Forest plots using R and ggplot2

Forest plots are most commonly used in reporting meta-analyses, but can be profitably used to summarise the results of a fitted model. They essentially display the estimates for model parameters and their corresponding confidence intervals.

Matt Shotwell just posted a message to the R-help mailing list with his lattice-based solution to the problem of creating forest plots in R. I just figured out how to create a forest plot for a consulting report using ggplot2. The availability of the geom_pointrange layer makes this process very easy!!

Update January 26, 2016: ggplot2 has changed a bit in the last five years. I’ve created a gist that will be easier to maintain. The link is here.

credplot.gg <- function(d){
 # d is a data frame with 4 columns
 # d$x gives variable names
 # d$y gives center point
 # d$ylo gives lower limits
 # d$yhi gives upper limits
 require(ggplot2)
 p <- ggplot(d, aes(x=x, y=y, ymin=ylo, ymax=yhi))+
 geom_pointrange()+
 geom_hline(yintercept = 0, linetype=2)+
 coord_flip()+
 xlab('Variable')
 return(p)
}

If we start with some dummy data, like

d <- data.frame(x = toupper(letters[1:10]),
                y = rnorm(10, 0, 0.1))
d <- transform(d, ylo = y-1/10, yhi=y+1/10)

credplot.gg(d)

we can get the following graph: