Creating 'structured' missing data in a data table in R - r

Question

I am doing research in a lab with a mentor who has developed a model that analyzes genetic data, which utilizes an ANOVA. I have simulated a dataset that I want to use in evaluating our model's ability to handle varying levels of missing data.
Our dataset consists of 15 species, with 4 individuals each, which we represent by naming the columns 'A'(x4) 'B'(x4)...etc. Each row represents a gene.
I'm trying to come up with a code that removes 1% of the data randomly, but such that each species has at least 2 individuals with valid data, because otherwise our model will just quit out (since it's ANOVA-based).
I realize this makes the 'randomly' missing data not so random, but we're trying different methods. It's important that the missing data is otherwise randomized. I'm hoping someone could help me with setting this up?

Answer 1

I try to do a toy example that maybe can help
is_valid_df<-function(df,col,val){
all(table(df[col])>val)
}
filter_function<-function(df,perc,col,val){
n=dim(df)[1]
filter<-sample(1:n,n*perc)
if(is_valid_df(df[-filter,],col,val)){
return(df[-filter,])
}else{
filter_function(df,perc,col,val)
cat("resampling\n")
}
}
set.seed(20)
a<-(filter_function(iris,0.1,"Species",44))