localExamples/airnow_downloadConditionsKML.R

In MazamaScience/PWFSLSmoke: Utilities for Working with Air Quality Monitoring Data

#' @keywords AirNow
#' @export
#' @importFrom utils read.table
#' @title Download Hourly Data from AirNow
#' @param datestamp integer or character representing YYYYMMDDHH
#' @param baseUrl base URL for archived hourly data
#' @return Dataframe of AirNow hourly data.
#' @description The \url{https://airnowtech.org} site provides both air pollution
#' monitoring data as well as monitoring site location metadata. This
#' function retrieves a single, hourly data file and returns it as a dataframe.
#' 
#' @note As of 2016-12-27, it appears that hourly data are available only for 2016 and
#' not for earlier years.
#' @seealso \link{airnow_createDataDataframes}
#' @seealso \link{airnow_downloadParseData}
#' @examples
#' \dontrun{
#' df <- airnow_downloadHourlyData(2016070112)
#' }

airnow_downloadConditionsKML <- function(datestamp=strftime(lubridate::now(),"%Y%m%d00",tz="UTC"),
                                         baseUrl='https://files.airnowtech.org/airnow') {
  
  # Strip off any final '/'
  baseUrl <- stringr::str_replace(baseUrl,'/$','')
  
  ###https://s3-us-west-1.amazonaws.com//files.airnowtech.org/airnow/today/airnow_conditions.kml
  
  # # Create URL
  # datestamp <- as.character(datestamp)
  # year <- stringr::str_sub(datestamp,1,4)
  # ymd <- stringr::str_sub(datestamp,1,8)
  # url <- paste0(baseUrl,'/',year,'/',ymd,'/HourlyData_',datestamp,'.dat')
  
  url <- paste0(baseUrl,'/today/airnow_conditions.kml')
  
  result <- try({
    
    r <- httr::GET(url)
    
  })
  
  # Handle the response -------------------------------------------------------
  
  status_code <- httr::status_code(r)
  content <- httr::content(r, as="raw", encoding='UTF-8')
  
  ###logger.debug("Status=%d, content='%s'", status_code, content)
  
  if ( httr::http_error(r) ) {  # web service failed to respond
    
  }


    
  fire <- rgdal::readOGR("~/desktop/fire.kml", "Fire (Final)")
  
  
  
  
  
  # col_names <- c('ValidDate', 'ValidTime', 'AQSID', 'SiteName', 'GMTOffset',
  #                'ParameterName', 'ReportingUnits', 'Value', 'DataSource')
  # 
  # col_types = 'cccciccdc'
  # 
  # # NOTE:  Even after the move to Amazon web services, they still have some enoding issues
  # # NOTE:  e.g. 12/26/16|00:00|000051501|Z<82>phirin|-5|OZONE|PPB|39|Meteorological Service of Canada
  # locale <- readr::locale(encoding="CP437")
  # 
  # # Read in text as a dataframe
  # df <- readr::read_delim(url, delim='|', col_names=col_names, col_types=col_types, locale=locale)
  
  return(df) 
}