This vignette covers changes between versions 0.1.0 and 0.2.0.

Hadley Wickhamâ€™s ggplot2 version 2.0 revolution, at the end of 2015,
triggered many crashes in dependent R packages, that finally led to
deletions of few packages from The
Comprehensive R Archive Network. It occured that
`survMisc`

package was removed from CRAN on 27th of January
2016 and R world remained helpless in the struggle with the elegant
visualizations of survival analysis. Then a new tool - survminer
package, created by Alboukadel
Kassambara - appeared on the R survival scene to fill the gap in
visualizing the Kaplan-Meier estimates of survival curves in elegant
grammar of graphics like way. This blog presents main features of core
`ggsurvplot()`

function from survminer package, *which
creates the most informative, elegant and flexible survival plots that I
have seen!*

During the development of RTCGA package there was a need
to provide the simplest possible interface to estimates of survival
curves for biotechnologists and the
discovery of `ggsurvplot()`

was a bullâ€™s-eye! Many have
tried to provide a package or function for ggplot2-like plots that
would present the basic tool of survival analysis: Kaplan-Meier
estimates of survival curves, but none of earlier attempts have provided
such a rich structure of features and flexibility as survminer. On basis
of estimates of survival curves one can infere on differences in
survival times between compared groups, so survival plots are very
useful and can be seen in almost every publication in the field of
survival analysis and time to event models.

After regular installation

```
install.packages('survminer')
BiocManager::install("RTCGA.clinical") # data for examples
```

we can create simple estimates of survival curves just after we put
`survfit`

(survival package) object into
`ggsurvplot()`

function. Letâ€™s have a look at differences in
survival times between patients suffering from Ovarian Cancer
(*Ovarian serous cystadenocarcinoma*) and patients suffering from
Breast Cancer (*Breast invasive carcinoma*), where data were
collected by The
Cancer Genome Atlas Project.

```
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)
```

This simple plot presents, in an elegant way, estimates of survival probability depending on days from cancer diagnostics grouped by cancer types and an informative risk set table, which shows the number of patients that were under observation in the specific period of time. Survival analysis is a specific field of data analysis because of the censored time to event data, so risk set size is a must in visual inference.

After few improvements (#1, #2, #3, #4, #7, #8, #12, #28), made by Alboukadel in version 0.2.0, one can create a powerful, informative survival plot with such specification of parameters

```
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
)
```