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#' Retrieves state, regional or national influenza statistics from the CDC (deprecated)
#'
#' Uses the data source from the
#' \href{https://gis.cdc.gov/grasp/fluview/fluportaldashboard.html}{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}",
#' "\code{state}"
#' @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}.\cr
#' For "\code{state}", should be a vector of state/territory names
#' or "\code{all}".
#' @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
get_flu_data <- function(region="hhs", sub_region=1:10,
data_source="ilinet",
years=as.numeric(format(Sys.Date(), "%Y"))) {
message(
paste0(
c("This function has been deprecated and will be removed in future releases.",
"Use either ilinet() or who_nrevss() instead."),
collapse="\n"
)
)
region <- tolower(region)
data_source <- tolower(data_source)
if (!(region %in% c("hhs", "census", "national", "state")))
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 = 0
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")
# Match names of states to numbers for API
if (region == "state") {
sub_region <- tolower(sub_region)
if (any(sub_region == "all")) {
sub_region_inpt <- 1:57
} else {
state_match <- data.frame(state = tolower(c(sort(c(datasets::state.name,
"District of Columbia")),
"American Samoa",
"Commonwealth of the Northern Mariana Islands",
"Puerto Rico",
"Virgin Islands",
"New York City",
"Los Angeles")),
num = 1:57,
stringsAsFactors = FALSE)
sub_region_inpt <- state_match$num[state_match$state %in% sub_region]
if (length(sub_region_inpt) == 0)
stop("Error: no eligible state/territory names provided")
}
} else sub_region_inpt <- sub_region
# format the input parameters to fit the CDC API
years <- years - 1960
reg <- as.numeric(c("hhs"=1, "census"=2, "national"=3, "state" = 5)[[region]])
# Format data source
if (data_source == "who") {
data_list <- list(list(ID = 0,
Name = "WHO_NREVSS"))
} else if (data_source == "ilinet") {
data_list <- list(list(ID = 1,
Name = "ILINet"))
} else data_list <- list(list(ID = 0,
Name = "WHO_NREVSS"),
list(ID = 1,
Name = "ILINet"))
# Format years
year_list <- lapply(seq_along(years),
function(x) list(ID = years[x],
Name = paste(years[x])))
# Format sub regions
sub_reg_list <- lapply(seq_along(sub_region_inpt),
function(x) list(ID = sub_region_inpt[x],
Name = paste(sub_region_inpt[x])))
params <- list(AppVersion = "Public",
DatasourceDT = data_list,
RegionTypeId = reg,
SeasonsDT = year_list,
SubRegionsDT = sub_reg_list)
out_file <- tempfile(fileext=".zip")
# CDC API returns a ZIP file so we grab, save & expand it to then read in CSVs
tmp <- httr::POST("https://gis.cdc.gov/grasp/flu2/PostPhase02DataDownload",
body = params,
encode = "json",
httr::write_disk(out_file))
httr::stop_for_status(tmp)
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))
lapply(files, function(x) {
pb$tick()$print()
ct <- ifelse(grepl("who", x, ignore.case=TRUE), 1, 1)
suppressMessages(readr::read_csv(x, skip=ct))
}) -> file_list
names(file_list) <- substr(basename(files), 1, nchar(basename(files)) - 4)
# If data are missing, X causes numeric columns to be read as character
lapply(file_list, function(x) {
# Create list of columns that should be numeric - exclude character columns
cols <- which(!colnames(x) %in% c("REGION", "REGION TYPE",
"SEASON_DESCRIPTION"))
suppressWarnings(x[cols] <- purrr::map(x[cols], as.numeric))
return(x)
}) -> file_list
# Depending on the parameters, there could be more than one
# file returned. When there's only one, return a more usable
# structure.
if (length(file_list) == 1) {
file_list <- file_list[[1]]
# when no rows, then it's likely the caller specified the
# current year and the flu season has technically not started yet.
# so help them out and move the year back and get current flu
# season data.
if ((nrow(file_list) == 0) &&
(length(years)==1) &&
(years == (as.numeric(format(Sys.Date(), "%Y"))-1960))) {
message("Adjusting [years] to get current season...")
return(get_flu_data(region=region, sub_region=sub_region,
data_source=data_source, years=years+1960-1))
} else {
return(file_list)
}
} else {
return(file_list)
}
}