ggsurvival

An R package that draws highly modifiable survival curves in ggplot2. The survival curves can easily be modified and added to an existing ggplot object. ggsurvival uses base R functions to convert the data into a format that is than vizualized using ggplot2's geom_path layers.

Installation

Install the package from the git repository:

devtools::install_github("nicolash2/ggsurvival")

Create a survival curve

Load the package, create your survival plot:

library(ggsurvival)
library(ggplot2)

#to showcase ggsurvival, let's create a test data.frame
survtest <- data.frame(
  time =       sample(seq(30),             50, replace = T),
  status =     sample(1:2,                 50, replace = T),
  condition1 = sample(c("wt","ko"),        50, replace = T),
  condition2 = sample(c("female", "male"), 50, replace = T)
)

ggplot() +
  geom_surv(data=survtest, mapping=aes(time, status, color=condition1, linetype=condition2))

That's already it! However, note that geom_surv does not take the data and mapping arguement from the ggplot general function. They always have to be provided directly in geom_surv.

Your data.frame needs to have at least 2 columns: one for the survival time and one for the survival status. In aes(x,y), time has to be assigned to x and status has to be assigned to y. You can name these columns however you want. For the status column, events are specified like this:

• NA: will be ignored
• 1: death event
• everything else: censored event

The survival curve follows a couple of rules:

1. The total sample number is normalized to 100 for each condition.
2. In the beginning a death event will lead to a drop of 1 unit (a unit is: 100 / number of samples).
3. With each censored event, death events that come afterwards or at the same time lead to a larger drop.
4. A censored event does not lead to a drop. Therefore, if the last event is censored, the curve will not drop to 0.

You probably also have a column by which you distinguish samples, e.g. mutation, sex, etc. In ggsurvival you can have up to 2 of such columns. The parameters in geom_surv's aes() you can use for this are color and linetype.

Modify the graph

First of all you can change the ticks if you like. Use the ticks parameter in the geom_surv function. By default this is "segment" (for geom_segment), and can be changed to "point" (for geom_point), but in theory also to "text" and other geoms (e.g. for geom_text you will then need define label in the aes).

ggplot() + 
  geom_surv(survtest, aes(time, status, color=condition1, linetype=condition2), ticks = "point")

This also allows you to change the shape of the points, although it will give you a warning message because the shape parameter is only known by the ticks, not by the line.

ggplot() + 
  geom_surv(survtest, aes(time, status, color=condition1, linetype=condition2), ticks = "point", shape=2)

You can modify the residual appearance of the graph as you usually would. However, geom_surv comes with an inbuilt option to set certain parameters to make the graph more pretty. Unlock this simply by specifying surv_pretty as TRUE.

ggplot() +
  geom_surv(survtest, aes(time, status, color=condition1, linetype=condition2), surv_pretty=T)

#this will create the exact same plot
ggplot() +
  geom_surv(survtest, aes(time, status, color=condition1, linetype=condition2)) +
  scale_color_manual(values=c("blue","red")) +
  scale_x_continuous(expand=c(0,0)) +
  scale_y_continuous(expand=c(0,0)) +
  theme_classic()

Use your usual ggplot modifications to change the graph as you whish.

ggplot() +
  geom_hline(yintercept=50, linetype="dashed") +
  geom_surv(survtest, aes(time, status, color=condition1, linetype=condition2), surv_pretty=T) +
  labs(x="time (months)", y="% survival", color="genotype", linetype="sex")

Plot lines and ticks seperately

ggsurvival also offers the functions geom_survLines and geom_survTicks to modify these components.

ggplot() +
  geom_survLines(survtest, aes(time, status), color="blue") +
  geom_survTicks(survtest, aes(time, status), color="red", ticks="point")