Survival Plots h/t @R_souvenirs

# How to build informative survival plots
# source: http://r-addict.com/2016/05/23/Informative-Survival-Plots.html

# setup
install.packages('survminer')
source("https://bioconductor.org/biocLite.R")
biocLite("RTCGA.clinical") # data for examples

Plot the difference in survival times between patients suffering from Ovarian Cancer & Breast Cancer using data collected by The Cancer Genome Atlas Project.

library(RTCGA.clinical)
survivalTCGA(BRCA.clinical, OV.clinical, extract.cols = "admin.disease_code") -> BRCAOV.survInfo
library(survival)
fit <- survfit(Surv(times, patient.vital_status) ~ admin.disease_code, data = BRCAOV.survInfo)
library(survminer)
ggsurvplot(fit, risk.table = TRUE)

Improved version with parameters:

ggsurvplot(
   fit,                     # survfit object with calculated statistics.
   risk.table = TRUE,       # show risk table.
   pval = TRUE,             # show p-value of log-rank test.
   conf.int = TRUE,         # show confidence intervals for
                            # point estimaes of survival curves.
   xlim = c(0,2000),        # present narrower X axis, but not affect
                            # survival estimates.
   break.time.by = 500,     # break X axis in time intervals by 500.
   ggtheme = theme_RTCGA(), # customize plot and risk table with a theme.
 risk.table.y.text.col = T, # colour risk table text annotations.
  risk.table.y.text = FALSE # show bars instead of names in text annotations
                            # in legend of risk table
)