When using facet_grid(), plot lines do not extend all the way to the y-axis

[uraniborg]

Expected behavior

Survival plot lines will appear to extend all the way to the y-axis on both facets of a plot.

Actual behavior

When using ggsurvplot() with facet_grid() on a survfit object, the second level of the faceting factor has line-segments displayed rather than extending the lines all the way to the y-axis. The first faceting factor does not appear to have this problem. When the factor levels are reversed, the new second level now displays line segments rather than extending to the y-axis.

In my example the survival fit is created with two genotypes and sex as factors. I am faceting the plots by sex, F for female and M for male. First the male curve has truncated line segments, but when the factor levels are reversed the male lines are drawn correctly but the female lines now don't extend to the y-axis.

I suspect that without faceting, the overlapping lines that converge at the y-axis are being masked in a particular order. When facet_grid() is used the masking is still taking place but now it becomes revealed. Is there a way to correct this? I've been unable to find any references to this issue elsewhere, and no features to control how the lines get drawn or if line masking can be enabled/disabled in the ggsurvplot package.

Steps to reproduce the problem

library(survival) library(ggplot2) library(survminer) library(dplyr)

fit <- survfit(Surv(survival, censor) ~ geneA + geneB + Sex, data = ss) ggsurvplot(fit, legend = 'none') + facet_grid(.~Sex)

default factor levels, F then M

reverse the factor levels, M then F

ss$Sex <- factor(ss$Sex, levels=c('M','F'))

session_info()

R version 3.4.0 (2017-04-21) Platform: x86_64-w64-mingw32/x64 (64-bit) Running under: Windows 7 x64 (build 7601) Service Pack 1

Matrix products: default

locale: [1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C
[5] LC_TIME=English_United States.1252

attached base packages: [1] stats graphics grDevices utils datasets methods base

loaded via a namespace (and not attached): [1] compiler_3.4.0 tools_3.4.0

# please paste here the result of
devtools::session_info()

[kassambara]

Please, follow the procedure described at: https://github.com/kassambara/survminer/issues/205

In your case, the code looks like this:

library(survival)
library(survminer)

fit <- survfit(Surv(survival, censor) ~ geneA + geneB , data = ss)
ggsurvplot(fit, legend = 'none', facet.by = "Sex") 

[uraniborg]

Thanks for your feedback,

I tried out 'facet.by' but this did not appear to fix the issue. It still results in drawing line segments that do not reach the y-axis. This time however, changing the order of the factor levels did not result in the lines being drawn correctly, like facet_grid() seemed to do in my original post.

I'm attaching two plots, one where I did not use any facets and the other which uses 'facet.by = gender'. This is a reduced dataset compared to my original one as I was trying to simplify things to narrow down where the problem originated. I'll include a printout of the survfit below:

Call: survfit(formula = Surv(survival, censor) ~ gender + genotype, data = ss2)

                           n events median 0.95LCL 0.95UCL

gender=Female, genotype=+/+/- 21 13 134 113 NA gender=Female, genotype=+/+/+ 8 3 NA 124 NA gender=Male, genotype=+/+/- 24 8 NA 407 NA gender=Male, genotype=+/+/+ 8 1 NA NA NA

Code: fit <- survfit(Surv(survival, censor) ~ gender + genotype, data = ss2)

First plot: ggsurvplot(fit)

Second plot: ggsurvplot(fit, facet.by="gender")

Thanks very much for your input. Chris

On Wed, Oct 4, 2017 at 1:49 PM, Alboukadel KASSAMBARA < notifications@github.com> wrote:

Please, follow the procedure described at: #205 https://github.com/kassambara/survminer/issues/205

In your case, the code looks like this:

library(survival) library(survminer) fit <- survfit(Surv(survival, censor) ~ geneA + geneB , data = ss) ggsurvplot(fit, legend = 'none', facet.by = "Sex")

— You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub https://github.com/kassambara/survminer/issues/254#issuecomment-334253905, or mute the thread https://github.com/notifications/unsubscribe-auth/Ae_A-1umY4aGZ2g00qCxIkxAzZ1jZ7uXks5so9NRgaJpZM4Psab2 .

[kassambara]

Please provide a reproducible example with a sample of your data.

To include data in a question, use dput() to generate the R code to recreate it. For example, to recreate the mtcars dataset in R, I’d perform the following steps:

• Run dput(mtcars) in R
• Copy the output
• In a reproducible script, type mtcars <- then paste.

[kassambara]

The demo data set is not attached to your message...

[kassambara]

Feel free to send the demo data set or to incorporate it in the script itself.

The only important thing is to make sure that your script can be executed, so that we can easily reproduce the issue in order to fix it.

Thank you in advance

[kassambara]

Hi,

Note that, When lefting github comment via e-mail reply, attached files don't follow the message.

You can send me directly the files at: alboukadel.kassambara@gmail.com

Thanks

[uraniborg]

My mistake, didn't realize that was happening.

My example dataset and code:

df <- structure(list(ID = c("897", "838", "839", "847", "842", "801", 
"718", "726", "730", "925", "926", "931", "936", "952", "953", 
"884", "891", "894", "895", "899", "902", "905", "908", "914", 
"807", "841", "844", "837", "846", "815", "818", "819", "822", 
"800", "722", "728", "809", "892", "900", "724", "885", "939", 
"940", "946", "810", "943", "934", "840", "947", "727", "937", 
"955", "954", "948", "889", "935", "929", "798", "716", "816", 
"824"), gender = structure(c(2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 
2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 
1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 
2L, 1L, 1L, 1L, 2L), .Label = c("Female", "Male"), class = "factor"), 
    genotype = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 2L, 2L, 2L, 2L), .Label = c("+/+/-", "+/+/+"), class = "factor"), 
    survival = c(122L, 394L, 394L, 394L, 394L, 400L, 401L, 401L, 
    401L, 519L, 519L, 519L, 519L, 519L, 519L, 520L, 520L, 520L, 
    520L, 520L, 520L, 520L, 520L, 520L, 72L, 394L, 394L, 394L, 
    394L, 399L, 399L, 399L, 399L, 400L, 401L, 401L, 75L, 78L, 
    80L, 83L, 87L, 106L, 106L, 113L, 123L, 125L, 126L, 134L, 
    143L, 151L, 164L, 171L, 203L, 226L, 239L, 379L, 407L, 88L, 
    124L, 159L, 299L), censor = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L)), row.names = c(NA, -61L
), class = "data.frame", .Names = c("ID", "gender", "genotype", 
"survival", "censor"))

library(survival)
library(survminer)

fit  <- survfit(Surv(survival, censor) ~ gender + genotype, data = df)

ggsurvplot(fit, facet.by="gender")

My output plot:

[kassambara]

I can see know where is the problem. I'll work on it.

The output should look like this:

library(survminer)
library(survival)
library(magrittr)

surv_fit(Surv(survival, censor) ~ genotype,
         data = df, group.by = "gender") %>%
  ggsurvplot(ggtheme = theme_bw()) %>%
  purrr::map(function(ggsurv) {ggsurv$plot}) %>%
  ggpubr::ggarrange(plotlist = .)

[Kabouik]

I am having the same issue. The code you posted above fixes the truncated segments on the plots, but I couldn't use all the graphical parameters I'm typically using when using ggsurvplot_facet().

Things like panel.labs or legend.labs that work with the method documented in help(ggsurvplot) throw errors with the method above, for instance (not sure whether it is due to magrittr, to the function, the ggarrange(), or the different formula). Additionally, is it statistically equivalent to use a secondary factor in group.by instead of directly in the formula of the Surv() model?

Is there any other fix for this issue?

[for8ver3]

same issue...any update on solution?

[Kabouik]

@kassambara, when using

surv_fit(Surv(survival, censor) ~ genotype, group.by = "gender")

instead of

survfit(Surv(survival, censor) ~ gender + genotype

to circumvent the plotting issue, and chosing to display the p-value with pval = TRUE, is the Log-rank test done the same way, i.e., a single test done for factors Gender and Genotype, or is it rather two distinct Log-rank tests, one for each level of Gender? Is there any correction applied?

Also, how can I access the statistics details of the test(s), in addition to the p-value(s)?

I need to finalize an analysis this week and adding my plot in the document reminded me that it is still unclear to me what is done under the hood for this figure.

[natapongtui]

@kassambara

I'm having the same problem with the survplot_facet function,

The survival curve displayed correctly when I plotted the survival curve using the normal ggsurvplot function. However, the parts of the plot line disappeared when I tried to facet the graph similar to what happened above.

The modification of the code to use group.by circumvent the issue but the graph did not come out as I wanted.

I am suspecting that there is something wrong with the facet function but cannot figure out the way to correct it. Could you please check if there there is something wrong with my code? Below is the dataset and the code I used.

Iso <- structure(list(Sample = 1:70, Treatment = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "Sa", class = "factor"), Dose = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("0 nM", "400 nM", "5 nM"), class = "factor"), Event = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), Time = c(1L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L)), class = "data.frame", row.names = c(NA, -70L))

fit <- survfit(Surv(Time, Event) ~Dose+Treatment , data = Iso) ggsurv <- ggsurvplot(fit,data = Iso,risk.table = F,pval = F, conf.int = FALSE,xlim = c(0,3),break.x.by = 1,xlab="Time (Days)", ggtheme = theme_bw(),risk.table.y.text.col = F, risk.table.y.text = FALSE) ggsurv

The ggsurv could produce the correct survival curve with all the dose in one graph

ggsurv$plot + theme_bw()+facet_grid(Treatment~Dose)

However, the facet function could not separate the data correctly

Thank you very much in advance for your help.

[LaserKate]

@kassambara Hi, First off, thanks for creating this package! I am having the same issues as those described above. Any updates on fixes? Issues: Without faceting, I get expected 0.95LCL and UCLs and the survival lines extend to y-axis

When faceting, I either get the error: 1) Error in f(...) : Aesthetics can not vary with a ribbon

https://github.com/kassambara/survminer/issues/315

2) Plot lines do not extend all the way to the y-axis

The workaround you describe above does not allow for multiple factors in the Surv function. For example genotype+gender+factorZ. It is not clear whether using group.by =c("gender", factorZ") is equivalent to genotype+gender+factorZ.

surv_fit(Surv(survival, censor) ~ genotype,
         data = df, group.by = "gender") %>%
  ggsurvplot(ggtheme = theme_bw()) %>%
  purrr::map(function(ggsurv) {ggsurv$plot}) %>%
  ggpubr::ggarrange(plotlist = .)

Any updates or workarounds appreciated!

[bernardose]

I believe I've found the bug that prevents the faceting from properly drawing the lines when there are more than one strata. I've attached the image of the data.frame created by ggsurvplot. See how the strata column is correctly built (red arrow), but the factors are all equal to the first level of strata combination (blue arrow).

I've worked around it by manually adding the initial states of all factor combinations and plot with the usual ggplot2's geom_step:

surv_fit(Surv(Sessão, Radiodermatite == "Sim") ~ Grupo + Grau, dsurv) %>%
  surv_summary(dsurv) %>%
  add_row(time = 0, 
          n.risk = tapply(.$n.risk, .$strata, max),
          n.event = 0, n.censor = 0,
          surv = 1, std.err = 0,
          upper = 1, lower = 1,
          strata = levels(.$strata),
          Grupo = rep(levels(.$Grupo), each = nlevels(.$Grau)),
          Grau = rep(levels(.$Grau), nlevels(.$Grupo)), .before = T) %>%
  ggplot(aes(time, surv, color = Grupo)) + geom_step(size = 1) + 
  facet_wrap(~ Grau, labeller = "label_both") + 
  scale_y_continuous("Sobrevivência acumulada", labels = scales::percent) + 
  xlab("Número de sessões") + 
  theme(legend.position = "top")

The plot produced is below (labels in portuguese, sorry!):

I still have to learn how to add the crosses for the censoring, but that shouldn't be very hard.

[BingxinS]

I have also notice the same issue, miss labled stratas, as in the thread from "bernardose" on Nov 13, 2018.

Below is my proposed fix:

library(survminer)
require("survival")

Test case 1

# testing dataframe
lungTest <- data.frame ("inst" = c(3, 3, 3, 5, 1, 12, 7, 11, 1, 7), 
                          "time" = c(306, 455, 1010, 210, 883, 1022, 310, 361, 218, 166),
                          "status" = c(2, 2, 1, 2, 2, 1, 2, 2, 2, 2),
                          "age" = c(74, 68, 56, 57, 60, 74, 68, 71, 53, 61),
                          "sex" = c(1, 2, 2, 1, 1, 1, 2, 2, 1, 1),
                          "ph.ecog" = c(1, 1, 1, 1, 1, 2, 2, 2, 2, 2),
                          "ph.karno" = c(90, 90, 90, 90, 100, 50, 70, 60, 70, 70),
                          "pat.karno" = c(100, 90, 90, 60, 90, 80, 60, 80, 80, 70),
                          "meal.cal" = c(1175, 1225, NA, 1150, NA, 513, 384, 538, 825, 271), 
                          "wt.loss" = c(NA, 15, 15, 11, 0, 0, 10, 1, 16, 34)
)

which is actural the first 10 data points in lung:

# testing dataframe
temprcd<-10
lungTest<-lung[1:temprcd,]

Plot with issues

One can facet the sex=1 and sex=2 plots by ph.ecog values:

survfit(Surv(time, status) ~ sex, lungTest) %>%
    ggsurvplot_facet(lungTest, facet.by="ph.ecog")

Here comes the problem. The function ggsurvplot_facet() missed the first intervals for each survival plot. It was caused by the returning dataframe .$data, where the starting point was not set correctly, shown in the table below. Pay attention to the first 4 rows and the last 2 columns, sex and ph.ecog values are not set correctly, compare to the column strata values.

A fix

The function, that causes the problem of missing the first interval in a survival plot, was function .connect2origin(), which was called by ggsurvplot_df(), which was called by ggsurvplot_core(), which was called by ggsurvplot_facet().

The orginial span .connect2origin() is as follows:

.connect2origin <- function (d, fit, data = NULL) 
{
    base <- d[1, , drop = FALSE]
    base[intersect(c("time", "n.censor", "std.err", "n.event"), 
        colnames(base))] <- 0
    base[c("surv", "upper", "lower")] <- 1
    n.strata <- length(levels(d$strata))
    if (n.strata > 1) {
        strata <- levels(d$strata)
        base <- base[rep(1, n.strata), , drop = FALSE]
        row.names(base) <- 1:nrow(base)
        base$strata <- strata
        base$strata <- factor(strata, levels = strata)
        if (!missing(fit)) {
            if (!inherits(fit, "survfit.cox")) {
                variables <- .get_variables(base$strata, fit, 
                  data)
                for (variable in variables) base[[variable]] <- .get_variable_value(variable, 
                  base$strata, fit, data)
            }
        }
    }
    d <- rbind(base, d)
    d
}

The code basically created and copy the origin for each subplot/faceted plot from the very first one.

I have added a block of code to modify the origin per strata per facet as follows as .connect2origin_fix():

.connect2origin_fix <- function(d, fit, data = NULL){

  base <- d[1, , drop = FALSE]
  base[intersect(c('time', 'n.censor', 'std.err', "n.event"), colnames(base))] <- 0
  base[c('surv', 'upper', 'lower')] <- 1.0
  n.strata <- length(levels(d$strata))

  # Connect each group to the origin
  if (n.strata > 1) {
    strata <- levels(d$strata)
    base <- base[rep(1, n.strata),, drop = FALSE]
    row.names(base) <- 1:nrow(base)
    base$strata <- strata
    base$strata <- factor(strata, levels = strata)

    #############################################################
    ##   added to modify values for each strata and facet      ##
    indStrata <- grep("strata",names(base))
    indMax    <- length(names(base))
    if(indStrata<indMax & n.strata>0){
      for (indRow in c(1:n.strata)) {
        tmpStrata = as.list(strsplit(strata[indRow], '\\,'))[[1]]
        tmpFacet  = as.numeric(gsub("\\D", "", tmpStrata)) 
        base[indRow, c((indStrata+1):indMax)] = tmpFacet
      }
    }
    ##                ending of the modification               ##
    #############################################################

    # update variable values
    if(!missing(fit)){
      if(!inherits(fit, "survfit.cox")){
        variables <- .get_variables(base$strata,  fit, data)
        for(variable in variables) base[[variable]] <- .get_variable_value(variable, base$strata, fit, data)
      }
    }
  }
  d <- rbind(base, d)
  d
}

Now the fixed table is as: The fixed survival plot is as:

Test case 2

This case is with more choices of strata and facets. Note that there is no data points for certain strata and facet.

require("survival")
temprcd<-100
lungTest<-lung[1:temprcd,]

# original ggsurvplot_facet missed the first intervals for each survival plot
survfit(Surv(time, status) ~  sex, lungTest) %>%
  ggsurvplot_facet(lungTest, facet.by=c("ph.ecog", "ph.karno"))

# fixed plot
survfit(Surv(time, status) ~  sex, lungTest) %>%
  ggsurvplot_facet_fix(lungTest, facet.by=c("ph.ecog", "ph.karno"))

Download and use the fix

Check the README.md file: https://github.com/BingxinS/survminer-fix

[kassambara]

To fix this issue, we can rewrite the .connect2origin() function as follow:

.connect2origin <- function(d, ...){
  n.risk <- strata <- NULL
  if("n.risk" %in% colnames(d)){d <- dplyr::arrange(d, dplyr::desc(n.risk))}
  origin <- d %>% distinct(strata, .keep_all = TRUE)
  origin[intersect(c('time', 'n.censor', 'std.err', "n.event"), colnames(origin))] <- 0
  origin[c('surv', 'upper', 'lower')] <- 1.0
  dplyr::bind_rows(origin, d)
}

[kassambara]

Fixed now, thanks!

Install the latest developmental version and test this:

df <- structure(list(ID = c("897", "838", "839", "847", "842", "801", 
"718", "726", "730", "925", "926", "931", "936", "952", "953", 
"884", "891", "894", "895", "899", "902", "905", "908", "914", 
"807", "841", "844", "837", "846", "815", "818", "819", "822", 
"800", "722", "728", "809", "892", "900", "724", "885", "939", 
"940", "946", "810", "943", "934", "840", "947", "727", "937", 
"955", "954", "948", "889", "935", "929", "798", "716", "816", 
"824"), gender = structure(c(2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 
2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 
1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 
2L, 1L, 1L, 1L, 2L), .Label = c("Female", "Male"), class = "factor"), 
    genotype = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 2L, 2L, 2L, 2L), .Label = c("+/+/-", "+/+/+"), class = "factor"), 
    survival = c(122L, 394L, 394L, 394L, 394L, 400L, 401L, 401L, 
    401L, 519L, 519L, 519L, 519L, 519L, 519L, 520L, 520L, 520L, 
    520L, 520L, 520L, 520L, 520L, 520L, 72L, 394L, 394L, 394L, 
    394L, 399L, 399L, 399L, 399L, 400L, 401L, 401L, 75L, 78L, 
    80L, 83L, 87L, 106L, 106L, 113L, 123L, 125L, 126L, 134L, 
    143L, 151L, 164L, 171L, 203L, 226L, 239L, 379L, 407L, 88L, 
    124L, 159L, 299L), censor = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L)), row.names = c(NA, -61L
), class = "data.frame", .Names = c("ID", "gender", "genotype", 
"survival", "censor"))

library(survival)
library(survminer)

fit  <- survfit(Surv(survival, censor) ~ gender + genotype, data = df)

ggsurvplot(fit, facet.by="gender")

Should give this output: