Nothing
R/testfun.R
In RObsDat: Store your observations: R-Package to the Observations Data Model from CUAHSI
exampleCommands <- function(){
doitall <- TRUE
#doitall <- FALSE
if(doitall){
###ADD DATA VALUES
try(getMetadata("Site"), silent=TRUE)
addSite(Code="testSpatialPoints", Name="Virtual test site", x=25, y=56,
LatLongDatum="WGS84", Elevation=350, State="Germany")
addVariable(Name="Temperature, transducer signal", Unit="degree celsius", ValueType="Field Observation",
GeneralCategory="Hydrology", Code="test_temp")
addQualityControlLevel(ID=2,Code="test_ok", Definition="The default values")
addISOMetadata(TopicCategory="Unknown", Title="Testdata",
Abstract="This data is created to test the functions of RObsDat")
addSource(Organization="Your Org", SourceDescription="Madeup data",
SourceLink="RObsDat Documentation", ContactName="Yourself",
Metadata="Testdata")
example.data <- xts(1:40, seq(as.POSIXct("2014-01-01", tz="UTC"),
as.POSIXct("2014-02-09", tz="UTC"), length.out=40))
example.data[40] <- 30
example.data[35] <- 22
addDataValues(example.data[1:20], Site="testSpatialPoints", Variable="test_temp",
Source="Madeup", QualityControlLevel="test_ok")
#Avoid duplicates autmatically
addDataValues(example.data, Site="testSpatialPoints", Variable="test_temp",
Source="Madeup", QualityControlLevel="test_ok")
### TEST STPLOT
testSiteData <- getDataValues(Site="test")
stplot(testSiteData, mode="ts")
### TEST SELECTION
#test selection Main Data
selectedData1 = testSiteData[, 10:20]
selectedData2 = testSiteData[,,'Temperatur, transducer signal']
#test selection for other Slots
selectedData3 = testSiteData@ValueIDs[,10:20]
selectedData4 = testSiteData@ValueIDs[,,'Temperatur, transducer signal']
### VERSION MANAGEMENT
### UPDATE
to.correct <- which(testSiteData@data > 30)
testSiteData@data[to.correct,] <- 20
testSiteData@data[39,] <- 32
updateDataValues(testSiteData, "Correction of wrong value")
ver2 <- testSiteData
ver2@data[10:13,] <- 60
updateDataValues(ver2, "Changing more data")
ver3 <- testSiteData
ver3@data[30:32,] <- 33
updateDataValues(ver3, "Ups, I used 60 instead of 33 by mistake")
### DELETE
# via ValueID:
deleteDataValues(testSiteData@ValueIDs[,36], "And finally remove one value via ID")
deleteDataValues(testSiteData@ValueIDs[,20:26], "And finally remove several values via ID")
# via direct access
deleteDataValues(testSiteData[,30:35], "And finally remove multi values via ID")
to.delete <- testSiteData@data == 60
if(any(to.delete)){
deleteDataValues(testSiteData@ValueIDs[,to.delete], "And finally remove several value")
}
### GET OLD VERSION
getDataVersions()
#show no deleted values
versionQuery <- getDataValues(Site=1, VersionID=1)
stplot(versionQuery, mode = 'ts')
versionQuery <- getDataValues(Site=1, VersionID=2)
stplot(versionQuery, mode = 'ts')
#show deleted values
versionQuery <- getDataValues(Site=1, VersionID=2, show.deleted=TRUE)
stplot(versionQuery, mode = 'ts')
}
}
cleanupMySQL <- function(con){
for(i in 1:6){
try(dbGetQuery(con, "DROP TABLE if exists DataValues, DataValuesRepository;"), silent=TRUE)
try(dbGetQuery(con, "DROP TABLE if exists `Categories`, `DerivedFrom`, `GeneralCategoryCV`, `GroupDescriptions`, `Groups`, `ISOMetadata`, `LabMethods`, `Methods`, `ODMVersion`, `OffsetTypes`, `Qualifiers`, `QualityControlLevels`, `SampleMediumCV`, `Samples`, `SampleTypeCV`, `SeriesCatalog`, `Sites`, `Sources`, `SpatialReferences`, `SpeciationCV`, `TopicCategoryCV`, `Units`, `ValueTypeCV`, `VariableNameCV`, `Variables`, `Versions`, `VerticalDatumCV`, CensorCodeCV, DataTypeCV , DataValues, Synonyms;") , silent=TRUE)
}
}
longExample <- function() {
options(error = recover)
if(!as.logical(getOption("testLongExample", FALSE))){
cat("Not testing long example. Use 'options(testLongExample = TRUE)' to do so\n")
return()
}
# prepare countries --> only once!
options(geonamesUsername="cite.all")
if(!requireNamespace("geonames", quietly=TRUE)){
cat("Please install geonames with install.packages(geonames) to run this test\n")
return()
}
cat("Importing Geonames from web\n")
all.countries <- geonames::GNcountryInfo(lang="EN")
cat("Importing other Geonames\n")
addSite(Code=all.countries$isoAlpha3, Name=all.countries$countryName, x=as.numeric(all.countries$west), y=as.numeric(all.countries$north),
LatLongDatum=rep("WGS84", NROW(all.countries)), Elevation=rep(0, NROW(all.countries)))
addSite(Code="ANT", Name="Netherlands Antilles", x=0, y=0,
LatLongDatum="WGS84", Elevation=0)
addSite(Code="YUG", Name="Yugoslavia", x=0, y=0,
LatLongDatum="WGS84", Elevation=0)
addSite(Code="WORLD", Name="The World", x=0, y=0, LatLongDatum="WGS84",
Elevation=0)
#############
## Data Sources
cat("Adding other Metadata\n")
addISOMetadata(TopicCategory = "farming", Title = "FAOSTAT", Abstract = "FAOSTAT time-series and cross sectional data relating to food and agriculture. Detailed country-level data
on food consumption and production", ProfileVersion = "Unknown", MetadataLink = NULL)
addSource(Organization="FAOSTAT", SourceDescription="FAOSTAT time-series and cross sectional data relating to food and agriculture",
SourceLink="faostat.fao.org", ContactName="LongExample Lissner",Metadata="FAOSTAT")
addISOMetadata(TopicCategory = "society", Title = "IDP", Abstract = "Institutional Profiles Database: indicators on institutional characteristics of 123 countries",
ProfileVersion = "Unknown", MetadataLink = NULL)
addSource(Organization="IPD", SourceDescription= "IDP database", SourceLink="www.cepii.fr/anglaisgraph/bdd/institutions.htm", ContactName="LongExample Lissner", Metadata = "IDP")
addISOMetadata(TopicCategory = "society", Title = "IIASA", Abstract = "IIASA downscaled population projections for IPCC SRES",
ProfileVersion = "Unknown", MetadataLink = NULL)
addSource(Organization="IIASA", SourceDescription= "IIASA downscaled population projections for IPCC SRES", SourceLink="http://ciesin.columbia.edu/datasets/downscaled/",
ContactName="LongExample Lissner", Metadata = "IIASA")
addISOMetadata(TopicCategory = "environment", Title = "WATCHproject", Abstract = "Results from the WATCH project",
ProfileVersion = "Unknown", MetadataLink = NULL)
addSource(Organization="WATCH Project", SourceDescription="Results from the WATCH project", SourceLink="ftp://watch-r:wWread77@ftp.iiasa.ac.at/", ContactName="LongExample Lissner",Metadata="WATCHproject")
addISOMetadata(TopicCategory = "health", Title = "WHO", Abstract = "Data on global health", ProfileVersion = "Unknown", MetadataLink = NULL)
addSource(Organization="WHO", SourceDescription="Data on global health", SourceLink="http://apps.who.int/ghodata/#", ContactName="LongExample",Metadata="WHO")
addISOMetadata(TopicCategory = "health", Title = "UNDP", Abstract = "Data on human development (Human Development Report)",
ProfileVersion = "Unknown", MetadataLink = NULL)
addSource(Organization="UNDP", SourceDescription="Data on human development (Human Development Report)", SourceLink="hdrstats.undp.org/en", ContactName="LongExample",Metadata="UNDP")
addISOMetadata(TopicCategory = "health", Title = "UNICEF", Abstract = "Childinfo - Monitoring the situation of women and children",
ProfileVersion = "Unknown", MetadataLink = NULL)
addSource(Organization="UNICEF", SourceDescription="Childinfo - Monitoring the situation of women and children",
SourceLink="http://www.childinfo.org/water_data.php", ContactName="LongExample", Metadata="UNICEF")
addISOMetadata(TopicCategory = "utilitiesCommunication", Title = "IEA", Abstract = "International Energy Agency - Information on electricity and energy generation and use",
ProfileVersion = "Unknown", MetadataLink = NULL)
addSource(Organization="IEA", SourceDescription="International Energy Agency", SourceLink="http://www.iea.org/weo/electricity.asp", ContactName="LongExample", Metadata="IEA")
#################
### Quality Control Levels
addQualityControlLevel(ID=6,Code="ok", Definition="The default")
#############
## Units
addUnits(Name="cubic kilometers per year",Type="water availability",Abbreviation="km^3/yr")
addUnits(Name="Kilocalories",Type="Energy",Abbreviation="kcal")
addUnits(Name="m3/cap/yr",Type="Water availability",Abbreviation="m3/cap/yr")
cat("Adding Synonyms\n")
importSynonyms(system.file("longexample/syn.txt", package="RObsDat"))
cat("Metadata prepared\n##############################\n")
#############
## Data
#############
###################
# Total annual surface and subsurface run-off from WaterGAP; 1981-2010 and 2011-2040, Scenario A2, three models --> still in km^3/yr, needs to be calculated per cap
cat("Importing water availability\n")
wateravail.2000 <- read.csv(system.file("longexample/ofile_watergap_wfdnat_qstot_mean_1971_2000.csv", package="RObsDat"),header=TRUE,sep=",",stringsAsFactors=FALSE,na.strings="NA",dec=".")
wateravail.2030_ipsl <- read.csv(system.file("longexample/ofile_watergap_ipsla2nat_qstot_mean_2011_2040.csv", package="RObsDat"),header=TRUE,sep=",",stringsAsFactors=FALSE,na.strings="NA",dec=".")
wateravail.2030_echam <- read.csv(system.file("longexample/ofile_watergap_echama2nat_qstot_mean_2011_2040.csv", package="RObsDat"),header=TRUE,sep=",",stringsAsFactors=FALSE,na.strings="NA",dec=".")
wateravail.2030_cncm3 <- read.csv(system.file("longexample/ofile_watergap_cncm3a2nat_qstot_mean_2011_2040.csv", package="RObsDat"),header=TRUE,sep=",",stringsAsFactors=FALSE,na.strings="NA",dec=".")
wateravail.2060_ipsl <- read.csv(system.file("longexample/ofile_watergap_ipsla2nat_qstot_mean_2041_2070.csv", package="RObsDat"),header=TRUE,sep=",",stringsAsFactors=FALSE,na.strings="NA",dec=".")
wateravail.2060_echam <- read.csv(system.file("longexample/ofile_watergap_echama2nat_qstot_mean_2041_2070.csv", package="RObsDat"),header=TRUE,sep=",",stringsAsFactors=FALSE,na.strings="NA",dec=".")
wateravail.2060_cncm3 <- read.csv(system.file("longexample/ofile_watergap_cncm3a2nat_qstot_mean_2041_2070.csv", package="RObsDat"),header=TRUE,sep=",",stringsAsFactors=FALSE,na.strings="NA",dec=".")
wateravail.2090_ipsl <- read.csv(system.file("longexample/ofile_watergap_ipsla2nat_qstot_mean_2071_2100.csv", package="RObsDat"),header=TRUE,sep=",",stringsAsFactors=FALSE,na.strings="NA",dec=".")
wateravail.2090_echam <- read.csv(system.file("longexample/ofile_watergap_echama2nat_qstot_mean_2071_2100.csv", package="RObsDat"),header=TRUE,sep=",",stringsAsFactors=FALSE,na.strings="NA",dec=".")
wateravail.2090_cncm3 <- read.csv(system.file("longexample/ofile_watergap_cncm3a2nat_qstot_mean_2071_2100.csv", package="RObsDat"),header=TRUE,sep=",",stringsAsFactors=FALSE,na.strings="NA",dec=".")
water.dat <- cbind(wateravail.2000,wateravail.2030_ipsl[,2],wateravail.2060_ipsl[,2],wateravail.2090_ipsl[,2],wateravail.2030_echam[,2],wateravail.2060_echam[,2],
wateravail.2090_echam[,2],wateravail.2030_cncm3[,2],wateravail.2060_cncm3[,2],wateravail.2090_cncm3[,2])
water.date <- strptime(c("1990-01-01","2030-01-01","2060-01-01","2090-01-01","2030-01-01","2060-01-01","2090-01-01","2030-01-01","2060-01-01","2090-01-01"), "%Y-%m-%d", tz="UTC")
water.vars <- c("water.available.cur","water.available.ipsl","water.available.ipsl","water.available.ipsl","water.available.echam","water.available.echam","water.available.echam",
"water.available.cncm3","water.available.cncm3","water.available.cncm3")
addCV("VariableName", term="water.available", definition="Total annual renewable water resources per country in km^3")
addVariable(Code="water.available.cur", Name="water.available", Unit="km^3/yr")
addVariable(Code="water.available.ipsl", Name="water.available", Unit="km^3/yr")
addVariable(Code="water.available.echam", Name="water.available", Unit="km^3/yr")
addVariable(Code="water.available.cncm3", Name="water.available", Unit="km^3/yr")
#Build synonym table
getID("Site", water.dat[,1])
#Import
addDataValues(Date=water.date, Value=water.dat[,2:11], Site = water.dat[,1], Variable = water.vars, Source = "WATCH Project", QualityControlLevel = "ok" )
###################
# Population projections SRES scenario A2
cat("Importing Population\n")
population.dat <- read.csv2(system.file("longexample/Natl_Pop_Proj_A2.csv", package="RObsDat"), header=TRUE, stringsAsFactors=FALSE, na.strings=c("na","nodata","-"), dec=".", encoding="utf8")
population.date <- strptime(c("1990-01-01","1995-01-01","2000-01-01","2005-01-01","2010-01-01","2015-01-01","2020-01-01","2025-01-01","2030-01-01","2035-01-01",
"2040-01-01","2045-01-01","2050-01-01","2055-01-01","2060-01-01","2065-01-01","2070-01-01","2075-01-01","2080-01-01","2085-01-01","2090-01-01","2095-01-01",
"2100-01-01"),"%Y-%m-%d", tz="UTC")
population.vars <- c("population.A2")
addCV("VariableName", term="population", definition="Projected number of inhabitants per country according to SRES")
addVariable(Code="population.A2", Name="population", Unit="count")
getID("Site", population.dat[,1])
#Import
addDataValues(Date=population.date, Value=population.dat[,2:24], Site = population.dat[,1], Variable = population.vars, Source = "IIASA", QualityControlLevel = "6" )
################
# Grand total: calorie availability/cap/day (2009 )from FAOSTAT
cat("Importing Calories\n")
calories.dat <- read.csv2(system.file("longexample/calorie_availability_2009_new.csv", package="RObsDat"), header=TRUE, dec=".", stringsAsFactors=FALSE, sep=";")
calories.date <- strptime("2009-01-01", "%Y-%m-%d")
calories.vars <- c("calories.total")
addCV("VariableName", term="calories.total", definition="Total available calories/cap/day")
addVariable(Code="calories.total", Name="Total available calories/cap/day", Unit="kcal")
#Build synonym table
getID("Site", calories.dat[,1])
#Import
addDataValues(Date=calories.date, Value=calories.dat[,2], Site = calories.dat[,1], Variable = calories.vars, Source = "FAOSTAT", QualityControlLevel = 6 )
################
# MDER: Minimum dietary requirements/cap/day
cat("Importing MDER\n")
mder.dat <- read.csv2(system.file("longexample/MinimumDietaryEnergyRequirement.csv", package="RObsDat"), header=TRUE, dec=".", stringsAsFactors=FALSE, sep=";")
mder.date <- strptime("2008-01-01", "%Y-%m-%d")
mder.vars <- c("mder")
addCV("VariableName", term="mder", definition="Minimum dietary requirement: calories/cap/day")
bla <- getMetadata("Variable", Name="dietary")
print(bla)
addVariable(Code="mder", Name="Minimum dietary requirement: calories/cap/day", Unit="kcal")
#Build synonym table
getID("Site", mder.dat[,2])
#Import
addDataValues(Date=mder.date, Value=mder.dat[,7], Site = mder.dat[,2], Variable = mder.vars, Source = "FAOSTAT", QualityControlLevel = 6 )
#########################
## IDP database, selected indicators: Population.participation;public.freedom_civil.society;stability.of.political.system;
## Domestic.public.security;Functioning.of.justice.system;Social.inclusion;geographic.coverage.of.public.services;Institutional.solidarity;
## Traditional.solidarity;Micro.lending;Existence/absence.of.labour.legislation.and.measures;weak.employment.contract.rigidity
########
cat("Importing Social Variables\n")
IDP.dat <- read.csv2(system.file("longexample/IPD_2009_selected_indicators1.csv", package="RObsDat"), header=TRUE, stringsAsFactors=FALSE, sep=",")
IDP.date <- strptime("2009-01-01", "%Y-%m-%d", tz="UTC")
IDP.vars <- c(NA,"population.participation","public.freedom_civil.society","stability.of.political.system","Domestic.public.security","Functioning.of.justice.system",
"Social.inclusion","geographic.coverage.of.public.services","Institutional.solidarity","Traditional.solidarity","Micro.lending","labour.legislation",
"employment.contract")
addCV("VariableName", term="Population.participation", definition="Population Participation")
addCV("VariableName", term="public.freedom_civil.society", definition="Public Freedom and Civil Society")
addCV("VariableName", term="stability.of.political.system", definition="Statibility of Political System")
addCV("VariableName", term="Domestic.public.security", definition="Domestic Public Security")
addCV("VariableName", term="Functioning.of.justice.system", definition="Functioning of Justice System")
addCV("VariableName", term="Social.inclusion", definition="Social Inclusion")
addCV("VariableName", term="geographic.coverage.of.public.services", definition="Geographic Coverage of Public Services")
addCV("VariableName", term="Institutional.solidarity", definition="Institutional Solidarity")
addCV("VariableName", term="Traditional.solidarity", definition="Traditional Solidarity")
addCV("VariableName", term="Micro.lending", definition="Micro Lending")
addCV("VariableName", term="labour.legislation", definition="Labour Legislation")
addCV("VariableName", term="employment.contract", definition="Weak Employment Contracts")
addVariable(Code="population.participation", Name="Population.participation", Unit="categorical")
addVariable(Code="public.freedom_civil.society", Name="public.freedom_civil.society", Unit="categorical")
addVariable(Code="stability.of.political.system", Name="stability.of.political.system", Unit="categorical")
addVariable(Code="Domestic.public.security", Name="Domestic.public.security", Unit="categorical")
addVariable(Code="Functioning.of.justice.system", Name="Functioning.of.justice.system", Unit="categorical")
addVariable(Code="Social.inclusion", Name="Social.inclusion", Unit="categorical")
addVariable(Code="geographic.coverage.of.public.services", Name="geographic.coverage.of.public.services", Unit="categorical")
addVariable(Code="Institutional.solidarity", Name="Institutional.solidarity", Unit="categorical")
addVariable(Code="Traditional.solidarity", Name="Traditional.solidarity", Unit="categorical")
addVariable(Code="Micro.lending", Name="Micro.lending", Unit="categorical")
addVariable(Code="labour.legislation", Name="labour.legislation", Unit="categorical")
addVariable(Code="employment.contract", Name="employment.contract", Unit="categorical")
#IDPcode.dat <- merge(IDP.dat, all.countries, by.x = "countrycode", by.y = "isoAlpha3")
#Build synonym table
getID("Site", IDP.dat[,1])
#Import
addDataValues(Date=IDP.date, Value=IDP.dat[,2:13], Site = IDP.dat[,1], Variable = IDP.vars[2:13], Source = "IDP database", QualityControlLevel = 6)
###################
# Life expectancy at birth as used in HDI
# added to "database_22_09_2011.db" and checked by LongExample
cat("Importing Life expect\n")
lifeexp.dat <- read.csv2(system.file("longexample/life_expectancy_HDI_2009.csv", package="RObsDat"), header=TRUE, stringsAsFactors=FALSE, sep=";", dec=".")
lifeexp.date <- strptime("2009-01-01", "%Y-%m-%d", tz="UTC")
lifeexp.vars <- c("lifeexp")
addCV("VariableName", term="lifeexp", definition="Life expectancy at birth")
addVariable(Code="lifeexp", Name="lifeexp", Unit="yr")
#Build synonym table
getID("Site", lifeexp.dat[,1])
#Import
addDataValues(Date=lifeexp.date, Value=lifeexp.dat[,2], Site = lifeexp.dat[,1], Variable = lifeexp.vars, Source = "UNDP", QualityControlLevel = 6 )
###################
# PM10 concentration, lastest values: 2007 - 2010
cat("Importing PM10\n")
pm10.dat <- read.csv2(system.file("longexample/pm10_latest_2009_Worldbank.csv", package="RObsDat"), header=TRUE, stringsAsFactors=FALSE, sep=",", dec=".")
pm10.date <- strptime("2009-01-01", "%Y-%m-%d", tz="UTC")
pm10.vars <- c("pm10")
addCV("VariableName", term="pm10", definition="Mean yearly PM10 concentrations")
addVariable(Code="pm10", Name="pm10", Unit="ug/m^3")
#Build synonym table
getID("Site", pm10.dat[,1])
#Import
addDataValues(Date=pm10.date, Value=pm10.dat[,2], Site = pm10.dat[,1], Variable = pm10.vars, Source = "WHO", QualityControlLevel = 6 )
###################
# Solid fuel use
cat("Importing Solid Fuel\n")
solid.fuel.dat <- read.csv2(system.file("longexample/solid_fuel_use2010.csv", package="RObsDat"), header=TRUE, stringsAsFactors=FALSE, sep=",", dec=".")
solid.fuel.date <- strptime("2010-01-01", "%Y-%m-%d", tz="UTC")
solid.fuel.vars <- c("solid.fuel")
addCV("VariableName", term="solid.fuel", definition="Mean yearly PM10 concentrations")
addVariable(Code="solid.fuel", Name="solid.fuel", Unit="%")
#Build synonym table
getID("Site", solid.fuel.dat[,1])
#Import
addDataValues(Date=solid.fuel.date, Value=solid.fuel.dat[,2], Site = solid.fuel.dat[,1], Variable = solid.fuel.vars, Source = "WHO", QualityControlLevel = 6 )
########################
## Water Data from Aquastat for Australia Analysis
cat("Importing Sanitation\n")
watsan.dat <- read.csv2(system.file("longexample/access_sanitation2010.csv", package="RObsDat"), header=TRUE, na.strings=c("na","nodata","-"), stringsAsFactors=TRUE, sep=",")
watsan.date <- strptime("2010-01-01", "%Y-%m-%d", tz="UTC")
watsan.vars <- c("wat.total.improved","wat.piped","wat.other.improved","wat.other.unimproved","san.total.improved","san.shared","san.other.unimproved","san.open.def")
addCV("VariableName", term="wat.total.improved", definition="Access to improved water sources (total)")
addCV("VariableName", term="wat.piped", definition="Water piped on premises")
addCV("VariableName", term="wat.other.improved", definition="Access to other improved water sources")
addCV("VariableName", term="wat.other.unimproved", definition="Unimproved water sources")
addCV("VariableName", term="san.total.improved", definition="Access to improved sanitation facilities")
addCV("VariableName", term="san.shared", definition="Ahred sanitation facilities")
addCV("VariableName", term="san.other.unimproved", definition="Unimproved sanitation access")
addCV("VariableName", term="san.open.def", definition="Open defecation")
addVariable(Code="wat.total.improved", Name="wat.total.improved", Unit="%")
addVariable(Code="wat.piped", Name="wat.piped", Unit="%")
addVariable(Code="wat.other.improved", Name="wat.other.improved", Unit="%")
addVariable(Code="wat.other.unimproved", Name="wat.other.unimproved", Unit="%")
addVariable(Code="san.total.improved", Name="san.total.improved", Unit="%")
addVariable(Code="san.shared", Name="san.shared", Unit="%")
addVariable(Code="san.other.unimproved", Name="san.other.unimproved", Unit="%")
addVariable(Code="san.open.def", Name="san.open.def", Unit="%")
getID("Site", watsan.dat[,1])
#Build synonym table
#Import
addDataValues(Date=watsan.date, Value=watsan.dat[,2:9], Site = watsan.dat[,1], Variable = watsan.vars, Source = "UNICEF", QualityControlLevel = 6 )
#######################
# Health care workforce --> is per 100000! needs to be converted
cat("Importing Health\n")
HCW.dat <- read.csv(system.file("longexample/healthcare_workforce_sum1000_latest.csv", package="RObsDat"), header=TRUE, stringsAsFactors=FALSE, sep=",")
HCW.date <- strptime(paste(HCW.dat[,2],"-01-01", sep=""), "%Y-%m-%d", tz="UTC")
HCW.vars <- c("health.workers")
addCV("VariableName", term="health.workers", definition="Density of health care work force (per 100000 population)")
addVariable(Code="health.workers", Name="health.workers", Unit="#")
#Build synonym table
getID("Site", HCW.dat[,1])
#Import
addDataValues(Date=HCW.date, Value=HCW.dat[,3], Site = HCW.dat[,1], Variable = HCW.vars, Source = "WHO", QualityControlLevel = 6 )
###################
# Under 5 mortality
# added to "database_22_09_2011.db" and checked by LongExample
u5.dat <- read.csv2(system.file("longexample/under5mortality_rate.csv", package="RObsDat"), header=TRUE, stringsAsFactors=FALSE, sep=",")
u5.date <- strptime("2010-01-01","%Y-%m-%d", tz="UTC")
u5.vars <- c("under5.mort")
addCV("VariableName", term="under5.mort", definition="Probability of dying by age 5 per 1000 live births")
addVariable(Code="under5.mort", Name="under5.mort", Unit="#")
#Build synonym table
getID("Site", u5.dat[,1])
#Import
addDataValues(Date=u5.date, Value=u5.dat[,2], Site = u5.dat[,1], Variable = u5.vars, Source = "WHO", QualityControlLevel = 6 )
###################
# Electrification rate
# new version of table: countries with HDI very high set to 99, HDI high to 95
# fully added to "database_22_09_2011.db" and checked by LongExample --> values updated by Lisei, added 2/12/2011
elect.dat <- read.csv2(system.file("longexample/electricity_rate_2008_new.csv", package="RObsDat"), header=TRUE, stringsAsFactors=FALSE, sep=";", dec=".")
elect.dat <- read.csv2(system.file("longexample/electrification_2009.csv", package="RObsDat"), header=TRUE, stringsAsFactors=FALSE, sep=",", dec=".")
elect.date <- strptime("2008-01-01", "%Y-%m-%d", tz="UTC")
elect.date <- strptime("2009-01-01", "%Y-%m-%d", tz="UTC")
elect.vars <- c("electrate.total")
addCV("VariableName", term="electrate.total", definition="Electrification rate of total population (percent of population with access to electricity)")
addVariable(Code="electrate.total", Name="electrate.total", Unit="%")
#Build synonym table
getID("Site", elect.dat[,1])
#Import
addDataValues(Date=elect.date, Value=elect.dat[,2], Site = elect.dat[,1], Variable = elect.vars, Source = "IEA", QualityControlLevel = 6 )
#########################
## Mean years of schooling and expected years of schooling as used in the new HDI
######## fully added to "database_22_09_2011.db" and checked by LongExample
schooling.dat <- read.csv2(system.file("longexample/years_of_schooling_HDI_2009.csv", package="RObsDat"), header=TRUE, stringsAsFactors=FALSE, sep=";", dec=".", na.strings="..")
schooling.date <- strptime("2009-01-01", "%Y-%m-%d", tz="UTC")
schooling.vars <- c("mean_schooling", "expected_schooling")
addCV("VariableName", term="mean_schooling", definition="Mean Years of Schooling")
addCV("VariableName", term="expected_schooling", definition="Expected mean Years of Schooling")
addVariable(Code="mean_schooling", Name="mean_schooling", Unit="yr")
addVariable(Code="expected_schooling", Name="expected_schooling", Unit="yr")
#Build synonym table
getID("Site", schooling.dat[,1])
#Import
addDataValues(Date=schooling.date, Value=schooling.dat[,2:3], Site = schooling.dat[,1], Variable = schooling.vars, Source = "UNDP", QualityControlLevel = 6 )
####################
req.vars.rest <- c("population.participation","public.freedom_civil.society","stability.of.political.system","Domestic.public.security","Functioning.of.justice.system",
"Social.inclusion","Institutional.solidarity","Traditional.solidarity","Micro.lending","labour.legislation","employment.contract","calories.total",
"mder","lifeexp","pm10","solid.fuel","wat.piped","wat.other.improved","wat.other.unimproved","san.total.improved",
"san.shared","san.other.unimproved","san.open.def","health.workers","under5.mort","electrate.total","mean_schooling","expected_schooling")
req.vars.water <- c("population.A2","water.available.cur","water.available.ipsl","water.available.echam","water.available.cncm3")
all.water.dat <- getDataValues(Variable=req.vars.water)
# all.data <- merge(all.water.dat, rest.data, by.x = "contryCode", by.y = "countryCode")
}
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RObsDat documentation built on May 2, 2019, 5:55 p.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
exampleCommands <- function(){
doitall <- TRUE
#doitall <- FALSE
if(doitall){
###ADD DATA VALUES
try(getMetadata("Site"), silent=TRUE)
addSite(Code="testSpatialPoints", Name="Virtual test site", x=25, y=56,
LatLongDatum="WGS84", Elevation=350, State="Germany")
addVariable(Name="Temperature, transducer signal", Unit="degree celsius", ValueType="Field Observation",
GeneralCategory="Hydrology", Code="test_temp")
addQualityControlLevel(ID=2,Code="test_ok", Definition="The default values")
addISOMetadata(TopicCategory="Unknown", Title="Testdata",
Abstract="This data is created to test the functions of RObsDat")
addSource(Organization="Your Org", SourceDescription="Madeup data",
SourceLink="RObsDat Documentation", ContactName="Yourself",
Metadata="Testdata")
example.data <- xts(1:40, seq(as.POSIXct("2014-01-01", tz="UTC"),
as.POSIXct("2014-02-09", tz="UTC"), length.out=40))
example.data[40] <- 30
example.data[35] <- 22
addDataValues(example.data[1:20], Site="testSpatialPoints", Variable="test_temp",
Source="Madeup", QualityControlLevel="test_ok")
#Avoid duplicates autmatically
addDataValues(example.data, Site="testSpatialPoints", Variable="test_temp",
Source="Madeup", QualityControlLevel="test_ok")
### TEST STPLOT
testSiteData <- getDataValues(Site="test")
stplot(testSiteData, mode="ts")
### TEST SELECTION
#test selection Main Data
selectedData1 = testSiteData[, 10:20]
selectedData2 = testSiteData[,,'Temperatur, transducer signal']
#test selection for other Slots
selectedData3 = testSiteData@ValueIDs[,10:20]
selectedData4 = testSiteData@ValueIDs[,,'Temperatur, transducer signal']
### VERSION MANAGEMENT
### UPDATE
to.correct <- which(testSiteData@data > 30)
testSiteData@data[to.correct,] <- 20
testSiteData@data[39,] <- 32
updateDataValues(testSiteData, "Correction of wrong value")
ver2 <- testSiteData
ver2@data[10:13,] <- 60
updateDataValues(ver2, "Changing more data")
ver3 <- testSiteData
ver3@data[30:32,] <- 33
updateDataValues(ver3, "Ups, I used 60 instead of 33 by mistake")
### DELETE
# via ValueID:
deleteDataValues(testSiteData@ValueIDs[,36], "And finally remove one value via ID")
deleteDataValues(testSiteData@ValueIDs[,20:26], "And finally remove several values via ID")
# via direct access
deleteDataValues(testSiteData[,30:35], "And finally remove multi values via ID")
to.delete <- testSiteData@data == 60
if(any(to.delete)){
deleteDataValues(testSiteData@ValueIDs[,to.delete], "And finally remove several value")
}
### GET OLD VERSION
getDataVersions()
#show no deleted values
versionQuery <- getDataValues(Site=1, VersionID=1)
stplot(versionQuery, mode = 'ts')
versionQuery <- getDataValues(Site=1, VersionID=2)
stplot(versionQuery, mode = 'ts')
#show deleted values
versionQuery <- getDataValues(Site=1, VersionID=2, show.deleted=TRUE)
stplot(versionQuery, mode = 'ts')
}
}
cleanupMySQL <- function(con){
for(i in 1:6){
try(dbGetQuery(con, "DROP TABLE if exists DataValues, DataValuesRepository;"), silent=TRUE)
try(dbGetQuery(con, "DROP TABLE if exists `Categories`, `DerivedFrom`, `GeneralCategoryCV`, `GroupDescriptions`, `Groups`, `ISOMetadata`, `LabMethods`, `Methods`, `ODMVersion`, `OffsetTypes`, `Qualifiers`, `QualityControlLevels`, `SampleMediumCV`, `Samples`, `SampleTypeCV`, `SeriesCatalog`, `Sites`, `Sources`, `SpatialReferences`, `SpeciationCV`, `TopicCategoryCV`, `Units`, `ValueTypeCV`, `VariableNameCV`, `Variables`, `Versions`, `VerticalDatumCV`, CensorCodeCV, DataTypeCV , DataValues, Synonyms;") , silent=TRUE)
}
}
longExample <- function() {
options(error = recover)
if(!as.logical(getOption("testLongExample", FALSE))){
cat("Not testing long example. Use 'options(testLongExample = TRUE)' to do so\n")
return()
}
# prepare countries --> only once!
options(geonamesUsername="cite.all")
if(!requireNamespace("geonames", quietly=TRUE)){
cat("Please install geonames with install.packages(geonames) to run this test\n")
return()
}
cat("Importing Geonames from web\n")
all.countries <- geonames::GNcountryInfo(lang="EN")
cat("Importing other Geonames\n")
addSite(Code=all.countries$isoAlpha3, Name=all.countries$countryName, x=as.numeric(all.countries$west), y=as.numeric(all.countries$north),
LatLongDatum=rep("WGS84", NROW(all.countries)), Elevation=rep(0, NROW(all.countries)))
addSite(Code="ANT", Name="Netherlands Antilles", x=0, y=0,
LatLongDatum="WGS84", Elevation=0)
addSite(Code="YUG", Name="Yugoslavia", x=0, y=0,
LatLongDatum="WGS84", Elevation=0)
addSite(Code="WORLD", Name="The World", x=0, y=0, LatLongDatum="WGS84",
Elevation=0)
#############
## Data Sources
cat("Adding other Metadata\n")
addISOMetadata(TopicCategory = "farming", Title = "FAOSTAT", Abstract = "FAOSTAT time-series and cross sectional data relating to food and agriculture. Detailed country-level data
on food consumption and production", ProfileVersion = "Unknown", MetadataLink = NULL)
addSource(Organization="FAOSTAT", SourceDescription="FAOSTAT time-series and cross sectional data relating to food and agriculture",
SourceLink="faostat.fao.org", ContactName="LongExample Lissner",Metadata="FAOSTAT")
addISOMetadata(TopicCategory = "society", Title = "IDP", Abstract = "Institutional Profiles Database: indicators on institutional characteristics of 123 countries",
ProfileVersion = "Unknown", MetadataLink = NULL)
addSource(Organization="IPD", SourceDescription= "IDP database", SourceLink="www.cepii.fr/anglaisgraph/bdd/institutions.htm", ContactName="LongExample Lissner", Metadata = "IDP")
addISOMetadata(TopicCategory = "society", Title = "IIASA", Abstract = "IIASA downscaled population projections for IPCC SRES",
ProfileVersion = "Unknown", MetadataLink = NULL)
addSource(Organization="IIASA", SourceDescription= "IIASA downscaled population projections for IPCC SRES", SourceLink="http://ciesin.columbia.edu/datasets/downscaled/",
ContactName="LongExample Lissner", Metadata = "IIASA")
addISOMetadata(TopicCategory = "environment", Title = "WATCHproject", Abstract = "Results from the WATCH project",
ProfileVersion = "Unknown", MetadataLink = NULL)
addSource(Organization="WATCH Project", SourceDescription="Results from the WATCH project", SourceLink="ftp://watch-r:wWread77@ftp.iiasa.ac.at/", ContactName="LongExample Lissner",Metadata="WATCHproject")
addISOMetadata(TopicCategory = "health", Title = "WHO", Abstract = "Data on global health", ProfileVersion = "Unknown", MetadataLink = NULL)
addSource(Organization="WHO", SourceDescription="Data on global health", SourceLink="http://apps.who.int/ghodata/#", ContactName="LongExample",Metadata="WHO")
addISOMetadata(TopicCategory = "health", Title = "UNDP", Abstract = "Data on human development (Human Development Report)",
ProfileVersion = "Unknown", MetadataLink = NULL)
addSource(Organization="UNDP", SourceDescription="Data on human development (Human Development Report)", SourceLink="hdrstats.undp.org/en", ContactName="LongExample",Metadata="UNDP")
addISOMetadata(TopicCategory = "health", Title = "UNICEF", Abstract = "Childinfo - Monitoring the situation of women and children",
ProfileVersion = "Unknown", MetadataLink = NULL)
addSource(Organization="UNICEF", SourceDescription="Childinfo - Monitoring the situation of women and children",
SourceLink="http://www.childinfo.org/water_data.php", ContactName="LongExample", Metadata="UNICEF")
addISOMetadata(TopicCategory = "utilitiesCommunication", Title = "IEA", Abstract = "International Energy Agency - Information on electricity and energy generation and use",
ProfileVersion = "Unknown", MetadataLink = NULL)
addSource(Organization="IEA", SourceDescription="International Energy Agency", SourceLink="http://www.iea.org/weo/electricity.asp", ContactName="LongExample", Metadata="IEA")
#################
### Quality Control Levels
addQualityControlLevel(ID=6,Code="ok", Definition="The default")
#############
## Units
addUnits(Name="cubic kilometers per year",Type="water availability",Abbreviation="km^3/yr")
addUnits(Name="Kilocalories",Type="Energy",Abbreviation="kcal")
addUnits(Name="m3/cap/yr",Type="Water availability",Abbreviation="m3/cap/yr")
cat("Adding Synonyms\n")
importSynonyms(system.file("longexample/syn.txt", package="RObsDat"))
cat("Metadata prepared\n##############################\n")
#############
## Data
#############
###################
# Total annual surface and subsurface run-off from WaterGAP; 1981-2010 and 2011-2040, Scenario A2, three models --> still in km^3/yr, needs to be calculated per cap
cat("Importing water availability\n")
wateravail.2000 <- read.csv(system.file("longexample/ofile_watergap_wfdnat_qstot_mean_1971_2000.csv", package="RObsDat"),header=TRUE,sep=",",stringsAsFactors=FALSE,na.strings="NA",dec=".")
wateravail.2030_ipsl <- read.csv(system.file("longexample/ofile_watergap_ipsla2nat_qstot_mean_2011_2040.csv", package="RObsDat"),header=TRUE,sep=",",stringsAsFactors=FALSE,na.strings="NA",dec=".")
wateravail.2030_echam <- read.csv(system.file("longexample/ofile_watergap_echama2nat_qstot_mean_2011_2040.csv", package="RObsDat"),header=TRUE,sep=",",stringsAsFactors=FALSE,na.strings="NA",dec=".")
wateravail.2030_cncm3 <- read.csv(system.file("longexample/ofile_watergap_cncm3a2nat_qstot_mean_2011_2040.csv", package="RObsDat"),header=TRUE,sep=",",stringsAsFactors=FALSE,na.strings="NA",dec=".")
wateravail.2060_ipsl <- read.csv(system.file("longexample/ofile_watergap_ipsla2nat_qstot_mean_2041_2070.csv", package="RObsDat"),header=TRUE,sep=",",stringsAsFactors=FALSE,na.strings="NA",dec=".")
wateravail.2060_echam <- read.csv(system.file("longexample/ofile_watergap_echama2nat_qstot_mean_2041_2070.csv", package="RObsDat"),header=TRUE,sep=",",stringsAsFactors=FALSE,na.strings="NA",dec=".")
wateravail.2060_cncm3 <- read.csv(system.file("longexample/ofile_watergap_cncm3a2nat_qstot_mean_2041_2070.csv", package="RObsDat"),header=TRUE,sep=",",stringsAsFactors=FALSE,na.strings="NA",dec=".")
wateravail.2090_ipsl <- read.csv(system.file("longexample/ofile_watergap_ipsla2nat_qstot_mean_2071_2100.csv", package="RObsDat"),header=TRUE,sep=",",stringsAsFactors=FALSE,na.strings="NA",dec=".")
wateravail.2090_echam <- read.csv(system.file("longexample/ofile_watergap_echama2nat_qstot_mean_2071_2100.csv", package="RObsDat"),header=TRUE,sep=",",stringsAsFactors=FALSE,na.strings="NA",dec=".")
wateravail.2090_cncm3 <- read.csv(system.file("longexample/ofile_watergap_cncm3a2nat_qstot_mean_2071_2100.csv", package="RObsDat"),header=TRUE,sep=",",stringsAsFactors=FALSE,na.strings="NA",dec=".")
water.dat <- cbind(wateravail.2000,wateravail.2030_ipsl[,2],wateravail.2060_ipsl[,2],wateravail.2090_ipsl[,2],wateravail.2030_echam[,2],wateravail.2060_echam[,2],
wateravail.2090_echam[,2],wateravail.2030_cncm3[,2],wateravail.2060_cncm3[,2],wateravail.2090_cncm3[,2])
water.date <- strptime(c("1990-01-01","2030-01-01","2060-01-01","2090-01-01","2030-01-01","2060-01-01","2090-01-01","2030-01-01","2060-01-01","2090-01-01"), "%Y-%m-%d", tz="UTC")
water.vars <- c("water.available.cur","water.available.ipsl","water.available.ipsl","water.available.ipsl","water.available.echam","water.available.echam","water.available.echam",
"water.available.cncm3","water.available.cncm3","water.available.cncm3")
addCV("VariableName", term="water.available", definition="Total annual renewable water resources per country in km^3")
addVariable(Code="water.available.cur", Name="water.available", Unit="km^3/yr")
addVariable(Code="water.available.ipsl", Name="water.available", Unit="km^3/yr")
addVariable(Code="water.available.echam", Name="water.available", Unit="km^3/yr")
addVariable(Code="water.available.cncm3", Name="water.available", Unit="km^3/yr")
#Build synonym table
getID("Site", water.dat[,1])
#Import
addDataValues(Date=water.date, Value=water.dat[,2:11], Site = water.dat[,1], Variable = water.vars, Source = "WATCH Project", QualityControlLevel = "ok" )
###################
# Population projections SRES scenario A2
cat("Importing Population\n")
population.dat <- read.csv2(system.file("longexample/Natl_Pop_Proj_A2.csv", package="RObsDat"), header=TRUE, stringsAsFactors=FALSE, na.strings=c("na","nodata","-"), dec=".", encoding="utf8")
population.date <- strptime(c("1990-01-01","1995-01-01","2000-01-01","2005-01-01","2010-01-01","2015-01-01","2020-01-01","2025-01-01","2030-01-01","2035-01-01",
"2040-01-01","2045-01-01","2050-01-01","2055-01-01","2060-01-01","2065-01-01","2070-01-01","2075-01-01","2080-01-01","2085-01-01","2090-01-01","2095-01-01",
"2100-01-01"),"%Y-%m-%d", tz="UTC")
population.vars <- c("population.A2")
addCV("VariableName", term="population", definition="Projected number of inhabitants per country according to SRES")
addVariable(Code="population.A2", Name="population", Unit="count")
getID("Site", population.dat[,1])
#Import
addDataValues(Date=population.date, Value=population.dat[,2:24], Site = population.dat[,1], Variable = population.vars, Source = "IIASA", QualityControlLevel = "6" )
################
# Grand total: calorie availability/cap/day (2009 )from FAOSTAT
cat("Importing Calories\n")
calories.dat <- read.csv2(system.file("longexample/calorie_availability_2009_new.csv", package="RObsDat"), header=TRUE, dec=".", stringsAsFactors=FALSE, sep=";")
calories.date <- strptime("2009-01-01", "%Y-%m-%d")
calories.vars <- c("calories.total")
addCV("VariableName", term="calories.total", definition="Total available calories/cap/day")
addVariable(Code="calories.total", Name="Total available calories/cap/day", Unit="kcal")
#Build synonym table
getID("Site", calories.dat[,1])
#Import
addDataValues(Date=calories.date, Value=calories.dat[,2], Site = calories.dat[,1], Variable = calories.vars, Source = "FAOSTAT", QualityControlLevel = 6 )
################
# MDER: Minimum dietary requirements/cap/day
cat("Importing MDER\n")
mder.dat <- read.csv2(system.file("longexample/MinimumDietaryEnergyRequirement.csv", package="RObsDat"), header=TRUE, dec=".", stringsAsFactors=FALSE, sep=";")
mder.date <- strptime("2008-01-01", "%Y-%m-%d")
mder.vars <- c("mder")
addCV("VariableName", term="mder", definition="Minimum dietary requirement: calories/cap/day")
bla <- getMetadata("Variable", Name="dietary")
print(bla)
addVariable(Code="mder", Name="Minimum dietary requirement: calories/cap/day", Unit="kcal")
#Build synonym table
getID("Site", mder.dat[,2])
#Import
addDataValues(Date=mder.date, Value=mder.dat[,7], Site = mder.dat[,2], Variable = mder.vars, Source = "FAOSTAT", QualityControlLevel = 6 )
#########################
## IDP database, selected indicators: Population.participation;public.freedom_civil.society;stability.of.political.system;
## Domestic.public.security;Functioning.of.justice.system;Social.inclusion;geographic.coverage.of.public.services;Institutional.solidarity;
## Traditional.solidarity;Micro.lending;Existence/absence.of.labour.legislation.and.measures;weak.employment.contract.rigidity
########
cat("Importing Social Variables\n")
IDP.dat <- read.csv2(system.file("longexample/IPD_2009_selected_indicators1.csv", package="RObsDat"), header=TRUE, stringsAsFactors=FALSE, sep=",")
IDP.date <- strptime("2009-01-01", "%Y-%m-%d", tz="UTC")
IDP.vars <- c(NA,"population.participation","public.freedom_civil.society","stability.of.political.system","Domestic.public.security","Functioning.of.justice.system",
"Social.inclusion","geographic.coverage.of.public.services","Institutional.solidarity","Traditional.solidarity","Micro.lending","labour.legislation",
"employment.contract")
addCV("VariableName", term="Population.participation", definition="Population Participation")
addCV("VariableName", term="public.freedom_civil.society", definition="Public Freedom and Civil Society")
addCV("VariableName", term="stability.of.political.system", definition="Statibility of Political System")
addCV("VariableName", term="Domestic.public.security", definition="Domestic Public Security")
addCV("VariableName", term="Functioning.of.justice.system", definition="Functioning of Justice System")
addCV("VariableName", term="Social.inclusion", definition="Social Inclusion")
addCV("VariableName", term="geographic.coverage.of.public.services", definition="Geographic Coverage of Public Services")
addCV("VariableName", term="Institutional.solidarity", definition="Institutional Solidarity")
addCV("VariableName", term="Traditional.solidarity", definition="Traditional Solidarity")
addCV("VariableName", term="Micro.lending", definition="Micro Lending")
addCV("VariableName", term="labour.legislation", definition="Labour Legislation")
addCV("VariableName", term="employment.contract", definition="Weak Employment Contracts")
addVariable(Code="population.participation", Name="Population.participation", Unit="categorical")
addVariable(Code="public.freedom_civil.society", Name="public.freedom_civil.society", Unit="categorical")
addVariable(Code="stability.of.political.system", Name="stability.of.political.system", Unit="categorical")
addVariable(Code="Domestic.public.security", Name="Domestic.public.security", Unit="categorical")
addVariable(Code="Functioning.of.justice.system", Name="Functioning.of.justice.system", Unit="categorical")
addVariable(Code="Social.inclusion", Name="Social.inclusion", Unit="categorical")
addVariable(Code="geographic.coverage.of.public.services", Name="geographic.coverage.of.public.services", Unit="categorical")
addVariable(Code="Institutional.solidarity", Name="Institutional.solidarity", Unit="categorical")
addVariable(Code="Traditional.solidarity", Name="Traditional.solidarity", Unit="categorical")
addVariable(Code="Micro.lending", Name="Micro.lending", Unit="categorical")
addVariable(Code="labour.legislation", Name="labour.legislation", Unit="categorical")
addVariable(Code="employment.contract", Name="employment.contract", Unit="categorical")
#IDPcode.dat <- merge(IDP.dat, all.countries, by.x = "countrycode", by.y = "isoAlpha3")
#Build synonym table
getID("Site", IDP.dat[,1])
#Import
addDataValues(Date=IDP.date, Value=IDP.dat[,2:13], Site = IDP.dat[,1], Variable = IDP.vars[2:13], Source = "IDP database", QualityControlLevel = 6)
###################
# Life expectancy at birth as used in HDI
# added to "database_22_09_2011.db" and checked by LongExample
cat("Importing Life expect\n")
lifeexp.dat <- read.csv2(system.file("longexample/life_expectancy_HDI_2009.csv", package="RObsDat"), header=TRUE, stringsAsFactors=FALSE, sep=";", dec=".")
lifeexp.date <- strptime("2009-01-01", "%Y-%m-%d", tz="UTC")
lifeexp.vars <- c("lifeexp")
addCV("VariableName", term="lifeexp", definition="Life expectancy at birth")
addVariable(Code="lifeexp", Name="lifeexp", Unit="yr")
#Build synonym table
getID("Site", lifeexp.dat[,1])
#Import
addDataValues(Date=lifeexp.date, Value=lifeexp.dat[,2], Site = lifeexp.dat[,1], Variable = lifeexp.vars, Source = "UNDP", QualityControlLevel = 6 )
###################
# PM10 concentration, lastest values: 2007 - 2010
cat("Importing PM10\n")
pm10.dat <- read.csv2(system.file("longexample/pm10_latest_2009_Worldbank.csv", package="RObsDat"), header=TRUE, stringsAsFactors=FALSE, sep=",", dec=".")
pm10.date <- strptime("2009-01-01", "%Y-%m-%d", tz="UTC")
pm10.vars <- c("pm10")
addCV("VariableName", term="pm10", definition="Mean yearly PM10 concentrations")
addVariable(Code="pm10", Name="pm10", Unit="ug/m^3")
#Build synonym table
getID("Site", pm10.dat[,1])
#Import
addDataValues(Date=pm10.date, Value=pm10.dat[,2], Site = pm10.dat[,1], Variable = pm10.vars, Source = "WHO", QualityControlLevel = 6 )
###################
# Solid fuel use
cat("Importing Solid Fuel\n")
solid.fuel.dat <- read.csv2(system.file("longexample/solid_fuel_use2010.csv", package="RObsDat"), header=TRUE, stringsAsFactors=FALSE, sep=",", dec=".")
solid.fuel.date <- strptime("2010-01-01", "%Y-%m-%d", tz="UTC")
solid.fuel.vars <- c("solid.fuel")
addCV("VariableName", term="solid.fuel", definition="Mean yearly PM10 concentrations")
addVariable(Code="solid.fuel", Name="solid.fuel", Unit="%")
#Build synonym table
getID("Site", solid.fuel.dat[,1])
#Import
addDataValues(Date=solid.fuel.date, Value=solid.fuel.dat[,2], Site = solid.fuel.dat[,1], Variable = solid.fuel.vars, Source = "WHO", QualityControlLevel = 6 )
########################
## Water Data from Aquastat for Australia Analysis
cat("Importing Sanitation\n")
watsan.dat <- read.csv2(system.file("longexample/access_sanitation2010.csv", package="RObsDat"), header=TRUE, na.strings=c("na","nodata","-"), stringsAsFactors=TRUE, sep=",")
watsan.date <- strptime("2010-01-01", "%Y-%m-%d", tz="UTC")
watsan.vars <- c("wat.total.improved","wat.piped","wat.other.improved","wat.other.unimproved","san.total.improved","san.shared","san.other.unimproved","san.open.def")
addCV("VariableName", term="wat.total.improved", definition="Access to improved water sources (total)")
addCV("VariableName", term="wat.piped", definition="Water piped on premises")
addCV("VariableName", term="wat.other.improved", definition="Access to other improved water sources")
addCV("VariableName", term="wat.other.unimproved", definition="Unimproved water sources")
addCV("VariableName", term="san.total.improved", definition="Access to improved sanitation facilities")
addCV("VariableName", term="san.shared", definition="Ahred sanitation facilities")
addCV("VariableName", term="san.other.unimproved", definition="Unimproved sanitation access")
addCV("VariableName", term="san.open.def", definition="Open defecation")
addVariable(Code="wat.total.improved", Name="wat.total.improved", Unit="%")
addVariable(Code="wat.piped", Name="wat.piped", Unit="%")
addVariable(Code="wat.other.improved", Name="wat.other.improved", Unit="%")
addVariable(Code="wat.other.unimproved", Name="wat.other.unimproved", Unit="%")
addVariable(Code="san.total.improved", Name="san.total.improved", Unit="%")
addVariable(Code="san.shared", Name="san.shared", Unit="%")
addVariable(Code="san.other.unimproved", Name="san.other.unimproved", Unit="%")
addVariable(Code="san.open.def", Name="san.open.def", Unit="%")
getID("Site", watsan.dat[,1])
#Build synonym table
#Import
addDataValues(Date=watsan.date, Value=watsan.dat[,2:9], Site = watsan.dat[,1], Variable = watsan.vars, Source = "UNICEF", QualityControlLevel = 6 )
#######################
# Health care workforce --> is per 100000! needs to be converted
cat("Importing Health\n")
HCW.dat <- read.csv(system.file("longexample/healthcare_workforce_sum1000_latest.csv", package="RObsDat"), header=TRUE, stringsAsFactors=FALSE, sep=",")
HCW.date <- strptime(paste(HCW.dat[,2],"-01-01", sep=""), "%Y-%m-%d", tz="UTC")
HCW.vars <- c("health.workers")
addCV("VariableName", term="health.workers", definition="Density of health care work force (per 100000 population)")
addVariable(Code="health.workers", Name="health.workers", Unit="#")
#Build synonym table
getID("Site", HCW.dat[,1])
#Import
addDataValues(Date=HCW.date, Value=HCW.dat[,3], Site = HCW.dat[,1], Variable = HCW.vars, Source = "WHO", QualityControlLevel = 6 )
###################
# Under 5 mortality
# added to "database_22_09_2011.db" and checked by LongExample
u5.dat <- read.csv2(system.file("longexample/under5mortality_rate.csv", package="RObsDat"), header=TRUE, stringsAsFactors=FALSE, sep=",")
u5.date <- strptime("2010-01-01","%Y-%m-%d", tz="UTC")
u5.vars <- c("under5.mort")
addCV("VariableName", term="under5.mort", definition="Probability of dying by age 5 per 1000 live births")
addVariable(Code="under5.mort", Name="under5.mort", Unit="#")
#Build synonym table
getID("Site", u5.dat[,1])
#Import
addDataValues(Date=u5.date, Value=u5.dat[,2], Site = u5.dat[,1], Variable = u5.vars, Source = "WHO", QualityControlLevel = 6 )
###################
# Electrification rate
# new version of table: countries with HDI very high set to 99, HDI high to 95
# fully added to "database_22_09_2011.db" and checked by LongExample --> values updated by Lisei, added 2/12/2011
elect.dat <- read.csv2(system.file("longexample/electricity_rate_2008_new.csv", package="RObsDat"), header=TRUE, stringsAsFactors=FALSE, sep=";", dec=".")
elect.dat <- read.csv2(system.file("longexample/electrification_2009.csv", package="RObsDat"), header=TRUE, stringsAsFactors=FALSE, sep=",", dec=".")
elect.date <- strptime("2008-01-01", "%Y-%m-%d", tz="UTC")
elect.date <- strptime("2009-01-01", "%Y-%m-%d", tz="UTC")
elect.vars <- c("electrate.total")
addCV("VariableName", term="electrate.total", definition="Electrification rate of total population (percent of population with access to electricity)")
addVariable(Code="electrate.total", Name="electrate.total", Unit="%")
#Build synonym table
getID("Site", elect.dat[,1])
#Import
addDataValues(Date=elect.date, Value=elect.dat[,2], Site = elect.dat[,1], Variable = elect.vars, Source = "IEA", QualityControlLevel = 6 )
#########################
## Mean years of schooling and expected years of schooling as used in the new HDI
######## fully added to "database_22_09_2011.db" and checked by LongExample
schooling.dat <- read.csv2(system.file("longexample/years_of_schooling_HDI_2009.csv", package="RObsDat"), header=TRUE, stringsAsFactors=FALSE, sep=";", dec=".", na.strings="..")
schooling.date <- strptime("2009-01-01", "%Y-%m-%d", tz="UTC")
schooling.vars <- c("mean_schooling", "expected_schooling")
addCV("VariableName", term="mean_schooling", definition="Mean Years of Schooling")
addCV("VariableName", term="expected_schooling", definition="Expected mean Years of Schooling")
addVariable(Code="mean_schooling", Name="mean_schooling", Unit="yr")
addVariable(Code="expected_schooling", Name="expected_schooling", Unit="yr")
#Build synonym table
getID("Site", schooling.dat[,1])
#Import
addDataValues(Date=schooling.date, Value=schooling.dat[,2:3], Site = schooling.dat[,1], Variable = schooling.vars, Source = "UNDP", QualityControlLevel = 6 )
####################
req.vars.rest <- c("population.participation","public.freedom_civil.society","stability.of.political.system","Domestic.public.security","Functioning.of.justice.system",
"Social.inclusion","Institutional.solidarity","Traditional.solidarity","Micro.lending","labour.legislation","employment.contract","calories.total",
"mder","lifeexp","pm10","solid.fuel","wat.piped","wat.other.improved","wat.other.unimproved","san.total.improved",
"san.shared","san.other.unimproved","san.open.def","health.workers","under5.mort","electrate.total","mean_schooling","expected_schooling")
req.vars.water <- c("population.A2","water.available.cur","water.available.ipsl","water.available.echam","water.available.cncm3")
all.water.dat <- getDataValues(Variable=req.vars.water)
# all.data <- merge(all.water.dat, rest.data, by.x = "contryCode", by.y = "countryCode")
}
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