Nothing

```
#' bin read depths of SNPs into categories having at least S observations
#'
#' @param D a matrix of read depths. Rows are individuals, columns are SNPs. Cells where data are missing
#' in the genotype matrix must be denoted as NA
#' @param S the min number of observations to have in each bin
#' @return This returns a list with two components. \code{dp_bins} is a matrix of the same
#' shape as D with the bin categories (as 1, 2, ...) and -1 for this cells
#' corresponding to missing genotypes. \code{num_cats} is the number of depth bins.
#' \code{tidy_bins} is a long format description of the bins.
#' \code{bin_stats} is a tibble giving summary information about the read depth bins which
#' is useful for plotting things, etc.
#' @export
#' @keywords internal
#' @examples
#'
#' # get a matrix of read depths and make it an integer matrix
#' depths <- vcfR::extract.gt(lobster_buz_2000, element = "DP")
#' storage.mode(depths) <- "integer"
#'
#' # get a character matrix of genotypes, so we can figure out which
#' # are missing and mask those from depths
#' genos <- vcfR::extract.gt(lobster_buz_2000, element = "GT")
#'
#' # make missing in depths if missing in genos
#' depths[is.na(genos)] <- NA
#'
#' # bin the read depths into bins with at least 1000 observations in each bin
#' bins <- bin_depths(depths, 1000)
bin_depths <- function(D, S) {
# first, count things up and get them into a reasonable format
dcnts <- as_tibble(as.data.frame(table(D))) %>%
mutate(D = as.integer(as.character(D))) %>%
setNames(c("D", "Freq")) %>%
rename(dp = D,
n = Freq) %>%
arrange(dp) # just to make sure it is in increasing order
# now we lump them up. We just use a for loop for this
idx <- 1
sum <- 0
n <- dcnts$n
res <- rep(NA, length(n))
for(i in seq_along(n)) {
res[i] <- idx
sum <- sum + n[i]
if(sum > S) {
idx <- idx + 1
sum <- 0
}
}
# at the end of that, we take the very last bin and just merge is with the
# preceding one, so that it doesn't have just a small number in it.
# but we only do this if there is more than one read depth bin!
if(max(res) > 1) {
res[res == max(res)] <- max(res) - 1
}
# and we add it on there with a mutate which will bark an error is something has gone awry
tidy_bins <- dcnts %>%
mutate(bin = as.integer(res))
# now we have to cut the original matrix up into these categories
left_endpoints <- tidy_bins %>%
group_by(bin) %>%
summarise(ends = max(dp) + 0.2)
cut_vec <- c(-0.01, left_endpoints$ends)
dp_bins <- D
dp_bins[] <- cut(D, breaks = cut_vec)
dp_bins[is.na(dp_bins)] <- -1L
# finally summarize the bin stats
bin_stats <- tidy_bins %>%
group_by(bin) %>%
summarise(total_n = sum(n),
mean_dp = sum(dp * n) / sum(n))
list(dp_bins = dp_bins,
num_cats = max(tidy_bins$bin),
tidy_bins = tidy_bins,
bin_stats = bin_stats
)
}
```

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