Kaplan-Meier plots using ggplots2 (updated)

About 3 years ago I published some code on this blog to draw a Kaplan-Meier plot using ggplot2. Since then, ggplot2 has been updated (from 0.8.9 to and has changed syntactically. Since that post, I have also become comfortable with Git and Github. I have updated the code, edited it for a small error, and published it in a Gist. This gist has two functions, ggkm (basic Kaplan-Meier plot) and ggkmTable (enhanced Kaplan-Meier plot with table showing numbers at risk at various times).

An enhanced Kaplan-Meier plot, updated

An enhanced Kaplan-Meier plot

We often see, in publications, a Kaplan-Meier survival plot, with a table of the number of subjects at risk at different time points aligned below the figure. I needed this type of plot (or really, matrices of such plots) for an upcoming publication. Of course, my preferred toolbox was R and the ggplot2 package. There were other attempts to do this type of plot in ggplot2, mainly by Gary Collins and an anonymous author as seen on the ggplot2 mailing list.

ggplot2 joy

I’ve been working on a long-term (25+yr) longitudinal study of rheumatoid arthritis with my boss. He just walked in and asked if I could create a plot showing the trajectory of pain scores over time for each subject, separated by educational level (4 groups). Having now worked with ggplot2 for a while, and learning more at the last two DC useR meetups, I realized that I could formulate this in ggplot very easily and in short order.

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.