Nothing
## ----setup, include = FALSE----------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ---- fig.height=6, fig.width=6------------------------------------------
library(survsup)
library(ggplot2)
library(survival)
library(dplyr)
fit <- survfit(Surv(time, status) ~ 1, data = lung)
plot_survfit(fit)
## ---- fig.height=6, fig.width=6------------------------------------------
lung %>%
survfit(Surv(time, status) ~ 1, data = .) %>%
plot_survfit()
## ---- fig.height=6, fig.width=6------------------------------------------
lung %>%
survfit(Surv(time, status) ~ 1, data = .) %>%
plot_survfit(cuminc = FALSE)
## ---- fig.height=6, fig.width=6------------------------------------------
lung %>%
survfit(Surv(time, status) ~ sex, data = .) %>%
plot_survfit(cuminc = FALSE)
## ---- fig.height=6, fig.width=6------------------------------------------
lung %>%
survfit(Surv(time, status) ~ sex, data = .) %>%
plot_survfit(cuminc = FALSE, ci = TRUE)
## ---- fig.height=6, fig.width=6------------------------------------------
lung %>%
survfit(Surv(time, status) ~ sex, data = .) %>%
plot_survfit(cuminc = FALSE, ci = TRUE) + # <--- NOTE!
labs(x = "Time (days)", y = "Survival (%)")
## ---- fig.height=6, fig.width=6------------------------------------------
lung %>%
survfit(Surv(time, status) ~ sex, data = .) %>%
plot_survfit(cuminc = FALSE) %>%
nar()
## ---- fig.height=6, fig.width=6------------------------------------------
lung %>%
survfit(Surv(time, status) ~ sex, data = .) %>%
plot_survfit(cuminc = FALSE) %>%
nar(size = 3)
## ---- fig.height=6, fig.width=6------------------------------------------
colon %>%
survfit(Surv(time, status) ~ rx, data = .) %>%
plot_survfit() %>%
nar() +
scale_color_manual(values = c("darkorange", "steelblue", "darkred"))
## ---- fig.height=6, fig.width=6------------------------------------------
colon %>%
survfit(Surv(time, status) ~ extent, data = .) %>%
plot_survfit() %>%
nar() %>%
skislopes()
## ---- fig.height=6, fig.width=6------------------------------------------
colon %>%
survfit(Surv(time, status) ~ extent, data = .) %>%
plot_survfit() %>%
nar() %>%
skislopes(reverse = TRUE)
## ---- fig.height=6, fig.width=6------------------------------------------
colon %>%
survfit(Surv(time, status) ~ extent, data = .) %>%
plot_survfit() %>%
nar() %>%
cat4()
## ---- fig.height=6, fig.width=6------------------------------------------
colon %>%
survfit(Surv(time, status) ~ extent, data = .) %>%
plot_survfit() %>%
nar() %>%
hcl_rainbow()
## ---- fig.height=6, fig.width=6------------------------------------------
colon %>%
survfit(Surv(time, status) ~ extent, data = .) %>%
plot_survfit() %>%
nar() %>%
hcl_rainbow(reverse = TRUE)
## ---- fig.height=6, fig.width=6------------------------------------------
colon %>%
survfit(Surv(time, status) ~ extent, data = .) %>%
plot_survfit(lwd = 2)
## ---- fig.height=6, fig.width=6------------------------------------------
colon %>%
survfit(Surv(time, status) ~ extent, data = .) %>%
plot_survfit(lwd = 0.5)
## ---- fig.height=6, fig.width=6------------------------------------------
colon %>%
survfit(Surv(time, status) ~ extent, data = .) %>%
plot_survfit(legend.title = "Extent of disease")
## ---- fig.height=6, fig.width=6------------------------------------------
colon %>%
survfit(Surv(time, status) ~ extent, data = .) %>%
plot_survfit(ylim = c(0, 100)) %>%
nar()
## ---- fig.height=6, fig.width=6------------------------------------------
colon %>%
survfit(Surv(time, status) ~ extent, data = .) %>%
plot_survfit(xmax = 2000) %>%
nar()
## ---- fig.height=6, fig.width=6------------------------------------------
colon %>%
survfit(Surv(time, status) ~ extent, data = .) %>%
plot_survfit(xmax = 2000, xbreaks = c(0, 1000, 2000)) %>%
nar()
## ---- fig.height=6, fig.width=6------------------------------------------
colon %>%
survfit(Surv(time, status) ~ sex, data = .) %>%
plot_survfit() %>%
nar()
## ---- fig.height=6, fig.width=6------------------------------------------
colon %>%
survfit(Surv(time, status) ~ sex, data = .) %>%
plot_survfit() %>%
nar(flip = TRUE)
## ---- fig.height=6, fig.width=6------------------------------------------
colon %>%
survfit(Surv(time, status) ~ sex, data = .) %>%
plot_survfit() %>%
nar(size = 5, flip = TRUE)
## ---- fig.height=6, fig.width=6------------------------------------------
colon %>%
survfit(Surv(time, status) ~ sex, data = .) %>%
plot_survfit() %>%
nar(size = 2, flip = TRUE)
## ---- fig.height=6, fig.width=6------------------------------------------
colon %>%
survfit(Surv(time, status) ~ sex, data = .) %>%
plot_survfit() %>%
nar(y_offset = 0.1, flip = TRUE)
## ---- fig.height=6, fig.width=6------------------------------------------
colon %>%
survfit(Surv(time, status) ~ sex, data = .) %>%
plot_survfit() %>%
nar(y_offset = 0.03, flip = TRUE)
## ---- fig.height=6, fig.width=6------------------------------------------
colon %>%
survfit(Surv(time, status) ~ sex, data = .) %>%
plot_survfit() %>%
nar(separator = FALSE)
## ---- fig.height=6, fig.width=6------------------------------------------
colon %>%
survfit(Surv(time, status) ~ sex, data = .) %>%
plot_survfit() %>%
nar(sep_color = "grey90", sep_lwd = 1.5)
## ------------------------------------------------------------------------
library(gridExtra)
## ---- fig.height=6, fig.width=10-----------------------------------------
p <- list(
p1 = colon %>%
survfit(Surv(time, status) ~ sex, data = .) %>%
plot_survfit(ylim = c(0, 100)) %>%
nar() +
labs(tag = "A"),
p2 = colon %>%
survfit(Surv(time, status) ~ node4, data = .) %>%
plot_survfit(ylim = c(0, 100)) %>%
nar() +
labs(tag = "B")
)
grid.arrange(grobs = p, ncol = 2)
## ---- fig.height=10, fig.width=9, out.width="90%"------------------------
# Store plots in a list
p <- list(
p1 = colon %>%
survfit(Surv(time, status) ~ 1, data = .) %>%
plot_survfit(ylim = c(0, 100)) +
labs(tag = "A"),
p2 = colon %>%
survfit(Surv(time, status) ~ rx, data = .) %>%
plot_survfit(ylim = c(0, 100)) %>%
nar(2, separator = FALSE) +
labs(tag = "B"),
p3 = colon %>%
survfit(Surv(time, status) ~ extent, data = .) %>%
plot_survfit(ylim = c(0, 100)) %>%
nar(2, separator = FALSE) +
labs(tag = "C"),
p4 = colon %>%
survfit(Surv(time, status) ~ sex, data = .) %>%
plot_survfit(ylim = c(0, 100)) %>%
nar(2, separator = FALSE) +
labs(tag = "D"),
p5 = colon %>%
survfit(Surv(time, status) ~ node4, data = .) %>%
plot_survfit(ylim = c(0, 100)) %>%
nar(2, separator = FALSE) +
labs(tag = "E"),
p6 = colon %>%
survfit(Surv(time, status) ~ surg, data = .) %>%
plot_survfit(ylim = c(0, 100)) %>%
nar(2, separator = FALSE) +
labs(tag = "F")
)
#Define layout matrix
lay <- rbind(c(1,1,2),
c(1,1,3),
c(4,5,6))
#Plot it all!
grid.arrange(grobs = p, layout_matrix = lay)
## ---- fig.height=6, fig.width=6------------------------------------------
p[["p3"]]
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