script/TablePlotsOverlay.R
In natoyaj/TestPackage: IBTSspatial
#
# Example of tabulated plot inspired by Gerritsen et al. 2006
#
library(ggplot2)
library(grid)
library(gridExtra)
library(egg)
#' @noRd
makeColorMap <- function(columns, color){
colors <- as.list(rep(color, length(columns)))
names(colors) <- columns
return(colors)
}
#' generates function for passing to tickmarks
#' tickfunctions have n number of ticks
#' lowest tick is 0
#' highes ttick is rounded of from ymax (digits determine rounding)
#' mid tick is rounded midpoint
genTickMarksFunction <- function(n=3, digits=1, slack=0.01){
roundf <- function(x){floor(x * (10**digits))/(10**digits)}
tickfun <- function(lim){
if (is.infinite(lim[2]) | is.na(lim[2])){
return(c(0.0))
}
maxl <- lim[2]-slack
minl <- 0
step <- maxl/(n-1)
ticks <- sapply(seq(minl, maxl, step), roundf)
return(ticks)
}
}
#' generates function for passing to tickmaks
#' tickfunctions tries a range of preset scales form 0 to ymax
#' and chooses the one that leaves fewer tickmars, exceding n
genTickMarksTester <- function(n=3, steps=c(.001, 0.0025, 0.005, 0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 1, 2.5, 5, 10, 25, 50, 100, 250, 500)){
stopifnot(all(steps == sort(steps)))
tickfun <- function(lim){
maxl <- lim[2]
if (is.infinite(maxl) | is.na(maxl)){
return(c(0.0))
}
minl <- 0
for (step in rev(steps)){
ticks <- seq(minl, maxl, step)
if (length(ticks) >= 3){
return(ticks)
}
}
stop("Could not make tickmarks")
}
}
ticks3 <- genTickMarksTester()
ticks2 <- genTickMarksFunction(2,2)
#' @noRd
panelPlotOverlay <- function(plotData, columnGroups, xVariable, yVariable, yVariableUpper, yVariableLower, xlimrow, ylimcol, showX=F, showY=F, ylabel="", basetheme=NULL, titletext="", pointcol=NULL, linecol=NULL, errorcol=NULL, tickmarks=NULL){
panelplot <- ggplot(plotData, aes_string(x=xVariable, y=yVariable)) + xlim(xlimrow) + ylab(ylabel) + ylim(ylimcol)
cols <- unique(plotData[,columnGroups])
for (col in cols){
colData <- plotData[plotData[,columnGroups] == col,]
if (!is.null(yVariableUpper) & !is.null(yVariableLower)){
panelplot <- panelplot + geom_linerange(data=colData, aes_string(ymin=yVariableLower, ymax=yVariableUpper), color=errorcol[[col]])
}
#points and lines
if (nrow(colData) == 0){
panelplot <- panelplot + geom_blank()
}
else{
panelplot <- panelplot + geom_line(data=colData, aes_string(color=columnGroups)) + geom_point(data=colData, shape=20, size=2, color=pointcol[[col]])
}
if (!is.null(title) & col == cols[1]){
panelplot <- panelplot + ggtitle(titletext)
}
}
panelplot <- panelplot + scale_color_manual(values = unlist(linecol))
panelplot <- panelplot + basetheme()
panelplot <- panelplot + theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank())
if (!is.null(tickmarks)){
panelplot <- panelplot + scale_y_continuous(breaks=tickmarks, limits = ylimcol)
}
if (!showX){
panelplot <- panelplot + theme(axis.title.x = element_blank(), axis.text.x=element_blank())
}
else{
panelplot <- panelplot + theme(axis.title.x = element_blank())
}
if (!showY){
panelplot <- panelplot + theme(axis.title.y = element_blank(), axis.text.y=element_blank())
}
return(panelplot)
}
#' Table plot
#' @description
#' makes a tabulated plot of estimates grouped by two covariates, one for columns, one for rows. axes of all plots are the same variables.
#' @details
#' @param data data.table() with data, must contain columns identified by parameters 'columnGroups', 'rowGroups', 'xVariable' and 'yVariable'
#' @param columnGroups character() identifies column in data that identifies the grouping used for columns in the plot
#' @param rowGRoups character() identifies column in data that identifies the grouping used for rows in the plot
#' @param xVariable character() identifies column in data containting variable to be used for x-axis
#' @param yVariable character() identifies column in data containing variable to be used for y-axis
#' @param overlayGroups character() identifies column in data specifying the groups to be overlayed
#' @param ylab character(), optional, label for y-axis, if not provided 'yVariable' will be used.
#' @param ymin numeric(), optional, lower bounds of y axis, if not NULL y axis will be adopted to data for each column
#' @param ymax numeric(), optional, upper bounds of y axis, if not provided y axis will be adopted to data for each column
#' @param xlim vector, optional, lower and upper bounds of x axis, if not provided y axis will be adapted to data for each row
#' @param pointcol character() or named list mapping the values in data[,columnGroups] to colors, color of points in plots
#' @param linecol character() or named list mapping the values in data[,columnGroups] to colors color of connecting lines in plots
#' @param errorcol character() or named list mapping the values in data[,columnGroups] to colors color of error bars
#' @param tickmarks numeric() specifies tickmarks for y-axis
#' @param basetheme ggplot2 - theme function to use for plotting. Default adjusts y-axis label alignments to account for variable width of tick-labels.
#' @param reverseX logical() whether to reverse X axes
#' @param hLineCol character() specify any column that should be used for horisontal reference lines
stackedPanelsOverlay <- function(data, columnGroups, rowGroups, xVariable, yVariable, overlayGroups, ylab=NULL, xlab=NULL, xlim=NULL, ymin=0, ymax=NULL, pointcol="black", linecol="#cb181d", errorcol="#cb181d", tickmarks=NULL, basetheme=function(x){ggplot2::theme_classic() + theme(plot.title = element_text(hjust = 0.5, size=10), axis.title.y = element_text(size=8), axis.title.x = element_text(size=8), axis.text.y = element_text(angle = 90, hjust = 1, size=6))}, reverseX=F, hLineCol=NULL){
if(is.numeric(columnGroups) | is.numeric(rowGroups)){
stop("ColumnGroups and rowGroups can not be numeric variables. Covert with as.character()")
}
if (!all(c(columnGroups, rowGroups, xVariable, yVariable) %in% names(data))){
stop("Some arguments (columnGroups, rowGroups, xVariable, yVariable) not found in data.")
}
if (any(is.na(data[,c(columnGroups, rowGroups, xVariable)]))){
stop("NAs in grouping variables or x variable")
}
if (is.null(ylab)){
ylab <- yVariable
}
if (is.null(xlab)){
xlab <- xVariable
}
rows <- sort(unique(unlist(data[,rowGroups])), decreasing = T)
cols <- sort(unique(unlist(data[,columnGroups])))
og <- sort(unique(unlist(data[,overlayGroups])))
if (is.character(linecol)){
linecol <- makeColorMap(og, linecol)
}
if (is.character(pointcol)){
pointcol <- makeColorMap(og, pointcol)
}
if (is.character(errorcol)){
errorcol <- makeColorMap(og, errorcol)
}
panels <- list()
for (row in rows){
#determine ylims if necessary
minvar <- yVariable
miny <- ymin
if (is.null(miny)){
miny <- min(data[data[,rowGroups] == row,minvar])
miny <- miny - abs(miny) * .1
}
maxvar <- yVariable
maxy <- ymax
if (is.null(maxy)){
maxy <- max(data[data[,rowGroups] == row,maxvar])
maxy <- maxy + abs(maxy) * .1
}
ylimcol <- c(miny, maxy)
for (col in cols){
xlimrow <- xlim
if (is.null(xlimrow)){
xlimrow <- c(min(data[data[,columnGroups] == col,xVariable]), max(data[data[,columnGroups] == col,xVariable]))
}
title <- NULL
if (row == rows[1]){
title = col
}
plotdata <- data[data[,columnGroups] == col & data[,rowGroups] == row,]
referenceline <- NULL
if (!is.null(hLineCol)){
stopifnot(length(unique(unlist(plotdata[,hLineCol])))==1)
referenceline <- unlist(plotdata[,hLineCol])[1]
}
panel <- panelPlotOverlay(plotdata, overlayGroups, xVariable, yVariable, yVariableUpper=NULL, yVariableLower=NULL, xlimrow, ylimcol, showX=(row == rows[length(rows)]), showY=(col == cols[1]), ylabel=row, basetheme=basetheme, title=title, pointcol=pointcol, linecol=linecol, errorcol=errorcol, tickmarks=tickmarks)
panel <- panel + theme(legend.position="none")
panels[[paste(row, col, sep="/")]] <- panel
}
}
g <- ggplotGrob(panels[[1]] + theme(legend.position="bottom", legend.title = element_blank()))$grobs
legend <- g[[which(sapply(g, function(x) x$name) == "guide-box")]]
lheight <- sum(legend$height)
grid.arrange(
ggarrange(
plots=panels,
nrow=length(rows),
left=textGrob(ylab, gp=gpar(fontsize=9), rot = 90),
bottom=textGrob(xlab, gp=gpar(fontsize=9)),draw=F
),
legend,
ncol = 1,
heights = unit.c(unit(1, "npc") - lheight, lheight))
}
#' Makes Two column plot-table
#' Same x-axis, and row-categories, but two different y-axis for the different groups.
#' values for each of the column groups are plotted on top of each other in the column to the right for yvariable1
#' values for each of the column groups are plotted on top of each other in the column to the left for yvariable2
overlayedColumnPlot <- function(data, columnGroups, rowGroups, xVariable, yVariable1, yVariable2, yVariable1Lower=NULL, yVariable1Upper=NULL, yVariable2Lower=NULL, yVariable2Upper=NULL, ylab1=NULL, ylab2=NULL, xlab=NULL, xlim=NULL, ymin1=0, ymin2=0, ymax1=NULL, ymax2=NULL, pointcol="black", linecol="#cb181d", errorcol="#cb181d", tickmarksY1=NULL, tickmarksY2=NULL, basetheme=function(x){ggplot2::theme_classic() + theme(plot.title = element_text(hjust = 0.5, size=10), axis.title.y = element_text(size=8), axis.title.x = element_text(size=8), axis.text.y = element_text(angle = 90, hjust = 1, size=6),axis.title.x.bottom = element_text(size=8), axis.text.x=element_text(size=6), legend.text = element_text(size=8))}){
if(is.numeric(columnGroups) | is.numeric(rowGroups)){
stop("ColumnGroups and rowGroups can not be numeric variables. Covert with as.character()")
}
if (!all(c(columnGroups, rowGroups, xVariable, yVariable1, yVariable2) %in% names(data))){
stop("Some arguments (columnGroups, rowGroups, xVariable, yVariable1, yvaraible2) not found in data.")
}
if (any(is.na(data[,c(columnGroups, rowGroups, xVariable)]))){
stop("NAs in grouping variables or x variable")
}
if (is.null(yVariable1Upper) + is.null(yVariable1Lower) == 1){
stop("Provide either both yVariableUpper and yVariableLower, or neither of them")
}
if (is.null(yVariable2Upper) + is.null(yVariable2Lower) == 1){
stop("Provide either both yVariableUpper and yVariableLower, or neither of them")
}
if (is.null(ylab1)){
ylab1 <- yVariable1
}
if (is.null(ylab2)){
ylab2 <- yVariable2
}
if (is.null(xlab)){
xlabel <- xVariable
}
else{
xlabel <- xlab
}
rows <- sort(unique(unlist(data[,rowGroups])), decreasing = T)
cols <- sort(unique(unlist(data[,columnGroups])))
if (is.character(linecol)){
linecol <- makeColorMap(cols, linecol)
}
if (is.character(pointcol)){
pointcol <- makeColorMap(cols, pointcol)
}
if (is.character(errorcol)){
errorcol <- makeColorMap(cols, errorcol)
}
panels <- list()
for (row in rows){
#determine ylims for yVariable1 if necessary
minvar1 <- yVariable1
if (!is.null(yVariable1Lower)){
minvar1 <- yVariable1Lower
}
miny1 <- ymin1
if (is.null(miny1)){
miny1 <- min(data[data[,rowGroups] == row,minvar1])
miny1 <- miny1 - abs(miny1) * .1
}
maxvar1 <- yVariable1
if (!is.null(yVariable1Upper)){
maxvar1 <- yVariable1Upper
}
maxy1 <- ymax1
if (is.null(maxy1)){
maxy1 <- max(data[data[,rowGroups] == row,maxvar1])
maxy1 <- maxy1 + abs(maxy1) * .1
}
ylim1col <- c(miny1, maxy1)
#determine ylims for yVariable2 if necessary
minvar2 <- yVariable2
if (!is.null(yVariable2Lower)){
minvar2 <- yVariable2Lower
}
miny2 <- ymin2
if (is.null(miny2)){
miny2 <- min(data[data[,rowGroups] == row,minvar2])
miny2 <- miny2 - abs(miny2) * .1
}
maxvar2 <- yVariable2
if (!is.null(yVariable2Upper)){
maxvar2 <- yVariable2Upper
}
maxy2 <- ymax2
if (is.null(maxy2)){
maxy2 <- max(data[data[,rowGroups] == row,maxvar2])
maxy2 <- maxy2 + abs(maxy2) * .1
}
ylim2col <- c(miny2, maxy2)
#get x-axis and title params
xlimrow <- xlim
if (is.null(xlimrow)){
xlimrow <- c(min(data[,xVariable]), max(data[,xVariable]))
}
title1 <- NULL
if (row == rows[1]){
title1 = yVariable1
}
title2 <- NULL
if (row == rows[1]){
title2 = yVariable2
}
plotdata <- data[data[,rowGroups] == row,]
#plot yvar1
panel <- panelPlotOverlay(plotdata, columnGroups, xVariable, yVariable1, yVariable1Upper, yVariable1Lower, xlimrow, ylim1col, showX=(row == rows[length(rows)]), showY=T, ylabel=row, basetheme=basetheme, title=title1, pointcol=pointcol, linecol=linecol, errorcol=errorcol, tickmarks=tickmarksY1)
panel <- panel + theme(legend.position="none")
panels[[paste(row, "yvar1", sep="/")]] <- panel
#plot yvar2
panel <- panelPlotOverlay(plotdata, columnGroups, xVariable, yVariable2, yVariable2Upper, yVariable2Lower, xlimrow, ylim2col, showX=(row == rows[length(rows)]), showY=T, ylabel=NULL, basetheme=basetheme, title=title2, pointcol=pointcol, linecol=linecol, errorcol=errorcol, tickmarks=tickmarksY2)
panel <- panel + theme(legend.position="none")
panels[[paste(row, "yvar2", sep="/")]] <- panel
}
g <- ggplotGrob(panels[[1]] + theme(legend.position="bottom", legend.title = element_blank()))$grobs
legend <- g[[which(sapply(g, function(x) x$name) == "guide-box")]]
lheight <- sum(legend$height)
grid.arrange(
ggarrange(
plots=panels,
nrow=length(rows),
left=ylab,
bottom=xlab,draw=F
),
legend,
ncol = 1,
heights = unit.c(unit(1, "npc") - lheight, lheight))
}
warning("Change file reference to package location")
warning("Check which CI to use")
warning("Check axis labels")
#
# compare the estimated mCPUE from the two ALKs for each age group (showing confidence intervals)
#
alkresult <- read.csv("data/ALKs_results.csv", sep=";", stringsAsFactors = F, dec = ",", na.strings = c("#N/A"))
areab <- alkresult$ALK %in% "area based"
haulb <- alkresult$ALK %in% "haul based"
alkresult[areab,5] <- "area-based"
alkresult[haulb,5] <- "haul-based"
alkresult$bs <- NULL
alkresult[alkresult$Bootstrap=="Stratified", "bs"] <- "S"
alkresult[alkresult$Bootstrap=="ICES-IBTS", "bs"] <- "I"
alkresult[alkresult$Bootstrap=="Modified-ICES", "bs"] <- "mI"
alkresult[alkresult$ALK == "area based", "ALK"] <- "area-based"
alkresult[alkresult$ALK == "haul based", "ALK"] <- "haul-based"
alkresult$ALKm <- paste(alkresult$ALK, alkresult$bs, sep=", ")
alkresult$age <- paste("Age", alkresult$age)
alkresult$RSE <- alkresult$sd / alkresult$mCPUE
estimator_colors <- list()
estimator_colors[["area-based, I"]] <- "#000000" # "#92c5de"
estimator_colors[["area-based, mI"]] <-"#00BFFF" #"#0000CD" #"#0571b0"
estimator_colors[["haul-based, S"]] <- "#ca0020"
####
# Plot 1
# Using Q1 and all years
#
#
#path= "G:/DOCUMENTS/IBTSgithub3/New-IBTS-Project/TestPackage/ices/figures/"
alkq1 <- alkresult[alkresult$Quarter=="Q1",]
alkq1 <- alkq1[alkq1$age != "Age 0",]
pdf(file = "ices/figures/ALK_Bootstrap_Plots_Edvin/mCpueRseQ1b.pdf", width=3.35, onefile = F) #85mm in inches (3.35) or 170mm (6.69 inches)
overlayedColumnPlot(data = alkq1, columnGroups = "ALKm", rowGroups = "age", xVariable = "Year", yVariable1 = "mCPUE", yVariable2 = "RSE", yVariable1Lower = "Q025", yVariable1Upper = "Q975", tickmarksY1 = c(0,2,5,10,15,20,25), tickmarksY2=c(0,.25,.5,.75,1,1.25,1.5), pointcol = estimator_colors, linecol = estimator_colors, errorcol = estimator_colors)
dev.off()
####
# Plot 2 - for supplementary
# As plot 1, but for Q3, retaining age group 0
# Using Q3 and all years
alkq3 <- alkresult[alkresult$Quarter=="Q3",]
pdf(file = "ices/figures/ALK_Bootstrap_Plots_Edvin/suppMatMcpueRseQ3.pdf", width=3.35, onefile = F) #85mm in inches
overlayedColumnPlot(data = alkq3, columnGroups = "ALKm", rowGroups = "age", xVariable = "Year", yVariable1 = "mCPUE", yVariable2 = "RSE", yVariable1Lower = "Q025", yVariable1Upper = "Q975", tickmarksY1 = ticks3, tickmarksY2=ticks3, pointcol = estimator_colors, linecol = estimator_colors, errorcol = estimator_colors)
dev.off()
####
# Plot 7
# overlay 1 and 5 for otholith per 5 cm
resamplingCol <- list()
resamplingCol[["1 fish per 5cm"]] <- "red"
resamplingCol[["5 fish per 5cm"]] <- "black"
resamplingOtolithsAndHauls <- read.csv("OtolithsAndHauls_Results_2015_2018/OtolAndHaul_1_5_per_5cm_areaBased2015_2018.csv", sep=";", stringsAsFactors = F, na.strings=c("NA"))
oneFish <- resamplingOtolithsAndHauls$Otolith_per5cm %in% 1
fiveFish <- resamplingOtolithsAndHauls$Otolith_per5cm %in% 5
resamplingOtolithsAndHauls[oneFish,5] <- "1 fish per 5cm"
resamplingOtolithsAndHauls[fiveFish,5] <- "5 fish per 5cm"
resamplingOtolithsAndHauls$cv <- resamplingOtolithsAndHauls$sd / resamplingOtolithsAndHauls$bootstrapMean
resamplingOtolithsAndHauls$age <- paste("Age", resamplingOtolithsAndHauls$age)
resamplingOtolithsAndHauls$Year <- as.character(resamplingOtolithsAndHauls$Year)
resamplingOtolithsAndHauls$Otolith_per5cm <- as.character(resamplingOtolithsAndHauls$Otolith_per5cm)
resamplingOtolithsAndHaulsQ1 <- resamplingOtolithsAndHauls[resamplingOtolithsAndHauls$Quarter=="Q1",]
resamplingOtolithsAndHaulsQ1 <- resamplingOtolithsAndHaulsQ1[resamplingOtolithsAndHaulsQ1$age != "Age 0",]
pdf(file = "ices/figures/ALK_Bootstrap_Plots_Edvin/resamplingAgeAndHaulsQ1.pdf", width=3.35, onefile = F) #85mm in inches
stackedPanelsOverlay(data = resamplingOtolithsAndHaulsQ1, columnGroups = "Year", rowGroups = "age", xVariable = "N", yVariable = "cv", overlayGroups="Otolith_per5cm", xlab="Number of hauls", ylab="RSE", ymin=0, pointcol = resamplingCol, linecol = resamplingCol, errorcol = resamplingCol, tickmarks = c(.4,.8,1.2), basetheme=function(x){ggplot2::theme_classic() + theme(plot.title = element_text(hjust = 0.5, size =10),axis.title.y = element_text(size =8),axis.title.x = element_text(size =8), axis.text.y = element_text(angle = 90, hjust = 1, size=6), axis.text.x = element_text(angle = 45, hjust = 1, size=6))})
dev.off()
####
# Plot 8 - supplementary
# Like plot 7, but for Q3 and including aGE GROUP 0
resamplingOtolithsAndHaulsQ3 <- resamplingOtolithsAndHauls[resamplingOtolithsAndHauls$Quarter=="Q3",]
pdf(file = "ices/figures/ALK_Bootstrap_Plots_Edvin/suppMatResamplingAgeAndHaulsQ3.pdf", width=3.35, onefile = F) #85mm in inches
stackedPanelsOverlay(data = resamplingOtolithsAndHaulsQ3, columnGroups = "Year", rowGroups = "age", xVariable = "N", yVariable = "cv", overlayGroups="Otolith_per5cm", xlab="Number of hauls", ylab="RSE", ymin=0, pointcol = resamplingCol, linecol = resamplingCol, errorcol = resamplingCol, tickmarks = c(.4,.8,1.2), basetheme=function(x){ggplot2::theme_classic() + theme(plot.title = element_text(hjust = 0.5, size =10),axis.title.y = element_text(size =8),axis.title.x = element_text(size =8), axis.text.y = element_text(angle = 90, hjust = 1, size=6), axis.text.x = element_text(angle = 45, hjust = 1, size=6))})
dev.off()
####
# Plot 9 - supplementary
# Like plot 7, but for 1997 - 1999
resamplingOtolithsAndHauls <- read.csv("OtolithsAndHauls_Results_1997_1999/OtolAndHaul_1_5_per_5cm_areaBased1997_1999.csv", sep=";", stringsAsFactors = F, na.strings=c("NA"))
oneFish <- resamplingOtolithsAndHauls$Otolith_per5cm %in% 1
fiveFish <- resamplingOtolithsAndHauls$Otolith_per5cm %in% 5
resamplingOtolithsAndHauls[oneFish,6] <- "1 fish per 5cm"
resamplingOtolithsAndHauls[fiveFish,6] <- "5 fish per 5cm"
resamplingOtolithsAndHauls$cv <- resamplingOtolithsAndHauls$sd / resamplingOtolithsAndHauls$bootstrapMean
resamplingOtolithsAndHauls$age <- paste("Age", resamplingOtolithsAndHauls$age)
resamplingOtolithsAndHauls$Year <- as.character(resamplingOtolithsAndHauls$Year)
resamplingOtolithsAndHauls$Otolith_per5cm <- as.character(resamplingOtolithsAndHauls$Otolith_per5cm)
resamplingOtolithsAndHaulsQ1 <- resamplingOtolithsAndHauls[resamplingOtolithsAndHauls$Quarter=="Q1",]
resamplingOtolithsAndHaulsQ1 <- resamplingOtolithsAndHaulsQ1[resamplingOtolithsAndHaulsQ1$age != "Age 0",]
pdf(file = "ices/figures/ALK_Bootstrap_Plots_Edvin/suppMatResamplingAgeAndHaulsQ197-99.pdf", width=3.35, onefile = F) #85mm in inches
stackedPanelsOverlay(data = resamplingOtolithsAndHaulsQ1, columnGroups = "Year", rowGroups = "age", xVariable = "N", yVariable = "cv", overlayGroups="Otolith_per5cm", xlab="Number of hauls", ylab="RSE", ymin=0, pointcol = resamplingCol, linecol = resamplingCol, errorcol = resamplingCol, tickmarks = ticks2, basetheme=function(x){ggplot2::theme_classic() + theme(plot.title = element_text(hjust = 0.5, size =10),axis.title.y = element_text(size =8), axis.title.x = element_text(size =8), axis.text.y = element_text(angle = 90, hjust = 1, size=6), axis.text.x = element_text(angle = 45, hjust = 1, size=6))})
dev.off()
####
# Plot 10 - supplementary
# Like plot 9, but for Q3 and including age group 0
resamplingOtolithsAndHaulsQ3 <- resamplingOtolithsAndHauls[resamplingOtolithsAndHauls$Quarter=="Q3",]
pdf(file = "ices/figures/ALK_Bootstrap_Plots_Edvin/suppMatResamplingAgeAndHaulsQ397-99.pdf", width=3.35, onefile = F) #85mm in inches
stackedPanelsOverlay(data = resamplingOtolithsAndHaulsQ3, columnGroups = "Year", rowGroups = "age", xVariable = "N", yVariable = "cv", overlayGroups="Otolith_per5cm", xlab="Number of hauls", ylab="RSE", ymin=0, pointcol = resamplingCol, linecol = resamplingCol, errorcol = resamplingCol, tickmarks = ticks2, basetheme=function(x){ggplot2::theme_classic() + theme(plot.title = element_text(hjust = 0.5, size = 10),axis.title.y = element_text(size = 8), axis.title.x = element_text(size=8), axis.text.y = element_text(angle = 90, hjust = 1, size=6), axis.text.x = element_text(angle = 45, hjust = 1, size=6))})
dev.off()
natoyaj/TestPackage documentation built on March 11, 2020, 7:46 a.m.
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#
# Example of tabulated plot inspired by Gerritsen et al. 2006
#
library(ggplot2)
library(grid)
library(gridExtra)
library(egg)
#' @noRd
makeColorMap <- function(columns, color){
colors <- as.list(rep(color, length(columns)))
names(colors) <- columns
return(colors)
}
#' generates function for passing to tickmarks
#' tickfunctions have n number of ticks
#' lowest tick is 0
#' highes ttick is rounded of from ymax (digits determine rounding)
#' mid tick is rounded midpoint
genTickMarksFunction <- function(n=3, digits=1, slack=0.01){
roundf <- function(x){floor(x * (10**digits))/(10**digits)}
tickfun <- function(lim){
if (is.infinite(lim[2]) | is.na(lim[2])){
return(c(0.0))
}
maxl <- lim[2]-slack
minl <- 0
step <- maxl/(n-1)
ticks <- sapply(seq(minl, maxl, step), roundf)
return(ticks)
}
}
#' generates function for passing to tickmaks
#' tickfunctions tries a range of preset scales form 0 to ymax
#' and chooses the one that leaves fewer tickmars, exceding n
genTickMarksTester <- function(n=3, steps=c(.001, 0.0025, 0.005, 0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 1, 2.5, 5, 10, 25, 50, 100, 250, 500)){
stopifnot(all(steps == sort(steps)))
tickfun <- function(lim){
maxl <- lim[2]
if (is.infinite(maxl) | is.na(maxl)){
return(c(0.0))
}
minl <- 0
for (step in rev(steps)){
ticks <- seq(minl, maxl, step)
if (length(ticks) >= 3){
return(ticks)
}
}
stop("Could not make tickmarks")
}
}
ticks3 <- genTickMarksTester()
ticks2 <- genTickMarksFunction(2,2)
#' @noRd
panelPlotOverlay <- function(plotData, columnGroups, xVariable, yVariable, yVariableUpper, yVariableLower, xlimrow, ylimcol, showX=F, showY=F, ylabel="", basetheme=NULL, titletext="", pointcol=NULL, linecol=NULL, errorcol=NULL, tickmarks=NULL){
panelplot <- ggplot(plotData, aes_string(x=xVariable, y=yVariable)) + xlim(xlimrow) + ylab(ylabel) + ylim(ylimcol)
cols <- unique(plotData[,columnGroups])
for (col in cols){
colData <- plotData[plotData[,columnGroups] == col,]
if (!is.null(yVariableUpper) & !is.null(yVariableLower)){
panelplot <- panelplot + geom_linerange(data=colData, aes_string(ymin=yVariableLower, ymax=yVariableUpper), color=errorcol[[col]])
}
#points and lines
if (nrow(colData) == 0){
panelplot <- panelplot + geom_blank()
}
else{
panelplot <- panelplot + geom_line(data=colData, aes_string(color=columnGroups)) + geom_point(data=colData, shape=20, size=2, color=pointcol[[col]])
}
if (!is.null(title) & col == cols[1]){
panelplot <- panelplot + ggtitle(titletext)
}
}
panelplot <- panelplot + scale_color_manual(values = unlist(linecol))
panelplot <- panelplot + basetheme()
panelplot <- panelplot + theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank())
if (!is.null(tickmarks)){
panelplot <- panelplot + scale_y_continuous(breaks=tickmarks, limits = ylimcol)
}
if (!showX){
panelplot <- panelplot + theme(axis.title.x = element_blank(), axis.text.x=element_blank())
}
else{
panelplot <- panelplot + theme(axis.title.x = element_blank())
}
if (!showY){
panelplot <- panelplot + theme(axis.title.y = element_blank(), axis.text.y=element_blank())
}
return(panelplot)
}
#' Table plot
#' @description
#' makes a tabulated plot of estimates grouped by two covariates, one for columns, one for rows. axes of all plots are the same variables.
#' @details
#' @param data data.table() with data, must contain columns identified by parameters 'columnGroups', 'rowGroups', 'xVariable' and 'yVariable'
#' @param columnGroups character() identifies column in data that identifies the grouping used for columns in the plot
#' @param rowGRoups character() identifies column in data that identifies the grouping used for rows in the plot
#' @param xVariable character() identifies column in data containting variable to be used for x-axis
#' @param yVariable character() identifies column in data containing variable to be used for y-axis
#' @param overlayGroups character() identifies column in data specifying the groups to be overlayed
#' @param ylab character(), optional, label for y-axis, if not provided 'yVariable' will be used.
#' @param ymin numeric(), optional, lower bounds of y axis, if not NULL y axis will be adopted to data for each column
#' @param ymax numeric(), optional, upper bounds of y axis, if not provided y axis will be adopted to data for each column
#' @param xlim vector, optional, lower and upper bounds of x axis, if not provided y axis will be adapted to data for each row
#' @param pointcol character() or named list mapping the values in data[,columnGroups] to colors, color of points in plots
#' @param linecol character() or named list mapping the values in data[,columnGroups] to colors color of connecting lines in plots
#' @param errorcol character() or named list mapping the values in data[,columnGroups] to colors color of error bars
#' @param tickmarks numeric() specifies tickmarks for y-axis
#' @param basetheme ggplot2 - theme function to use for plotting. Default adjusts y-axis label alignments to account for variable width of tick-labels.
#' @param reverseX logical() whether to reverse X axes
#' @param hLineCol character() specify any column that should be used for horisontal reference lines
stackedPanelsOverlay <- function(data, columnGroups, rowGroups, xVariable, yVariable, overlayGroups, ylab=NULL, xlab=NULL, xlim=NULL, ymin=0, ymax=NULL, pointcol="black", linecol="#cb181d", errorcol="#cb181d", tickmarks=NULL, basetheme=function(x){ggplot2::theme_classic() + theme(plot.title = element_text(hjust = 0.5, size=10), axis.title.y = element_text(size=8), axis.title.x = element_text(size=8), axis.text.y = element_text(angle = 90, hjust = 1, size=6))}, reverseX=F, hLineCol=NULL){
if(is.numeric(columnGroups) | is.numeric(rowGroups)){
stop("ColumnGroups and rowGroups can not be numeric variables. Covert with as.character()")
}
if (!all(c(columnGroups, rowGroups, xVariable, yVariable) %in% names(data))){
stop("Some arguments (columnGroups, rowGroups, xVariable, yVariable) not found in data.")
}
if (any(is.na(data[,c(columnGroups, rowGroups, xVariable)]))){
stop("NAs in grouping variables or x variable")
}
if (is.null(ylab)){
ylab <- yVariable
}
if (is.null(xlab)){
xlab <- xVariable
}
rows <- sort(unique(unlist(data[,rowGroups])), decreasing = T)
cols <- sort(unique(unlist(data[,columnGroups])))
og <- sort(unique(unlist(data[,overlayGroups])))
if (is.character(linecol)){
linecol <- makeColorMap(og, linecol)
}
if (is.character(pointcol)){
pointcol <- makeColorMap(og, pointcol)
}
if (is.character(errorcol)){
errorcol <- makeColorMap(og, errorcol)
}
panels <- list()
for (row in rows){
#determine ylims if necessary
minvar <- yVariable
miny <- ymin
if (is.null(miny)){
miny <- min(data[data[,rowGroups] == row,minvar])
miny <- miny - abs(miny) * .1
}
maxvar <- yVariable
maxy <- ymax
if (is.null(maxy)){
maxy <- max(data[data[,rowGroups] == row,maxvar])
maxy <- maxy + abs(maxy) * .1
}
ylimcol <- c(miny, maxy)
for (col in cols){
xlimrow <- xlim
if (is.null(xlimrow)){
xlimrow <- c(min(data[data[,columnGroups] == col,xVariable]), max(data[data[,columnGroups] == col,xVariable]))
}
title <- NULL
if (row == rows[1]){
title = col
}
plotdata <- data[data[,columnGroups] == col & data[,rowGroups] == row,]
referenceline <- NULL
if (!is.null(hLineCol)){
stopifnot(length(unique(unlist(plotdata[,hLineCol])))==1)
referenceline <- unlist(plotdata[,hLineCol])[1]
}
panel <- panelPlotOverlay(plotdata, overlayGroups, xVariable, yVariable, yVariableUpper=NULL, yVariableLower=NULL, xlimrow, ylimcol, showX=(row == rows[length(rows)]), showY=(col == cols[1]), ylabel=row, basetheme=basetheme, title=title, pointcol=pointcol, linecol=linecol, errorcol=errorcol, tickmarks=tickmarks)
panel <- panel + theme(legend.position="none")
panels[[paste(row, col, sep="/")]] <- panel
}
}
g <- ggplotGrob(panels[[1]] + theme(legend.position="bottom", legend.title = element_blank()))$grobs
legend <- g[[which(sapply(g, function(x) x$name) == "guide-box")]]
lheight <- sum(legend$height)
grid.arrange(
ggarrange(
plots=panels,
nrow=length(rows),
left=textGrob(ylab, gp=gpar(fontsize=9), rot = 90),
bottom=textGrob(xlab, gp=gpar(fontsize=9)),draw=F
),
legend,
ncol = 1,
heights = unit.c(unit(1, "npc") - lheight, lheight))
}
#' Makes Two column plot-table
#' Same x-axis, and row-categories, but two different y-axis for the different groups.
#' values for each of the column groups are plotted on top of each other in the column to the right for yvariable1
#' values for each of the column groups are plotted on top of each other in the column to the left for yvariable2
overlayedColumnPlot <- function(data, columnGroups, rowGroups, xVariable, yVariable1, yVariable2, yVariable1Lower=NULL, yVariable1Upper=NULL, yVariable2Lower=NULL, yVariable2Upper=NULL, ylab1=NULL, ylab2=NULL, xlab=NULL, xlim=NULL, ymin1=0, ymin2=0, ymax1=NULL, ymax2=NULL, pointcol="black", linecol="#cb181d", errorcol="#cb181d", tickmarksY1=NULL, tickmarksY2=NULL, basetheme=function(x){ggplot2::theme_classic() + theme(plot.title = element_text(hjust = 0.5, size=10), axis.title.y = element_text(size=8), axis.title.x = element_text(size=8), axis.text.y = element_text(angle = 90, hjust = 1, size=6),axis.title.x.bottom = element_text(size=8), axis.text.x=element_text(size=6), legend.text = element_text(size=8))}){
if(is.numeric(columnGroups) | is.numeric(rowGroups)){
stop("ColumnGroups and rowGroups can not be numeric variables. Covert with as.character()")
}
if (!all(c(columnGroups, rowGroups, xVariable, yVariable1, yVariable2) %in% names(data))){
stop("Some arguments (columnGroups, rowGroups, xVariable, yVariable1, yvaraible2) not found in data.")
}
if (any(is.na(data[,c(columnGroups, rowGroups, xVariable)]))){
stop("NAs in grouping variables or x variable")
}
if (is.null(yVariable1Upper) + is.null(yVariable1Lower) == 1){
stop("Provide either both yVariableUpper and yVariableLower, or neither of them")
}
if (is.null(yVariable2Upper) + is.null(yVariable2Lower) == 1){
stop("Provide either both yVariableUpper and yVariableLower, or neither of them")
}
if (is.null(ylab1)){
ylab1 <- yVariable1
}
if (is.null(ylab2)){
ylab2 <- yVariable2
}
if (is.null(xlab)){
xlabel <- xVariable
}
else{
xlabel <- xlab
}
rows <- sort(unique(unlist(data[,rowGroups])), decreasing = T)
cols <- sort(unique(unlist(data[,columnGroups])))
if (is.character(linecol)){
linecol <- makeColorMap(cols, linecol)
}
if (is.character(pointcol)){
pointcol <- makeColorMap(cols, pointcol)
}
if (is.character(errorcol)){
errorcol <- makeColorMap(cols, errorcol)
}
panels <- list()
for (row in rows){
#determine ylims for yVariable1 if necessary
minvar1 <- yVariable1
if (!is.null(yVariable1Lower)){
minvar1 <- yVariable1Lower
}
miny1 <- ymin1
if (is.null(miny1)){
miny1 <- min(data[data[,rowGroups] == row,minvar1])
miny1 <- miny1 - abs(miny1) * .1
}
maxvar1 <- yVariable1
if (!is.null(yVariable1Upper)){
maxvar1 <- yVariable1Upper
}
maxy1 <- ymax1
if (is.null(maxy1)){
maxy1 <- max(data[data[,rowGroups] == row,maxvar1])
maxy1 <- maxy1 + abs(maxy1) * .1
}
ylim1col <- c(miny1, maxy1)
#determine ylims for yVariable2 if necessary
minvar2 <- yVariable2
if (!is.null(yVariable2Lower)){
minvar2 <- yVariable2Lower
}
miny2 <- ymin2
if (is.null(miny2)){
miny2 <- min(data[data[,rowGroups] == row,minvar2])
miny2 <- miny2 - abs(miny2) * .1
}
maxvar2 <- yVariable2
if (!is.null(yVariable2Upper)){
maxvar2 <- yVariable2Upper
}
maxy2 <- ymax2
if (is.null(maxy2)){
maxy2 <- max(data[data[,rowGroups] == row,maxvar2])
maxy2 <- maxy2 + abs(maxy2) * .1
}
ylim2col <- c(miny2, maxy2)
#get x-axis and title params
xlimrow <- xlim
if (is.null(xlimrow)){
xlimrow <- c(min(data[,xVariable]), max(data[,xVariable]))
}
title1 <- NULL
if (row == rows[1]){
title1 = yVariable1
}
title2 <- NULL
if (row == rows[1]){
title2 = yVariable2
}
plotdata <- data[data[,rowGroups] == row,]
#plot yvar1
panel <- panelPlotOverlay(plotdata, columnGroups, xVariable, yVariable1, yVariable1Upper, yVariable1Lower, xlimrow, ylim1col, showX=(row == rows[length(rows)]), showY=T, ylabel=row, basetheme=basetheme, title=title1, pointcol=pointcol, linecol=linecol, errorcol=errorcol, tickmarks=tickmarksY1)
panel <- panel + theme(legend.position="none")
panels[[paste(row, "yvar1", sep="/")]] <- panel
#plot yvar2
panel <- panelPlotOverlay(plotdata, columnGroups, xVariable, yVariable2, yVariable2Upper, yVariable2Lower, xlimrow, ylim2col, showX=(row == rows[length(rows)]), showY=T, ylabel=NULL, basetheme=basetheme, title=title2, pointcol=pointcol, linecol=linecol, errorcol=errorcol, tickmarks=tickmarksY2)
panel <- panel + theme(legend.position="none")
panels[[paste(row, "yvar2", sep="/")]] <- panel
}
g <- ggplotGrob(panels[[1]] + theme(legend.position="bottom", legend.title = element_blank()))$grobs
legend <- g[[which(sapply(g, function(x) x$name) == "guide-box")]]
lheight <- sum(legend$height)
grid.arrange(
ggarrange(
plots=panels,
nrow=length(rows),
left=ylab,
bottom=xlab,draw=F
),
legend,
ncol = 1,
heights = unit.c(unit(1, "npc") - lheight, lheight))
}
warning("Change file reference to package location")
warning("Check which CI to use")
warning("Check axis labels")
#
# compare the estimated mCPUE from the two ALKs for each age group (showing confidence intervals)
#
alkresult <- read.csv("data/ALKs_results.csv", sep=";", stringsAsFactors = F, dec = ",", na.strings = c("#N/A"))
areab <- alkresult$ALK %in% "area based"
haulb <- alkresult$ALK %in% "haul based"
alkresult[areab,5] <- "area-based"
alkresult[haulb,5] <- "haul-based"
alkresult$bs <- NULL
alkresult[alkresult$Bootstrap=="Stratified", "bs"] <- "S"
alkresult[alkresult$Bootstrap=="ICES-IBTS", "bs"] <- "I"
alkresult[alkresult$Bootstrap=="Modified-ICES", "bs"] <- "mI"
alkresult[alkresult$ALK == "area based", "ALK"] <- "area-based"
alkresult[alkresult$ALK == "haul based", "ALK"] <- "haul-based"
alkresult$ALKm <- paste(alkresult$ALK, alkresult$bs, sep=", ")
alkresult$age <- paste("Age", alkresult$age)
alkresult$RSE <- alkresult$sd / alkresult$mCPUE
estimator_colors <- list()
estimator_colors[["area-based, I"]] <- "#000000" # "#92c5de"
estimator_colors[["area-based, mI"]] <-"#00BFFF" #"#0000CD" #"#0571b0"
estimator_colors[["haul-based, S"]] <- "#ca0020"
####
# Plot 1
# Using Q1 and all years
#
#
#path= "G:/DOCUMENTS/IBTSgithub3/New-IBTS-Project/TestPackage/ices/figures/"
alkq1 <- alkresult[alkresult$Quarter=="Q1",]
alkq1 <- alkq1[alkq1$age != "Age 0",]
pdf(file = "ices/figures/ALK_Bootstrap_Plots_Edvin/mCpueRseQ1b.pdf", width=3.35, onefile = F) #85mm in inches (3.35) or 170mm (6.69 inches)
overlayedColumnPlot(data = alkq1, columnGroups = "ALKm", rowGroups = "age", xVariable = "Year", yVariable1 = "mCPUE", yVariable2 = "RSE", yVariable1Lower = "Q025", yVariable1Upper = "Q975", tickmarksY1 = c(0,2,5,10,15,20,25), tickmarksY2=c(0,.25,.5,.75,1,1.25,1.5), pointcol = estimator_colors, linecol = estimator_colors, errorcol = estimator_colors)
dev.off()
####
# Plot 2 - for supplementary
# As plot 1, but for Q3, retaining age group 0
# Using Q3 and all years
alkq3 <- alkresult[alkresult$Quarter=="Q3",]
pdf(file = "ices/figures/ALK_Bootstrap_Plots_Edvin/suppMatMcpueRseQ3.pdf", width=3.35, onefile = F) #85mm in inches
overlayedColumnPlot(data = alkq3, columnGroups = "ALKm", rowGroups = "age", xVariable = "Year", yVariable1 = "mCPUE", yVariable2 = "RSE", yVariable1Lower = "Q025", yVariable1Upper = "Q975", tickmarksY1 = ticks3, tickmarksY2=ticks3, pointcol = estimator_colors, linecol = estimator_colors, errorcol = estimator_colors)
dev.off()
####
# Plot 7
# overlay 1 and 5 for otholith per 5 cm
resamplingCol <- list()
resamplingCol[["1 fish per 5cm"]] <- "red"
resamplingCol[["5 fish per 5cm"]] <- "black"
resamplingOtolithsAndHauls <- read.csv("OtolithsAndHauls_Results_2015_2018/OtolAndHaul_1_5_per_5cm_areaBased2015_2018.csv", sep=";", stringsAsFactors = F, na.strings=c("NA"))
oneFish <- resamplingOtolithsAndHauls$Otolith_per5cm %in% 1
fiveFish <- resamplingOtolithsAndHauls$Otolith_per5cm %in% 5
resamplingOtolithsAndHauls[oneFish,5] <- "1 fish per 5cm"
resamplingOtolithsAndHauls[fiveFish,5] <- "5 fish per 5cm"
resamplingOtolithsAndHauls$cv <- resamplingOtolithsAndHauls$sd / resamplingOtolithsAndHauls$bootstrapMean
resamplingOtolithsAndHauls$age <- paste("Age", resamplingOtolithsAndHauls$age)
resamplingOtolithsAndHauls$Year <- as.character(resamplingOtolithsAndHauls$Year)
resamplingOtolithsAndHauls$Otolith_per5cm <- as.character(resamplingOtolithsAndHauls$Otolith_per5cm)
resamplingOtolithsAndHaulsQ1 <- resamplingOtolithsAndHauls[resamplingOtolithsAndHauls$Quarter=="Q1",]
resamplingOtolithsAndHaulsQ1 <- resamplingOtolithsAndHaulsQ1[resamplingOtolithsAndHaulsQ1$age != "Age 0",]
pdf(file = "ices/figures/ALK_Bootstrap_Plots_Edvin/resamplingAgeAndHaulsQ1.pdf", width=3.35, onefile = F) #85mm in inches
stackedPanelsOverlay(data = resamplingOtolithsAndHaulsQ1, columnGroups = "Year", rowGroups = "age", xVariable = "N", yVariable = "cv", overlayGroups="Otolith_per5cm", xlab="Number of hauls", ylab="RSE", ymin=0, pointcol = resamplingCol, linecol = resamplingCol, errorcol = resamplingCol, tickmarks = c(.4,.8,1.2), basetheme=function(x){ggplot2::theme_classic() + theme(plot.title = element_text(hjust = 0.5, size =10),axis.title.y = element_text(size =8),axis.title.x = element_text(size =8), axis.text.y = element_text(angle = 90, hjust = 1, size=6), axis.text.x = element_text(angle = 45, hjust = 1, size=6))})
dev.off()
####
# Plot 8 - supplementary
# Like plot 7, but for Q3 and including aGE GROUP 0
resamplingOtolithsAndHaulsQ3 <- resamplingOtolithsAndHauls[resamplingOtolithsAndHauls$Quarter=="Q3",]
pdf(file = "ices/figures/ALK_Bootstrap_Plots_Edvin/suppMatResamplingAgeAndHaulsQ3.pdf", width=3.35, onefile = F) #85mm in inches
stackedPanelsOverlay(data = resamplingOtolithsAndHaulsQ3, columnGroups = "Year", rowGroups = "age", xVariable = "N", yVariable = "cv", overlayGroups="Otolith_per5cm", xlab="Number of hauls", ylab="RSE", ymin=0, pointcol = resamplingCol, linecol = resamplingCol, errorcol = resamplingCol, tickmarks = c(.4,.8,1.2), basetheme=function(x){ggplot2::theme_classic() + theme(plot.title = element_text(hjust = 0.5, size =10),axis.title.y = element_text(size =8),axis.title.x = element_text(size =8), axis.text.y = element_text(angle = 90, hjust = 1, size=6), axis.text.x = element_text(angle = 45, hjust = 1, size=6))})
dev.off()
####
# Plot 9 - supplementary
# Like plot 7, but for 1997 - 1999
resamplingOtolithsAndHauls <- read.csv("OtolithsAndHauls_Results_1997_1999/OtolAndHaul_1_5_per_5cm_areaBased1997_1999.csv", sep=";", stringsAsFactors = F, na.strings=c("NA"))
oneFish <- resamplingOtolithsAndHauls$Otolith_per5cm %in% 1
fiveFish <- resamplingOtolithsAndHauls$Otolith_per5cm %in% 5
resamplingOtolithsAndHauls[oneFish,6] <- "1 fish per 5cm"
resamplingOtolithsAndHauls[fiveFish,6] <- "5 fish per 5cm"
resamplingOtolithsAndHauls$cv <- resamplingOtolithsAndHauls$sd / resamplingOtolithsAndHauls$bootstrapMean
resamplingOtolithsAndHauls$age <- paste("Age", resamplingOtolithsAndHauls$age)
resamplingOtolithsAndHauls$Year <- as.character(resamplingOtolithsAndHauls$Year)
resamplingOtolithsAndHauls$Otolith_per5cm <- as.character(resamplingOtolithsAndHauls$Otolith_per5cm)
resamplingOtolithsAndHaulsQ1 <- resamplingOtolithsAndHauls[resamplingOtolithsAndHauls$Quarter=="Q1",]
resamplingOtolithsAndHaulsQ1 <- resamplingOtolithsAndHaulsQ1[resamplingOtolithsAndHaulsQ1$age != "Age 0",]
pdf(file = "ices/figures/ALK_Bootstrap_Plots_Edvin/suppMatResamplingAgeAndHaulsQ197-99.pdf", width=3.35, onefile = F) #85mm in inches
stackedPanelsOverlay(data = resamplingOtolithsAndHaulsQ1, columnGroups = "Year", rowGroups = "age", xVariable = "N", yVariable = "cv", overlayGroups="Otolith_per5cm", xlab="Number of hauls", ylab="RSE", ymin=0, pointcol = resamplingCol, linecol = resamplingCol, errorcol = resamplingCol, tickmarks = ticks2, basetheme=function(x){ggplot2::theme_classic() + theme(plot.title = element_text(hjust = 0.5, size =10),axis.title.y = element_text(size =8), axis.title.x = element_text(size =8), axis.text.y = element_text(angle = 90, hjust = 1, size=6), axis.text.x = element_text(angle = 45, hjust = 1, size=6))})
dev.off()
####
# Plot 10 - supplementary
# Like plot 9, but for Q3 and including age group 0
resamplingOtolithsAndHaulsQ3 <- resamplingOtolithsAndHauls[resamplingOtolithsAndHauls$Quarter=="Q3",]
pdf(file = "ices/figures/ALK_Bootstrap_Plots_Edvin/suppMatResamplingAgeAndHaulsQ397-99.pdf", width=3.35, onefile = F) #85mm in inches
stackedPanelsOverlay(data = resamplingOtolithsAndHaulsQ3, columnGroups = "Year", rowGroups = "age", xVariable = "N", yVariable = "cv", overlayGroups="Otolith_per5cm", xlab="Number of hauls", ylab="RSE", ymin=0, pointcol = resamplingCol, linecol = resamplingCol, errorcol = resamplingCol, tickmarks = ticks2, basetheme=function(x){ggplot2::theme_classic() + theme(plot.title = element_text(hjust = 0.5, size = 10),axis.title.y = element_text(size = 8), axis.title.x = element_text(size=8), axis.text.y = element_text(angle = 90, hjust = 1, size=6), axis.text.x = element_text(angle = 45, hjust = 1, size=6))})
dev.off()
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