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map.R 37.91 KiB
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# \\\
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# Copyright 2021-2022 Louis Héraut*1
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#
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# *1   INRAE, France
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#      louis.heraut@inrae.fr
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#
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# This file is part of ash R toolbox.
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#
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# ash R toolbox is free software: you can redistribute it and/or modify
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# it under the terms of the GNU General Public License as published by
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# the Free Software Foundation, either version 3 of the License, or (at
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# your option) any later version.
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#
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# ash R toolbox is distributed in the hope that it will be useful, but
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# WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
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# General Public License for more details.
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#
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# You should have received a copy of the GNU General Public License
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# along with ash R toolbox.  If not, see <https://www.gnu.org/licenses/>.
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# ///
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#
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#
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# plotting/map.R
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#
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# Deals with the creation of a map for presenting the trend analysis of hydrological variables
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## 1. MAP PANEL ______________________________________________________
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# Generates a map plot of the tendancy of a hydrological variable
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map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
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                      trend_period, mean_period, colorForce=FALSE,
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                      outdirTmp='', codeLight=NULL,
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                      margin=NULL, showSea=TRUE,
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                      foot_note=FALSE,
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                      foot_height=0, resources_path=NULL,
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                      logo_dir=NULL,
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                      AEAGlogo_file=NULL, INRAElogo_file=NULL,
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                      FRlogo_file=NULL, df_page=NULL,
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                      verbose=TRUE) {
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    # Extract shapefiles
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    df_france = df_shapefile$france
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    df_bassin = df_shapefile$bassin
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    df_subbassin = df_shapefile$subbassin
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    df_river = df_shapefile$river
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    # Number of variable/plot
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    nbp = length(list_df2plot)
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    # Get all different stations code
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    Code = levels(factor(df_meta$code))
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    nCode = length(Code)
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    if (!is.null(trend_period)) {
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        # Convert 'trend_period' to list
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        trend_period = as.list(trend_period)
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        # Number of trend period
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        nPeriod_trend = length(trend_period)
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        # Extracts the min and the max of the mean trend for all the station
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        res = short_trendExtremes(list_df2plot, Code, nPeriod_trend, nbp, nCode, colorForce)
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        minTrendValue = res$min
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        maxTrendValue = res$max
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    }
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    # If there is a 'mean_period'
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    if (!is.null(mean_period)) {
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        # Convert 'mean_period' to list
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        mean_period = as.list(mean_period)
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# Number of mean period nPeriod_mean = length(mean_period) res = short_meanExtremes(list_df2plot, Code, nPeriod_mean, nbp, nCode) minBreakValue = res$min maxBreakValue = res$max breakValue_code = res$value } else { 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 # Explanations about the variable glose = list_df2plot[[i]]$glose # Createsa name for the map if (j > 1) { outname = paste('map_d', var, sep='') } else { outname = paste('map_', var, sep='') } n_loop = i + nbp*(j-1) N_loop = nbp*nPeriod_mean # If there is the verbose option if (verbose) { if (j > 1) { mapName = 'difference' } else { mapName = 'tendence' } # Prints the name of the map print(paste('Map of ', mapName, ' for : ', var, " (", round(n_loop/N_loop*100, 0), " %)", sep='')) } # If there is no specified station code to highlight # (mini map) if (is.null(codeLight)) { # Sets the size of the countour sizefr = 0.45 sizebs = 0.4 sizerv = 0.3 } else { sizefr = 0.35 sizebs = 0.3 sizerv = 0.2 } # Stores the coordonate system cf = coord_fixed() # Makes it the default one to remove useless warning
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cf$default = TRUE # Open a new plot with the personalise theme map = ggplot() + theme_void() + # theme( # # Ticks marker # axis.ticks.x=element_line(color='grey75', size=0.3), # axis.ticks.y=element_line(color='grey75', size=0.3), # # Ticks label # axis.text.x=element_text(color='grey40'), # axis.text.y=element_text(color='grey40')) + # 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 (is.null(codeLight)) { xBasin = c(410000, 520000, 630000, 620000, 510000, 450000, 390000) yBasin = c(6280000, 6290000, 6320000, 6385000, 6450000, 6530000, 6361000) nameBasin = c('Adour', 'Garonne', 'Tarn-Aveyron', 'Lot', 'Dordogne', 'Charente', 'Fleuves-\nCôtiers') nBasin = length(xBasin) plot_basin = tibble(x=xBasin, y=yBasin, label=nameBasin) map = map + geom_shadowtext(data=plot_basin, aes(x=x, y=y, label=label), fontface="bold", color="grey85", bg.colour="grey97", hjust=0.5, vjust=0.5, size=5) } # If the sea needs to be shown if (showSea) { # Leaves space around the France xlim = c(295000, 790000) ylim = c(6125000, 6600000)
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# 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
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map = map + theme(plot.margin=margin) } # Blank vector to store data about station lon = c() lat = c() fill = c() shape = c() Value = c() OkVal = 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] pVal = 0 } else if (is.null(trend_period)) { value = NA minValue = NULL maxValue = NULL pVal = 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$code == code,] # Extract start and end of trend periods Start = df_trend_code$period_start[idPer_trend] End = df_trend_code$period_end[idPer_trend] # Extracts the corresponding data for the period df_data_code_per = df_data_code[df_data_code$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)
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# 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] pVal = df_trend_code_per$p } # Computes the color associated to the mean trend color_res = get_color(value, minValue, maxValue, palette_name='perso', reverse=TRUE, ncolor=256) # Computes the colorbar info palette_res = get_palette(minValue, maxValue, palette_name='perso', reverse=TRUE, ncolor=256, nbTick=nbTick) if (j > 1 | is.null(trend_period)) { # The computed color is stored filltmp = color_res # The marker is a circle shapetmp = 21 } else { # If it is significative if (pVal <= alpha){ # The computed color is stored filltmp = color_res # If the mean tend is positive if (value >= 0) { # Uses a triangle up for the shape # of the marker shapetmp = 24 # If negative } else { # Uses a triangle down for the shape # of the marker shapetmp = 25 } } else if (pVal > alpha & colorForce) { # The computed color is stored filltmp = color_res # The marker is a circle shapetmp = 21 # If it is not significative } else { # The fill color is grey filltmp = 'grey97' # The marker is a circle shapetmp = 21 } } # Extracts the localisation of the current station lontmp = df_meta[df_meta$code == code,]$L93X_m_BH lattmp = df_meta[df_meta$code == code,]$L93Y_m_BH
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# Stores all the parameters lon = c(lon, lontmp) lat = c(lat, lattmp) fill = c(fill, filltmp) shape = c(shape, shapetmp) Value = c(Value, value) # If the trend analysis is significative a TRUE is stored OkVal = c(OkVal, pVal <= alpha) } # Creates a tibble to stores all the data to plot plot_map = tibble(lon=lon, lat=lat, fill=fill, shape=shape, code=Code, OkVal=OkVal) # If there is no specified station code to highlight # (mini map) if (is.null(codeLight)) { plot_map_NOk = plot_map[!plot_map$OkVal,] plot_map_Ok = plot_map[plot_map$OkVal,] if (nrow(plot_map_NOk) > 0) { map = map + # Plots the point that are not # significant first geom_point(data=plot_map_NOk, aes(x=lon, y=lat), shape=shape[!OkVal], size=5, stroke=1, color='grey50', fill=fill[!OkVal]) } if (nrow(plot_map_Ok) > 0) { map = map + # Plots the point that are significant last geom_point(data=plot_map_Ok, aes(x=lon, y=lat), shape=shape[OkVal], size=5, stroke=1, color='grey50', fill=fill[OkVal]) } # If there is a specified station code } else { # Extract data of all stations not to highlight plot_map_codeNo = plot_map[plot_map$code != codeLight,] # Extract data of the station to highlight plot_map_code = plot_map[plot_map$code == codeLight,] # Plots only the localisation map = map + # For all stations not to highlight geom_point(data=plot_map_codeNo, aes(x=lon, y=lat), shape=21, size=0.5, stroke=0.5, color='grey50', fill='grey50') + # For the station to highlight geom_point(data=plot_map_code, aes(x=lon, y=lat), shape=21, size=2, stroke=0.5, color='grey97', fill='#00A3A8') } if (is.null(codeLight)) { # Extracts the position of the tick of the colorbar posTick = palette_res$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
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# 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(signif(labTick*100, 2)) # If it is a date variable } else if (type == 'saisonnalité') { # Formatting of label labTick = as.character(signif(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) nbLine = as.integer(nchar(glose)/40) + 1 nbNewline = 0 nbLim = 43 gloseName = glose nbChar = nchar(gloseName) while (nbChar > nbLim) { nbNewline = nbNewline + 1 posSpace = which(strsplit(gloseName, "")[[1]] == " ") idNewline = which.min(abs(posSpace - nbLim * nbNewline)) posNewline = posSpace[idNewline] gloseName = paste(substring(gloseName, c(1, posNewline + 1), c(posNewline, nchar(gloseName))), collapse="\n") Newline = substr(gloseName, posNewline + 2, nchar(gloseName)) nbChar = nchar(Newline) } Yline = 0.6 + 0.47*nbNewline Ytitle = Yline + 0.15 # New plot with void theme title = ggplot() + theme_void() + # Plots separation lines geom_line(aes(x=c(-0.3, 3.9), y=c(0.05, 0.05)), size=0.6, color="#00A3A8") + geom_line(aes(x=c(-0.3, 3.9), y=c(Yline, Yline)), size=0.6, color="#00A3A8") + # Writes title geom_shadowtext(data=tibble(x=-0.3, y=Ytitle, label=var), aes(x=x, y=y, label=label), fontface="bold", color="#00A3A8", bg.colour="white", hjust=0, vjust=0, size=10) + # Writes title geom_shadowtext(data=tibble(x=-0.3, y=0.2, label=gloseName), aes(x=x, y=y, label=label),
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fontface="bold", color="#00A3A8", bg.colour="white", hjust=0, vjust=0, size=3) + # 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) periodName_trend = paste( format(as.Date(trend_period[[idPer_trend]][1]), '%Y'), format(as.Date(trend_period[[idPer_trend]][2]), '%Y'), sep='-') periodName1_mean = paste( format(as.Date(mean_period[[1]][1]), '%Y'), format(as.Date(mean_period[[1]][2]), '%Y'), sep='-') periodName2_mean = paste( format(as.Date(mean_period[[2]][1]), '%Y'), format(as.Date(mean_period[[2]][2]), '%Y'), sep='-') if (j > 1) { ValueName1 = "Écarts observés entre" ValueName2 = paste(periodName1_mean, " et ", periodName2_mean, sep='') # 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 { ValueName1 = "Tendances observées" ValueName2 = paste("sur la période ", periodName_trend, sep='') # 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 +
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# Name of the colorbar annotate('text', x=-0.3, y= valNorm + 37, label=ValueName1, hjust=0, vjust=0.5, size=6, color='grey40') + # Second line annotate('text', x=-0.3, y= valNorm + 26, label=ValueName2, hjust=0, vjust=0.5, size=6, color='grey40') + # Unit legend of the colorbar annotate('text', x=-0.3, y= valNorm + 14, 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') } if (j == 1) { upLabel = bquote(bold("Hausse significative à 10%")) noneLabel = bquote(bold("Non significatif à 10%")) downLabel = bquote(bold("Baisse significative à 10%")) yUp = -20 yNone = -29 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') 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',
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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 yValueOk = yValue[OkVal] yValueNOk = yValue[!OkVal] # Histogram distribution # Computes the histogram of values res_hist = hist(yValueOk, breaks=ytick, plot=FALSE) # Extracts the number of counts per cells countsOk = res_hist$counts # Extracts middle of cells midsOk = res_hist$mids # Histogram distribution # Computes the histogram of values res_hist = hist(yValueNOk, breaks=ytick, plot=FALSE) # Extracts the number of counts per cells countsNOk = res_hist$counts counts = countsOk + countsNOk # Blank vectors to store position of points of # the distribution to plot xValue = c() yValue = c() color = c() shape = c() # Start X position of the distribution start_hist = 1 # X separation bewteen point hist_sep = 0.15 # Gets the maximun number of point of the distribution maxCount = max(counts, na.rm=TRUE) # Limit of the histogram lim_hist = 2 # If the number of point will exceed the limit if (maxCount * hist_sep > lim_hist) { # Computes the right amount of space between points hist_sep = lim_hist / maxCount } # For all cells of the histogram for (ii in 1:length(midsOk)) { # 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
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# current cell the number of times it has been counted yValue = c(yValue, rep(midsOk[ii], times=counts[ii])) color = c(color, rep('grey50', times=countsOk[ii])) color = c(color, rep('grey85', times=countsNOk[ii])) if (j > 1) { shape = 21 } else { if (midsOk[ii] > 0) { shapetmp = 25 } else { shapetmp = 24 } shape = c(shape, rep(shapetmp, times=countsOk[ii])) shape = c(shape, rep(21, times=countsNOk[ii])) } } # Makes a tibble to plot the distribution plot_value = tibble(xValue=xValue, yValue=yValue) pal = pal + # Plots the point of the distribution geom_point(data=plot_value, aes(x=xValue, y=yValue), shape=shape, color=color, fill=color, stroke=0.4, alpha=1) if (type == 'sévérité') { labelArrow = 'Plus sévère' } else if (type == 'saisonnalité') { labelArrow = 'Plus tôt' } # Position of the arrow xArrow = 3.3 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, 4), expand=c(0, 0)) + # Y axis of the colorbar scale_y_continuous(limits=c(-47, valNorm + 48), expand=c(0, 0)) + # Margin of the colorbar theme(plot.margin=margin(t=0, r=0, b=0, l=0, unit="mm")) } else {
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pal = void title = void } if (!is.null(df_page)) { if (j > 1) { section = 'Carte des écarts observés' } else { section = 'Carte des tendances observées' } subsection = var n_page = df_page$n[nrow(df_page)] + 1 df_page = bind_rows( df_page, tibble(section=section, subsection=subsection, n=n_page)) } # If there is a foot note if (foot_note) { if (j > 1) { footName = 'carte des écarts observés' } else { footName = 'carte des tendances observées' } if (is.null(df_page)) { n_page = n_loop } foot = foot_panel(footName, n_page, resources_path, logo_dir, 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_size = 0.5 height = 21 width = 29.7 row_height = (height - 2*margin_size - foot_height) / (LMrow - 3) Hcut = LM[, 2] heightLM = rep(row_height, times=LMrow) heightLM[Hcut == id_foot] = foot_height
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heightLM[Hcut == 99] = margin_size col_width = (width - 2*margin_size) / (LMcol - 2) Wcut = LM[(nrow(LM)-1),] widthLM = rep(col_width, times=LMcol) widthLM[Wcut == 99] = margin_size # 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) } } } # If there is no specified station code to highlight # (mini map) if (is.null(codeLight)) { return (df_page) # Returns the map object } else { return (map) } }