ggsurv: Survival curves with ggplot2

Description

This function produces Kaplan-Meier plots using ggplot2. As a first argument it needs a survfit object, created by the survival package. Default settings differ for single stratum and multiple strata objects.

Usage

ggsurv(s, CI = "def", plot.cens = TRUE, surv.col = "gg.def", cens.col = "gg.def", lty.est = 1, lty.ci = 2, size.est = 0.5, size.ci = size.est, cens.size = 2, cens.shape = 3, back.white = FALSE, xlab = "Time", ylab = "Survival", main = "")

Arguments

an object of class survfit

should a confidence interval be plotted? Defaults to TRUE for single stratum objects and FALSE for multiple strata objects.

plot.cens

mark the censored observations?

surv.col

colour of the survival estimate. Defaults to black for one stratum, and to the default ggplot2 colours for multiple strata. Length of vector with colour names should be either 1 or equal to the number of strata.

cens.col

colour of the points that mark censored observations.

lty.est

linetype of the survival curve(s). Vector length should be either 1 or equal to the number of strata.

lty.ci

linetype of the bounds that mark the 95% CI.

size.est

line width of the survival curve

size.ci

line width of the 95% CI

cens.size

point size of the censoring points

cens.shape

shape of the points that mark censored observations.

back.white

if TRUE the background will not be the default grey of ggplot2 but will be white with borders around the plot.

xlab

the label of the x-axis.

ylab

the label of the y-axis.

main

the plot label.

Value

An object of class ggplot

Examples

Run this code


if (require(survival) && require(scales)) {
  data(lung, package = "survival")
  sf.lung <- survival::survfit(Surv(time, status) ~ 1, data = lung)
  ggsurv(sf.lung)

  # Multiple strata examples
  sf.sex <- survival::survfit(Surv(time, status) ~ sex, data = lung)
  pl.sex <- ggsurv(sf.sex)
  pl.sex

  # Adjusting the legend of the ggsurv fit
  pl.sex +
    ggplot2::guides(linetype = FALSE) +
    ggplot2::scale_colour_discrete(
      name   = 'Sex',
      breaks = c(1,2),
      labels = c('Male', 'Female')
    )

  # We can still adjust the plot after fitting
  data(kidney, package = "survival")
  sf.kid <- survival::survfit(Surv(time, status) ~ disease, data = kidney)
  pl.kid <- ggsurv(sf.kid, plot.cens = FALSE)
  pl.kid

  # Zoom in to first 80 days
  pl.kid + ggplot2::coord_cartesian(xlim = c(0, 80), ylim = c(0.45, 1))

  # Add the diseases names to the plot and remove legend
  pl.kid +
    ggplot2::annotate(
      "text",
      label  = c("PKD", "Other", "GN", "AN"),
      x      = c(90, 125, 5, 60),
      y      = c(0.8, 0.65, 0.55, 0.30),
      size   = 5,
      colour = scales::hue_pal(
        h         = c(0, 360) + 15,
        c         = 100,
        l         = 65,
        h.start   = 0,
        direction = 1
      )(4)
    ) +
    ggplot2::guides(color = FALSE, linetype = FALSE)
}

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