#' Get download statistics for Bioconductor packages distributed via
#' Anaconda.
#'
#' @details Anaconda provide daily download counts for all software packages
#' they distribute. These are summarised into monthly tables of counts and made
#' available from https://github.com/grimbough/anaconda-download-stats
#' This function provides a mechanism to download these monthly counts for
#' Bioconductor packages distributed through Anaconda.
#'
#' @importFrom dplyr mutate select arrange desc
#' @importFrom utils read.table download.file
#' @importFrom tibble as_tibble
#'
#' @return A \code{data.frame} of download statistics for
#' all Bioconductor packages distributed by Anaconda, in tidy format.
#' Note: Anaconda do not provide counts for unique IP addresses. This column
#' is listed as \code{NA} for all packages to provide continuity with data from
#' Bioconductor.org obtained by \code{\link{biocDownloadStats}}. The counts are
#' updated monthly, so do not expect to see counts for the current month.
#'
#' @author Mike L. Smith
#'
#' @examples
#' anacondaDownloadStats()
#'
#' @export
anacondaDownloadStats <- function() {
temp_file <- tempfile()
url <- paste0(
"https://github.com/grimbough/anaconda-download-stats",
"/raw/master/rdata/bioc_counts.rds"
)
download.file(url, destfile = temp_file, quiet = TRUE, mode = "wb")
tmp <- readRDS(temp_file)
tmp$repo <- "Anaconda"
tmp$Nb_of_distinct_IPs <- NA_integer_
tmp <- as_tibble(tmp) |>
dplyr::mutate(Date = as.Date(
paste(.data$Year, .data$Month, "01"),
"%Y %b %d"
)) |>
select(
"Package", "Year", "Month", "Nb_of_distinct_IPs",
"Nb_of_downloads", "repo", "Date"
) |>
## put this into the same order as the BioC table
arrange(tmp, .data$Package, desc(.data$Year), .data$Date)
class(tmp) <- c("bioc_downloads", class(tmp))
tmp
}
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