The result was big difference between R survival when no event in one group.

[sqf10000]

coxph_testdata.csv

coxph result for R:

z:0.004 
Pr(>|z|):0.997
Warning message:
In fitter(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  1 ; coefficient may be infinite. 

coxph result for julia:

z:Inf
Pr(>|z|):<1e-99

Why is there such a big difference between the R and julia and which one should be trusted?

##################the test run code for R and julia################
R code
library(survival)
test1 <- read.csv(file = '/Users/guan.wang/Downloads/coxph_testdata.csv',sep="\t")
re<-coxph(Surv(survivalMonth, survivalEvent) ~ myclass, test1)
summary(re)
##R outputs:
Warning message:
In fitter(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  1 ; coefficient may be infinite. 
> summary(re)
Call:
coxph(formula = Surv(survivalMonth, survivalEvent) ~ myclass, 
    data = test1)

  n= 148, number of events= 41 

             coef exp(coef)  se(coef)     z Pr(>|z|)
myclass 1.948e+01 2.885e+08 4.568e+03 0.004    0.997

        exp(coef) exp(-coef) lower .95 upper .95
myclass 288459349  3.467e-09         0       Inf

Concordance= 0.623  (se = 0.02 )
Likelihood ratio test= 23.75  on 1 df,   p=1e-06
Wald test            = 0  on 1 df,   p=1
Score (logrank) test = 13.8  on 1 df,   p=2e-04
#########

## julia  code:
rossi = CSV.read("coxph_testdata.csv", DataFrame,header=1,delim="\t",)
rossi.event = EventTime.(rossi.survivalMonth, rossi.survivalEvent .== 1)
outcome = coxph(@formula(event ~ myclass), rossi)
outcome_coefmat = coeftable(outcome)
print(outcome_coefmat)
## julia  output:
         Estimate  Std.Error  z value  Pr(>|z|)
───────────────────────────────────────────────
myclass   35.4617        0.0      Inf    <1e-99
───────────────────────────────────────────────

[sqf10000]

Or can julia coxph provide the Likelihood ratio test，Wald test ，Score (logrank) test results like R?