performance - R Optimise a while loop nested in a for loop to introduce missing values in a dataframe -

i have (70 rows x 4 columns) dataframe (data) has 10% of nas. dataframe has no more 1 na per row. dataset, produce 10 dataframes 60% of nas. not want have entirely empty (=all-na) rows. made while loop nested loop. code working taking long time run. need run loop many datasets know if there easy way improve it.

my dataframe looks that:

library(missforest) data<-iris[1:70,1:4] for(i in 1:28){   data[i,]<-prodna(data[i,],nona =0.25) } 

and here loop:

    missing.data<-list()    for(j in 1:10){     missing.data[[j]]<-prodna(data, nona = 0.6)       while(sum(rowsums(is.na(missing.data[[j]]))==4)!=0) {         missing.data[[j]]<-prodna(data, nona = 0.6)     } } 

edit: loop becomes slow nona > 0.55 unfortunately need introduce 60% of na's.. also, na's introduced in loop introduced @ random, can "replace" na's in original dataframe (data).

i not sure if looking for:

library(missforest) data1<-iris[1:70,1:4] for(i in 1:28){      data1[i,]<-prodna(mydata[i,],nona =0.10)  } table(is.na(data1)) n<-10 data2<-do.call("rbind", replicate(n, data1, simplify=false)) table(is.na(data2))  data3<-prodna(data2,nona=0.55) > table(is.na(data3))  false  true   1133  1667