Nothing
## ----include = FALSE--------------------------------------------------------------------------------------------------------------------------------
library(knitr)
opts_chunk$set(
comment = "",
fig.width = 12,
message = FALSE,
warning = FALSE,
tidy.opts = list(
keep.blank.line = TRUE,
width.cutoff = 150
),
options(width = 150),
eval = TRUE
)
## ---- eval = FALSE----------------------------------------------------------------------------------------------------------------------------------
# install.packages('survminer')
# BiocManager::install("RTCGA.clinical") # data for examples
## ---- fig.width=10, eval = FALSE--------------------------------------------------------------------------------------------------------------------
# library(survminer)
# 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)
# # Visualize with survminer
# ggsurvplot(fit, data = BRCAOV.survInfo, risk.table = TRUE)
## ---- echo = FALSE, fig.width=10--------------------------------------------------------------------------------------------------------------------
library(survminer)
data(BRCAOV.survInfo)
library(survival)
fit <- survfit(Surv(times, patient.vital_status) ~ admin.disease_code,
data = BRCAOV.survInfo)
ggsurvplot(fit, data = BRCAOV.survInfo, risk.table = TRUE)
## ---- fig.width=10----------------------------------------------------------------------------------------------------------------------------------
ggsurvplot(
fit, # survfit object with calculated statistics.
data = BRCAOV.survInfo, # data used to fit survival curves.
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_minimal(), # 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
)
## ---- fig.width=10----------------------------------------------------------------------------------------------------------------------------------
plot(fit) # base
## ---- fig.width=10----------------------------------------------------------------------------------------------------------------------------------
plot(fit, col=c("orange","purple"), lty=c(1:2), lwd=3, # base with some customization
conf.int = TRUE, xmax = 2000)
# add a legend
legend(100, .2, c("Ovarian Cancer", "Breast Cancer"),
lty = c(1:2), col=c("orange","purple"))
## ---- fig.width=10, eval=FALSE----------------------------------------------------------------------------------------------------------------------
# # install.packages('survMisc')
# library(survMisc)
# survMisc:::autoplot.survfit(fit) # no customization
#
## ---- fig.width=10, eval = FALSE--------------------------------------------------------------------------------------------------------------------
# survMisc:::autoplot.survfit( # with some hard customization
# fit,
# type = "fill",
# pVal=TRUE
# ) -> fit.survMisc
# fit.survMisc$table <- fit.survMisc$table +
# theme_minimal() + # theme(legend.position = "top")
# coord_cartesian(xlim = c(0,2000))
# fit.survMisc$plot <- fit.survMisc$plot +
# theme_minimal() +
# coord_cartesian(xlim = c(0,2000))
# survMisc:::print.tableAndPlot(fit.survMisc)
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