c(1, 2.1, 1.9, 1.8, 1.7, 1.2), std = c(0.3, 0.4, 0.2, 0.6, 0.2, 0.3))
ggplot(example, aes(x = tp, y = means)) +  
  geom_bar(position = position_dodge()) + 
  geom_errorbar(aes(ymin=means-std, ymax=means+std))

Now my timepoints are a factor, but the fact that there is an unequal distribution of measurements across time makes the plot less nice.!

This is how I imagine the graph :

I find the ggplot2 package can give you very nice graphs, but I have a lot more difficulty understanding it than I have with other R stuff.

Answer 1

Before we get into R, you have to realize that even in a bar plot the x axis needs a numeric value. If you treat them as factors then the software assumes equal spacing between the bars by default. What would be the x-values for each of the bars in this case? It can be (0, 14, 14, 24, 48, 72) but then it will plot two bars at point 14 which you don't seem to want. So you have to come up with the x-values.

Joran provides an elegant solution by modifying the width of the bars at position 14. Modifying the code given by joran to make the bars fall at the right position in the x-axis, the final solution is:

library(ggplot2)
example <- data.frame(tp = factor(c(0, "14a", "14b", 24, 48, 72)), means  = c(1, 2.1, 1.9, 1.8, 1.7, 1.2), std = c(0.3, 0.4, 0.2, 0.6, 0.2, 0.3))

example$tp1 <- gsub("a|b","",example$tp)
example$grp <- c('a','a','b','a','a','a')
example$tp2 <- as.numeric(example$tp1)

ggplot(example, aes(x = tp2, y = means,fill = grp)) +  
  geom_bar(position = "dodge",stat = "identity") + 
  geom_errorbar(aes(ymin=means-std, ymax=means+std),position = "dodge")