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Merge branch 'master' of github.com:hrbrmstr/cdcfluview

tags/v0.5.2^0 v0.5.2
boB Rudis 3 years ago
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6 changed files with 248 additions and 23 deletions
  1. +2
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      DESCRIPTION
  2. +1
    -0
      NAMESPACE
  3. +77
    -19
      R/get_flu_data.r
  4. +119
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      R/get_hosp_data.R
  5. +5
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      man/get_flu_data.Rd
  6. +44
    -0
      man/get_hosp_data.Rd

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DESCRIPTION View File

@ -3,8 +3,8 @@ Type: Package
Title: Retrieve U.S. Flu Season Data from the CDC FluView Portal
Version: 0.5.2
Date: 2017-03-14
Author: Bob Rudis (bob@rud.is)
Maintainer: Bob Rudis <bob@rud.is>
Authors@R: c(person("Bob","Rudis", email = "bob@rud.is", role = c("aut", "cre")),
person("Craig", "McGowan", email = "mcgowan.cj@gmail.com", role = "ctb"))
Encoding: UTF-8
Description: The U.S. Centers for Disease Control (CDC) maintains a portal
<http://gis.cdc.gov/grasp/fluview/fluportaldashboard.html> for


+ 1
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NAMESPACE View File

@ -1,6 +1,7 @@
# Generated by roxygen2: do not edit by hand
export(get_flu_data)
export(get_hosp_data)
export(get_mortality_surveillance_data)
export(get_state_data)
export(get_weekly_flu_report)


+ 77
- 19
R/get_flu_data.r View File

@ -9,11 +9,14 @@
#' 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 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}
#' 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
@ -37,13 +40,13 @@ get_flu_data <- function(region="hhs", sub_region=1:10,
region <- tolower(region)
data_source <- tolower(data_source)
if (!(region %in% c("hhs", "census", "national")))
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 = ""
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'")
@ -57,31 +60,75 @@ get_flu_data <- function(region="hhs", sub_region=1:10,
if (any(years < 1997))
stop("Error: years should be > 1997")
# format the input parameters to fit the CDC API
years <- years - 1960
# Match names of states to numbers for API
if (region == "state") {
sub_region <- tolower(sub_region)
reg <- as.numeric(c("hhs"=1, "census"=2, "national"=3)[[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 = F)
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
if ("all" %in% data_source) data_source <- c("who", "ilinet")
# format the input parameters to fit the CDC API
data_source <- gsub("who", "WHO_NREVSS", data_source)
data_source <- gsub("ilinet", "ILINet", data_source)
years <- years - 1960
params <- list(SubRegionsList = paste0(sub_region, collapse=","),
DataSources = paste0(data_source, collapse=","),
RegionID = reg,
SeasonsList = paste0(years, collapse=","))
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/fluview/FluViewPhase2CustomDownload.ashx",
tmp <- httr::POST("https://gis.cdc.gov/grasp/flu2/PostPhase02DataDownload",
body = params,
write_disk(out_file))
encode = "json",
httr::write_disk(out_file))
stop_for_status(tmp)
httr::stop_for_status(tmp)
if (!(file.exists(out_file)))
stop("Error: cannot process downloaded data")
@ -97,7 +144,17 @@ get_flu_data <- function(region="hhs", sub_region=1:10,
suppressMessages(readr::read_csv(x, skip=ct))
}) -> file_list
names(file_list) <- substr(basename(files), 1, 3)
names(file_list) <- substr(basename(files), 1, nchar(basename(files)) - 4)
# If data are missing, X causes numeric columns to be read as character
purrr::map(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
@ -128,3 +185,4 @@ get_flu_data <- function(region="hhs", sub_region=1:10,
}
}

+ 119
- 0
R/get_hosp_data.R View File

@ -0,0 +1,119 @@
#' Retrieves influenza hospitalization statistics from the CDC
#'
#' Uses the data source from the
#' \href{https://gis.cdc.gov/GRASP/Fluview/FluHospRates.html}{CDC FluView}
#' and provides influenza hospitalization reporting data as a data frame.
#'
#' @param area one of "\code{flusurvnet}", "\code{eip}", "\code{ihsp}", or two
#' digit state abbreviation for an individual site. Exceptions are
#' New York - Albany ("\code{nya}") and New York - Rochester
#' ("\code{nyr}")
#' @param age_group a vector of age groups to pull data for. Possible values are:
#' "\code{overall}", "\code{0-4y}", "\code{5-17y}, "\code{18-49y},
#' "\code{50-64y}, "\code{65+y}".
#' @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{2009}
#' @return A single \code{data.frame}.
#' @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{
#' # All of FluSurv-NET, 50-64 years old, 2010/11-2014/15 flu seasons
#' flu <- get_hosp_data("flusurvnet", "50-64y", years=2010:2014)
#' }
get_hosp_data <- function(area="flusurvnet", age_group="overall",
years=as.numeric(format(Sys.Date(), "%Y")) - 1) {
area <- tolower(area)
age_group <- tolower(age_group)
if (!(area %in% c("flusurvnet", "eip", "ihsp", "ca", "co", "ct", "ga", "md",
"mn", "nm", "nya", "nyr", "or", "tn", "id", "ia", "mi",
"oh", "ok", "ri", "sd", "ut")))
stop("Error: area must be one of flusurvnet, eip, ihsp, or a valid state abbreviation")
if (length(area) != 1)
stop("Error: can only select one area")
if (!all(age_group %in% c("overall", "0-4y", "5-17y", "18-49y",
"50-64y", "65+y")))
stop("Error: invalid age group specified")
if (any(years < 2009))
stop("Error: years should be >= 2009")
# Match names of age groups to numbers for API
age_match <- data.frame(age_group = c("overall", "0-4y", "5-17y",
"18-49y", "50-64y", "65+y"),
code = c(6, 1, 2, 3, 4, 5))
age_group_num <- age_match$code[age_match$age_group %in% age_group]
# format the input parameters to fit the CDC API
years <- years - 1960
area_match <- data.frame(area = c("flusurvnet", "eip", "ca", "co", "ct",
"ga", "md", "mn", "nm", "nya", "nyr", "or",
"tn", "ihsp", "id", "ia", "mi", "oh", "ok",
"ri", "sd", "ut"),
catch = c(22, 22, 1, 2, 3, 4, 7, 9, 11, 13, 14, 17,
20, 22, 6, 5, 8, 15, 16, 18, 19, 21),
network = c(1, rep(2, 12), rep(3, 9)))
# Format years
year_list <- lapply(seq_along(years),
function(x) list(ID = years[x]))
# Format age group
age_list <- lapply(seq_along(age_group_num),
function(x) list(ID = age_group_num[x]))
params <- list(AppVersion = "Public",
agegroups = age_list,
catchmentid = area_match$catch[area_match$area == area],
networkid = area_match$network[area_match$area == area],
seasons = year_list)
out_file <- tempfile(fileext=".json")
# 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/Flu3/PostPhase03DownloadData",
body = params,
encode = "json",
httr::write_disk(out_file, overwrite = T))
httr::stop_for_status(tmp)
if (!(file.exists(out_file)))
stop("Error: cannot process downloaded data")
file <- jsonlite::fromJSON(out_file)[[1]]
# pb <- dplyr::progress_estimated(length(file))
# purrr::map(file, 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)
# Depending on the parameters, there could be more than one
# file returned. When there's only one, return a more usable
# structure.
# 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.
return(file)
}

+ 5
- 2
man/get_flu_data.Rd View File

@ -8,12 +8,15 @@ get_flu_data(region = "hhs", sub_region = 1:10, data_source = "ilinet",
years = as.numeric(format(Sys.Date(), "\%Y")))
}
\arguments{
\item{region}{one of "\code{hhs}", "\code{census}", "\code{national}"}
\item{region}{one of "\code{hhs}", "\code{census}", "\code{national}",
"\code{state}"}
\item{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}}
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}".}
\item{data_source}{either of "\code{who}" (for WHO NREVSS) or "\code{ilinet}"
or "\code{all}" (for both)}


+ 44
- 0
man/get_hosp_data.Rd View File

@ -0,0 +1,44 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/get_hosp_data.R
\name{get_hosp_data}
\alias{get_hosp_data}
\title{Retrieves influenza hospitalization statistics from the CDC}
\usage{
get_hosp_data(area = "flusurvnet", age_group = "overall",
years = as.numeric(format(Sys.Date(), "\%Y")) - 1)
}
\arguments{
\item{area}{one of "\code{flusurvnet}", "\code{eip}", "\code{ihsp}", or two
digit state abbreviation for an individual site. Exceptions are
New York - Albany ("\code{nya}") and New York - Rochester
("\code{nyr}")}
\item{age_group}{a vector of age groups to pull data for. Possible values are:
"\code{overall}", "\code{0-4y}", "\code{5-17y}, "\code{18-49y},
"\code{50-64y}, "\code{65+y}".}
\item{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{2009}}
}
\value{
A single \code{data.frame}.
}
\description{
Uses the data source from the
\href{https://gis.cdc.gov/GRASP/Fluview/FluHospRates.html}{CDC FluView}
and provides influenza hospitalization reporting data as a data frame.
}
\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.
}
\examples{
\dontrun{
# All of FluSurv-NET, 50-64 years old, 2010/11-2014/15 flu seasons
flu <- get_hosp_data("flusurvnet", "50-64y", years=2010:2014)
}
}

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