How to randomly sample dataframe rows with unique column values

The ultimate objective is to compare the variance and standard deviation of a simple statistic (numerator / denominator / true_count) from the avg_score for 10 trials of incrementally sized random samples per word from a dataset similar to:

library (data.table)
set.seed(1)
df <- data.frame(
  word_ID = c(rep(1,4),rep(2,3),rep(3,2),rep(4,5),rep(5,5),rep(6,3),rep(7,4),rep(8,4),rep(9,6),rep(10,4)),
  word = c(rep("cat",4), rep("house", 3), rep("sung",2), rep("door",5), rep("pretty", 5), rep("towel",3), rep("car",4), rep("island",4), rep("ran",6), rep("pizza", 4)), 
  true_count = c(rep(234,4),rep(39,3),rep(876,2),rep(4,5),rep(67,5),rep(81,3),rep(90,4),rep(43,4),rep(54,6),rep(53,4)),
  occurrences = c(rep(234,4),rep(34,3),rep(876,2),rep(4,5),rep(65,5),rep(81,3),rep(90,4),rep(43,4),rep(54,6),rep(51,4)),
  item_score = runif(40),
  avg_score = rnorm(40),
  line = c(71,234,71,34,25,32,573,3,673,899,904,2,4,55,55,1003,100,432,100,29,87,326,413,32,54,523,87,988,988,12,24,754,987,12,4276,987,93,65,45,49),
  validity = sample(c("T", "F"), 40, replace = T)

)
dt <- data.table(df)
dt[ , denominator := 1:.N, by=word_ID]
dt[ , numerator := 1:.N, by=c("word_ID", "validity")]
dt$numerator[df$validity=="F"] <- 0
df <- dt

<df
    word_ID  word  true_count occurrences item_score   avg_score line validity denominator numerator
 1:       1    cat        234         234 0.25497614  0.15268651   71        F           1         0
 2:       1    cat        234         234 0.18662407  1.77376261  234        F           2         0
 3:       1    cat        234         234 0.74554352 -0.64807093   71        T           3         1
 4:       1    cat        234         234 0.93296878 -0.19981748   34        T           4         2
 5:       2  house         39          34 0.49471189  0.68924373   25        F           1         0
 6:       2  house         39          34 0.64499368  0.03614551   32        T           2         1
 7:       2  house         39          34 0.17580259  1.94353631  573        F           3         0
 8:       3   sung        876         876 0.60299465  0.73721373    3        T           1         1
 9:       3   sung        876         876 0.88775767  2.32133393  673        F           2         0
10:       4   door          4           4 0.49020940  0.34890935  899        T           1         1
11:       4   door          4           4 0.01838357 -1.13391666  904        T           2         2

The data represents each detection of a word in a document, so it's possible for a word to appear on the same line more than once. The task is for the sample size to represent unique column values (line), but to return all instances where the line number is the same- meaning the actual number of rows returned could be more than the specified sample size. So, for one two-word sample size trial for "cat", the form of the desired result would be:

    word_ID  word  true_count occurrences item_score   avg_score line validity denominator numerator
 1:       1    cat        234         234 0.25497614  0.15268651   71        F           1         0
 2:       1    cat        234         234 0.18662407  1.77376261  234        F           2         0
 3:       1    cat        234         234 0.74554352 -0.64807093   71        T           3         1

My basic iteration (found on this site) currently looks like:

for (i in 1:10) {

  a2[[i]] <- lapply(split(df, df$word_ID), function(x) x[sample(nrow(x), 2, replace = T), ])

  b3[[i]] <- lapply(split(df, df$word_ID), function(x) x[sample(nrow(x), 3, replace = T), ])}

}

So, I can do the standard random sample sizes, but am unsure (and couldn't find something similar or wasn't looking the right way) how to approach the goal stated above. Is there a straight-forward way to approach this?

Thanks,