R: how can I do data[[k]] calculation - r

Question

I have a data set from a sample without replacement look like this:
The picture shows the frequency of each species, and there are 50 data.c[[k]] like this.
Now I'm trying the Jackknife resampling(without replacement) to estimate coverage, codes below:
data.c <- sapply(1:50, function(k)table(data[,k])) #freq
mdata <- sapply(1:50, function(k)sum(data.c[[k]]==1))
True_c <- 1- sum(np*(exp(lchoose(N-data.c[[k]], i))/exp(lchoose(N,i))))
##True_c function shows error message##
my result shows "Error in N - data.c : non-numeric argument to binary operator"
I want to do True_c with N(population size) minus species' frequncies and do the 'lchoose' function, how can I do or adjust my codes?
My entire codes show below:
### without replacement
for (seed in c(99,100)){
set.seed(seed)
for (s in c(100,1000)){
sdata <- rlnorm(s,0,1)
p <- sdata/sum(sdata)
gn <- p*s*10
gn <- round(gn)
M <-replace(gn, gn==0,1) #or M=gn[gn==0]=1
N <- sum(M); N
np <- M/N #new prob
pop_index = rep(1:s, time=M)
for (i in c(100,500,1000,5000,N))
{
data=replicate(50, sample(pop_index, i,
replace = FALSE, prob = NULL))
data.c=sapply(1:50, function(k)table(data[,k])) #freq
mdata=sapply(1:50, function(k)sum(data.c[[k]]==1)) #each group, total freq=1
True_c <- 1- sum(np*(exp(lchoose(N-data.c, i))/exp(lchoose(N,i))))
c.hat <- (1-(1-(i/N))*(mdata/i)) #geo
bias=mean(c.hat)-True_c
var=var(c.hat)
cat("sample_size",i,"\n",
"True_C=",True_c,"\n",
"bias =",bias,"\n",
"variance=",var,"\n","\n")
}
}
}