R: Storing the Results of a Loop

Question

I am working with R. I wrote the following program which creates a table for a series of data manipulations steps:

#load library 
library(dplyr)
 set.seed(123)
 
 # data
 a1 = rnorm(1000,100,10)
 b1 = rnorm(1000,100,5)
 c1 = sample.int(1000, 1000, replace = TRUE)
 train_data = data.frame(a1,b1,c1)

#generate random numbers
 random_1 =  runif(1, 80, 120)
random_2 =  runif(1, random_1, 120)
 random_3 =  runif(1, 85, 120)
 random_4 =  runif(1, random_3, 120)

#bin data according to random criteria
train_data <- train_data %>% mutate(cat = ifelse(a1 <= random_1 & b1 <= random_3, "a", ifelse(a1 <= random_2 & b1 <= random_4, "b", "c"))) 

#calculate 60th quantile ("quant") for each bin
final_table = data.frame(train_data %>% group_by(cat) %>%
mutate(quant = quantile(c1, prob = .6)))

#create a new variable ("diff") that measures if the quantile is bigger tha the value of "c1"
final_table$diff = ifelse(final_table$quant > final_table$c1,1,0)

#create a table: for each bin, calculate the average of "diff"
final_table_2 = data.frame(final_table %>% 
  group_by(cat) %>% 
  summarize(
   mean = mean(diff)
  ))

#add "total mean" to this table
final_table_2 = data.frame(final_table_2 %>% add_row(cat = "total", mean = mean(final_table$diff)))

#format this table: add the random criteria to this table for reference
final_table_2$random_1 = random_1

final_table_2$random_2 = random_2

final_table_2$random_3 = random_3

final_table_2$random_4 = random_4

 #optional: view table
 head(final_table_2)

    cat      mean random_1 random_2 random_3 random_4
1     a 0.5897436 95.67371 111.8133 94.00313 102.0569
2     b 0.5992366 95.67371 111.8133 94.00313 102.0569
3     c 0.5995423 95.67371 111.8133 94.00313 102.0569
4 total 0.5990000 95.67371 111.8133 94.00313 102.0569

Now, I am trying to create a loop which repeats this process 10 times - for each iteration, it should "stack" the new results on top of the older results (i.e. keep everything).

I tried to set up the basic loop structure:

for (i in 1:10 ) { 
for (j in 1:10 ) { 
    for (k in 1:10) {
        for (l in 1:10)  {

for (m in 1:10)  {

 random_i =  runif(1, 80, 120)
random_j =  runif(1, random_1, 120)


 random_k =  runif(1, 85, 120)
 random_l =  runif(1, random_3, 120)


train_data <- train_data %>% mutate(cat = ifelse(a1 <= random_i & b1 <= random_k, "a", ifelse(a1 <= random_j & b1 <= random_l, "b", "c"))) 

final_table = data.frame(train_data %>% group_by(cat) %>%
mutate(quant = quantile(c1, prob = .6)))

final_table$diff = ifelse(final_table$quant > final_table$c1,1,0)


final_table_m = data.frame(final_table %>% 
  group_by(cat) %>% 
  summarize(
   mean = mean(diff)
  ))

final_table_m = data.frame(final_table_m %>% add_row(cat = "total", mean = mean(final_table$diff)))

final_table_m$random_1 = random_i

final_table_m$random_2 = random_j


final_table_m$random_3 = random_k


final_table_m$random_4 = random_l

}
    }
        }
            }

But this only keeps the "latest" iteration.

Does anyone know how to keep all of the iterations, and store/keep all the results in the same table?

Thanks