Survival curves — ggsurv • GGally

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.

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 = "",
  order.legend = TRUE
)

Arguments

s: an object of class survfit
CI: 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.
order.legend: boolean to determine if the legend display should be ordered by final survival time

Value

An object of class ggplot

Author

Edwin Thoen

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

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

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

  # Adjusting the legend of the ggsurv fit
  p_(pl.sex +
    ggplot2::guides(linetype = "none") +
    ggplot2::scale_colour_discrete(
      name   = "Sex",
      breaks = c(1, 2),
      labels = c("Male", "Female")
    ))

  # Multiple factors
  lung2 <- dplyr::mutate(lung, older = as.factor(age > 60))
  sf.sex2 <- survival::survfit(Surv(time, status) ~ sex + older, data = lung2)
  pl.sex2 <- ggsurv(sf.sex2)
  p_(pl.sex2)

  # Change legend title
  p_(pl.sex2 + labs(color = "New Title", linetype = "New Title"))

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

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

  # Add the diseases names to the plot and remove legend
  p_(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 = "none", linetype = "none"))
}
#> Loading required package: survival
#> Loading required package: scales

#> Scale for colour is already present.
#> Adding another scale for colour, which will replace the existing scale.