Removing rows from a data frame until a condition is met - r

Question

I have a function, remove_fun, that removes rows from a data frame based on some conditions (this function is too verbose to include, so here's a simplified example:).
Let's say I have a data frame called block_2, with two columns:
Treatment seq
1 29
1 23
3 60
1 6
2 41
1 5
2 44
For the sake of this example, let's say my function removes 1 row from block_2 at a time based on the highest value of seq in block_2$seq. This function works well when I run it once, i.e. remove_fun(block_2) would return the following output:
Treatment seq
1 29
1 23
1 6
2 41
1 5
2 44
However, what I'm not figuring out is how to repeatedly implement my remove_fun until I reduce block_2 to a certain dimension.
My idea is to do something like this:
while (dim(block_2_df)[1]>1)#The number of rows of block_2_df{
remove_fun(block_2_df)
}
This would theoretically reduce block_2_df until only the observation corresponding to the lowest seq number remains.
However, this doesn't work. I think my problem relates to me not knowing how to use my 'updated' block_2_df iteratively. What I'd like to accomplish is some code that does something like this:
new_df_1<-remove_fun(block_2)
new_df_2<-remove_fun(new_df_1)
new_df_3<-remove_fun(new_df_2)
etc...
I'm not necessarily looking for an exact solution to this problem (as I didn't provide remove_fun), but I'd appreciate some insight re: a general approach to the problem.
Edit: here's my actual code with some example data:
#Start from a block of 10*6 balls, with lambda*(wj) balls of each class
#Allocation ratios
class_1<-"a"
class_2<-"b"
class_3<-"c"
ratio_a<-3
ratio_b<-2
ratio_c<-1
#Min_set
min_set<-c(rep(class_1,ratio_a),rep(class_2,ratio_b),rep(class_3,ratio_c))
min_set_num<-ifelse(min_set=='a',1,ifelse(min_set=='b',2,3))
table_key <- table(min_set_num)
#Number of min_sets
lamb<-10
#Active urn
block_1<-matrix(0,lamb,length(min_set))
for (i in 1:lamb){
block_1[i,]<-min_set
}
#Turn classes into a vector
block_1<-as.vector(block_1)
block_1<-ifelse(block_1=='a',1,ifelse(block_1=='b',2,3))
#Turn into a df w/ identifying numbers:
block_1_df<-data.frame(block_1,seq(1:length(block_1)))
#Enumerate all sampling outcome permutations
library('dplyr')
#Create inactive urn
#Sample from block_1 until min_set is achieved, store in block_2#####
#Random sample :
block_2<-sample(block_1,length(block_1),replace=F)
block_2_df<-block_1_df[sample(nrow(block_1_df), length(block_1)), ]
colnames(block_2_df)<-c('Treatment','seq')
#Generally:####
remove_fun<-function(dat){
#For df
min_set_obs_mat<-matrix(0,length(block_1),2)
min_set_obs_df<-as.data.frame(min_set_obs_mat)
colnames(min_set_obs_df)<-c('Treatment','seq')
for (i in 1:length(block_1)){
if ((sum(min_set_obs_df[,1]==1)<3) || (sum(min_set_obs_df[,1]==2)<2) || (sum(min_set_obs_df[,1]==3)<1)){
min_set_obs_df[i,]<-dat[i,]
}
}
#Get rid of empty rows in df:
min_set_obs_df<-min_set_obs_df%>%filter(Treatment>0)
#Return the sampled 'balls' which satisfy the minimum set into block_2_df (randomized block_!), ####
#keeping the 'extra' balls in a new df: extra_df:####
#Question: does the order of returning matter?####
#Identify min_set
outcome_df<-min_set_obs_df %>% group_by(Treatment) %>% do({
head(., coalesce(table_key[as.character(.$Treatment[1])], 0L))
})
#This removes extra observations 'chronologically'
#Identify extra balls
#Extra_df is the 'inactive' urn####
extra_df<-min_set_obs_df%>%filter(!(min_set_obs_df$seq%in%outcome_df$seq))
#Question: is the number of pts equal to the block size? (lambda*W)?######
#Return min_df back to block_2_df, remove extra_df from block_2_df:
dat<-dat%>%filter(!(seq%in%extra_df$seq))
return(dat)
}

Answer 1

Your while-loop doesn't redefine block2_df. This should work:
while (dim(block_2_df)[1]>1) {
block_2_df <- remove_fun(block_2_df)
}

Answer 2

If all you need is a way to subset the data frame...
df <- data.frame(Treatment = c(1, 1, 3, 1, 2, 1, 2),
seq = c(29, 23, 60, 6, 41, 5, 44))
df
Treatment seq
1 1 29
2 1 23
3 3 60
4 1 6
5 2 41
6 1 5
7 2 44
# Decide how many rows you want in output
n <- 6
# Find the top "n" values in the seq variable
head(sort(df$seq), n)
[1] 5 6 23 29 41 44
# Use them in the subset criteria
df[df$seq %in% head(sort(df$seq), n), ]
Treatment seq
1 1 29
2 1 23
4 1 6
5 2 41
6 1 5
7 2 44