R/ws_data_na_span_2_pgdb.R
In rabravo/ws2pgdb: Retrieves, manipulates, and stores data from NOAA, U.S.Census, and Epimodels.org. Uses location of weather stations (ws) to create a clustering of counties around the ws
#' ws_data_na_span_2_pgdb() retrieves the subset of ws with data available in the intersecting ws
#'
#' Retrieves the weather data from the intersecting stations. It is known that NOAA listed
#' available data for certain years, months, or days; however, some of this information is missing.
#' This function replaces the missing data with a NA value of the dataset. These value is read by
#' the Postgres database as an empty cell. You can confirm this using pgAdmin3 or other database manager.
#' The purpose of this function is to store the raw data, as it is, so that, the user can decide
#' what to do with missing points ( i.e. extrapolate). The example shows the use of ws_metadata_span_2_pgdb()
#' , but it can be very well be replaced by ws_metadata_2_pgdb().
#'
#' @keywords NOAA, weather station, rgdal, RPostgreSQL, rnoaa
#' @param ghcnd String that refers to the Global Historical Climate Network (daily) dataset
#' @param geoid FIPS number from census tables
#' @param type Variable under investigation i.e. TMAX, TMIN, PRCP
#' @param span Look-back time to search and retrieve weather data information
#' @param ws_metadata The table name of the subset of weather stations with intersecting data
#' @return Returns the name of the table that has been created. When fail, it returns 1.
#' @examples
#' ghcnd <- 'GHCND'
#' geoid <- '12087'
#' type <- 'TMAX'
#' stations <- as.data.frame( all_coor_ws( ghcnd, geoid, type) )
#' span <- '2'
#' ws_metadata <- ws_metadata_span_2_pgdb( geoid, type, stations, span)
#' ws_data_na_span_2_pgdb(ghcnd, geoid, type, span, ws_metadata)
#'
#' @export
ws_data_na_span_2_pgdb <- function( ghcnd, geoid, type, span, ws_metadata){
file <- base::paste(Sys.getenv("HOME"), "/","pg_config.yml", sep="")
config <- yaml::yaml.load_file( file )
driver <- "PostgreSQL"
drv <- RPostgres::Postgres()
conn <- RPostgres::dbConnect(drv, host= config$dbhost, port= config$dbport, dbname= config$dbname, user= config$dbuser, password= config$dbpwd)
if ( as.integer(geoid) < 100) {
q1 <- base::paste("select NAME from cb_2013_us_state_20m where GEOID='", geoid,"'", sep="")
res <- RPostgres::dbSendQuery(conn, q1)
state <- RPostgres::dbFetch(res)
RPostgres::dbClearResult(res)
tableName <- base::paste(state, "_",geoid,"_ws_data_span_",span,"_na_", sep="")
} else {
q2 <- base::paste("select NAME from cb_2013_us_county_20m where GEOID='", geoid,"'", sep="")
res <- RPostgres::dbSendQuery(conn, q2)
county <- RPostgres::dbFetch(res)
RPostgres::dbClearResult(res)
q3 <- base::paste("select NAME from cb_2013_us_state_20m where GEOID='", substr(geoid, 1, 2),"'", sep="")
res <- RPostgres::dbSendQuery(conn, q3)
state <- RPostgres::dbFetch(res)
RPostgres::dbClearResult(res)
tableName <- base::paste(state, "_",county,"_",geoid,"_ws_data_span_",span,"_na_",sep="")
}# endIF/ELSE
type <- tolower( type )
tableName <- gsub(" ", "_", tableName)
tableName <- tolower( tableName )
tableName <- base::paste( tableName, type, sep="")
cat("Creating Postgres weather information table \t\t\t\t")
if (RPostgres::dbExistsTable(conn, tableName)) {
msg <- base::paste("Done - Table ", tableName, " exists.\t\t\t\t\n", sep="")
cat(msg)
cat("Check Postgres table!\n")
RPostgres::dbDisconnect(conn)
return(tableName)
} else {
q1 <- base::paste("select name as id, mindate as mindate, maxdate as maxdate, longitude as longitude, latitude as latitude from ", ws_metadata, sep="")
res <- RPostgres::dbSendQuery(conn, q1)
station <- data.frame(RPostgres::dbFetch(res))
RPostgres::dbClearResult(res)
minDate <- base::as.Date( lubridate::ymd( station$mindate[1] ) )
maxDate <- base::as.Date( lubridate::ymd( station$maxdate[1] ) )
# One is end in a new year.
ndays <- ( lubridate::int_length( lubridate::interval( base::as.Date( minDate ) , base::as.Date( maxDate )))/(3600*24))+1
dates <- as.character( seq.Date( minDate , by ="days", length.out= ndays) )
# The NOAA API only allows queries from users to retrieve data sets by year;
# thus, this code section limits each query to a different year for each iteration.
# WARNING: There is 500 limit request per day with the current token
for (i in 1:nrow( station ) ) {
ws <- station$id[i]
# startDate
sDate <- minDate
# finishDate
fDate <- minDate
# Used for testing Comment this out after testing ( maybe deprecated)
# year( sDate ) <- year( maxDate ) - 5
# year( fDate ) <- year( maxDate ) - 5
lubridate::year( fDate ) <- lubridate::year( fDate ) + 1 #Interval (sDate, fDate) is one year.
# Temporal strings attached to the dates for consistency with NOAA
ssDate <- base::paste(sDate, "T00:00:00", sep="")
ffDate <- base::paste(fDate, "T00:00:00", sep="")
estacion <- base::paste("GHCND:", ws, sep="")
weatherVar <- rnoaa::ncdc(datasetid=ghcnd, stationid=estacion, datatypeid=type, startdate=ssDate, enddate=ffDate , limit=366, token=config$token)
# Verify that available information exist
if (length(as.character( weatherVar$meta$totalCount)) == 0 ) {
msg <- base::paste("Not Data Available for station ", estacion, " for variable type: ", type, ".", sep="" )
cat( msg )
colnames( valores ) <- station$id[1:(i-1)]
RPostgres::dbWriteTable( conn, tableName, as.data.frame( valores ) )
RPostgres::dbDisconnect( conn )
return(tableName)
}#endIF
weatherYear<- weatherVar$data
repeat
{
# Replaces starting year, year( sDate ), with a value of new time series
lubridate::year( sDate ) <- lubridate::year( sDate ) + 1
# Replaces starting day, day( sDate ), with a value of new time
lubridate::day( sDate ) <- lubridate::day( fDate ) + 1
# Replaces ending year, year( fDate ), with a value of new time series
lubridate::year( fDate ) <- lubridate::year( fDate ) + 1
ssDate <- base::paste(sDate, "T00:00:00", sep="")
ffDate <- base::paste(fDate, "T00:00:00", sep="")
if ( isTRUE( lubridate::year( fDate ) < lubridate::year( maxDate ) ) ) {
weatherVar<-rnoaa::ncdc(datasetid = ghcnd, stationid = estacion, datatypeid=type, startdate = ssDate, enddate= ffDate , limit=366, token=config$token)
#Verify that available information exist
if (length(as.character( weatherVar$meta$totalCount)) == 0 ) {
msg <- base::paste("Not Data Available for statino ", estacion, " for variable type: ", type, ".\t\n", sep="" )
cat( msg )
colnames( valores ) <- station$id[1:(i-1)]
RPostgres::dbWriteTable( conn, tableName, as.data.frame( valores ) )
RPostgres::dbDisconnect( conn )
return(tableName)
}#endIF
# Delay needed since NOAA service accepts a maximum of 5 request per second.
Sys.sleep(0.2)
weatherYear <- rbind(weatherYear, weatherVar$data)
} else {
lubridate::month( fDate ) <- lubridate::month( maxDate )
lubridate::day( fDate ) <- lubridate::day( maxDate )
ssDate <- base::paste( sDate, "T00:00:00", sep="")
ffDate <- base::paste( fDate, "T00:00:00", sep="")
weatherVar<-rnoaa::ncdc(datasetid = ghcnd, stationid = estacion, datatypeid=type, startdate = ssDate, enddate= ffDate , limit=366, token=config$token)
# Verify that available information exist
if (length(as.character( weatherVar$meta$totalCount)) == 0 ) {
msg <- base::paste("Not Data Available for station ", estacion, " for variable type: ", type, ".\t\n", sep="" )
cat( msg )
colnames( valores ) <- station$id[1:(i-1)]
RPostgres::dbWriteTable( conn, tableName, as.data.frame( valores ) )
RPostgres::dbDisconnect( conn )
return(tableName)
}# endIF
weatherYear <- rbind( weatherYear, weatherVar$data)
break
}# endIF/ELSE
}# endRepeat
# TMAX = Maximum temperature (tenths of degrees C)
# promedio <- sprintf( "%.4f", mean( weatherYear$value/10 ) )
# Return year mean value
# print( promedio )
# NOAA specification of available data within a range often is not complete.
# Individual dates are missing even within an specified range.
# SOLUTIONS:
# 1) Average from the available number of samples.
# 2) Interpolation when possible
if ( i == 1) {
intervalAsDate <- base::as.Date( weatherYear$date )
# TMAX = Maximum temperature (tenths of degrees C)
intervalAsValue <- sprintf("%.4f", weatherYear$value/10)
stationDataHash <- hash::hash( intervalAsDate , intervalAsValue )
allDatesHash <- hash::hash( dates , rep(1, ndays ) ) # h1 <- hash( dates , rep(1, ndays ) )
hash::values( allDatesHash, keys=intervalAsDate ) <- intervalAsValue
v1.df <- as.data.frame( hash::has.key( dates , stationDataHash ) )
colnames(v1.df) <- "value"
# Section that guarantees that v1.df has at least one value
if ( length( which( v1.df$value == FALSE) ) > 0 ) {
sub_v1.df <- as.data.frame( subset( v1.df, value == FALSE ) )
# Dates from station which values do not exist in allDatasHash
dateKeys <- as.character( rownames( sub_v1.df ) ) #print(dateKeys)
sizeSubV1 <- nrow( sub_v1.df )
# Main change from conn_rnooa_postgresql_writeOGR_locs_and_weatherData_v2 (v2)
# is the following line. We replace average for Interpolated values.
# In v2 the vecPromedio variable hold the average value of the available data.
# Currently as can be observed, it is replaced by NA that is replaced by an empty
# cell when exported to a database with column holding a variable of type double
vecPromedio <- rep( NA, sizeSubV1 )
#Update allDatesHash values found in 'dataKeys' with NA
hash::values( allDatesHash, keys = dateKeys) <- vecPromedio
valores <- as.data.frame( hash::values( allDatesHash, keys=NULL ) )
} else { valores <- data.frame( intervalAsValue ) }#endIF/ELSE
} else {
intervalAsDate <- base::as.Date( weatherYear$date )
# TMAX = Maximum temperature (tenths of degrees C)
intervalAsValue <- sprintf( "%.4f", weatherYear$value/10 )
stationDataHash <- hash::hash( intervalAsDate , intervalAsValue )
hash::values( allDatesHash, keys=dates) <- rep(1, ndays )
hash::values( allDatesHash, keys=intervalAsDate) <- intervalAsValue
v1.df <- as.data.frame( hash::has.key( dates , stationDataHash ) )
colnames(v1.df) <- "value"
# Section guarantees that v1.df has at least one value
if ( length( which( v1.df$value == FALSE) ) > 0 ) {
sub_v1.df <- as.data.frame( subset( v1.df, value == FALSE) )
dateKeys <- as.character( rownames( sub_v1.df ) ) #print(dateKeys)
sizeSubV1 <- nrow( sub_v1.df)
# Main change from v2 is this line. We replace average for Interpolated values.
# In v2 the vecPromedio the current NA values were replaced instead by an average value.
vecPromedio <- rep( NA, sizeSubV1 )
hash::values( allDatesHash, keys=dateKeys) <- vecPromedio
subValores <- as.data.frame( hash::values( allDatesHash, keys=NULL ) )
valores <- as.data.frame( cbind( valores, subValores ) )
} else { valores <- as.data.frame( cbind( valores, intervalAsValue ) ) }#endIF/ELSE
}# endIF/ELSE
rm( stationDataHash )
}# endFOR (stick time series of different stations together)
colnames( valores ) <- station$id
msg <- base::paste("Creating table ", tableName, ".\t\t\t\t\t", sep="")
cat(msg)
RPostgres::dbWriteTable( conn, tableName, as.data.frame( valores ) )
Sys.sleep(4)
cat("Check Postgres table!")
}# endIF/ELSE
RPostgres::dbDisconnect( conn )
return(tableName)
}
rabravo/ws2pgdb documentation built on May 26, 2019, 8:51 p.m.
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#' ws_data_na_span_2_pgdb() retrieves the subset of ws with data available in the intersecting ws
#'
#' Retrieves the weather data from the intersecting stations. It is known that NOAA listed
#' available data for certain years, months, or days; however, some of this information is missing.
#' This function replaces the missing data with a NA value of the dataset. These value is read by
#' the Postgres database as an empty cell. You can confirm this using pgAdmin3 or other database manager.
#' The purpose of this function is to store the raw data, as it is, so that, the user can decide
#' what to do with missing points ( i.e. extrapolate). The example shows the use of ws_metadata_span_2_pgdb()
#' , but it can be very well be replaced by ws_metadata_2_pgdb().
#'
#' @keywords NOAA, weather station, rgdal, RPostgreSQL, rnoaa
#' @param ghcnd String that refers to the Global Historical Climate Network (daily) dataset
#' @param geoid FIPS number from census tables
#' @param type Variable under investigation i.e. TMAX, TMIN, PRCP
#' @param span Look-back time to search and retrieve weather data information
#' @param ws_metadata The table name of the subset of weather stations with intersecting data
#' @return Returns the name of the table that has been created. When fail, it returns 1.
#' @examples
#' ghcnd <- 'GHCND'
#' geoid <- '12087'
#' type <- 'TMAX'
#' stations <- as.data.frame( all_coor_ws( ghcnd, geoid, type) )
#' span <- '2'
#' ws_metadata <- ws_metadata_span_2_pgdb( geoid, type, stations, span)
#' ws_data_na_span_2_pgdb(ghcnd, geoid, type, span, ws_metadata)
#'
#' @export
ws_data_na_span_2_pgdb <- function( ghcnd, geoid, type, span, ws_metadata){
file <- base::paste(Sys.getenv("HOME"), "/","pg_config.yml", sep="")
config <- yaml::yaml.load_file( file )
driver <- "PostgreSQL"
drv <- RPostgres::Postgres()
conn <- RPostgres::dbConnect(drv, host= config$dbhost, port= config$dbport, dbname= config$dbname, user= config$dbuser, password= config$dbpwd)
if ( as.integer(geoid) < 100) {
q1 <- base::paste("select NAME from cb_2013_us_state_20m where GEOID='", geoid,"'", sep="")
res <- RPostgres::dbSendQuery(conn, q1)
state <- RPostgres::dbFetch(res)
RPostgres::dbClearResult(res)
tableName <- base::paste(state, "_",geoid,"_ws_data_span_",span,"_na_", sep="")
} else {
q2 <- base::paste("select NAME from cb_2013_us_county_20m where GEOID='", geoid,"'", sep="")
res <- RPostgres::dbSendQuery(conn, q2)
county <- RPostgres::dbFetch(res)
RPostgres::dbClearResult(res)
q3 <- base::paste("select NAME from cb_2013_us_state_20m where GEOID='", substr(geoid, 1, 2),"'", sep="")
res <- RPostgres::dbSendQuery(conn, q3)
state <- RPostgres::dbFetch(res)
RPostgres::dbClearResult(res)
tableName <- base::paste(state, "_",county,"_",geoid,"_ws_data_span_",span,"_na_",sep="")
}# endIF/ELSE
type <- tolower( type )
tableName <- gsub(" ", "_", tableName)
tableName <- tolower( tableName )
tableName <- base::paste( tableName, type, sep="")
cat("Creating Postgres weather information table \t\t\t\t")
if (RPostgres::dbExistsTable(conn, tableName)) {
msg <- base::paste("Done - Table ", tableName, " exists.\t\t\t\t\n", sep="")
cat(msg)
cat("Check Postgres table!\n")
RPostgres::dbDisconnect(conn)
return(tableName)
} else {
q1 <- base::paste("select name as id, mindate as mindate, maxdate as maxdate, longitude as longitude, latitude as latitude from ", ws_metadata, sep="")
res <- RPostgres::dbSendQuery(conn, q1)
station <- data.frame(RPostgres::dbFetch(res))
RPostgres::dbClearResult(res)
minDate <- base::as.Date( lubridate::ymd( station$mindate[1] ) )
maxDate <- base::as.Date( lubridate::ymd( station$maxdate[1] ) )
# One is end in a new year.
ndays <- ( lubridate::int_length( lubridate::interval( base::as.Date( minDate ) , base::as.Date( maxDate )))/(3600*24))+1
dates <- as.character( seq.Date( minDate , by ="days", length.out= ndays) )
# The NOAA API only allows queries from users to retrieve data sets by year;
# thus, this code section limits each query to a different year for each iteration.
# WARNING: There is 500 limit request per day with the current token
for (i in 1:nrow( station ) ) {
ws <- station$id[i]
# startDate
sDate <- minDate
# finishDate
fDate <- minDate
# Used for testing Comment this out after testing ( maybe deprecated)
# year( sDate ) <- year( maxDate ) - 5
# year( fDate ) <- year( maxDate ) - 5
lubridate::year( fDate ) <- lubridate::year( fDate ) + 1 #Interval (sDate, fDate) is one year.
# Temporal strings attached to the dates for consistency with NOAA
ssDate <- base::paste(sDate, "T00:00:00", sep="")
ffDate <- base::paste(fDate, "T00:00:00", sep="")
estacion <- base::paste("GHCND:", ws, sep="")
weatherVar <- rnoaa::ncdc(datasetid=ghcnd, stationid=estacion, datatypeid=type, startdate=ssDate, enddate=ffDate , limit=366, token=config$token)
# Verify that available information exist
if (length(as.character( weatherVar$meta$totalCount)) == 0 ) {
msg <- base::paste("Not Data Available for station ", estacion, " for variable type: ", type, ".", sep="" )
cat( msg )
colnames( valores ) <- station$id[1:(i-1)]
RPostgres::dbWriteTable( conn, tableName, as.data.frame( valores ) )
RPostgres::dbDisconnect( conn )
return(tableName)
}#endIF
weatherYear<- weatherVar$data
repeat
{
# Replaces starting year, year( sDate ), with a value of new time series
lubridate::year( sDate ) <- lubridate::year( sDate ) + 1
# Replaces starting day, day( sDate ), with a value of new time
lubridate::day( sDate ) <- lubridate::day( fDate ) + 1
# Replaces ending year, year( fDate ), with a value of new time series
lubridate::year( fDate ) <- lubridate::year( fDate ) + 1
ssDate <- base::paste(sDate, "T00:00:00", sep="")
ffDate <- base::paste(fDate, "T00:00:00", sep="")
if ( isTRUE( lubridate::year( fDate ) < lubridate::year( maxDate ) ) ) {
weatherVar<-rnoaa::ncdc(datasetid = ghcnd, stationid = estacion, datatypeid=type, startdate = ssDate, enddate= ffDate , limit=366, token=config$token)
#Verify that available information exist
if (length(as.character( weatherVar$meta$totalCount)) == 0 ) {
msg <- base::paste("Not Data Available for statino ", estacion, " for variable type: ", type, ".\t\n", sep="" )
cat( msg )
colnames( valores ) <- station$id[1:(i-1)]
RPostgres::dbWriteTable( conn, tableName, as.data.frame( valores ) )
RPostgres::dbDisconnect( conn )
return(tableName)
}#endIF
# Delay needed since NOAA service accepts a maximum of 5 request per second.
Sys.sleep(0.2)
weatherYear <- rbind(weatherYear, weatherVar$data)
} else {
lubridate::month( fDate ) <- lubridate::month( maxDate )
lubridate::day( fDate ) <- lubridate::day( maxDate )
ssDate <- base::paste( sDate, "T00:00:00", sep="")
ffDate <- base::paste( fDate, "T00:00:00", sep="")
weatherVar<-rnoaa::ncdc(datasetid = ghcnd, stationid = estacion, datatypeid=type, startdate = ssDate, enddate= ffDate , limit=366, token=config$token)
# Verify that available information exist
if (length(as.character( weatherVar$meta$totalCount)) == 0 ) {
msg <- base::paste("Not Data Available for station ", estacion, " for variable type: ", type, ".\t\n", sep="" )
cat( msg )
colnames( valores ) <- station$id[1:(i-1)]
RPostgres::dbWriteTable( conn, tableName, as.data.frame( valores ) )
RPostgres::dbDisconnect( conn )
return(tableName)
}# endIF
weatherYear <- rbind( weatherYear, weatherVar$data)
break
}# endIF/ELSE
}# endRepeat
# TMAX = Maximum temperature (tenths of degrees C)
# promedio <- sprintf( "%.4f", mean( weatherYear$value/10 ) )
# Return year mean value
# print( promedio )
# NOAA specification of available data within a range often is not complete.
# Individual dates are missing even within an specified range.
# SOLUTIONS:
# 1) Average from the available number of samples.
# 2) Interpolation when possible
if ( i == 1) {
intervalAsDate <- base::as.Date( weatherYear$date )
# TMAX = Maximum temperature (tenths of degrees C)
intervalAsValue <- sprintf("%.4f", weatherYear$value/10)
stationDataHash <- hash::hash( intervalAsDate , intervalAsValue )
allDatesHash <- hash::hash( dates , rep(1, ndays ) ) # h1 <- hash( dates , rep(1, ndays ) )
hash::values( allDatesHash, keys=intervalAsDate ) <- intervalAsValue
v1.df <- as.data.frame( hash::has.key( dates , stationDataHash ) )
colnames(v1.df) <- "value"
# Section that guarantees that v1.df has at least one value
if ( length( which( v1.df$value == FALSE) ) > 0 ) {
sub_v1.df <- as.data.frame( subset( v1.df, value == FALSE ) )
# Dates from station which values do not exist in allDatasHash
dateKeys <- as.character( rownames( sub_v1.df ) ) #print(dateKeys)
sizeSubV1 <- nrow( sub_v1.df )
# Main change from conn_rnooa_postgresql_writeOGR_locs_and_weatherData_v2 (v2)
# is the following line. We replace average for Interpolated values.
# In v2 the vecPromedio variable hold the average value of the available data.
# Currently as can be observed, it is replaced by NA that is replaced by an empty
# cell when exported to a database with column holding a variable of type double
vecPromedio <- rep( NA, sizeSubV1 )
#Update allDatesHash values found in 'dataKeys' with NA
hash::values( allDatesHash, keys = dateKeys) <- vecPromedio
valores <- as.data.frame( hash::values( allDatesHash, keys=NULL ) )
} else { valores <- data.frame( intervalAsValue ) }#endIF/ELSE
} else {
intervalAsDate <- base::as.Date( weatherYear$date )
# TMAX = Maximum temperature (tenths of degrees C)
intervalAsValue <- sprintf( "%.4f", weatherYear$value/10 )
stationDataHash <- hash::hash( intervalAsDate , intervalAsValue )
hash::values( allDatesHash, keys=dates) <- rep(1, ndays )
hash::values( allDatesHash, keys=intervalAsDate) <- intervalAsValue
v1.df <- as.data.frame( hash::has.key( dates , stationDataHash ) )
colnames(v1.df) <- "value"
# Section guarantees that v1.df has at least one value
if ( length( which( v1.df$value == FALSE) ) > 0 ) {
sub_v1.df <- as.data.frame( subset( v1.df, value == FALSE) )
dateKeys <- as.character( rownames( sub_v1.df ) ) #print(dateKeys)
sizeSubV1 <- nrow( sub_v1.df)
# Main change from v2 is this line. We replace average for Interpolated values.
# In v2 the vecPromedio the current NA values were replaced instead by an average value.
vecPromedio <- rep( NA, sizeSubV1 )
hash::values( allDatesHash, keys=dateKeys) <- vecPromedio
subValores <- as.data.frame( hash::values( allDatesHash, keys=NULL ) )
valores <- as.data.frame( cbind( valores, subValores ) )
} else { valores <- as.data.frame( cbind( valores, intervalAsValue ) ) }#endIF/ELSE
}# endIF/ELSE
rm( stationDataHash )
}# endFOR (stick time series of different stations together)
colnames( valores ) <- station$id
msg <- base::paste("Creating table ", tableName, ".\t\t\t\t\t", sep="")
cat(msg)
RPostgres::dbWriteTable( conn, tableName, as.data.frame( valores ) )
Sys.sleep(4)
cat("Check Postgres table!")
}# endIF/ELSE
RPostgres::dbDisconnect( conn )
return(tableName)
}
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