Separating samples when importing a csv in R - r

Question

I'm new to R, but want to use it for its statistics tools on some collected data. I'm trying to import raw data from an instrument output, but to do so, I need to take out the useless comments left over from the machine's display, then separate the multiple samples into their own dataframes. The data comes out as:
////this is some preamble
////for sample 1 that would graph
////data on the machines display
1 10
2 20
3 30
///This is the preamble
////for the second sample
1 11
2 19
3 32
4 41
5 50
////this is closing statements
////and final plot command
////for the machine's display
I'm currently trying to import it with whitespace delimiters. If I only had the one sample, I know I can just skip the first four lines and add the titles for the columns later, as
library(readr)
DATA <- read_table2("DATA.txt", col_names = FALSE, skip = 4)
colnames(DATA) <- c("X","Y")
But I can't figure out how to separate sample 2 and the remainder of the unimportant text.
Another problem that might arise is that separation of sample one and two happen on different lines depending on the file. So I figure I need to import the text file to scan through it before even making tables.
I know this is a bit of a cluster, but I appreciate any help.

Answer 1

This should get you started. I'm using base R but you can always convert to a tibble later if you want.
DATA <- read.table("DATA.txt", header=FALSE, comment="/")
colnames(DATA) <- c("X","Y")
begin <- which(DATA$X==1)
end <- c(diff(begin), nrow(DATA))
groups <- mapply(":", begin, end)
DATA.lst <- lapply(groups, function(g) DATA[g, ])
names(DATA.lst) <- sprintf("Group%0.2i", seq(length(groups)))
DATA.lst
# $Group01
# X Y
# 1 1 10
# 2 2 20
# 3 3 30
#
# $Group02
# X Y
# 4 1 11
# 5 2 19
# 6 3 32
# 7 4 41
# 8 5 50
DATA.lst is a list of data frames. You can extract them with the following code but it may not be your best option if you are planning to perform the same analyses on each. R makes it easy to process all of the data frames in a list which saves you writing code for each one:
for (i in 1:2) {assign(names(DATA.lst)[i], DATA.lst[[i]])}