Updating dataframe based on conditions (over loop) R - r

Question

I'm having difficulty developing a function/algorithm that that updates a dataframe based on certain conditions. I've looked at some answers related to "updating" a dataframe via for loops, but I'm still stuck.
Say I have a dataframe:
df <- data.frame("data_low" = .2143, "data_high" = .7149)
where data_low and data_high are the max and min of some column in a dataframe
I also have two functions:
checker(b[1,])
Takes the value of data_high and data_low, and returns a scalar. If the scalar is less than 1, I'd like to store this in another dataframe, say "d". Else, I want to split "b" with the following function:
splitter()
splits "b" by the median of data_high and data_low.
I've considered trying to develop this with a loop:
storage <- data.frame(data_low = double(), data_high = double()
for( i in 1:nrow(b)){
if(checker(b[i,]) <1){
storage <- splitter(b[i,])
} else {
temp <- splitter(b[i,])
b <- rbind(b,temp)
}
}
My desired output after two iterations (where check >1 for each row:
** Obviously these numbers are picked at random, I'm just hoping to gain some intuition related to looping/updating dataframes based on cases..
starting at i = 0:
| .2143 | .7149 |,
i = 2
| .2143 | .4442 | ** Note at splitter() should break this into 2 rows after i = 2 is complete.
| .4442 | .7149 | ** And again here
i = 3
| .2143 | .3002 |
| .3002 | .4442 |
| .4442 | .5630 |
| .5630 | .7149 |
Can anyone give me some tips on how to organize this loop? I'm thinking my issue here is related to rbind and/or the actual updating of b.
I recognize that much of this code isn't reproducible, but am more interested in the though process here.
Any help would be greatly appreciated!

Answer 1

You can do this with a nested loop (one for the number of iterations and one for the number of rows in b), or using nested Reduce calls, as shown here.
Reduce(function(x, y) {
List=apply(x, 1, function(z) {
med=median(c(z[1], z[2]))
dat=data.frame(data_low=c(z[1], med), data_high=c(med, z[2]))
rownames(dat)=NULL
return(dat)
})
Reduce(function(w, z) rbind(w, z), List)
}, rep(NA, 2), init=df)
One rep:
data_low data_high
1 0.2143 0.4646
2 0.4646 0.7149
Two reps:
data_low data_high
1 0.21430 0.33945
2 0.33945 0.46460
3 0.46460 0.58975
4 0.58975 0.71490
Three reps:
data_low data_high
1 0.214300 0.276875
2 0.276875 0.339450
3 0.339450 0.402025
4 0.402025 0.464600
5 0.464600 0.527175
6 0.527175 0.589750
7 0.589750 0.652325
8 0.652325 0.714900