[R] Adjusted survival curves

[Ted Beginner (RStudio)]

Adjusted survival curves (Thanks to sample code:  https://rpubs.com/daspringate/survival )
Thanks to Moderator/Admin's Great Work! For a successful solution I used advice that could be understood:
1. Peter Dalgaard: The code does not work, because the covariates are not factors.
2. Jeff Newmiller: "Change the columns into factors before you give them to the coxph function, e.g. df$treatment <- as.factor(df$treatment)"
And I study David Winsemius's instructions.THANKS!!! 
Code works:

library(survival)
library(survminer)
df<-read.csv("F:/R/data/edgr-orig.csv", header = TRUE, sep = ";")

head(df)
# "age" means the age groups
ID start stop censor sex age stage treatment
1   0    66    0     2   1   3     1
2   0    18    0     1   2   4     2
3   0    43    1     2   3   3     1
4   0    47    1     2   3   NA    2
5   0    26    0     1   4   3    NA

# Change continuous var. as factors
df$sex<-as.factor(df$sex)
df$age<-as.factor(df$age)
df$stage<-as.factor(df$stage)
df$treatment<-as.factor(df$treatment)

S <- Surv(
  time = df$start, 
  time2 = df$stop, 
  event = df$censor)

model <- coxph( S ~ treatment + age + sex + stage, data = df)

plot(survfit(model), 
     las=1,
     xscale = 1.00,
     xlab = "Months after diagnosis",
     ylab = "Proportion survived",
     main = "Baseline Hazard Curve")

treat <- with(df,
              data.frame(
              treatment = levels(treatment),
              age = rep(levels(age)[1], 2),
              sex = rep(levels(sex)[1], 2),
              stage = rep(levels(stage)[1], 2)))

plot(survfit(model, newdata = treat), 
     las=1,
     xscale = 1.00,
     conf.int = TRUE,
     xlab = "Months after diagnosis",
     ylab = "Proportion survived",
     col = c("red", "green"))

legend(8, 0.9, 
       legend = c("Beta blockers", 
                  "No beta blockers"), 
       lty = 1, 
       col = c("green", "red"))
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