ggplot2 log scale of y axis causing curved lines

Question

I’d like to graph some data as means of groups over time, with lines connecting the different mean time points for each group.

The code for this is:

line<-ggplot(dat, aes(Time, Cortisol.ngmL, shape=T))

line+
stat_summary(fun.y=mean, geom="point", size=4, aes(group=T))+
stat_summary(fun.y=mean, geom="line", aes(group=T), linetype="dashed", lwd=0.7)

But…I want the y axis logged (log10). And when I do this the lines connecting the groups across time become curved (code below)

line<-ggplot(dat, aes(Time, Cortisol.ngmL, shape=T))

line+
stat_summary(fun.y=mean, geom="point", size=4, aes(group=T))+
stat_summary(fun.y=mean, geom="line", aes(group=T), linetype="dashed", lwd=0.7)+
coord_trans(y="log10")

Does anyone know a way I can have a log scale and straight lines?

Answer 1

I use this function to connect points with straight lines in log scale:

log_line <- function(x, y, n = 1000) {
  l <- lapply(2:length(x),
              function(i, n) {
                xl <- seq(x[i - 1], x[i], (x[i] - x[i - 1]) / n)
                yl <- exp(log(y[i]) + (xl - x[i]) * (log(y[i]) - log(y[i - 1])) / (x[i] - x[i - 1]))
                return(data.frame(x = xl, y = yl))
              },
              n)

  return(do.call(rbind, l))
}

The arguments are the x and y coordinates of the points you want to connect with a straight line in log scale and the n number of points you want to predict between each pair of original points.

This function fits a straight line in log scale between each points, predicts n new points between the two original points and than converts them back to the original scale. The output is a data frame with the predicted values x and y coordinates.

It can be easily added to a ggplot:

v1 <- 1:10
v2 <- exp(-v1)

ggplot() +
  geom_point(aes(v1, v2)) +
  geom_line(aes(x, y), data = log_line(v1, v2)) +
  coord_trans(y = "log")