R/bitacora-internal.R
In imarpe/imarpe: Tools for graphics and summary reports for IMARPE
Defines functions
plotEffort.numeric
getCpue_relative
getEffort
effortSpeciesData.bitacora
.effortData.bitacora
.distributionCatch.bitacora
.plotSpeciesComposition.bitacora
.speciesComposition.bitacora
.plotFishingPresence.bitacora
.plotFishingPoints.bitacora
.fishingPoints.bitacora
.fishingHaul.bitacora
.observedTrip.bitacora
.getBitacoraData
Documented in
effortSpeciesData.bitacora
getCpue_relative
getEffort
plotEffort.numeric
# Internal function for the class bitacora --------------------------------
#Funcion para obtener la data del programa de bitacoras (PBP)
.getBitacoraData = function(file, colTrip, colPort, colDateOut, colDateStart,
colSearchTime, colStorageCapacity,
colLat, colLon, colHaul, colHaulTotal, colCatchHaul,
capAnch, capSar, capJur, capCab, capBon, ...) {
dataBase = read.csv(file = file, header = TRUE, na.strings = "", stringsAsFactors = FALSE, ...)
#Fishing trip
tripVector = dataBase[, colTrip]
if(sum(is.na(tripVector)) != 0) {warning("Existen viajes sin el registro del codigo de viaje")}
tripVector = tripVector[tripVector != 0 & tripVector != ""]
tripVector = tripVector[!duplicated(tripVector)]
#Ports (seguir poniendo a prueba el vector de puertos)
portsVector = dataBase[, colPort]
if(sum(is.na(portsVector)) != 0) {warning("Existen Viajes sin el registro del puerto")}
portsVector = gsub(pattern = "\\([[:print:]]+\\)", replacement = "", x = portsVector, perl = TRUE)
portList = getPort(myPorts = portsVector)
dataBase$puerto = portList$data$name
dataBase$latitudAux = portList$data$lat
#Dates
datesVector = dataBase[, colDateOut]
if(sum(is.na(datesVector)) != 0) {warning("Existen viajes sin el registro de la fecha")}
datesVector = datesVector[datesVector != "" & !is.na(datesVector)]
newDatesVector = as.Date(datesVector, format="%d/%m/%Y %H:%M:%S")
yearVector = as.numeric(format(newDatesVector, "%Y"))
monthVector = as.numeric(format(newDatesVector, "%m"))
dayVector = as.numeric(format(newDatesVector, "%d"))
#Fleet
fleetVector = dataBase[, colStorageCapacity]
if(sum(is.na(fleetVector)) != 0) {warning("Existen viajes sin el registro del tipo de flota")}
dataBase$flota = cut(x = fleetVector, breaks = c(0, 10, 32.5, 110, Inf),
labels = c("Artesanal", "Menor escala", "Industrial madera", "Industrial"))
info = list(file = file,
trips = length(unique(tripVector)),
ports = length(unique(dataBase$puerto[!is.na(dataBase$puerto)])),
years = unique(yearVector),
months = length(rle(monthVector)$values),
fleets = as.vector(unique(dataBase$flota[!is.na(dataBase$flota)])),
colTrip = colTrip, colDateOut = colDateOut, colDateStart = colDateStart,
colSearchTime = colSearchTime, colStorageCapacity = colStorageCapacity,
colLat = colLat, colLon = colLon,
colHaul = colHaul, colHaulTotal = colHaulTotal, colCatchHaul = colCatchHaul,
capAnch = capAnch, capSar = capSar, capJur = capJur, capCab = capCab, capBon = capBon)
output = list(data = dataBase, info = info)
class(output) = c("bitacora")
return(output)
}
#Funcion para obtener los viajes observados
.observedTrip.bitacora = function(object, language) {
dataBase = object$data
dataBase = dataBase[, c(object$info$colTrip, "puerto", "flota", "latitudAux") ]
colnames(dataBase) = c("codeTrip", "puerto", "flota", "latitudAux")
dataBase = dataBase[!is.na(dataBase$codeTrip) & dataBase$codeTrip != 0 & dataBase$codeTrip != "" &
!is.na(dataBase$puerto) & dataBase$puerto != 0 & dataBase$puerto != "" &
!is.na(dataBase$flota) & dataBase$flota != 0 & dataBase$flota != "" , ]
#to get the trips
dataBase = dataBase[!duplicated(dataBase), ]
dataTable = table(dataBase$puerto, dataBase$flota)
dataTable = data.frame(dataTable)
dataTable = reshape(dataTable, idvar = "Var1", timevar = "Var2", direction = "wide")
namesData = unlist(strsplit(colnames(dataTable), "\\."))
namesData = namesData[!namesData == "Freq"]
colnames(dataTable) = c("Puerto", namesData[-1])
#Order by colnames
fleetNames = c("Puerto", "Artesanal", "Menor escala", "Industrial madera", "Industrial")
sortFleet = sort(match(colnames(dataTable), fleetNames), decreasing = FALSE)
dataTable = dataTable[sortFleet]
dataTable$Total = rowSums(dataTable[, seq(from = 2, to = dim(dataTable)[2], by = 1)])
dataTable$Puerto = as.character(dataTable$Puerto)
#Order by ports
dataTable$lat = dataBase$latitudAux[match(dataTable$Puerto, dataBase$puerto)]
dataTable = dataTable[with(dataTable, order(dataTable$lat, decreasing = TRUE)), ]
dataTable$lat = NULL
rownames(dataTable) = NULL
dataTable = rbind(dataTable, c("Total", as.vector(colSums(dataTable[, seq(from = 2, to = dim(dataTable)[2], by = 1)]))))
#Language
if(language == "english"){
vectorNames = match(colnames(dataTable), c("Puerto", "Artesanal", "Menor escala", "Industrial madera", "Industrial", "Total"))
colnames(dataTable) = cut(x = vectorNames, breaks = c(0,1,2,3,4,5,6),
labels = c("Port", "Artisanal", "Small scale", "Industrial wood", "Industrial", "Total"))}
if(language == "spanish"){colnames(dataTable) = colnames(dataTable)}
return(dataTable)
}
#Funcion para obtener los lances pesqueros muestreados
.fishingHaul.bitacora = function(object, language, latByPort) {
dataBase = object$data
dataBase = dataBase[, c(object$info$colLat, "latitudAux", object$info$colHaul, "flota") ]
dataBase[, 1] = -abs(dataBase[, 1])
colnames(dataBase) = c("lat", "latAux", "haul", "fleet")
dataBase = dataBase[!is.na(dataBase$lat) & dataBase$lat != 0 & dataBase$lat != "" &
!is.na(dataBase$haul) & dataBase$haul != 0 & dataBase$haul != "" &
!is.na(dataBase$fleet) & dataBase$fleet != 0 & dataBase$fleet != "", ]
if(isTRUE(latByPort)) {
indexLat = which(is.na(dataBase$lat))
dataBase$lat[indexLat] = dataBase$latAux[indexLat]
}
dataBase$latAux = NULL
rownames(dataBase) = NULL
dataBase$lat = cut(dataBase$lat,
breaks = seq(from = -19, to = -3, by = 1),
labels = seq(from = -18, to = -3, by = 1))
dataTable = table(dataBase$lat, dataBase$fleet)
dataTable = data.frame(dataTable)
dataTable = reshape(dataTable, idvar = "Var1", timevar = "Var2", direction = "wide")
namesData = unlist(strsplit(colnames(dataTable), "\\."))
namesData = namesData[!namesData == "Freq"]
colnames(dataTable) = c("Latitud", namesData[-1])
dataTable = dataTable[sort(dataTable$Latitud, decreasing = TRUE),]
dataTable$Latitud = as.character(dataTable$Latitud)
dataTable$Latitud = paste0(substring(dataTable$Latitud, 2), "S")
#dataTable$Latitud = paste0(substring(dataTable$Latitud, 2), "\U00B0", "S")
#Order by colnames
fleetNames = c("Latitud", "Artesanal", "Menor escala", "Industrial madera", "Industrial")
sortFleet = sort(match(colnames(dataTable), fleetNames), decreasing = FALSE)
dataTable = dataTable[sortFleet]
dataTable$Total = rowSums(dataTable[, seq(from = 2, to = dim(dataTable)[2], by = 1)])
dataTable = rbind(dataTable, c("Total", as.vector(colSums(dataTable[, seq(from = 2, to = dim(dataTable)[2], by = 1)]))))
#Language
if(language == "english"){
vectorNames = match(colnames(dataTable), c("Latitud", "Artesanal", "Menor escala", "Industrial madera", "Industrial", "Total"))
colnames(dataTable) = cut(x = vectorNames, breaks = c(0,1,2,3,4,5,6),
labels = c("Latitude", "Artisanal", "Small scale", "Industrial wood", "Industrial", "Total"))}
if(language == "spanish"){colnames(dataTable) = colnames(dataTable)}
rownames(dataTable) = NULL
return(dataTable)
}
#Funcion para obtener los puntos de pesca (por especie y grupo taxonomico)
.fishingPoints.bitacora = function(object){
dataBase = object$data
dataBase = dataBase[, c(object$info$colLat, object$info$colLon, "flota", object$info$colHaul,
object$info$colCatchHaul, catchSpecies$catch) ]
colnames(dataBase)[1:5] = c("lat", "lon", "fleet", "num_haul", "catch_haul")
#remove null catched
dataBase = dataBase[!is.na(dataBase$num_haul) & dataBase$num_haul != 0 & dataBase$num_haul != "", ]
#remove null fleet type
dataBase = dataBase[!is.na(dataBase$fleet) & dataBase$fleet != 0, ]
#removing null lat and lon
dataBase = dataBase[!is.na(dataBase$lat) & dataBase$lat != "", ]
dataBase = dataBase[!is.na(dataBase$lon) & dataBase$lon != "", ]
rownames(dataBase) = NULL
dataBase$lat = -abs(dataBase$lat)
dataBase$lon = -abs(dataBase$lon)
#data for plot the 5 species
dataAnch = dataBase[, c("lat", "lon", "fleet", "num_haul", "catch_haul", object$info$capAnch)]
dataSar = dataBase[, c("lat", "lon", "fleet", "num_haul", "catch_haul", object$info$capSar)]
dataJur = dataBase[, c("lat", "lon", "fleet", "num_haul", "catch_haul", object$info$capJur)]
dataCab = dataBase[, c("lat", "lon", "fleet", "num_haul", "catch_haul", object$info$capCab)]
dataBon = dataBase[, c("lat", "lon", "fleet", "num_haul", "catch_haul", object$info$capBon)]
colnames(dataAnch) = c("lat", "lon", "fleet", "num_haul", "catch_haul", "catchSpecies")
dataAnch = dataAnch[!is.na(dataAnch$catchSpecies) & dataAnch$catchSpecies !=0, ]
rownames(dataAnch) = NULL
colnames(dataSar) = c("lat", "lon", "fleet", "num_haul", "catch_haul", "catchSpecies")
dataSar = dataSar[!is.na(dataSar$catchSpecies) & dataSar$catchSpecies !=0, ]
rownames(dataSar) = NULL
colnames(dataJur) = c("lat", "lon", "fleet", "num_haul", "catch_haul", "catchSpecies")
dataJur = dataJur[!is.na(dataJur$catchSpecies) & dataJur$catchSpecies !=0, ]
rownames(dataJur) = NULL
colnames(dataCab) = c("lat", "lon", "fleet", "num_haul", "catch_haul", "catchSpecies")
dataCab = dataCab[!is.na(dataCab$catchSpecies) & dataCab$catchSpecies !=0, ]
rownames(dataCab) = NULL
colnames(dataBon) = c("lat", "lon", "fleet", "num_haul", "catch_haul", "catchSpecies")
dataBon = dataBon[!is.na(dataBon$catchSpecies) & dataBon$catchSpecies !=0, ]
rownames(dataBon) = NULL
#presence for other species
dataGroups = dataBase[, -which(names(dataBase) %in%
c(object$info$capAnch, object$info$capSar, object$info$capJur, object$info$capCab, object$info$capBon))]
dataGroups = melt(dataGroups, id.vars=c("lat", "lon", "fleet", "num_haul", "catch_haul"))
indexGroup = match(dataGroups$variable, catchSpecies$catch)
dataGroups$group = catchSpecies$group[indexGroup]
dataGroups$species = catchSpecies$species[indexGroup]
dataGroups = dataGroups[!is.na(dataGroups$value),]
rownames(dataGroups) = NULL
output = list(dataAnch = dataAnch, dataSar = dataSar, dataJur = dataJur, dataCab = dataCab,
dataBon = dataBon, dataGroups = dataGroups, dataTotal = dataBase)
return(output)
}
#Funcion para plotear los mapas con puntos de pesca
.plotFishingPoints.bitacora = function(x, language, dataType,
colMap, colFleet,
cexPointCatch, cexPoint, cex.axis, cexPorts, ...){
dataBase = x[[dataType]]
ports = portData[portData$importance == 1, ]
#Colours by fleet
colVector = match(dataBase$fleet, c("Artesanal", "Menor escala", "Industrial madera", "Industrial"))
#Legend colours
if(language == "spanish"){fleetName = c("AR", "ME", "IM", "IN")[sort(unique(colVector), decreasing = FALSE)]}
if(language == "english"){fleetName = c("AR", "SS", "IW", "IN")[sort(unique(colVector), decreasing = FALSE)]}
colLegend = colFleet[sort(unique(colVector), decreasing = FALSE)]
colVector = cut(colVector, breaks = c(-Inf, 1, 2, 3, 4), labels = colFleet)
#Size according the catch
sizePoints = cut(x = dataBase$catchSpecies, breaks = pretty(dataBase$catchSpecies),
labels = seq(from = 1, by = 1, length.out = length(pretty(dataBase$catchSpecies))-1))
if(isTRUE(cexPointCatch)){cexPoints = as.numeric(as.character(sizePoints))} else {cexPoints = cexPoint}
#plot
par(mar = c(3.5, 3.5, 1, 1))
plot(x = dataBase$lon, y = dataBase$lat, xlim = c(-85, -70), ylim = c(-20,-2),
xlab = "", ylab = "", col = as.character(colVector), axes = FALSE, type = "p", pch = 16,
cex = cexPoints, ...)
map(add = TRUE, fill = TRUE, col = colMap)
box()
axis(2, at = seq(from = -20, to = -2, by = 2), las = 2, cex.axis = cex.axis,
labels = paste(seq(from = 20, to = 2, by = -2), "S", sep = " "))
axis(1, at = seq(from = -82, to = -70, by = 2), cex.axis = cex.axis,
labels = paste(seq(from = 82, to = 70, by = -2), "W", sep = " "))
text(x = ports$lon, y = ports$lat, labels = ports$name, adj = -0.1, cex = cexPorts)
legend("bottomleft", legend = fleetName, col = colLegend,
pch = 20, horiz = FALSE, bty = "n", cex = 1)
return(invisible())
}
#Funcion para plotear la presencia de otras especies por grupo taxonomico
.plotFishingPresence.bitacora = function(x, byGroup, group,
colMap, colSpecies, colLegend,
cexPoint, cex.axis, cexPorts, cexLegend, ...){
dataBase = x$dataGroups
if(isTRUE(byGroup)){
dataBase = dataBase[dataBase$group %in% group, ]
specieVector = match(dataBase$species, unique(dataBase$species))
if(is.null(colSpecies)){
colPlot = vectorColours(length(dataBase$species))[rank(dataBase$species)]
colLegend = unique(vectorColours(length(dataBase$species))[rank(dataBase$species)])
} else {
colPlot = colSpecies
colLegend = colLegend
}
namesLegend = capitalizeFirstLetter(unique(dataBase$species))
} else {
dataBase = dataBase[!dataBase$group %in% c("neritico", "transzonal", "oceanico", "demersal"),]
dataBase = dataBase[!dataBase$group == "", ]
specieVector = match(dataBase$group, unique(dataBase$group))
if(is.null(colSpecies)){
colPlot = vectorColours(length(dataBase$group))[rank(dataBase$group)]
colLegend = unique(vectorColours(length(dataBase$group))[rank(dataBase$group)])
} else {
colPlot = colSpecies
colLegend = colLegend
}
namesLegend = capitalizeFirstLetter(unique(dataBase$group))
}
ports = portData[portData$importance == 1, ]
#plot
par(mar = c(3.5, 3.5, 1,1))
plot(x = dataBase$lon, y = dataBase$lat, xlim = c(-85, -70), ylim = c(-20,-2),
xlab = "", ylab = "", col = colPlot, axes = FALSE, type = "p", pch = 16, cex = cexPoint, ...)
map(add = TRUE, fill = TRUE, col = colMap)
box()
axis(2, at = seq(from = -20, to = -2, by = 2), las = 2, cex.axis = cex.axis,
labels = paste(seq(from = 20, to = 2, by = -2), "S", sep = " "))
axis(1, at = seq(from = -82, to = -70, by = 2), cex.axis = cex.axis,
labels = paste(seq(from = 82, to = 70, by = -2), "W", sep = " "))
text(x = ports$lon, y = ports$lat, labels = ports$name, adj = -0.1, cex = cexPorts)
legend("bottomleft", legend = namesLegend, col = colLegend,
pch = 20, horiz = FALSE, bty = "n", cex = cexLegend)
return(invisible())
}
#Funcion para obtener la composicion de especies de las capturas
.speciesComposition.bitacora = function(object, language) {
#cleaning data
dataBase = object$data
dataBase = dataBase[, c(object$info$colCatchHaul, catchSpecies$catch) ]
dataBase[, 1] = apply(dataBase[2:dim(dataBase)[2]], 1, sum, na.rm = TRUE)
dataBase = dataBase[!dataBase[, 1] == 0, ]
dataBase[, 1] = NULL
#remove species with catch = 0
index = apply(dataBase, 2, sum, na.rm = TRUE)
index = which(index != 0); names(index) = NULL
dataBase = dataBase[, index]
#data.frame in a new format (species, catch, porcentaje)
dataTable = apply(dataBase, 2, sum, na.rm = TRUE)
dataTable = as.data.frame(dataTable)
dataTable$species = capitalizeFirstLetter(catchSpecies$species[match(rownames(dataTable), catchSpecies$catch)])
dataTable = dataTable[order(dataTable$dataTable, decreasing = TRUE), ]
#remove Unidentified species
dataTable = dataTable[!dataTable$species == "", ]
dataTable = data.frame(Species = dataTable$species,
Catch = round(dataTable$dataTable, 4),
Percentage = round((dataTable$dataTable * 100) / sum(dataTable$dataTable), 4) )
dataTable$Species = as.character(dataTable$Species)
dataTable = rbind(dataTable, c("Total", round(sum(dataTable$Catch),2), round(sum(dataTable$Percentage), 2)))
dataTable$Catch = as.numeric(dataTable$Catch)
dataTable$Percentage = as.numeric(dataTable$Percentage)
#Language
if(language == "english") {colnames(dataTable) = colnames(dataTable)}
if(language == "spanish") {colnames(dataTable) = c("Especie", "Captura", "Porcentaje")}
return(dataTable)
}
#Funcion para plotear el pie de composicion de especies
.plotSpeciesComposition.bitacora = function(x, threshold, minPercentage, ...) {
dataBase = x
dataBase = dataBase[- dim(dataBase)[1],]
if(isTRUE(threshold)){
dataBase = dataBase[dataBase[, 3] >= minPercentage, ]
dataBase$Porcentaje = dataBase$Captura * 100 / sum(dataBase$Captura)
} else {
dataBase = dataBase
}
#plot
par(oma = c(1,1,1,1))
pie3D(dataBase[,3], labels = dataBase[, 1], col = rainbow(dim(dataBase)[1]),
radius = 1, height = 0.15, theta = 1.1, start = 1,
labelcex = 1.2, labelrad = 1.3, ...)
return(invisible())
}
#Funcion para obtener la distribucion espacial de las capturas
.distributionCatch.bitacora = function(object, language, specie) {
dataBase = object$data
dataBase = dataBase[, c(object$info$colLat, object$info$colLon, catchSpecies$catch) ]
colnames(dataBase)[c(1,2)] = c("lat", "lon")
dataBase$lat = -abs(dataBase$lat)
dataBase$lon = -abs(dataBase$lon)
dataBase = dataBase[!is.na(dataBase$lat) & !is.na(dataBase$lon), ]
aip = isopArea.assigner(dataBase)
getAipInfo = getAIPInfo(aipVector = aip)
dataBase$lon = getAipInfo$dc
colnames(dataBase)[2] = "dc"
colnames(dataBase)[3:dim(dataBase)[2]] = catchSpecies$species[match(colnames(dataBase[3:dim(dataBase)[2]]), catchSpecies$catch)]
#Indexing for name specie
dataTable = dataBase[, c(1, 2)]
dataTable$specie = round(dataBase[, specie], 2)
dataTable$lat = cut(dataTable$lat, breaks = seq(from = -19, to = -3, by = 1), labels = seq(from = -18, to = -3, by = 1))
dataTable = aggregate(specie ~ lat + dc, data = dataTable, FUN = sum, na.rm = TRUE)
dataTable = dcast(dataTable, lat ~ dc, value.var = "specie")
#Order by latitude
dataTable$lat = as.numeric(as.character(dataTable$lat))
dataTable = dataTable[order(dataTable$lat, decreasing = TRUE), ]
dataTable[is.na(dataTable)] = 0
rownames(dataTable) = NULL
#Latitud
dataTable$lat = as.character(dataTable$lat)
dataTable$lat = paste0(substring(dataTable$lat, 2), "S")
#Total
dataTable$Total = rowSums(dataTable[, seq(from = 2, to = dim(dataTable)[2], by = 1)])
dataTable = rbind(dataTable, c("Total", as.vector(colSums(dataTable[, seq(from = 2, to = dim(dataTable)[2], by = 1)]))))
#Language
if(language == "spanish"){colnames(dataTable)[1] = "Latitud"}
if(language == "spanish"){colnames(dataTable)[1] = "Latitude"}
return(dataTable)
}
#Funcion para obtener la data para analizar el esfuerzo y el cpue
.effortData.bitacora = function(object) {
dataBase = object$data
dataBase = dataBase[, c(object$info$colTrip, object$info$colStorageCapacity, "flota", "puerto",
object$info$colDateStart, object$info$colDateOut,
object$info$colLat, object$info$colLon, object$info$colSearchTime,
object$info$colHaul, object$info$colHaulTotal, object$info$colCatchHaul,
object$info$capAnch, object$info$capSar, object$info$capJur, object$info$capCab, object$info$capBon)]
colnames(dataBase) = c("codeTrip", "storageCapacity", "fleet", "port", "dateStart", "dateOut",
"lat", "lon", "searchTime", "haulNumber", "haulTotal","haulCatch",
"anchoveta", "sardina", "jurel", "caballa", "bonito")
#Obtencion de variables para el esfuerzo
#travel time (duracion viaje)
dataBase = dataBase[!is.na(dataBase$dateStart) & !is.na(dataBase$dateOut), ]
dataBase$dateStart = as.POSIXct(strptime(dataBase$dateStart, format = "%d/%m/%Y %H:%M:%S"))
dataBase$dateOut = as.POSIXct(strptime(dataBase$dateOut, format = "%d/%m/%Y %H:%M:%S"))
dataBase$travelTime = as.numeric(dataBase$dateOut - dataBase$dateStart) / 60
#search time (tiempo busqueda)
hours = suppressWarnings(as.numeric(substring(gsub("^\\s+|\\s+$", "", dataBase$searchTime), 1, 2)))
mins = suppressWarnings(as.numeric(substring(gsub("^\\s+|\\s+$", "", dataBase$searchTime), 4, 5)))
dataBase$searchTime = hours + mins/60
# variables time
dataBase$year = as.numeric(format(dataBase$dateStart, "%Y"))
dataBase$month = as.numeric(format(dataBase$dateStart, "%m"))
dataBase$day = as.numeric(format(dataBase$dateStart, "%d"))
dataBase$dates = as.Date(paste(dataBase$day, dataBase$month, dataBase$year, sep = "/"),
format = "%d/%m/%Y")
# code trip haul
dataBase = dataBase[!is.na(dataBase$codeTrip) & !is.na(dataBase$haulTotal), ]
dataBase$codeTripHaul = paste(dataBase$codeTrip, dataBase$haulTotal, sep = "-")
return(dataBase)
}
#Funcion para extraer informacion de la especies y la temporada
#' Get species effort data
#' @description Receive an object of bitacora class, indexes the data by species,
#' estimates the season by species, for anchovy the season depend of region
#' (north-central and south region) but for other species
#' (sardine, jack mackerel, chub mackerel and bonito) the season is annual.
#' @param data A \code{data.frame} obtained from effortData.bitacora function (internal function
#' of bitacora class). This data frame could be obtained from
#' getMainResults.bitacora function too.
#' @param species A \code{character} with the common name of anchovy ("anchoveta"),
#' sardine ("sardina"), jack mackerel ("jurel"), chub mackerel ("caballa") and bonito ("bonito").
#' @param region If the species is anchovy (\code{anchoveta}) is necessary
#' indicate for each region the effort data is going to be calculate. By default is \code{NULL}.
#' @return A \code{data.frame} with effort data for a given species.
#' @author Criscely Lujan-Paredes, \email{criscelylujan@gmail.com}.
#' @examples
#' # Read a example of data base
#' bitacoraData = system.file("extdata", "bitacoraData.csv", package = "imarpe")
#'
#' # Create a object of bitacora class
#' bitacoraObject = getBitacoraData(file = bitacoraData)
#' class(bitacoraObject)
#'
#' # Get the results
#' mainBitacoraData = getMainResults.bitacora(object = bitacoraObject, language = "spanish",
#' specie = "anchoveta", effortData = TRUE)
#'
#' # Effort data
#' effortData = mainBitacoraData$effortData
#'
#' # Use of effortSpeciesData.bitacora
#' effortNC = effortSpeciesData.bitacora(data = effortData, species = "anchoveta", region = "norte-centro")
#'
#' @export
effortSpeciesData.bitacora = function(data, species, region = NULL){
dataBase = data
nameSpecies = c("anchoveta", "sardina", "jurel", "caballa", "bonito")
nameSpecies = nameSpecies[!nameSpecies %in% species]
dataBase = dataBase[, -which(colnames(dataBase) %in% nameSpecies)]
dataBase = dataBase[!is.na(dataBase[species]), ]
catchSpecies = tapply(dataBase[, species], dataBase[, "codeTripHaul"], sum)
dataBase = subset(dataBase, !duplicated(subset(dataBase, select=c(codeTripHaul))))
dataBase[, species] = catchSpecies[match(dataBase$codeTripHaul, names(catchSpecies)) ]
#building season by species
if(species %in% c("sardina", "jurel", "caballa", "bonito")) {
dataBase$season = dataBase$year}
if(species == "anchoveta") {
if(!is.null(region)){
if(region == "norte-centro") {
dataBase = dataBase[!is.na(dataBase$lat), ]
dataBase = dataBase[which(dataBase$lat > 2 & dataBase$lat <= 15.99), ]
dataBase$season = assignAnchovy_season(x = dataBase$dates, region = region) }
if(region == "sur") {
dataBase = dataBase[!is.na(dataBase$lat), ]
dataBase = dataBase[which(dataBase$lat >= 16 & dataBase$lat < 18.9), ]
dataBase$season = assignAnchovy_season(x = dataBase$dates, region = region) }
} else {
dataBase$season = dataBase$year
}
}
colnames(dataBase)[13] = "catch"
rownames(dataBase) = NULL
return(data = dataBase)
}
#Funcion para obtener el esfuerzo
#' Get fishing effort
#' @description Generical function to get the fishing effort. The type of fishing effort could be four types:
#' \itemize{
#' \item travelTime for the effort as a function of the travel time.
#' \item haulTotal for the effort as a function of the total number of fishing haul.
#' \item storageCapacity for the effort as a function of the storage capacity.
#' \item searchTime for the effort as a function of the search time to catch the species.
#' }
#' @param data A \code{data.frame} with information to estimated the fishing effort (in columns).
#' This data base have to contain columns of the time (days, months, years, season), ports and the
#' fleet type.
#' @param efforType A \code{character}. It could be one of the fishing effort type ("travelTime",
#' "haulTotal", "storageCapacity", "searchTime").
#' @param effortBy Parameter to indicate whether the effort will be estimated by
#' time (\code{effortBy = time}) or by port (\code{effortBy = port}).
#' @param timeBy If the effort is estimated over the time, it could be by days
#' (\code{timeBy = days}), months (\code{timeBy = months}), years (\code{timeBy = years}) or
#' by seasons (\code{timeBy = seasons}).
#' @param fleeType The effort could be estimated for the whole data given or for a data by fleeType.
#' By default this parameter is \code{NULL}.
#' @return A \code{vector} with the estimated effort.
#' @author Criscely Lujan-Paredes, \email{criscelylujan@gmail.com}.
#' @examples
#' # Read a example of data base
#' bitacoraData = system.file("extdata", "bitacoraData.csv", package = "imarpe")
#'
#' # Create a object of bitacora class
#' bitacoraObject = getBitacoraData(file = bitacoraData)
#'
#' # Get the results
#' mainBitacoraData = getMainResults.bitacora(object = bitacoraObject, language = "spanish",
#' specie = "anchoveta", effortData = TRUE)
#'
#' # Effort data
#' effortData = mainBitacoraData$effortData
#'
# # Filter data: anchovy data for anchovy and the north-central region
#' effortNC = effortSpeciesData.bitacora(data = effortData, species = "anchoveta", region = "norte-centro")
#'
#' # Get the cpue
#' effortNC_tt = getEffort(data = effortNC, efforType = "travelTime", effortBy = "port")
#' effortNC_hn = getEffort(data = effortNC, efforType = "haulTotal", effortBy = "port")
#'
#' @export
getEffort = function(data, efforType, effortBy, timeBy, fleeType = NULL) {
dataBase = data
#indexacion tipo de flota
if(!is.null(fleeType)) {dataBase = dataBase[dataBase$fleet %in% fleeType, ]}
#remove NAs in efforType
dataBase = dataBase[!is.na(dataBase[efforType]), ]
effort = dataBase[efforType]
names(effort) = NULL
dataBase["effort"] = effort
if(effortBy %in% "time") {
if(timeBy == "days") {effortVector = tapply(X = dataBase$effort, INDEX = list(dataBase$dates), FUN = sum, na.rm = TRUE)}
if(timeBy == "months") {effortVector = tapply(X = dataBase$effort, INDEX = list(dataBase$year, dataBase$month), FUN = sum, na.rm = TRUE) }
if(timeBy == "years") {effortVector = tapply(X = dataBase$effort, INDEX = dataBase$year, FUN = sum, na.rm = TRUE) }
if(timeBy == "seasons") {effortVector = tapply(X = dataBase$effort, INDEX = list(dataBase$season), FUN = sum, na.rm = TRUE) }
}
if(effortBy %in% "port") {
effortVector = tapply(X = dataBase$effort, INDEX = list(dataBase$port), FUN = mean, na.rm = TRUE)
effortVector = effortVector[order(match(names(effortVector), portData$name))]
}
return(effort = effortVector)
}
#Funcion para obtener la captura por unidad de esfuerzo (cpue) relativizada
#' Get the relative catch per unit effort (cpue)
#' @details The relative cpue is a index used on bitacora report.
#' @param data A \code{data.frame} with information about catchs and fishing effort.
#' @param toTons A logical parameter. \code{TRUE} (dafault) it assume that the catches is in kilograms
#' and converts it into tonnes (divided by 1000). \code{FALSE} it assume the the information is in tons
#' and don't convert the data.
#' @param efforType A \code{character} indicating the fishing effort type. It could be four types:
#' \itemize{
#' \item travelTime for the effort as a function of the travel time.
#' \item haulTotal for the effort as a function of the total number of fishing haul.
#' \item storageCapacity for the effort as a function of the storage capacity.
#' \item searchTime for the effort as a function of the search time to catch the species.
#' }
#' @param cpueBy Parameter to indicate whether the relative cpue will be estimated by
#' time (\code{cpueBy = time}) or by port (\code{cpueBy = port}).
#' @param timeBy If the relative cpue is estimated over the time, it could be by days
#' (\code{timeBy = days}), months (\code{timeBy = months}), years (\code{timeBy = years}) or
#' by seasons (\code{timeBy = seasons}).
#' @param fleeType The relative cpue could be estimated for the whole data given or for a data by fleeType.
#' By default this parameter is \code{NULL}.
#' @return A \code{vector} with the relative cpue.
#' @author Criscely Lujan-Paredes, \email{criscelylujan@gmail.com}.
#' @examples
#' # Read a example of data base
#' bitacoraData = system.file("extdata", "bitacoraData.csv", package = "imarpe")
#'
#' # Create a object of bitacora class
#' bitacoraObject = getBitacoraData(file = bitacoraData)
#'
#' # Get the results
#' mainBitacoraData = getMainResults.bitacora(object = bitacoraObject, language = "spanish",
#' specie = "anchoveta", effortData = TRUE)
#'
#' # Effort data
#' effortData = mainBitacoraData$effortData
#'
#' # Filter data: anchovy data for anchovy and the north-central region
#' effortNC = effortSpeciesData.bitacora(data = effortData, species = "anchoveta", region = "norte-centro")
#'
#' # Get the cpue relative
#' getCpue_relative(data = effortNC, efforType = "travelTime", cpueBy = "port")
#' getCpue_relative(data = effortNC, efforType = "travelTime", cpueBy = "time", timeBy = "months")
#'
#' @export
getCpue_relative = function(data, toTons=FALSE, efforType, cpueBy = "time", timeBy, fleeType = NULL) {
dataBase = data
dataBase$catch = dataBase$catch / ifelse(isTRUE(toTons), 1000, 1)
#indexacion tipo de flota
if(!is.null(fleeType)) {dataBase = dataBase[dataBase$fleet %in% fleeType, ]}
#remove NAs in the dataBase
dataBase = dataBase[!is.na(dataBase$catch) & dataBase$catch != 0 &
!is.na(dataBase$storageCapacity) & dataBase$storageCapacity != 0, ]
dataBase = dataBase[!is.na(dataBase[efforType]) & dataBase[efforType] != 0, ]
cpue = dataBase$catch / dataBase$storageCapacity / dataBase[efforType]
names(cpue) = NULL
dataBase["cpue"] = cpue
if(cpueBy %in% "time"){
if(timeBy == "days") {cpueVector = tapply(X = dataBase$cpue, INDEX = list(dataBase$dates), FUN = mean, na.rm = TRUE, i)}
if(timeBy == "months") {cpueVector = tapply(X = dataBase$cpue, INDEX = list(dataBase$year, dataBase$month), FUN = mean, na.rm = TRUE) }
if(timeBy == "years") {cpueVector = tapply(X = dataBase$cpue, INDEX = dataBase$year, FUN = mean, na.rm = TRUE) }
if(timeBy == "seasons") {cpueVector = tapply(X = dataBase$cpue, INDEX = list(dataBase$season), FUN = mean, na.rm = TRUE) }
}
if(cpueBy %in% "port") {
cpueVector = tapply(X = dataBase$cpue, INDEX = list(dataBase$port), FUN = mean, na.rm = TRUE)
cpueVector = cpueVector[order(match(names(cpueVector), portData$name))]
}
return(cpue = cpueVector)
}
#' Plot series of fishing effort by ports
#' @description This method take a series of fishing effort by ports and produces a graph
#' that is useful for the PBP program.
#' @param effort1 A vector of the first serie of fishing effort.
#' @param effort2 A vector of the second serie of fishing effort.
#' @param labAxis2 Text to be included on the left vertical axis of the graph
#' @param labAxis4 Text to be included on the right vertical axis of the graph
#' @param colBar The colour of the barplot. Sea \code{\link{barplot}}.
#' @param colLine The colour of the line on the graph.
#' @param legend The names to include on the legend of the graph.
#' @param ... Extra arguments.
#' @return A graph for two types of fishing effort by ports. One type is on the left
#' vertical axis and the other on the right vertical axis, the ports are on the horizontal axis.
#' @author Criscely Lujan-Paredes, \email{criscelylujan@gmail.com}.
#' @examples
#' # Read a example of data base
#' bitacoraData = system.file("extdata", "bitacoraData.csv", package = "imarpe")
#'
#' # Create a object of bitacora class
#' bitacoraObject = getBitacoraData(file = bitacoraData)
#'
#' # Get the results
#' mainBitacoraData = getMainResults.bitacora(object = bitacoraObject, language = "spanish",
#' specie = "anchoveta", effortData = TRUE)
#'
#' # Effort data
#' effortData = mainBitacoraData$effortData
#'
#' # Filter data: anchovy data for anchovy and the north-central region
#' effortNC = effortSpeciesData.bitacora(data = effortData, species = "anchoveta",
#' region = "norte-centro")
#'
#' # Get effort
#' effortNC_tt = getEffort(data = effortNC, efforType = "travelTime", effortBy = "port")
#' effortNC_hn = getEffort(data = effortNC, efforType = "haulTotal", effortBy = "port")
#'
#' # Graph
#' plotEffort(effort1 = effortNC_tt, effort2 = effortNC_hn, labAxis2 = "Horas de viaje",
#' labAxis4 = "N\u{00FA}mero de calas", colBar="gray", colLine = "black",
#' legend = c("Duracion del viaje", "N\u{00FA}mero de calas"))
#'
#' @export
plotEffort.numeric = function(effort1, effort2, labAxis2, labAxis4, colBar = NULL,
colLine = NULL, legend = NULL, ...) {
maxAxis2 = ceiling(1.2*max(effort1))
maxAxis4 = ceiling(1.2*max(effort2))
par(mar=c(3.5,4.5,1,4.5))
#effort 1
barplot(effort1, cex.names = 0.8, ylim = c(0, maxAxis2), axes = FALSE, ylab = labAxis2, col = colBar, ...)
axis(side = 2, at = pretty(effort1), las = 1)
#effort 2
vectorEffort2 = barplot(effort2, names.arg = seq(from = 1, to = length(effort2), by = 1), plot = FALSE)
lines(vectorEffort2, effort2*maxAxis2/maxAxis4, type="o", pch=19, lwd=1, col = colLine)
axis(side = 4, at = pretty(effort2)/maxAxis4*maxAxis2, labels = pretty(effort2), las = 2)
mtext(text = labAxis4, side = 4, line = 2.5)
box()
if(!is.null(legend)){
legend("topleft", legend = c(legend[1], legend[2]), bty = "n", lty = c(1,4),
cex = 0.8, lwd = c(5, 2), col = c("gray", "black"))
}
return(invisible())
}
imarpe/imarpe documentation built on Aug. 8, 2020, 8:40 p.m.
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Defines functions plotEffort.numeric getCpue_relative getEffort effortSpeciesData.bitacora .effortData.bitacora .distributionCatch.bitacora .plotSpeciesComposition.bitacora .speciesComposition.bitacora .plotFishingPresence.bitacora .plotFishingPoints.bitacora .fishingPoints.bitacora .fishingHaul.bitacora .observedTrip.bitacora .getBitacoraData
Documented in effortSpeciesData.bitacora getCpue_relative getEffort plotEffort.numeric
# Internal function for the class bitacora --------------------------------
#Funcion para obtener la data del programa de bitacoras (PBP)
.getBitacoraData = function(file, colTrip, colPort, colDateOut, colDateStart,
colSearchTime, colStorageCapacity,
colLat, colLon, colHaul, colHaulTotal, colCatchHaul,
capAnch, capSar, capJur, capCab, capBon, ...) {
dataBase = read.csv(file = file, header = TRUE, na.strings = "", stringsAsFactors = FALSE, ...)
#Fishing trip
tripVector = dataBase[, colTrip]
if(sum(is.na(tripVector)) != 0) {warning("Existen viajes sin el registro del codigo de viaje")}
tripVector = tripVector[tripVector != 0 & tripVector != ""]
tripVector = tripVector[!duplicated(tripVector)]
#Ports (seguir poniendo a prueba el vector de puertos)
portsVector = dataBase[, colPort]
if(sum(is.na(portsVector)) != 0) {warning("Existen Viajes sin el registro del puerto")}
portsVector = gsub(pattern = "\\([[:print:]]+\\)", replacement = "", x = portsVector, perl = TRUE)
portList = getPort(myPorts = portsVector)
dataBase$puerto = portList$data$name
dataBase$latitudAux = portList$data$lat
#Dates
datesVector = dataBase[, colDateOut]
if(sum(is.na(datesVector)) != 0) {warning("Existen viajes sin el registro de la fecha")}
datesVector = datesVector[datesVector != "" & !is.na(datesVector)]
newDatesVector = as.Date(datesVector, format="%d/%m/%Y %H:%M:%S")
yearVector = as.numeric(format(newDatesVector, "%Y"))
monthVector = as.numeric(format(newDatesVector, "%m"))
dayVector = as.numeric(format(newDatesVector, "%d"))
#Fleet
fleetVector = dataBase[, colStorageCapacity]
if(sum(is.na(fleetVector)) != 0) {warning("Existen viajes sin el registro del tipo de flota")}
dataBase$flota = cut(x = fleetVector, breaks = c(0, 10, 32.5, 110, Inf),
labels = c("Artesanal", "Menor escala", "Industrial madera", "Industrial"))
info = list(file = file,
trips = length(unique(tripVector)),
ports = length(unique(dataBase$puerto[!is.na(dataBase$puerto)])),
years = unique(yearVector),
months = length(rle(monthVector)$values),
fleets = as.vector(unique(dataBase$flota[!is.na(dataBase$flota)])),
colTrip = colTrip, colDateOut = colDateOut, colDateStart = colDateStart,
colSearchTime = colSearchTime, colStorageCapacity = colStorageCapacity,
colLat = colLat, colLon = colLon,
colHaul = colHaul, colHaulTotal = colHaulTotal, colCatchHaul = colCatchHaul,
capAnch = capAnch, capSar = capSar, capJur = capJur, capCab = capCab, capBon = capBon)
output = list(data = dataBase, info = info)
class(output) = c("bitacora")
return(output)
}
#Funcion para obtener los viajes observados
.observedTrip.bitacora = function(object, language) {
dataBase = object$data
dataBase = dataBase[, c(object$info$colTrip, "puerto", "flota", "latitudAux") ]
colnames(dataBase) = c("codeTrip", "puerto", "flota", "latitudAux")
dataBase = dataBase[!is.na(dataBase$codeTrip) & dataBase$codeTrip != 0 & dataBase$codeTrip != "" &
!is.na(dataBase$puerto) & dataBase$puerto != 0 & dataBase$puerto != "" &
!is.na(dataBase$flota) & dataBase$flota != 0 & dataBase$flota != "" , ]
#to get the trips
dataBase = dataBase[!duplicated(dataBase), ]
dataTable = table(dataBase$puerto, dataBase$flota)
dataTable = data.frame(dataTable)
dataTable = reshape(dataTable, idvar = "Var1", timevar = "Var2", direction = "wide")
namesData = unlist(strsplit(colnames(dataTable), "\\."))
namesData = namesData[!namesData == "Freq"]
colnames(dataTable) = c("Puerto", namesData[-1])
#Order by colnames
fleetNames = c("Puerto", "Artesanal", "Menor escala", "Industrial madera", "Industrial")
sortFleet = sort(match(colnames(dataTable), fleetNames), decreasing = FALSE)
dataTable = dataTable[sortFleet]
dataTable$Total = rowSums(dataTable[, seq(from = 2, to = dim(dataTable)[2], by = 1)])
dataTable$Puerto = as.character(dataTable$Puerto)
#Order by ports
dataTable$lat = dataBase$latitudAux[match(dataTable$Puerto, dataBase$puerto)]
dataTable = dataTable[with(dataTable, order(dataTable$lat, decreasing = TRUE)), ]
dataTable$lat = NULL
rownames(dataTable) = NULL
dataTable = rbind(dataTable, c("Total", as.vector(colSums(dataTable[, seq(from = 2, to = dim(dataTable)[2], by = 1)]))))
#Language
if(language == "english"){
vectorNames = match(colnames(dataTable), c("Puerto", "Artesanal", "Menor escala", "Industrial madera", "Industrial", "Total"))
colnames(dataTable) = cut(x = vectorNames, breaks = c(0,1,2,3,4,5,6),
labels = c("Port", "Artisanal", "Small scale", "Industrial wood", "Industrial", "Total"))}
if(language == "spanish"){colnames(dataTable) = colnames(dataTable)}
return(dataTable)
}
#Funcion para obtener los lances pesqueros muestreados
.fishingHaul.bitacora = function(object, language, latByPort) {
dataBase = object$data
dataBase = dataBase[, c(object$info$colLat, "latitudAux", object$info$colHaul, "flota") ]
dataBase[, 1] = -abs(dataBase[, 1])
colnames(dataBase) = c("lat", "latAux", "haul", "fleet")
dataBase = dataBase[!is.na(dataBase$lat) & dataBase$lat != 0 & dataBase$lat != "" &
!is.na(dataBase$haul) & dataBase$haul != 0 & dataBase$haul != "" &
!is.na(dataBase$fleet) & dataBase$fleet != 0 & dataBase$fleet != "", ]
if(isTRUE(latByPort)) {
indexLat = which(is.na(dataBase$lat))
dataBase$lat[indexLat] = dataBase$latAux[indexLat]
}
dataBase$latAux = NULL
rownames(dataBase) = NULL
dataBase$lat = cut(dataBase$lat,
breaks = seq(from = -19, to = -3, by = 1),
labels = seq(from = -18, to = -3, by = 1))
dataTable = table(dataBase$lat, dataBase$fleet)
dataTable = data.frame(dataTable)
dataTable = reshape(dataTable, idvar = "Var1", timevar = "Var2", direction = "wide")
namesData = unlist(strsplit(colnames(dataTable), "\\."))
namesData = namesData[!namesData == "Freq"]
colnames(dataTable) = c("Latitud", namesData[-1])
dataTable = dataTable[sort(dataTable$Latitud, decreasing = TRUE),]
dataTable$Latitud = as.character(dataTable$Latitud)
dataTable$Latitud = paste0(substring(dataTable$Latitud, 2), "S")
#dataTable$Latitud = paste0(substring(dataTable$Latitud, 2), "\U00B0", "S")
#Order by colnames
fleetNames = c("Latitud", "Artesanal", "Menor escala", "Industrial madera", "Industrial")
sortFleet = sort(match(colnames(dataTable), fleetNames), decreasing = FALSE)
dataTable = dataTable[sortFleet]
dataTable$Total = rowSums(dataTable[, seq(from = 2, to = dim(dataTable)[2], by = 1)])
dataTable = rbind(dataTable, c("Total", as.vector(colSums(dataTable[, seq(from = 2, to = dim(dataTable)[2], by = 1)]))))
#Language
if(language == "english"){
vectorNames = match(colnames(dataTable), c("Latitud", "Artesanal", "Menor escala", "Industrial madera", "Industrial", "Total"))
colnames(dataTable) = cut(x = vectorNames, breaks = c(0,1,2,3,4,5,6),
labels = c("Latitude", "Artisanal", "Small scale", "Industrial wood", "Industrial", "Total"))}
if(language == "spanish"){colnames(dataTable) = colnames(dataTable)}
rownames(dataTable) = NULL
return(dataTable)
}
#Funcion para obtener los puntos de pesca (por especie y grupo taxonomico)
.fishingPoints.bitacora = function(object){
dataBase = object$data
dataBase = dataBase[, c(object$info$colLat, object$info$colLon, "flota", object$info$colHaul,
object$info$colCatchHaul, catchSpecies$catch) ]
colnames(dataBase)[1:5] = c("lat", "lon", "fleet", "num_haul", "catch_haul")
#remove null catched
dataBase = dataBase[!is.na(dataBase$num_haul) & dataBase$num_haul != 0 & dataBase$num_haul != "", ]
#remove null fleet type
dataBase = dataBase[!is.na(dataBase$fleet) & dataBase$fleet != 0, ]
#removing null lat and lon
dataBase = dataBase[!is.na(dataBase$lat) & dataBase$lat != "", ]
dataBase = dataBase[!is.na(dataBase$lon) & dataBase$lon != "", ]
rownames(dataBase) = NULL
dataBase$lat = -abs(dataBase$lat)
dataBase$lon = -abs(dataBase$lon)
#data for plot the 5 species
dataAnch = dataBase[, c("lat", "lon", "fleet", "num_haul", "catch_haul", object$info$capAnch)]
dataSar = dataBase[, c("lat", "lon", "fleet", "num_haul", "catch_haul", object$info$capSar)]
dataJur = dataBase[, c("lat", "lon", "fleet", "num_haul", "catch_haul", object$info$capJur)]
dataCab = dataBase[, c("lat", "lon", "fleet", "num_haul", "catch_haul", object$info$capCab)]
dataBon = dataBase[, c("lat", "lon", "fleet", "num_haul", "catch_haul", object$info$capBon)]
colnames(dataAnch) = c("lat", "lon", "fleet", "num_haul", "catch_haul", "catchSpecies")
dataAnch = dataAnch[!is.na(dataAnch$catchSpecies) & dataAnch$catchSpecies !=0, ]
rownames(dataAnch) = NULL
colnames(dataSar) = c("lat", "lon", "fleet", "num_haul", "catch_haul", "catchSpecies")
dataSar = dataSar[!is.na(dataSar$catchSpecies) & dataSar$catchSpecies !=0, ]
rownames(dataSar) = NULL
colnames(dataJur) = c("lat", "lon", "fleet", "num_haul", "catch_haul", "catchSpecies")
dataJur = dataJur[!is.na(dataJur$catchSpecies) & dataJur$catchSpecies !=0, ]
rownames(dataJur) = NULL
colnames(dataCab) = c("lat", "lon", "fleet", "num_haul", "catch_haul", "catchSpecies")
dataCab = dataCab[!is.na(dataCab$catchSpecies) & dataCab$catchSpecies !=0, ]
rownames(dataCab) = NULL
colnames(dataBon) = c("lat", "lon", "fleet", "num_haul", "catch_haul", "catchSpecies")
dataBon = dataBon[!is.na(dataBon$catchSpecies) & dataBon$catchSpecies !=0, ]
rownames(dataBon) = NULL
#presence for other species
dataGroups = dataBase[, -which(names(dataBase) %in%
c(object$info$capAnch, object$info$capSar, object$info$capJur, object$info$capCab, object$info$capBon))]
dataGroups = melt(dataGroups, id.vars=c("lat", "lon", "fleet", "num_haul", "catch_haul"))
indexGroup = match(dataGroups$variable, catchSpecies$catch)
dataGroups$group = catchSpecies$group[indexGroup]
dataGroups$species = catchSpecies$species[indexGroup]
dataGroups = dataGroups[!is.na(dataGroups$value),]
rownames(dataGroups) = NULL
output = list(dataAnch = dataAnch, dataSar = dataSar, dataJur = dataJur, dataCab = dataCab,
dataBon = dataBon, dataGroups = dataGroups, dataTotal = dataBase)
return(output)
}
#Funcion para plotear los mapas con puntos de pesca
.plotFishingPoints.bitacora = function(x, language, dataType,
colMap, colFleet,
cexPointCatch, cexPoint, cex.axis, cexPorts, ...){
dataBase = x[[dataType]]
ports = portData[portData$importance == 1, ]
#Colours by fleet
colVector = match(dataBase$fleet, c("Artesanal", "Menor escala", "Industrial madera", "Industrial"))
#Legend colours
if(language == "spanish"){fleetName = c("AR", "ME", "IM", "IN")[sort(unique(colVector), decreasing = FALSE)]}
if(language == "english"){fleetName = c("AR", "SS", "IW", "IN")[sort(unique(colVector), decreasing = FALSE)]}
colLegend = colFleet[sort(unique(colVector), decreasing = FALSE)]
colVector = cut(colVector, breaks = c(-Inf, 1, 2, 3, 4), labels = colFleet)
#Size according the catch
sizePoints = cut(x = dataBase$catchSpecies, breaks = pretty(dataBase$catchSpecies),
labels = seq(from = 1, by = 1, length.out = length(pretty(dataBase$catchSpecies))-1))
if(isTRUE(cexPointCatch)){cexPoints = as.numeric(as.character(sizePoints))} else {cexPoints = cexPoint}
#plot
par(mar = c(3.5, 3.5, 1, 1))
plot(x = dataBase$lon, y = dataBase$lat, xlim = c(-85, -70), ylim = c(-20,-2),
xlab = "", ylab = "", col = as.character(colVector), axes = FALSE, type = "p", pch = 16,
cex = cexPoints, ...)
map(add = TRUE, fill = TRUE, col = colMap)
box()
axis(2, at = seq(from = -20, to = -2, by = 2), las = 2, cex.axis = cex.axis,
labels = paste(seq(from = 20, to = 2, by = -2), "S", sep = " "))
axis(1, at = seq(from = -82, to = -70, by = 2), cex.axis = cex.axis,
labels = paste(seq(from = 82, to = 70, by = -2), "W", sep = " "))
text(x = ports$lon, y = ports$lat, labels = ports$name, adj = -0.1, cex = cexPorts)
legend("bottomleft", legend = fleetName, col = colLegend,
pch = 20, horiz = FALSE, bty = "n", cex = 1)
return(invisible())
}
#Funcion para plotear la presencia de otras especies por grupo taxonomico
.plotFishingPresence.bitacora = function(x, byGroup, group,
colMap, colSpecies, colLegend,
cexPoint, cex.axis, cexPorts, cexLegend, ...){
dataBase = x$dataGroups
if(isTRUE(byGroup)){
dataBase = dataBase[dataBase$group %in% group, ]
specieVector = match(dataBase$species, unique(dataBase$species))
if(is.null(colSpecies)){
colPlot = vectorColours(length(dataBase$species))[rank(dataBase$species)]
colLegend = unique(vectorColours(length(dataBase$species))[rank(dataBase$species)])
} else {
colPlot = colSpecies
colLegend = colLegend
}
namesLegend = capitalizeFirstLetter(unique(dataBase$species))
} else {
dataBase = dataBase[!dataBase$group %in% c("neritico", "transzonal", "oceanico", "demersal"),]
dataBase = dataBase[!dataBase$group == "", ]
specieVector = match(dataBase$group, unique(dataBase$group))
if(is.null(colSpecies)){
colPlot = vectorColours(length(dataBase$group))[rank(dataBase$group)]
colLegend = unique(vectorColours(length(dataBase$group))[rank(dataBase$group)])
} else {
colPlot = colSpecies
colLegend = colLegend
}
namesLegend = capitalizeFirstLetter(unique(dataBase$group))
}
ports = portData[portData$importance == 1, ]
#plot
par(mar = c(3.5, 3.5, 1,1))
plot(x = dataBase$lon, y = dataBase$lat, xlim = c(-85, -70), ylim = c(-20,-2),
xlab = "", ylab = "", col = colPlot, axes = FALSE, type = "p", pch = 16, cex = cexPoint, ...)
map(add = TRUE, fill = TRUE, col = colMap)
box()
axis(2, at = seq(from = -20, to = -2, by = 2), las = 2, cex.axis = cex.axis,
labels = paste(seq(from = 20, to = 2, by = -2), "S", sep = " "))
axis(1, at = seq(from = -82, to = -70, by = 2), cex.axis = cex.axis,
labels = paste(seq(from = 82, to = 70, by = -2), "W", sep = " "))
text(x = ports$lon, y = ports$lat, labels = ports$name, adj = -0.1, cex = cexPorts)
legend("bottomleft", legend = namesLegend, col = colLegend,
pch = 20, horiz = FALSE, bty = "n", cex = cexLegend)
return(invisible())
}
#Funcion para obtener la composicion de especies de las capturas
.speciesComposition.bitacora = function(object, language) {
#cleaning data
dataBase = object$data
dataBase = dataBase[, c(object$info$colCatchHaul, catchSpecies$catch) ]
dataBase[, 1] = apply(dataBase[2:dim(dataBase)[2]], 1, sum, na.rm = TRUE)
dataBase = dataBase[!dataBase[, 1] == 0, ]
dataBase[, 1] = NULL
#remove species with catch = 0
index = apply(dataBase, 2, sum, na.rm = TRUE)
index = which(index != 0); names(index) = NULL
dataBase = dataBase[, index]
#data.frame in a new format (species, catch, porcentaje)
dataTable = apply(dataBase, 2, sum, na.rm = TRUE)
dataTable = as.data.frame(dataTable)
dataTable$species = capitalizeFirstLetter(catchSpecies$species[match(rownames(dataTable), catchSpecies$catch)])
dataTable = dataTable[order(dataTable$dataTable, decreasing = TRUE), ]
#remove Unidentified species
dataTable = dataTable[!dataTable$species == "", ]
dataTable = data.frame(Species = dataTable$species,
Catch = round(dataTable$dataTable, 4),
Percentage = round((dataTable$dataTable * 100) / sum(dataTable$dataTable), 4) )
dataTable$Species = as.character(dataTable$Species)
dataTable = rbind(dataTable, c("Total", round(sum(dataTable$Catch),2), round(sum(dataTable$Percentage), 2)))
dataTable$Catch = as.numeric(dataTable$Catch)
dataTable$Percentage = as.numeric(dataTable$Percentage)
#Language
if(language == "english") {colnames(dataTable) = colnames(dataTable)}
if(language == "spanish") {colnames(dataTable) = c("Especie", "Captura", "Porcentaje")}
return(dataTable)
}
#Funcion para plotear el pie de composicion de especies
.plotSpeciesComposition.bitacora = function(x, threshold, minPercentage, ...) {
dataBase = x
dataBase = dataBase[- dim(dataBase)[1],]
if(isTRUE(threshold)){
dataBase = dataBase[dataBase[, 3] >= minPercentage, ]
dataBase$Porcentaje = dataBase$Captura * 100 / sum(dataBase$Captura)
} else {
dataBase = dataBase
}
#plot
par(oma = c(1,1,1,1))
pie3D(dataBase[,3], labels = dataBase[, 1], col = rainbow(dim(dataBase)[1]),
radius = 1, height = 0.15, theta = 1.1, start = 1,
labelcex = 1.2, labelrad = 1.3, ...)
return(invisible())
}
#Funcion para obtener la distribucion espacial de las capturas
.distributionCatch.bitacora = function(object, language, specie) {
dataBase = object$data
dataBase = dataBase[, c(object$info$colLat, object$info$colLon, catchSpecies$catch) ]
colnames(dataBase)[c(1,2)] = c("lat", "lon")
dataBase$lat = -abs(dataBase$lat)
dataBase$lon = -abs(dataBase$lon)
dataBase = dataBase[!is.na(dataBase$lat) & !is.na(dataBase$lon), ]
aip = isopArea.assigner(dataBase)
getAipInfo = getAIPInfo(aipVector = aip)
dataBase$lon = getAipInfo$dc
colnames(dataBase)[2] = "dc"
colnames(dataBase)[3:dim(dataBase)[2]] = catchSpecies$species[match(colnames(dataBase[3:dim(dataBase)[2]]), catchSpecies$catch)]
#Indexing for name specie
dataTable = dataBase[, c(1, 2)]
dataTable$specie = round(dataBase[, specie], 2)
dataTable$lat = cut(dataTable$lat, breaks = seq(from = -19, to = -3, by = 1), labels = seq(from = -18, to = -3, by = 1))
dataTable = aggregate(specie ~ lat + dc, data = dataTable, FUN = sum, na.rm = TRUE)
dataTable = dcast(dataTable, lat ~ dc, value.var = "specie")
#Order by latitude
dataTable$lat = as.numeric(as.character(dataTable$lat))
dataTable = dataTable[order(dataTable$lat, decreasing = TRUE), ]
dataTable[is.na(dataTable)] = 0
rownames(dataTable) = NULL
#Latitud
dataTable$lat = as.character(dataTable$lat)
dataTable$lat = paste0(substring(dataTable$lat, 2), "S")
#Total
dataTable$Total = rowSums(dataTable[, seq(from = 2, to = dim(dataTable)[2], by = 1)])
dataTable = rbind(dataTable, c("Total", as.vector(colSums(dataTable[, seq(from = 2, to = dim(dataTable)[2], by = 1)]))))
#Language
if(language == "spanish"){colnames(dataTable)[1] = "Latitud"}
if(language == "spanish"){colnames(dataTable)[1] = "Latitude"}
return(dataTable)
}
#Funcion para obtener la data para analizar el esfuerzo y el cpue
.effortData.bitacora = function(object) {
dataBase = object$data
dataBase = dataBase[, c(object$info$colTrip, object$info$colStorageCapacity, "flota", "puerto",
object$info$colDateStart, object$info$colDateOut,
object$info$colLat, object$info$colLon, object$info$colSearchTime,
object$info$colHaul, object$info$colHaulTotal, object$info$colCatchHaul,
object$info$capAnch, object$info$capSar, object$info$capJur, object$info$capCab, object$info$capBon)]
colnames(dataBase) = c("codeTrip", "storageCapacity", "fleet", "port", "dateStart", "dateOut",
"lat", "lon", "searchTime", "haulNumber", "haulTotal","haulCatch",
"anchoveta", "sardina", "jurel", "caballa", "bonito")
#Obtencion de variables para el esfuerzo
#travel time (duracion viaje)
dataBase = dataBase[!is.na(dataBase$dateStart) & !is.na(dataBase$dateOut), ]
dataBase$dateStart = as.POSIXct(strptime(dataBase$dateStart, format = "%d/%m/%Y %H:%M:%S"))
dataBase$dateOut = as.POSIXct(strptime(dataBase$dateOut, format = "%d/%m/%Y %H:%M:%S"))
dataBase$travelTime = as.numeric(dataBase$dateOut - dataBase$dateStart) / 60
#search time (tiempo busqueda)
hours = suppressWarnings(as.numeric(substring(gsub("^\\s+|\\s+$", "", dataBase$searchTime), 1, 2)))
mins = suppressWarnings(as.numeric(substring(gsub("^\\s+|\\s+$", "", dataBase$searchTime), 4, 5)))
dataBase$searchTime = hours + mins/60
# variables time
dataBase$year = as.numeric(format(dataBase$dateStart, "%Y"))
dataBase$month = as.numeric(format(dataBase$dateStart, "%m"))
dataBase$day = as.numeric(format(dataBase$dateStart, "%d"))
dataBase$dates = as.Date(paste(dataBase$day, dataBase$month, dataBase$year, sep = "/"),
format = "%d/%m/%Y")
# code trip haul
dataBase = dataBase[!is.na(dataBase$codeTrip) & !is.na(dataBase$haulTotal), ]
dataBase$codeTripHaul = paste(dataBase$codeTrip, dataBase$haulTotal, sep = "-")
return(dataBase)
}
#Funcion para extraer informacion de la especies y la temporada
#' Get species effort data
#' @description Receive an object of bitacora class, indexes the data by species,
#' estimates the season by species, for anchovy the season depend of region
#' (north-central and south region) but for other species
#' (sardine, jack mackerel, chub mackerel and bonito) the season is annual.
#' @param data A \code{data.frame} obtained from effortData.bitacora function (internal function
#' of bitacora class). This data frame could be obtained from
#' getMainResults.bitacora function too.
#' @param species A \code{character} with the common name of anchovy ("anchoveta"),
#' sardine ("sardina"), jack mackerel ("jurel"), chub mackerel ("caballa") and bonito ("bonito").
#' @param region If the species is anchovy (\code{anchoveta}) is necessary
#' indicate for each region the effort data is going to be calculate. By default is \code{NULL}.
#' @return A \code{data.frame} with effort data for a given species.
#' @author Criscely Lujan-Paredes, \email{criscelylujan@gmail.com}.
#' @examples
#' # Read a example of data base
#' bitacoraData = system.file("extdata", "bitacoraData.csv", package = "imarpe")
#'
#' # Create a object of bitacora class
#' bitacoraObject = getBitacoraData(file = bitacoraData)
#' class(bitacoraObject)
#'
#' # Get the results
#' mainBitacoraData = getMainResults.bitacora(object = bitacoraObject, language = "spanish",
#' specie = "anchoveta", effortData = TRUE)
#'
#' # Effort data
#' effortData = mainBitacoraData$effortData
#'
#' # Use of effortSpeciesData.bitacora
#' effortNC = effortSpeciesData.bitacora(data = effortData, species = "anchoveta", region = "norte-centro")
#'
#' @export
effortSpeciesData.bitacora = function(data, species, region = NULL){
dataBase = data
nameSpecies = c("anchoveta", "sardina", "jurel", "caballa", "bonito")
nameSpecies = nameSpecies[!nameSpecies %in% species]
dataBase = dataBase[, -which(colnames(dataBase) %in% nameSpecies)]
dataBase = dataBase[!is.na(dataBase[species]), ]
catchSpecies = tapply(dataBase[, species], dataBase[, "codeTripHaul"], sum)
dataBase = subset(dataBase, !duplicated(subset(dataBase, select=c(codeTripHaul))))
dataBase[, species] = catchSpecies[match(dataBase$codeTripHaul, names(catchSpecies)) ]
#building season by species
if(species %in% c("sardina", "jurel", "caballa", "bonito")) {
dataBase$season = dataBase$year}
if(species == "anchoveta") {
if(!is.null(region)){
if(region == "norte-centro") {
dataBase = dataBase[!is.na(dataBase$lat), ]
dataBase = dataBase[which(dataBase$lat > 2 & dataBase$lat <= 15.99), ]
dataBase$season = assignAnchovy_season(x = dataBase$dates, region = region) }
if(region == "sur") {
dataBase = dataBase[!is.na(dataBase$lat), ]
dataBase = dataBase[which(dataBase$lat >= 16 & dataBase$lat < 18.9), ]
dataBase$season = assignAnchovy_season(x = dataBase$dates, region = region) }
} else {
dataBase$season = dataBase$year
}
}
colnames(dataBase)[13] = "catch"
rownames(dataBase) = NULL
return(data = dataBase)
}
#Funcion para obtener el esfuerzo
#' Get fishing effort
#' @description Generical function to get the fishing effort. The type of fishing effort could be four types:
#' \itemize{
#' \item travelTime for the effort as a function of the travel time.
#' \item haulTotal for the effort as a function of the total number of fishing haul.
#' \item storageCapacity for the effort as a function of the storage capacity.
#' \item searchTime for the effort as a function of the search time to catch the species.
#' }
#' @param data A \code{data.frame} with information to estimated the fishing effort (in columns).
#' This data base have to contain columns of the time (days, months, years, season), ports and the
#' fleet type.
#' @param efforType A \code{character}. It could be one of the fishing effort type ("travelTime",
#' "haulTotal", "storageCapacity", "searchTime").
#' @param effortBy Parameter to indicate whether the effort will be estimated by
#' time (\code{effortBy = time}) or by port (\code{effortBy = port}).
#' @param timeBy If the effort is estimated over the time, it could be by days
#' (\code{timeBy = days}), months (\code{timeBy = months}), years (\code{timeBy = years}) or
#' by seasons (\code{timeBy = seasons}).
#' @param fleeType The effort could be estimated for the whole data given or for a data by fleeType.
#' By default this parameter is \code{NULL}.
#' @return A \code{vector} with the estimated effort.
#' @author Criscely Lujan-Paredes, \email{criscelylujan@gmail.com}.
#' @examples
#' # Read a example of data base
#' bitacoraData = system.file("extdata", "bitacoraData.csv", package = "imarpe")
#'
#' # Create a object of bitacora class
#' bitacoraObject = getBitacoraData(file = bitacoraData)
#'
#' # Get the results
#' mainBitacoraData = getMainResults.bitacora(object = bitacoraObject, language = "spanish",
#' specie = "anchoveta", effortData = TRUE)
#'
#' # Effort data
#' effortData = mainBitacoraData$effortData
#'
# # Filter data: anchovy data for anchovy and the north-central region
#' effortNC = effortSpeciesData.bitacora(data = effortData, species = "anchoveta", region = "norte-centro")
#'
#' # Get the cpue
#' effortNC_tt = getEffort(data = effortNC, efforType = "travelTime", effortBy = "port")
#' effortNC_hn = getEffort(data = effortNC, efforType = "haulTotal", effortBy = "port")
#'
#' @export
getEffort = function(data, efforType, effortBy, timeBy, fleeType = NULL) {
dataBase = data
#indexacion tipo de flota
if(!is.null(fleeType)) {dataBase = dataBase[dataBase$fleet %in% fleeType, ]}
#remove NAs in efforType
dataBase = dataBase[!is.na(dataBase[efforType]), ]
effort = dataBase[efforType]
names(effort) = NULL
dataBase["effort"] = effort
if(effortBy %in% "time") {
if(timeBy == "days") {effortVector = tapply(X = dataBase$effort, INDEX = list(dataBase$dates), FUN = sum, na.rm = TRUE)}
if(timeBy == "months") {effortVector = tapply(X = dataBase$effort, INDEX = list(dataBase$year, dataBase$month), FUN = sum, na.rm = TRUE) }
if(timeBy == "years") {effortVector = tapply(X = dataBase$effort, INDEX = dataBase$year, FUN = sum, na.rm = TRUE) }
if(timeBy == "seasons") {effortVector = tapply(X = dataBase$effort, INDEX = list(dataBase$season), FUN = sum, na.rm = TRUE) }
}
if(effortBy %in% "port") {
effortVector = tapply(X = dataBase$effort, INDEX = list(dataBase$port), FUN = mean, na.rm = TRUE)
effortVector = effortVector[order(match(names(effortVector), portData$name))]
}
return(effort = effortVector)
}
#Funcion para obtener la captura por unidad de esfuerzo (cpue) relativizada
#' Get the relative catch per unit effort (cpue)
#' @details The relative cpue is a index used on bitacora report.
#' @param data A \code{data.frame} with information about catchs and fishing effort.
#' @param toTons A logical parameter. \code{TRUE} (dafault) it assume that the catches is in kilograms
#' and converts it into tonnes (divided by 1000). \code{FALSE} it assume the the information is in tons
#' and don't convert the data.
#' @param efforType A \code{character} indicating the fishing effort type. It could be four types:
#' \itemize{
#' \item travelTime for the effort as a function of the travel time.
#' \item haulTotal for the effort as a function of the total number of fishing haul.
#' \item storageCapacity for the effort as a function of the storage capacity.
#' \item searchTime for the effort as a function of the search time to catch the species.
#' }
#' @param cpueBy Parameter to indicate whether the relative cpue will be estimated by
#' time (\code{cpueBy = time}) or by port (\code{cpueBy = port}).
#' @param timeBy If the relative cpue is estimated over the time, it could be by days
#' (\code{timeBy = days}), months (\code{timeBy = months}), years (\code{timeBy = years}) or
#' by seasons (\code{timeBy = seasons}).
#' @param fleeType The relative cpue could be estimated for the whole data given or for a data by fleeType.
#' By default this parameter is \code{NULL}.
#' @return A \code{vector} with the relative cpue.
#' @author Criscely Lujan-Paredes, \email{criscelylujan@gmail.com}.
#' @examples
#' # Read a example of data base
#' bitacoraData = system.file("extdata", "bitacoraData.csv", package = "imarpe")
#'
#' # Create a object of bitacora class
#' bitacoraObject = getBitacoraData(file = bitacoraData)
#'
#' # Get the results
#' mainBitacoraData = getMainResults.bitacora(object = bitacoraObject, language = "spanish",
#' specie = "anchoveta", effortData = TRUE)
#'
#' # Effort data
#' effortData = mainBitacoraData$effortData
#'
#' # Filter data: anchovy data for anchovy and the north-central region
#' effortNC = effortSpeciesData.bitacora(data = effortData, species = "anchoveta", region = "norte-centro")
#'
#' # Get the cpue relative
#' getCpue_relative(data = effortNC, efforType = "travelTime", cpueBy = "port")
#' getCpue_relative(data = effortNC, efforType = "travelTime", cpueBy = "time", timeBy = "months")
#'
#' @export
getCpue_relative = function(data, toTons=FALSE, efforType, cpueBy = "time", timeBy, fleeType = NULL) {
dataBase = data
dataBase$catch = dataBase$catch / ifelse(isTRUE(toTons), 1000, 1)
#indexacion tipo de flota
if(!is.null(fleeType)) {dataBase = dataBase[dataBase$fleet %in% fleeType, ]}
#remove NAs in the dataBase
dataBase = dataBase[!is.na(dataBase$catch) & dataBase$catch != 0 &
!is.na(dataBase$storageCapacity) & dataBase$storageCapacity != 0, ]
dataBase = dataBase[!is.na(dataBase[efforType]) & dataBase[efforType] != 0, ]
cpue = dataBase$catch / dataBase$storageCapacity / dataBase[efforType]
names(cpue) = NULL
dataBase["cpue"] = cpue
if(cpueBy %in% "time"){
if(timeBy == "days") {cpueVector = tapply(X = dataBase$cpue, INDEX = list(dataBase$dates), FUN = mean, na.rm = TRUE, i)}
if(timeBy == "months") {cpueVector = tapply(X = dataBase$cpue, INDEX = list(dataBase$year, dataBase$month), FUN = mean, na.rm = TRUE) }
if(timeBy == "years") {cpueVector = tapply(X = dataBase$cpue, INDEX = dataBase$year, FUN = mean, na.rm = TRUE) }
if(timeBy == "seasons") {cpueVector = tapply(X = dataBase$cpue, INDEX = list(dataBase$season), FUN = mean, na.rm = TRUE) }
}
if(cpueBy %in% "port") {
cpueVector = tapply(X = dataBase$cpue, INDEX = list(dataBase$port), FUN = mean, na.rm = TRUE)
cpueVector = cpueVector[order(match(names(cpueVector), portData$name))]
}
return(cpue = cpueVector)
}
#' Plot series of fishing effort by ports
#' @description This method take a series of fishing effort by ports and produces a graph
#' that is useful for the PBP program.
#' @param effort1 A vector of the first serie of fishing effort.
#' @param effort2 A vector of the second serie of fishing effort.
#' @param labAxis2 Text to be included on the left vertical axis of the graph
#' @param labAxis4 Text to be included on the right vertical axis of the graph
#' @param colBar The colour of the barplot. Sea \code{\link{barplot}}.
#' @param colLine The colour of the line on the graph.
#' @param legend The names to include on the legend of the graph.
#' @param ... Extra arguments.
#' @return A graph for two types of fishing effort by ports. One type is on the left
#' vertical axis and the other on the right vertical axis, the ports are on the horizontal axis.
#' @author Criscely Lujan-Paredes, \email{criscelylujan@gmail.com}.
#' @examples
#' # Read a example of data base
#' bitacoraData = system.file("extdata", "bitacoraData.csv", package = "imarpe")
#'
#' # Create a object of bitacora class
#' bitacoraObject = getBitacoraData(file = bitacoraData)
#'
#' # Get the results
#' mainBitacoraData = getMainResults.bitacora(object = bitacoraObject, language = "spanish",
#' specie = "anchoveta", effortData = TRUE)
#'
#' # Effort data
#' effortData = mainBitacoraData$effortData
#'
#' # Filter data: anchovy data for anchovy and the north-central region
#' effortNC = effortSpeciesData.bitacora(data = effortData, species = "anchoveta",
#' region = "norte-centro")
#'
#' # Get effort
#' effortNC_tt = getEffort(data = effortNC, efforType = "travelTime", effortBy = "port")
#' effortNC_hn = getEffort(data = effortNC, efforType = "haulTotal", effortBy = "port")
#'
#' # Graph
#' plotEffort(effort1 = effortNC_tt, effort2 = effortNC_hn, labAxis2 = "Horas de viaje",
#' labAxis4 = "N\u{00FA}mero de calas", colBar="gray", colLine = "black",
#' legend = c("Duracion del viaje", "N\u{00FA}mero de calas"))
#'
#' @export
plotEffort.numeric = function(effort1, effort2, labAxis2, labAxis4, colBar = NULL,
colLine = NULL, legend = NULL, ...) {
maxAxis2 = ceiling(1.2*max(effort1))
maxAxis4 = ceiling(1.2*max(effort2))
par(mar=c(3.5,4.5,1,4.5))
#effort 1
barplot(effort1, cex.names = 0.8, ylim = c(0, maxAxis2), axes = FALSE, ylab = labAxis2, col = colBar, ...)
axis(side = 2, at = pretty(effort1), las = 1)
#effort 2
vectorEffort2 = barplot(effort2, names.arg = seq(from = 1, to = length(effort2), by = 1), plot = FALSE)
lines(vectorEffort2, effort2*maxAxis2/maxAxis4, type="o", pch=19, lwd=1, col = colLine)
axis(side = 4, at = pretty(effort2)/maxAxis4*maxAxis2, labels = pretty(effort2), las = 2)
mtext(text = labAxis4, side = 4, line = 2.5)
box()
if(!is.null(legend)){
legend("topleft", legend = c(legend[1], legend[2]), bty = "n", lty = c(1,4),
cex = 0.8, lwd = c(5, 2), col = c("gray", "black"))
}
return(invisible())
}
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