R/Atlas_i4_SpeciesMonthByOcean_julien.R

In jsubei/IRDTunaAtlas: IRD (www.ird.fr) - UMR EME - Tuna atlas

# 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)

}