#Atlas_i3_SpeciesYearByGearMonth.R
#Tuna Atlas - IRD / MR EME
#
#This indicator build a graph of catches of a given species for a given year by gear type and by month. Provide comparaison with monthly mean of the 5 previous years. 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
#2013/03/15: Norbert - Initial version
##################################################################
#Use example:
# library(IRDTunaAtlas)
# csv.df <- read.csv("/home/norbert/Boulot/iMarine/WPS/Atlas/CSV/i3.csv", stringsAsFactors=FALSE)
# csv.df <- csv.df[csv.df$species == "MLS",]
# Atlas_i3_SpeciesYearByGearMonth(csv.df,
# yearAttributeName="year",
# monthAttributeName="month",
# speciesAttributeName="species",
# gearTypeAttributeName="gear_type",
# valueAttributeName="value",
# meanPrev5YearsAttributeName="mean_prev_5_years",
# stddevPrev5YearsAttributeName="stddev_prev_5_years")
##################################################################
Atlas_i3_SpeciesYearByGearMonth <- function(df,
yearAttributeName="year",
monthAttributeName="month",
speciesAttributeName="species",
gearTypeAttributeName="gear_type",
valueAttributeName="value",
meanPrev5YearsAttributeName="mean_prev_5_years",
stddevPrev5YearsAttributeName="stddev_prev_5_years",
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) == 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) == gearTypeAttributeName) == 0) {
stop("Cannot found gear attribute")
}
if(sum(names(df) == valueAttributeName) == 0) {
stop("Cannot found value attribute")
}
if(sum(names(df) == meanPrev5YearsAttributeName) == 0) {
stop("Cannot found mean for previous years attribute")
}
if(sum(names(df) == stddevPrev5YearsAttributeName) == 0) {
stop("Cannot found std_dev for previous years attribute")
}
#format columns
df[, yearAttributeName] <- as.numeric(df[, yearAttributeName])
df[, monthAttributeName] <- as.numeric(df[, monthAttributeName])
df[, speciesAttributeName] <- as.factor(df[, speciesAttributeName])
df[, gearTypeAttributeName] <- as.factor(df[, gearTypeAttributeName])
df[, valueAttributeName] <- as.numeric(df[, valueAttributeName])
df[, meanPrev5YearsAttributeName] <- as.numeric(df[, meanPrev5YearsAttributeName])
df[, stddevPrev5YearsAttributeName] <- as.numeric(df[, stddevPrev5YearsAttributeName])
#rename columns
names(df)[which(names(df) == yearAttributeName)] <- "year"
names(df)[which(names(df) == monthAttributeName)] <- "month"
names(df)[which(names(df) == speciesAttributeName)] <- "species"
names(df)[which(names(df) == gearTypeAttributeName)] <- "gear_type"
names(df)[which(names(df) == valueAttributeName)] <- "value"
names(df)[which(names(df) == meanPrev5YearsAttributeName)] <- "mean_prev_5_years"
names(df)[which(names(df) == stddevPrev5YearsAttributeName)] <- "stddev_prev_5_years"
#from std deviation to variance, and root square the sum of variances
fct <- function(vec)
{
var <- vec * vec
var <- sum(var)
return(sqrt(var))
}
#test if FAO usual gear codes are used
#if (length(intersect(levels(df$gear_type), c("BB", "GILL", "LL", "PS", "OTHER_I", "OTHER_A", "TROL", "TRAP"))) == length(levels(df$gear_type))) {
# df$gear_type <- factor(df$gear_type, levels=c("BB", "GILL", "LL", "PS", "OTHER_I", "OTHER_A", "TROL", "TRAP"), labels=c("Baitboat", "Gillnet", "Longline", "Purse seine", "Unclass. art. Indian O.", "Unclass. art. Atl. O.", "Trol.", "Trap"))
#}
#setup the palette
my.colors <- brewer.pal(length(levels(df$gear_type)), "Set1")
names(my.colors) <- levels(df$gear_type)
#define the result
result.df <- c()
#for each species
for (species.current in unique(df$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
#for each year
for (year.current in unique(df[df$species == species.current,]$year)) {
current.df <- df[df$species == species.current & df$year == year.current,]
if (! all(table(current.df$month) == 1)) {
if (all(is.na(current.df$stddev_prev_5_years))) {
stddev.agg <- cbind(month=unique(current.df$month), stddev_prev_5_years=NA)
} else {
stddev.agg <- aggregate(stddev_prev_5_years ~ month, data=current.df, fct)
}
if (all(is.na(current.df$mean_prev_5_years))) {
mean.agg <- cbind(month=unique(current.df$month), mean_prev_5_years=NA)
} else {
mean.agg <- aggregate(mean_prev_5_years ~ month, data=current.df, sum)
}
dfPrev5Years <- merge(mean.agg, stddev.agg)
} else {
dfPrev5Years <- current.df
}
#order gear factor levels by value
current.df$gear_type <- factor(current.df$gear_type, levels=rev(levels(reorder(current.df$gear_type, current.df$value))))
#set proper month label
current.df$month <- factor(month.abb[current.df$month], levels=levels(reorder(month.abb[current.df$month], current.df$month)))
dfPrev5Years$month <- factor(month.abb[dfPrev5Years$month], levels=levels(reorder(month.abb[dfPrev5Years$month], dfPrev5Years$month)))
#build the plot
resultPlot <- ggplot() +
layer(data=current.df,
mapping=aes(x=month, y=value, fill=gear_type, order=gear_type),
stat="identity",
geom="bar") +
layer(data=dfPrev5Years,
mapping=aes(x=month, y=mean_prev_5_years, group=1),
stat="identity",
geom="line") +
layer(data=dfPrev5Years,
mapping=aes(x=month, ymax=mean_prev_5_years + stddev_prev_5_years, ymin=mean_prev_5_years - stddev_prev_5_years),
width=0.25,
color="dimgray",
stat="identity",
geom="errorbar") +
scale_fill_manual(name="Gear type", values=my.colors) +
xlab("Month") + ylab("Catches in tons") +
ggtitle(paste(species.label, "monthly catches by gear type on", year.current))
#draw the plot
tempfile.base <- tempfile(pattern=paste("I3_", gsub(" ", "_", species.label), "_", as.character(year.current), "_", sep=""))
plot.filepath <- paste(tempfile.base, ".png", sep="")
ggsave(filename=plot.filepath, plot=resultPlot, dpi=300)
#
# #create the RDF metadata
rdf.filepath <- paste(tempfile.base, ".rdf", sep="")
# buildRdf(rdf_file_path=rdf.filepath,
# #rdf_subject="http://ecoscope.org/indicatorI3",
# rdf_subject=paste("http://www.ecoscope.org/ontologies/resources", tempfile.base, sep=""),
# titles=c("IRD Tuna Atlas: indicator #3 - catches by species for a given year by gear type and by month",
# "IRD Atlas thonier : indicateur #3 - captures par espèces pour une année donnée par mois et par type d'engin"),
# descriptions=c(paste(species.label, "catches by gear type and by month on", as.character(year.current)),
# paste("Captures de", species.label, "par mois et par type d'engin pour l'année", as.character(year.current))),
# subjects=c(as.character(species.current), as.character(unique(current.df$gear_type))),
# #processes="&localfile;/processI3",
# processes="http://www.ecoscope.org/ontologies/resources/processI3",
# data_output_identifier=plot.filepath,
# start=as.character(year.current),
# end=as.character(year.current),
# spatial="POLYGON((-180 -90,-180 90,180 90,180 -90,-180 -90))",
# withSparql)
result.df <- rbind(result.df, c(plot.file.path=plot.filepath, rdf.file.path=rdf.filepath))
}
}
return(result.df)
}
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