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#' Retrieves state, regional or national influenza statistics from the CDC
#' Uses the data source from the
#' \href{}{CDC FluView}
#' and provides flu reporting data as either a single data frame or a list of
#' data frames (depending on whether either \code{WHO NREVSS} or \code{ILINet}
#' (or both) is chosen.
#' A lookup table between HHS regions and their member states/territories
#' is provided in \code{\link{hhs_regions}}.
#' @param region one of "\code{hhs}", "\code{census}", "\code{national}"
#' @param sub_region depends on the \code{region_type}.\cr
#' For "\code{national}", the \code{sub_region} should be \code{NA}.\cr
#' For "\code{hhs}", should be a vector between \code{1:10}.\cr
#' For "\code{census}", should be a vector between \code{1:9}
#' @param data_source either of "\code{who}" (for WHO NREVSS) or "\code{ilinet}"
#' or "\code{all}" (for both)
#' @param years a vector of years to retrieve data for (i.e. \code{2014} for CDC
#' flu season 2014-2015). Default value is the current year and all
#' \code{years} values should be > \code{1997}
#' @return If only a single \code{data_source} is specified, then a single
#' \code{data.frame} is returned, otherwise a named list with each
#' \code{data.frame} is returned.
#' @note There is often a noticeable delay when making the API request to the CDC.
#' This is not due to a large download size, but the time it takes for their
#' servers to crunch the data. Wrap the function call in \code{httr::with_verbose}
#' if you would like to see what's going on.
#' @export
#' @examples \dontrun{
#' flu <- get_flu_data("hhs", 1:10, c("who", "ilinet"), years=2000:2014)
#' }
get_flu_data <- function(region="hhs", sub_region=1:10,
years=as.numeric(format(Sys.Date(), "%Y"))) {
region <- tolower(region)
data_source <- tolower(data_source)
if (!(region %in% c("hhs", "census", "national")))
stop("Error: region must be one of hhs, census or national")
if (length(region) != 1)
stop("Error: can only select one region")
if (region=="national") sub_region = ""
if ((region=="hhs") && !all(sub_region %in% 1:10))
stop("Error: sub_region values must fall between 1:10 when region is 'hhs'")
if ((region=="census") && !all(sub_region %in% 1:19))
stop("Error: sub_region values must fall between 1:10 when region is 'census'")
if (!all(data_source %in% c("who", "ilinet", "all")))
stop("Error: data_source must be either 'who', 'ilinet', 'all' or c('who', 'ilinet')")
if (any(years < 1997))
stop("Error: years should be > 1997")
# format the input parameters to fit the CDC API
years <- years - 1960
reg <- as.numeric(c("hhs"=1, "census"=2, "national"=3)[[region]])
if ("all" %in% data_source) data_source <- c("who", "ilinet")
data_source <- gsub("who", "WHO_NREVSS", data_source)
data_source <- gsub("ilinet", "ILINet", data_source)
params <- list(SubRegionsList=paste0(sub_region, collapse=","),
DataSources=paste0(data_source, collapse=","),
SeasonsList=paste0(years, collapse=","))
out_file <- tempfile(fileext=".zip")
tmp <- httr::POST("",
if (!(file.exists(out_file)))
stop("Error: cannot process downloaded data")
out_dir <- tempdir()
files <- unzip(out_file, exdir=out_dir, overwrite=TRUE)
pb <- dplyr::progress_estimated(length(files))
purrr::map(files, function(x) {
ct <- ifelse(grepl("who", x,, 1, 1)
suppressMessages(readr::read_csv(x, skip=ct))
}) -> file_list
names(file_list) <- substr(basename(files), 1, 3)
if (length(file_list) == 1) {
} else {