Hadley Wickham’s ggplot2 version 2.0 revolution, at the end of 2015, triggered many crashes in dependent R packages, that finally led to deletions of few packages from The Comprehensive R Archive Network. It occured that
survMisc package was removed from CRAN on 27th of January 2016 and R world remained helpless in the struggle with the elegant visualizations of survival analysis. Then a new tool – survminer package, created by Alboukadel Kassambara – appeared on the R survival scene to fill the gap in visualizing the Kaplan-Meier estimates of survival curves in elegant grammar of graphics like way. This blog presents main features of core
ggsurvplot() function from survminer package, which creates the most informative, elegant and flexible survival plots that I have seen!
During the development of RTCGA package (about which I wrote here) we encountered a need to provide the simplest possible interface to estimates of survival curves for biotechnologists and the discovery of
ggsurvplot() was a bull’s-eye! Many have tried to provide a package or function for ggplot2-like plots that would present the basic tool of survival analysis: Kaplan-Meier estimates of survival curves, but none of earlier attempts have provided such a rich structure of features and flexibility as survminer.
On basis of estimates of survival curves one can infere on differences in survival times between compared groups, so survival plots are very useful and can be seen in almost every publication in the field of survival analysis and time to event models.
After regular installation
we can create simple estimates of survival curves just after we put
survfit (survival package) object into
ggsurvplot() function. Let’s have a look at differences in survival times between patients suffering from Ovarian Cancer (Ovarian serous cystadenocarcinoma) and patients suffering from Breast Cancer (Breast invasive carcinoma), where data were collected by The Cancer Genome Atlas Project.
This simple plot presents, in an elegant way, estimates of survival probability depending on days from cancer diagnostics grouped by cancer types and an informative risk set table, which shows the number of patients that were under observation in the specific period of time. Survival analysis is a specific field of data analysis because of the censored time to event data, so risk set size is a must in visual inference.
Each parameter is described in the correspoding comment, but I would like to emphasize the
xlim parameter which controls limits of the X axis but does not affect the survival curves, that are taking into account all possible times. Another brilliant parameter is
break.time.by that affects survival plots and the risk set table – this would not be so easy to create it by yourself. Also a
ggtheme parameter is beneficial for simple plot customization. Finally,
risk.table.y.text.col (for which I have provided a user requests) are useful parameters that change text (often too long and redundand) from risk table legend into narrow, coloured bar. This saves a lot of space in the final plot.
survMisc have returned on CRAN, I’ve appreciated survminer so much that I would not look anymore for other solutions. Check why: at the end I present survival curves that can be obtained with the usage of
base package and
It looks pretty… base…
I haven’t seen examples with risk table and adding legend isn’t so quick as in survminer. Moreover, there are no minor axis breaks lines.
Why colours are not asigned to any group? Where is the legend? Why there is so much white space to the right?
Where is risk table? Why I can’t pass
break.by.time to have informative minor X axis breaks? Why the plot gets narrower when the legend in risk table gets wider and why I can’t do anything to workaround this?
Never mind ->