# Atlas_i4_SpeciesMonthByOcean_julien.R
# Tuna Atlas - IRD / MR EME
#
# This indicator build a graph of monthly (seasonal) catches by ocean for a species. An associated RDF file is also produced.
##################################################################
# Norbert Billet - IRD
# 2013/11/04: V2 version: add RDF export and allow production of multiple graph (i.e. species)
# 2013/09/03: Norbert - Add attributes names as parameters
# 2013/08/30: Norbert - Modifications to use with IRDTunaAtlas package
# 2013/04/22: Norbert - First version
##################################################################
# Use example:
# library(IRDTunaAtlas)
# csv.df <- read.csv("/home/norbert/Boulot/iMarine/WPS/Atlas/CSV/i4.csv", stringsAsFactors=FALSE)
# csv.df <- csv.df[csv.df$species == "ALB",]
# Atlas_i4_SpeciesMonthByOcean(csv.df,
# oceanAttributeName="ocean",
# yearAttributeName="year",
# monthAttributeName="month",
# speciesAttributeName="espece",
# valueAttributeName="value")
##################################################################
# library(rCharts)
# library(jsonlite)
# source("/home/tomcat7/temp/IRDTunaAtlas.R")
# source("/home/julien/SVNs/GIT/IRDTunaAtlas/R/IRDTunaAtlas_julien.R")
Atlas_i4_SpeciesMonthByOcean_julien <- function(df,
oceanAttributeName="ocean",
yearAttributeName="year",
monthAttributeName="month",
speciesAttributeName="species",
valueAttributeName="value",
withSparql=TRUE)
{
if (! require(XML) | ! require(ggplot2) | ! require(RColorBrewer)) {
stop("Missing library")
}
if (missing(df)) {
stop("Input data frame not specified")
}
#check for input attributes
if(sum(names(df) == oceanAttributeName) == 0) {
stop("Cannot found ocean attribute")
}
if(sum(names(df) == yearAttributeName) == 0) {
stop("Cannot found year attribute")
}
if(sum(names(df) == monthAttributeName) == 0) {
stop("Cannot found month attribute")
}
if(sum(names(df) == speciesAttributeName) == 0) {
stop("Cannot found species attribute")
}
if(sum(names(df) == valueAttributeName) == 0) {
stop("Cannot found value attribute")
}
#format columns
df[, oceanAttributeName] <- as.factor(df[, oceanAttributeName])
df[, yearAttributeName] <- as.numeric(df[, yearAttributeName])
df[, monthAttributeName] <- as.numeric(df[, monthAttributeName])
df[, speciesAttributeName] <- as.factor(df[, speciesAttributeName])
df[, valueAttributeName] <- as.numeric(df[, valueAttributeName])
#aggregate to cut other columns
df <- aggregate(x=df[, valueAttributeName],
by=list(df[, oceanAttributeName], df[, yearAttributeName], df[, monthAttributeName], df[, speciesAttributeName]),
FUN=sum)
#rename columns
names(df) <- c("ocean", "year", "month", "species", "value")
#setup month factor
df$month <- factor(df$month, labels=month.name)
#test if FAO usual gear codes are used
if (length(intersect(levels(df$ocean), c("ATL", "IND", "PAC_E", "PAC_W"))) == length(levels(df$ocean))) {
df$ocean <- factor(df$ocean, levels=c("ATL", "IND", "PAC_E", "PAC_W"), labels=c("Atlantic O.", "Indian O.", "East Pacific O.", "West Pacific O."))
}
#setup the palette
my.colors <- brewer.pal(length(levels(df$ocean)), "Set1")
names(my.colors) <- levels(df$ocean)
#define the resulr df
result.df <- c()
#List to store URLs of the set of files generated for each species
#Comment RDF part julie
liste <- list()
# store = new.rdf(ontology=FALSE)
# add.prefix(store,
# prefix="resources_def",
# namespace="http://www.ecoscope.org/ontologies/resources_def/")
# add.prefix(store,
# prefix="ical",
# namespace="http://www.w3.org/2002/12/cal/ical/")
# add.prefix(store,
# prefix="dct",
# namespace="http://purl.org/dc/terms/")
# tableauResult$results <- data.frame(titre=character(),
tableauResult <- data.frame(stringsAsFactors=FALSE)
URL<-"http://mdst-macroes.ird.fr/tmp/SpeciesByMonthByOcean/default/"
repository<-paste(mywd,"outputs/www/html/tmp/SpeciesByMonthByOcean/default/",sep="")
# URL<-"http://mdst-macroes.ird.fr/tmp/SpeciesByMonthByOcean/cdn/"
# repository<-"/data/www/html/tmp/SpeciesByMonthByOcean/cdn/"
#
for (species.current in unique(df$species)) {
#
# if (withSparql) {
# #get species scientific name from ecoscope sparql
# sparqlResult <- getSpeciesFromEcoscope(as.character(species.current))
# if (length(sparqlResult) > 0) {
# species.label <- sparqlResult[1,"scientific_name"]
# species.URI <- sparqlResult[1,"uri"]
# } else {
# species.label <- species.current
# species.URI <- species.current
# }
# } else {
# species.label <- species.current
# species.URI <- species.current
# }
species.label <- species.current
species.URI <- species.current
species.df <- df[df$species == species.current,]
#compute the decade
species.df$decade <- species.df$year - (species.df$year %% 10)
decades.factor <- sort(unique(species.df$decade))
species.df$decade <- factor(species.df$decade,
levels=decades.factor,
labels=unlist(lapply(X=decades.factor, FUN=function(dec) paste(min(species.df[species.df$decade == dec,]$year), "-", max(species.df[species.df$decade == dec,]$year), sep=""))))
for (decade.current in unique(species.df$decade)) {
current.df <- species.df[species.df$decade == decade.current,]
#aggregate values by years and month
valuesSum <- aggregate(value ~ year + month, data=current.df, FUN=sum)
names(valuesSum) <- c("year", "month", "valuesSum")
mergedDf <- merge(current.df, valuesSum)
#build the plot
#pie plot
resultPlot <- ggplot(data=mergedDf, mapping=aes(x=valuesSum/2, fill=ocean, y=value, width=valuesSum)) +
facet_grid(facets=year ~ month) +
geom_bar(aes(order = ocean), position="fill", stat="identity") +
scale_fill_manual(name="Ocean", values=my.colors) +
coord_polar(theta="y") +
theme(axis.text.y=element_text(size=6), axis.text.x=element_blank(), panel.grid.minor=element_blank(), axis.title.x=element_blank(), axis.title.y=element_blank()) +
labs(title=paste(species.label, "monthly catches by ocean for", decade.current))
#bar plot
#resultPlot <- ggplot(data=mergedDf) + facet_grid(facets=year ~ month) + geom_bar(mapping=aes(x=ocean, y=value, fill=ocean), stat="identity") + theme(axis.text.x=element_blank(), panel.grid.minor=element_blank(), axis.title.x=element_blank(), axis.title.y=element_blank()) + labs(title="Montly catches by ocean")
#draw the plot
# tempfile.base <- tempfile(pattern=paste("I4_", gsub(" ", "_", species.label), "_", as.character(decade.current), "_", sep=""))
filename <- paste("I4", gsub(" ", "_", species.label), decade.current,sep="_")
tempfile.base <- paste(repository,filename, sep="")
plot.filepath <- paste(tempfile.base, ".png", sep="")
ggsave(filename=plot.filepath, plot=resultPlot, dpi=300)
plot.URLpng <- paste(URL,filename, ".png", sep="")
## plot Rcharts Highcharts
plotRchartsHighcharts <- hPlot(x = "ocean", y = "value", data = mergedDf, type = "pie")
plotRchartsHighcharts
## {title: Pie Chart}
plotRchartsNVD3 <- nPlot(~ ocean, data = mergedDf, type = 'pieChart')
plotRchartsNVD3
## Dataset in HTML
Datatable <- dTable(
mergedDf,
sPaginationType= "full_numbers"
)
Datatable
## Storage of files in a given repository (temporary or permanent)
plot.filepathtml <- paste(tempfile.base, ".html", sep="")
plot.URLhtml <- paste(URL,filename, ".html", sep="")
plotRchartsHighcharts$save(plot.filepathtml,standalone=TRUE)
# plotRchartsHighcharts$save(plot.filepathtml,cdn=TRUE)
plot.filepathtmlNVD3 <- paste(tempfile.base, "_NVD3.html", sep="")
plotRchartsNVD3$save(plot.filepathtmlNVD3,standalone=TRUE)
# plotRchartsNVD3$save(plot.filepathtmlNVD3,cdn=TRUE)
# Datatable
plot.filepathtmltable <- paste(tempfile.base, "_table.html", sep="")
Datatable$save(plot.filepathtmltable,standalone=TRUE)
# Datatable$save(plot.filepathtmltable,cdn=TRUE)
plot.URLhtmlTable <- paste(URL,filename, "_table.html", sep="")
################################################################################################
# ligne <- data.frame(TYPE="URI", URL=URI, stringsAsFactors=FALSE)
################################################################################################
#
# #Metadata elements (in addition to OGC WPS metadata) to describe the current indicator which will be used by other applications (Ecoscope and Tuna Atlas Websites)
# titles=c(paste(species.label, "IRD Tuna Atlas: indicator #4 - monthly catches by ocean and by year"),
# paste("IRD Atlas thonier : indicateur #4 - captures mensuelles de", species.label, " par océan et par année"))
#
# #
# # descriptions=c(c("en",paste(species.label, "monthly catches by ocean and by year"),
# # c("fr",paste("Captures mensuelles de", species.label, "par océan et par année"))
# #
#
# descriptions=c(c("en",paste(species.label, "monthly catches by ocean and by year")),
# c("fr",paste("Captures mensuelles de", species.label, "par océan et par année")))
#
# subjects=c(as.character(species.current),as.character(unique(current.df$ocean)))
#
# #Collect the URIs of related Topics from Ecoscope SPARQL endpoint
# URI <- FAO2URIFromEcoscope(as.character(species.current))
# tabURIs<- data.frame(type="species",URI=URI,stringsAsFactors=FALSE)
#
# # for (gear_type.current in unique(df$gear_type)) {
#
# # URIGear <- FAO2URIFromEcoscope(as.character(unique(current.df$gear_type)))
# # ligne<- c(x="gear",y=URIGear)
# # tabURIs<- rbind(tabURIs,ligne)
#
# # }
# # EVENTUELLEMENT AJOUTER D'AUTRES SUJETS COMME LA ZONE
# #subjects=c(as.character(species.current), as.character(gear_type.current), as.character(unique(current.df$gear_type))),
# #subjects=c(as.character(species.current)),
# #subjects=c(as.character(species.current), as.character(gear_type.current)),
# #data_input=url,
#
# #TODO julien => A ADAPTER AVEC LA CONVEX HULL / ou la collection DE TOUTES LES GEOMETRIES CONCERNEES
# spatial_extent="POLYGON((-180 -90,-180 90,180 90,180 -90,-180 -90))"
# temporal_extent_begin=as.character(min(current.df$year))
# temporal_extent_end=as.character(max(current.df$year))
#
# #create the RDF metadata
# rdf.filepath <- paste(repository, "La_totale.rdf", sep="")
# rdf.URL <- paste(URL,filename, ".rdf", sep="")
# # il faudrait ajouter un attribut qui précise le type de visualisation: carte, chart...
# data_output_identifiers=data.frame(titre="1 en fait y a pas besoin de cet attribut",type="image",year=temporal_extent_begin, fileURL=plot.filepath, stringsAsFactors=FALSE)
# ligne <- c(titre="2 en fait y a pas besoin de cet attribut",type="pie", year=temporal_extent_begin, fileURL=plot.URLhtml)
# data_output_identifiers <- rbind(data_output_identifiers, ligne)
# ligne <- c(titre="3 en fait y a pas besoin de cet attribut",type="pie",year=temporal_extent_begin, fileURL=plot.filepathtmlNVD3)
# data_output_identifiers <- rbind(data_output_identifiers, ligne)
# ligne <- c(titre="6 en fait y a pas besoin de cet attribut",type="dataTable",year=temporal_extent_begin, fileURL=plot.filepathtmltable)
# data_output_identifiers <- rbind(data_output_identifiers, ligne)
#
#
# download=data.frame(format="csv",URL="http://mdst-macroes.ird.fr/tmp/SpeciesByGear/XXX.csv", stringsAsFactors=FALSE)
# ligne <- c(format="shp",URL="http://mdst-macroes.ird.fr/tmp/SpeciesByGear/XXX.shp")
# download <- rbind(download, ligne)
# ligne <- c(format="GML|WKT|shp|netCDF",URL="http://mdst-macroes.ird.fr/tmp/SpeciesByGear/XXX.nc....")
# download <- rbind(download, ligne)
#
#
# tableauResult <- buildRdf(store=store,
# tableauResult = tableauResult,
# RDFMetadata=rdf.URL,
# rdf_file_path=rdf.filepath,
# rdf_subject=paste("http://www.ecoscope.org/ontologies/resources", tempfile.base, sep=""),
# #rdf_subject="http://ecoscope.org/indicatorI1",
# titles=titles,
# descriptions=descriptions,
# subjects=subjects,
# tabURIs=tabURIs,
# processes="http://www.ecoscope.org/ontologies/resources/processI4",
# image=plot.URLpng,
# data_output_identifiers=data_output_identifiers,
# download=download,
# start=temporal_extent_begin,
# end=temporal_extent_end,
# spatial=spatial_extent,
# withSparql)
}
#if multiple decade we produce a graph by decade
if (length(unique(species.df$decade)) > 1) {
#aggregate values by decade and month
valuesSum <- aggregate(value ~ decade + month, data=species.df, FUN=sum)
names(valuesSum) <- c("decade", "month", "valuesSum")
values <- aggregate(value ~ decade + month + ocean, data=species.df, FUN=sum)
mergedDf <- merge(values, valuesSum)
#build the plot
#pie plot
resultPlot <- ggplot(data=mergedDf, mapping=aes(x=valuesSum/2, fill=ocean, y=value, width=valuesSum)) +
facet_grid(facets=decade ~ month) +
geom_bar(aes(order = ocean), position="fill", stat="identity") +
scale_fill_manual(name="Ocean", values=my.colors) +
coord_polar(theta="y") +
theme(axis.text.y=element_text(size=6), axis.text.x=element_blank(), panel.grid.minor=element_blank(), axis.title.x=element_blank(), axis.title.y=element_blank()) +
labs(title=paste(species.label, "monthly catches by ocean and by decade"))
#draw the plot
filename <- paste("I4", gsub(" ", "_", species.label), "_byDecade_", decade.current, sep="")
tempfile.base <- paste(repository,filename, sep="")
plot.filepath <- paste(tempfile.base, ".png", sep="")
ggsave(filename=plot.filepath, plot=resultPlot, dpi=100)
plot.URLpng <- paste(URL,filename, ".png", sep="")
## plot Rcharts Highcharts
# plotRchartsHighcharts <- hPlot(x = "ocean", y = "value", data = mergedDf, type = "pie")
plotRchartsHighcharts <- hPlot(x = "ocean", y = "value", data = mergedDf, type = "pie",title = "Tableau de pies", subtitle = "species.label", size = "value", group = "ocean")
plotRchartsHighcharts
## {title: Pie Chart}
plotRchartsNVD3 <- nPlot(~ ocean, data = mergedDf, type = 'pieChart')
plotRchartsNVD3
## Dataset in HTML
Datatable <- dTable(
mergedDf,
sPaginationType= "full_numbers"
)
Datatable
## Storage of files in a given repository (temporary or permanent)
plot.filepathtml <- paste(tempfile.base, ".html", sep="")
plot.URLhtml <- paste(URL,filename, ".html", sep="")
plotRchartsHighcharts$save(plot.filepathtml,standalone=TRUE)
# plotRchartsHighcharts$save(plot.filepathtml,cdn=TRUE)
plot.filepathtmlNVD3 <- paste(tempfile.base, "_NVD3.html", sep="")
plotRchartsNVD3$save(plot.filepathtmlNVD3,standalone=TRUE)
# plotRchartsNVD3$save(plot.filepathtmlNVD3,cdn=TRUE)
# Datatable
plot.filepathtmltable <- paste(tempfile.base, "_table.html", sep="")
Datatable$save(plot.filepathtmltable,standalone=TRUE)
# Datatable$save(plot.filepathtmltable,cdn=TRUE)
plot.URLhtmlTable <- paste(URL,filename, "_table.html", sep="")
################################################################################################
# ligne <- data.frame(TYPE="URI", URL=URI, stringsAsFactors=FALSE)
################################################################################################
#Metadata elements (in addition to OGC WPS metadata) to describe the current indicator which will be used by other applications (Ecoscope and Tuna Atlas Websites)
#
# titles=c(paste(species.label, "IRD Tuna Atlas: indicator #4 - monthly catches by ocean and by decade"),
# paste("IRD Atlas thonier : indicateur #4 - captures mensuelles de", species.label, " par océan et par décénie"))
#
#
# # descriptions=c(c("en",paste(species.label, "monthly catches by ocean and by decade"),
# # c("fr",paste("Captures mensuelles de", species.label, "par océan et par décénie"))
# descriptions=c(paste(species.label, "monthly catches by ocean and by decade")
# ,paste("Captures mensuelles de", species.label, "par océan et par décénie"))
#
#
# subjects=c(as.character(species.current),levels(current.df$ocean))
#
# #Collect the URIs of related Topics from Ecoscope SPARQL endpoint
# URI <- FAO2URIFromEcoscope(as.character(species.current))
# tabURIs<- data.frame(type="species",URI=URI,stringsAsFactors=FALSE)
# for (gear_type.current in unique(df$gear_type)) {
# URIGear <- FAO2URIFromEcoscope(as.character(unique(current.df$gear_type)))
# ligne<- c(x="gear",y=URIGear)
# tabURIs<- rbind(tabURIs,ligne)
# }
# EVENTUELLEMENT AJOUTER D'AUTRES SUJETS COMME LA ZONE
#subjects=c(as.character(species.current), as.character(gear_type.current), as.character(unique(current.df$gear_type))),
#subjects=c(as.character(species.current)),
#subjects=c(as.character(species.current), as.character(gear_type.current)),
#data_input=url,
#
# #TODO julien => A ADAPTER AVEC LA CONVEX HULL / ou la collection DE TOUTES LES GEOMETRIES CONCERNEES
# spatial_extent="POLYGON((-180 -90,-180 90,180 90,180 -90,-180 -90))"
# temporal_extent_begin=as.character(min(current.df$year))
# temporal_extent_end=as.character(max(current.df$year))
#
#
# #create the RDF metadata
# rdf.filepath <- paste(repository, "La_totale.rdf", sep="")
# rdf.URL <- paste(URL,filename, ".rdf", sep="")
# # il faudrait ajouter un attribut qui précise le type de visualisation: carte, chart...
# data_output_identifiers=data.frame(titre="1 en fait y a pas besoin de cet attribut",type="image",year=temporal_extent_begin, fileURL=plot.filepath, stringsAsFactors=FALSE)
# ligne <- c(titre="2 en fait y a pas besoin de cet attribut",type="pie", year=temporal_extent_begin, fileURL=plot.URLhtml)
# data_output_identifiers <- rbind(data_output_identifiers, ligne)
# ligne <- c(titre="3 en fait y a pas besoin de cet attribut",type="pie",year=temporal_extent_begin, fileURL=plot.filepathtmlNVD3)
# data_output_identifiers <- rbind(data_output_identifiers, ligne)
# ligne <- c(titre="6 en fait y a pas besoin de cet attribut",type="dataTable",year=temporal_extent_begin, fileURL=plot.filepathtmltable)
# data_output_identifiers <- rbind(data_output_identifiers, ligne)
#
#
# download=data.frame(format="csv",URL="http://mdst-macroes.ird.fr/tmp/SpeciesByGear/XXX.csv", stringsAsFactors=FALSE)
# ligne <- c(format="shp",URL="http://mdst-macroes.ird.fr/tmp/SpeciesByGear/XXX.shp")
# download <- rbind(download, ligne)
# ligne <- c(format="GML|WKT|shp|netCDF",URL="http://mdst-macroes.ird.fr/tmp/SpeciesByGear/XXX.nc....")
# download <- rbind(download, ligne)
#
#
#
# tableauResult <- buildRdf(store=store,
# tableauResult = tableauResult,
# RDFMetadata=rdf.URL,
# rdf_file_path=rdf.filepath,
# rdf_subject=paste("http://www.ecoscope.org/ontologies/resources", tempfile.base, sep=""),
# #rdf_subject="http://ecoscope.org/indicatorI1",
# titles=titles,
# descriptions=descriptions,
# subjects=subjects,
# tabURIs=tabURIs,
# processes="http://www.ecoscope.org/ontologies/resources/processI4",
# image=plot.URLpng,
# data_output_identifiers=data_output_identifiers,
# download=download,
# start=temporal_extent_begin,
# end=temporal_extent_end,
# spatial=spatial_extent,
# withSparql)
}
}
# julien<-buildJson(type="Pies Table", description="Rapport d'exécution du traitement i4",processSourceCode="http://mdst-macroes.ird.fr:8084/wps/R/scripts/Atlas_i4_XXXX.R",results=tableauResult)
return(resultPlot)
}
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