# \\\
# Copyright 2021-2022 Louis Héraut*1
#
# *1 INRAE, France
# louis.heraut@inrae.fr
#
# This file is part of ash R toolbox.
#
# ash R toolbox is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or (at
# your option) any later version.
#
# ash R toolbox is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with ash R toolbox. If not, see <https://www.gnu.org/licenses/>.
# ///
#
#
# plotting/map.R
#
# Deals with the creation of a map for presenting the trend analysis of hydrological variables
## 1. MAP PANEL
# Generates a map plot of the tendancy of a hydrological variable
map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
mean_period=NULL, outdirTmp='', codeLight=NULL,
margin=NULL, showSea=TRUE,
foot_note=FALSE,
foot_height=0, resources_path=NULL,
AEAGlogo_file=NULL, INRAElogo_file=NULL,
FRlogo_file=NULL,
verbose=TRUE) {
# Extract shapefiles
df_france = df_shapefile$france
df_bassin = df_shapefile$bassin
df_subbassin = df_shapefile$subbassin
df_river = df_shapefile$river
# Number of variable/plot
nbp = length(list_df2plot)
# Get all different stations code
Code = levels(factor(df_meta$code))
nCode = length(Code)
# Gets a trend example
df_trend = list_df2plot[[1]]$trend
nPeriod_max = 0
for (code in Code) {
# Extracts the trend corresponding to the code
df_trend_code = df_trend[df_trend$code == code,]
# Extract start and end of trend periods
Start = df_trend_code$period_start
End = df_trend_code$period_end
# Get the name of the different period
UStart = levels(factor(Start))
UEnd = levels(factor(End))
# Compute the max of different start and end
# so the number of different period
nPeriod = max(length(UStart), length(UEnd))
# If the number of period for the trend is greater
# than the current max period, stocks it
if (nPeriod > nPeriod_max) {
nPeriod_max = nPeriod
}
}
# Blank array to store time info
tab_Start = array(rep('', nCode*nbp*nPeriod_max),
dim=c(nCode, nbp, nPeriod_max))
tab_End = array(rep('', nCode*nbp*nPeriod_max),
dim=c(nCode, nbp, nPeriod_max))
tab_Code = array(rep('', nCode*nbp*nPeriod_max),
dim=c(nCode, nbp, nPeriod_max))
tab_Periods = array(rep('', nCode*nbp*nPeriod_max),
dim=c(nCode, nbp, nPeriod_max))
# For all code
for (k in 1:nCode) {
# Gets the code
code = Code[k]
# For all the variable
for (i in 1:nbp) {
df_trend = list_df2plot[[i]]$trend
# Extracts the trend corresponding to the code
df_trend_code = df_trend[df_trend$code == code,]
# Extract start and end of trend periods
Start = df_trend_code$period_start
End = df_trend_code$period_end
# Get the name of the different period
UStart = levels(factor(Start))
UEnd = levels(factor(End))
# Compute the max of different start and end
# so the number of different period
nPeriod = max(length(UStart), length(UEnd))
# For all the period
for (j in 1:nPeriod_max) {
# Stocks period
Periods = paste(Start[j],
End[j],
sep=' / ')
# Saves the time info
tab_Start[k, i, j] = as.character(Start[j])
tab_End[k, i, j] = as.character(End[j])
tab_Code[k, i, j] = code
tab_Periods[k, i, j] = Periods
}
}
}
# Blank array to store mean of the trend for each
# station, perdiod and variable
TrendValue_code = array(rep(1, nPeriod_max*nbp*nCode),
dim=c(nPeriod_max, nbp, nCode))
# For all the period
for (j in 1:nPeriod_max) {
# For all the code
for (k in 1:nCode) {
# Gets the code
code = Code[k]
# For all variable
for (i in 1:nbp) {
# Extracts the data corresponding to the
# current variable
df_data = list_df2plot[[i]]$data
# Extracts the trend corresponding to the
# current variable
df_trend = list_df2plot[[i]]$trend
# Extracts the type of the variable
type = list_df2plot[[i]]$type
alpha = list_df2plot[[i]]$alpha
# Extracts the data corresponding to the code
df_data_code = df_data[df_data$code == code,]
# Extracts the trend corresponding to the code
df_trend_code = df_trend[df_trend$code == code,]
# Gets the associated time info
Start = tab_Start[k, i, j]
End = tab_End[k, i, j]
Periods = tab_Periods[k, i, j]
# Extracts the corresponding data for the period
df_data_code_per =
df_data_code[df_data_code$Date >= Start
& df_data_code$Date <= End,]
# Same for trend
df_trend_code_per =
df_trend_code[df_trend_code$period_start == Start
& df_trend_code$period_end == End,]
# Computes the number of trend analysis selected
Ntrend = nrow(df_trend_code_per)
# If there is more than one trend on the same period
if (Ntrend > 1) {
# Takes only the first because they are similar
df_trend_code_per = df_trend_code_per[1,]
}
# If it is a flow variable
if (type == 'sévérité') {
# Computes the mean of the data on the period
dataMean = mean(df_data_code_per$Value, na.rm=TRUE)
# Normalises the trend value by the mean of the data
trendValue = df_trend_code_per$trend / dataMean
# If it is a date variable
} else if (type == 'saisonnalité') {
trendValue = df_trend_code_per$trend
}
# If the p value is under the threshold
if (df_trend_code_per$p <= alpha){
# Stores the mean trend
TrendValue_code[j, i, k] = trendValue
# Otherwise
} else {
# Do not stocks it
TrendValue_code[j, i, k] = NA
}
}
}
}
# Compute the min and the max of the mean trend for all the station
minTrendValue = apply(TrendValue_code, c(1, 2), min, na.rm=TRUE)
maxTrendValue = apply(TrendValue_code, c(1, 2), max, na.rm=TRUE)
# If there is a 'mean_period'
if (!is.null(mean_period)) {
# Blank vectors to store info about breaking analysis
Var_mean = c()
Type_mean = c()
Code_mean = c()
DataMean_mean = c()
breakValue_mean = c()
# Convert 'mean_period' to list
mean_period = as.list(mean_period)
# Number of mean period
nPeriod_mean = length(mean_period)
# Blank array to store difference of mean between two periods
breakValue_code = array(rep(1, nPeriod_mean*nbp*nCode),
dim=c(nPeriod_mean, nbp, nCode))
# Blank array to store mean for a temporary period in order
# to compute the difference of mean with a second period
dataMeantmp = array(rep(NA, nbp*nCode),
dim=c(nbp, nCode))
# For all period of breaking analysis
for (j in 1:nPeriod_mean) {
# For all the code
for (k in 1:nCode) {
# Gets the code
code = Code[k]
# For all variable
for (i in 1:nbp) {
# Extracts the data corresponding to
# the current variable
df_data = list_df2plot[[i]]$data
# Extract the variable of the plot
var = list_df2plot[[i]]$var
# Extract the type of the variable to plot
type = list_df2plot[[i]]$type
# Extracts the data corresponding to the code
df_data_code = df_data[df_data$code == code,]
# Get the current start and end of the sub period
Start_mean = mean_period[[j]][1]
End_mean = mean_period[[j]][2]
# Extract the data corresponding to this sub period
df_data_code_per =
df_data_code[df_data_code$Date >= Start_mean
& df_data_code$Date <= End_mean,]
# Min max for the sub period
Datemin = min(df_data_code_per$Date)
Datemax = max(df_data_code_per$Date)
# Mean of the flow over the sub period
dataMean = mean(df_data_code_per$Value,
na.rm=TRUE)
# If this in not the first period
if (j > 1) {
# Compute the difference of mean
Break = dataMean - dataMeantmp[i, k]
# Otherwise for the first period
} else {
# Stocks NA
Break = NA
}
# If it is a flow variable
if (type == 'sévérité') {
# Normalises the break by the mean of the
# initial period
breakValue = Break / dataMeantmp[i, k]
# If it is a date variable
} else if (type == 'saisonnalité') {
# Just stocks the break value
breakValue = Break
}
# Stores the result
breakValue_code[j, i, k] = breakValue
# Stores temporarily the mean of the current period
dataMeantmp[i, k] = dataMean
}
}
}
# Computes the min and the max of the averaged trend for
# all the station
minBreakValue = apply(breakValue_code, c(1, 2),
min, na.rm=TRUE)
maxBreakValue = apply(breakValue_code, c(1, 2),
max, na.rm=TRUE)
}
if (is.null(mean_period)) {
nPeriod_mean = 1
}
# Number of ticks for the colorbar
nbTick = 10
for (j in 1:nPeriod_mean) {
# For all variable
for (i in 1:nbp) {
# If there is a specified station code to highlight (mini map)
# and there has already been one loop
if ((i > 1 | j > 1) & !is.null(codeLight)) {
# Stop the for loop over the variable
break
}
# Extracts the variable of the plot
var = list_df2plot[[i]]$var
# Extracts the type of variable of the plot
type = list_df2plot[[i]]$type
# Createsa name for the map
if (j > 1) {
outname = paste('map_d', var, sep='')
} else {
outname = paste('map_', var, sep='')
}
n_page = i + nbp*(j-1)
N_page = nbp*nPeriod_mean
# If there is the verbose option
if (verbose) {
# Prints the name of the map
print(paste('Map for variable : ', var,
" (", round(n_page/N_page*100, 0), " %)",
sep=''))
}
# If there is no specified station code to highlight
# (mini map)
if (is.null(codeLight)) {
# Sets the size of the countour
sizefr = 0.45
sizebs = 0.4
sizerv = 0.3
} else {
sizefr = 0.35
sizebs = 0.3
sizerv = 0.2
}
# Stores the coordonate system
cf = coord_fixed()
# Makes it the default one to remove useless warning
cf$default = TRUE
# Open a new plot with the personalise theme
map = ggplot() + theme_void() +
# theme(plot.background=element_rect(fill=NA,
# color="#EC4899")) +
# Fixed coordinate system (remove useless warning)
cf +
# Plot the background of France
geom_polygon(data=df_france,
aes(x=long, y=lat, group=group),
color=NA, fill="grey97")
# If the river shapefile exists
if (!is.null(df_river)) {
# Plot the river
map = map +
geom_path(data=df_river,
aes(x=long, y=lat, group=group),
color="grey85", size=sizerv)
}
map = map +
# Plot the hydrological basin
geom_polygon(data=df_bassin,
aes(x=long, y=lat, group=group),
color="grey70", fill=NA, size=sizebs) +
# Plot the hydrological sub-basin
geom_polygon(data=df_subbassin,
aes(x=long, y=lat, group=group),
color="grey70", fill=NA, size=sizebs) +
# Plot the countour of France
geom_polygon(data=df_france,
aes(x=long, y=lat, group=group),
color="grey40", fill=NA, size=sizefr)
# If the sea needs to be shown
if (showSea) {
# Leaves space around the France
xlim = c(295000, 790000)
ylim = c(6125000, 6600000)
# Otherwise
} else {
# Leaves minimal space around France
xlim = c(305000, 790000)
ylim = c(6135000, 6600000)
}
# If there is no specified station code to highlight (mini map)
if (is.null(codeLight)) {
# Sets a legend scale start
xmin = gpct(4, xlim, shift=TRUE)
# Sets graduations
xint = c(0, 10*1E3, 50*1E3, 100*1E3)
# Sets the y postion
ymin = gpct(5, ylim, shift=TRUE)
# Sets the height of graduations
ymax = ymin + gpct(1, ylim)
# Size of the value
size = 3
# Size of the 'km' unit
sizekm = 2.5
# If there is a specified station code
} else {
# Same but with less graduation and smaller size
xmin = gpct(2, xlim, shift=TRUE)
xint = c(0, 100*1E3)
ymin = gpct(1, ylim, shift=TRUE)
ymax = ymin + gpct(3, ylim)
size = 2
sizekm = 1.5
}
map = map +
# Adds the base line of the scale
geom_line(aes(x=c(xmin, max(xint)+xmin),
y=c(ymin, ymin)),
color="grey40", size=0.2) +
# Adds the 'km' unit
annotate("text",
x=max(xint)+xmin+gpct(1, xlim), y=ymin,
vjust=0, hjust=0, label="km",
color="grey40", size=sizekm)
# For all graduations
for (x in xint) {
map = map +
# Draws the tick
annotate("segment",
x=x+xmin, xend=x+xmin, y=ymin, yend=ymax,
color="grey40", size=0.2) +
# Adds the value
annotate("text",
x=x+xmin, y=ymax+gpct(0.5, ylim),
vjust=0, hjust=0.5, label=x/1E3,
color="grey40", size=size)
}
map = map +
# Allows to crop shapefile without graphical problem
coord_sf(xlim=xlim, ylim=ylim,
expand=FALSE)
# If there is no margins specified
if (is.null(margin)) {
# Sets all margins to 0
map = map +
theme(plot.margin=margin(t=0, r=0, b=0, l=0,
unit="mm"))
# Otherwise
} else {
# Sets margins to the given ones
map = map +
theme(plot.margin=margin)
}
# Blank vector to store data about station
lon = c()
lat = c()
fill = c()
shape = c()
Value = c()
alpha_Ok = c()
# For all code
for (k in 1:nCode) {
# Gets the code
code = Code[k]
if (j > 1) {
value = breakValue_code[j, i, k]
minValue = minBreakValue[j, i]
maxValue = maxBreakValue[j, i]
pvalue = 0
} else {
# Extracts the data corresponding to the
# current variable
df_data = list_df2plot[[i]]$data
# Extracts the trend corresponding to the
# current variable
df_trend = list_df2plot[[i]]$trend
# Gets the risk of the test
alpha = list_df2plot[[i]]$alpha
# Extracts the data corresponding to the code
df_data_code = df_data[df_data$code == code,]
# Extracts the trend corresponding to the code
df_trend_code = df_trend[df_trend$code == code,]
# Gets the associated time info
Start = tab_Start[k, i, idPer_trend]
End = tab_End[k, i, idPer_trend]
Periods = tab_Periods[k, i, idPer_trend]
# Extracts the corresponding data for the period
df_data_code_per =
df_data_code[df_data_code$Date >= Start
& df_data_code$Date <= End,]
# Same for trend
df_trend_code_per =
df_trend_code[df_trend_code$period_start == Start
& df_trend_code$period_end == End,]
# Computes the number of trend analysis selected
Ntrend = nrow(df_trend_code_per)
# If there is more than one trend on the same period
if (Ntrend > 1) {
# Takes only the first because they are similar
df_trend_code_per = df_trend_code_per[1,]
}
# If it is a flow variable
if (type == 'sévérité') {
# Computes the mean of the data on the period
dataMean = mean(df_data_code_per$Value,
na.rm=TRUE)
# Normalises the trend value by the mean
# of the data
value = df_trend_code_per$trend / dataMean
# If it is a date variable
} else if (type == 'saisonnalité') {
value = df_trend_code_per$trend
}
minValue = minTrendValue[idPer_trend, i]
maxValue = maxTrendValue[idPer_trend, i]
pvalue = df_trend_code_per$p
}
# Computes the color associated to the mean trend
color_res = get_color(value,
minValue,
maxValue,
palette_name='perso',
reverse=TRUE,
ncolor=256)
# Computes the colorbar info
palette_res = get_palette(minValue,
maxValue,
palette_name='perso',
reverse=TRUE,
ncolor=256,
nbTick=nbTick)
# If it is significative
if (pvalue <= alpha){
# The computed color is stored
filltmp = color_res
# If the mean tend is positive
if (value >= 0) {
# Uses a triangle up for the shape
# of the marker
shapetmp = 24
# If negative
} else {
# Uses a triangle down for the shape
# of the marker
shapetmp = 25
}
# If it is not significative
} else {
# The fill color is grey
filltmp = 'grey97'
# The marker is a circle
shapetmp = 21
}
# Extracts the localisation of the current station
lontmp =
df_meta[df_meta$code == code,]$L93X_m_BH
lattmp =
df_meta[df_meta$code == code,]$L93Y_m_BH
# Stores all the parameters
lon = c(lon, lontmp)
lat = c(lat, lattmp)
fill = c(fill, filltmp)
shape = c(shape, shapetmp)
Value = c(Value, value)
# If the trend analysis is significative a TRUE is stored
alpha_Ok = c(alpha_Ok,
pvalue <= alpha)
}
# Creates a tibble to stores all the data to plot
plot_map = tibble(lon=lon, lat=lat, fill=fill,
shape=shape, code=Code)
# If there is no specified station code to highlight
# (mini map)
if (is.null(codeLight)) {
map = map +
# Plots the trend point
geom_point(data=plot_map,
aes(x=lon, y=lat),
shape=shape, size=5, stroke=1,
color='grey50', fill=fill)
# If there is a specified station code
} else {
# Extract data of all stations not to highlight
plot_map_codeNo = plot_map[plot_map$code != codeLight,]
# Extract data of the station to highlight
plot_map_code = plot_map[plot_map$code == codeLight,]
# Plots only the localisation
map = map +
# For all stations not to highlight
geom_point(data=plot_map_codeNo,
aes(x=lon, y=lat),
shape=21, size=0.5, stroke=0.5,
color='grey50', fill='grey50') +
# For the station to highlight
geom_point(data=plot_map_code,
aes(x=lon, y=lat),
shape=21, size=1.5, stroke=0.5,
color='#00A3A8', fill='#00A3A8')
}
# Extracts the position of the tick of the colorbar
posTick = palette_res$posTick
# Extracts the label of the tick of the colorbar
labTick = palette_res$labTick
# Extracts the color corresponding to the tick of the colorbar
colTick = palette_res$colTick
# Spreading of the colorbar
valNorm = nbTick * 10
# Normalisation of the position of ticks
ytick = posTick / max(posTick) * valNorm
# If it is a flow variable
if (type == 'sévérité') {
# Formatting of label in pourcent
labTick = as.character(round(labTick*100, 2))
# If it is a date variable
} else if (type == 'saisonnalité') {
# Formatting of label
labTick = as.character(round(labTick, 2))
}
# X position of ticks all similar
xtick = rep(0, times=nbTick)
# Creates a tibble to store all parameters of colorbar
plot_palette = tibble(xtick=xtick, ytick=ytick,
colTick=colTick, labTick=labTick)
# New plot with void theme
title = ggplot() + theme_void() +
# Plots separation line
geom_line(aes(x=c(-0.3, 3.7), y=c(0.05, 0.05)),
size=0.6, color="#00A3A8") +
# Writes title
geom_shadowtext(data=tibble(x=-0.3, y=0.2,
label=var),
aes(x=x, y=y, label=label),
fontface="bold",
color="#00A3A8",
bg.colour="white",
hjust=0, vjust=0, size=10) +
# X axis
scale_x_continuous(limits=c(-0.3, 1 + 3),
expand=c(0, 0)) +
# Y axis
scale_y_continuous(limits=c(0, 10),
expand=c(0, 0)) +
# Margin
theme(plot.margin=margin(t=0, r=0, b=0, l=0, unit="mm"))
# New plot with void theme
pal = ggplot() + theme_void() +
# Plots the point of the colorbar
geom_point(data=plot_palette,
aes(x=xtick, y=ytick),
shape=21, size=5, stroke=1,
color='white', fill=colTick)
if (j > 1) {
ValueName = "Écarts observées"
# If it is a flow variable
if (type == 'sévérité') {
unit = bquote(bold("(%)"))
# If it is a date variable
} else if (type == 'saisonnalité') {
unit = bquote(bold("(jour)"))
}
} else {
ValueName = "Tendances observées"
# If it is a flow variable
if (type == 'sévérité') {
unit = bquote(bold("(% par an)"))
# If it is a date variable
} else if (type == 'saisonnalité') {
unit = bquote(bold("(jour par an)"))
}
}
pal = pal +
# Name of the colorbar
annotate('text',
x=-0.3, y= valNorm + 23,
label=ValueName,
hjust=0, vjust=0.5,
size=6, color='grey40') +
# Unit legend of the colorbar
annotate('text',
x=-0.2, y= valNorm + 13,
label=unit,
hjust=0, vjust=0.5,
size=4, color='grey40')
# For all the ticks
for (id in 1:nbTick) {
pal = pal +
# Adds the value
annotate('text', x=xtick[id]+0.3,
y=ytick[id],
label=bquote(bold(.(labTick[id]))),
hjust=0, vjust=0.7,
size=3, color='grey40')
}
yUp = -20
yNone = -29
if (j > 1) {
upLabel = bquote(bold("Hausse"))
noneLabel = NULL
downLabel = bquote(bold("Baisse"))
yDown = -29
} else {
upLabel = bquote(bold("Hausse significative à 10%"))
noneLabel = bquote(bold("Non significatif à 10%"))
downLabel = bquote(bold("Baisse significative à 10%"))
yDown = -40
}
pal = pal +
# Up triangle in the marker legend
geom_point(aes(x=0, y=yUp),
shape=24, size=4, stroke=1,
color='grey50', fill='grey97') +
# Up triangle text legend
annotate('text',
x=0.3, y=yUp,
label=upLabel,
hjust=0, vjust=0.5,
size=3, color='grey40')
if (!is.null(noneLabel)) {
pal = pal +
# Circle in the marker legend
geom_point(aes(x=0, y=yNone),
shape=21, size=4, stroke=1,
color='grey50', fill='grey97') +
# Circle text legend
annotate('text',
x=0.3, y=yNone,
label=noneLabel,
hjust=0, vjust=0.7,
size=3, color='grey40')
}
pal = pal +
# Down triangle in the marker legend
geom_point(aes(x=0, y=yDown),
shape=25, size=4, stroke=1,
color='grey50', fill='grey97') +
# Down triangle text legend
annotate('text',
x=0.3, y=yDown,
label=downLabel,
hjust=0, vjust=0.5,
size=3, color='grey40')
# Normalises all the trend values for each station
# according to the colorbar
if (j > 1) {
yValue = (Value - minBreakValue[j, i]) / (maxBreakValue[j, i] - minBreakValue[j, i]) * valNorm
} else {
yValue = (Value - minTrendValue[idPer_trend, i]) / (maxTrendValue[idPer_trend, i] - minTrendValue[idPer_trend, i]) * valNorm
}
# Takes only the significative ones
yValue = yValue[alpha_Ok]
# Histogram distribution
# Computes the histogram of the trend
res_hist = hist(yValue, breaks=ytick, plot=FALSE)
# Extracts the number of counts per cells
counts = res_hist$counts
# Extracts limits of cells
breaks = res_hist$breaks
# Extracts middle of cells
mids = res_hist$mids
# Blank vectors to store position of points of
# the distribution to plot
xValue = c()
yValue = c()
# Start X position of the distribution
start_hist = 1.25
# X separation bewteen point
hist_sep = 0.15
# For all cells of the histogram
for (ii in 1:length(mids)) {
# If the count in the current cell is not zero
if (counts[ii] != 0) {
# Stores the X positions of points of the distribution
# for the current cell
xValue = c(xValue,
seq(start_hist,
start_hist+(counts[ii]-1)*hist_sep,
by=hist_sep))
}
# Stores the Y position which is the middle of the
# current cell the number of times it has been counted
yValue = c(yValue, rep(mids[ii], times=counts[ii]))
}
# Makes a tibble to plot the trend distribution
plot_value = tibble(xValue=xValue, yValue=yValue)
pal = pal +
# Plots the point of the trend distribution
geom_point(data=plot_value,
aes(x=xValue, y=yValue),
# shape=21, size=1,
# color="grey20", fill="grey20")
alpha=0.4)
if (type == 'sévérité') {
labelArrow = 'Plus sévère'
} else if (type == 'saisonnalité') {
labelArrow = 'Plus tôt'
}
xArrow = 3.2
pal = pal +
# Arrow to show a worsening of the situation
geom_segment(aes(x=xArrow, y=valNorm*0.75,
xend=xArrow, yend=valNorm*0.25),
color='grey50', size=0.3,
arrow=arrow(length=unit(2, "mm"))) +
# Text associated to the arrow
annotate('text',
x=xArrow+0.1, y=valNorm*0.5,
label=labelArrow,
angle=90,
hjust=0.5, vjust=1,
size=3, color='grey50')
pal = pal +
# X axis of the colorbar
scale_x_continuous(limits=c(-0.3, 1 + 3),
expand=c(0, 0)) +
# Y axis of the colorbar
scale_y_continuous(limits=c(-60, valNorm + 35),
expand=c(0, 0)) +
# Margin of the colorbar
theme(plot.margin=margin(t=0, r=0, b=0, l=0, unit="mm"))
if (j > 1) {
footName = 'carte des écarts observés'
} else {
footName = 'carte des tendances observées'
}
# If there is a foot note
if (foot_note) {
foot = foot_panel(footName,
n_page, N_page, resources_path,
AEAGlogo_file, INRAElogo_file,
FRlogo_file, foot_height)
# Stores the map, the title and the colorbar in a list
P = list(map, title, pal, foot)
LM = matrix(c(1, 1, 1, 2,
1, 1, 1, 3,
4, 4, 4, 4),
nrow=3, byrow=TRUE)
} else {
foot_height = 0
# Stores the map, the title and the colorbar in a list
P = list(map, title, pal)
LM = matrix(c(1, 1, 1, 2,
1, 1, 1, 3),
nrow=2, byrow=TRUE)
}
id_foot = 4
LMcol = ncol(LM)
LMrow = nrow(LM)
LM = rbind(rep(99, times=LMcol), LM, rep(99, times=LMcol))
LMrow = nrow(LM)
LM = cbind(rep(99, times=LMrow), LM, rep(99, times=LMrow))
LMcol = ncol(LM)
margin_height = 0.5
height = 21
width = 29.7
row_height = (height - 2*margin_height - foot_height) / (LMrow - 3)
Hcut = LM[, 2]
heightLM = rep(row_height, times=LMrow)
heightLM[Hcut == id_foot] = foot_height
heightLM[Hcut == 99] = margin_height
col_width = (width - 2*margin_height) / (LMcol - 2)
Wcut = LM[(nrow(LM)-1),]
widthLM = rep(col_width, times=LMcol)
widthLM[Wcut == 99] = margin_height
# Arranges the graphical object
plot = grid.arrange(grobs=P, layout_matrix=LM,
heights=heightLM, widths=widthLM)
# If there is no specified station code to highlight (mini map)
if (is.null(codeLight)) {
# Saving matrix plot
ggsave(plot=plot,
path=outdirTmp,
filename=paste(outname, '.pdf', sep=''),
width=width, height=height, units='cm', dpi=100)
}
}
}
# Returns the map object
return (map)
}