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matrix.R 40.11 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/matrix.R
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#
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# Allows the creation of a summarizing matrix of trend and break analyses
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## 1. MATRIX PANEL
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# Generates a summarizing matrix of the trend analyses of all station for different hydrological variables and periods. Also shows difference of means between specific periods.
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matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice=NULL, outdirTmp='', outnameTmp='matrix', title=NULL, A3=FALSE) {
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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$code))
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    nCode = length(Code)
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    # Gets a trend example
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    df_trend = list_df2plot[[1]]$trend
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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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    # Fix the maximal number of period to the minimal possible
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    nPeriod_max = 0
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    # For all code
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    for (code in Code) {
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        # Extracts the trend corresponding to the code
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        df_trend_code = df_trend[df_trend$code == code,]
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        # Extract start and end of trend periods
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        Start = df_trend_code$period_start
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        End = df_trend_code$period_end
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        # Get the name of the different period
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        UStart = levels(factor(Start))
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        UEnd = levels(factor(End))
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        # Compute the max of different start and end
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        # so the number of different period
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        nPeriod = max(length(UStart), length(UEnd))
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        # If the number of period for the trend is greater
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        # than the current max period, stocks it
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        if (nPeriod > nPeriod_max) {
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            nPeriod_max = nPeriod
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        }
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} # Blank list to store time info by station code Start_code = vector(mode='list', length=nCode) End_code = vector(mode='list', length=nCode) Code_code = vector(mode='list', length=nCode) Periods_code = vector(mode='list', length=nCode) # For all the code for (j in 1:nCode) { # Gets the code code = Code[j] # Extracts the trend corresponding to the code df_trend_code = df_trend[df_trend$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)) # Vector to store trend period Periods = c() # For all the trend period for (i in 1:nPeriod_trend) { # Stocks period Periods = append(Periods, paste(Start[i], End[i], sep=' / ')) } # Stores time info by station Start_code[[j]] = Start End_code[[j]] = End Code_code[[j]] = code Periods_code[[j]] = Periods } # Blank array to store mean of the trend for each # station, perdiod and variable TrendMean_code = array(rep(1, nPeriod_trend*nbp*nCode), dim=c(nPeriod_trend, nbp, nCode)) # For all the trend period for (j in 1:nPeriod_trend) { # 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 p_threshold = list_df2plot[[i]]$p_threshold # Extracts the data corresponding to the code df_data_code = df_data[df_data$code == code,] # Extracts the trend corresponding to the code df_trend_code = df_trend[df_trend$code == code,] # Gets the associated time info Start = Start_code[Code_code == code][[1]][j] End = End_code[Code_code == code][[1]][j] Periods = Periods_code[Code_code == code][[1]][j]
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# Extracts the corresponding data for the period df_data_code_per = df_data_code[df_data_code$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$period_end == End,] # Computes the number of trend analysis selected Ntrend = nrow(df_trend_code_per) # If there is more than one trend on the same period if (Ntrend > 1) { # Takes only the first because they are similar df_trend_code_per = df_trend_code_per[1,] } # Computes the mean of the data on the period dataMean = mean(df_data_code_per$Value, na.rm=TRUE) # Normalises the trend value by the mean of the data trendMean = df_trend_code_per$trend / dataMean # If the p value is under the threshold if (df_trend_code_per$p <= p_threshold){ # Stores the averaged trend TrendMean_code[j, i, k] = trendMean # Otherwise } else { # Do not stocks it TrendMean_code[j, i, k] = NA } } } } # Computes the min and the max of the mean trend for # all the station minTrendMean = apply(TrendMean_code, c(1, 2), min, na.rm=TRUE) maxTrendMean = apply(TrendMean_code, c(1, 2), max, na.rm=TRUE) # Blank vectors to store info about trend analyses Periods_trend = c() NPeriod_trend = c() Var_trend = list() Code_trend = c() Pthresold_trend = c() TrendMean_trend = c() DataMean_trend = c() Fill_trend = c() Color_trend = c() # For all the trend period for (j in 1:nPeriod_trend) { # For all code for (code in Code) { # 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 p_threshold = list_df2plot[[i]]$p_threshold # Extract the variable of the plot var = list_df2plot[[i]]$var # 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 = Start_code[Code_code == code][[1]][j]
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End = End_code[Code_code == code][[1]][j] Periods = Periods_code[Code_code == code][[1]][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,] } # Computes the mean of the data on the period dataMean = mean(df_data_code_per$Value, na.rm=TRUE) # Normalises the trend value by the mean of the data trendMean = df_trend_code_per$trend / dataMean # If the p value is under the threshold if (df_trend_code_per$p <= p_threshold){ # Gets the color associated to the averaged trend color_res = get_color(trendMean, minTrendMean[j, i], maxTrendMean[j, i], palette_name='perso', reverse=TRUE) # Specifies the color fill and contour of # table cells fill = color_res color = 'white' Pthresold = p_thresold # Otherwise it is not significative } else { fill = 'white' color = 'grey85' Pthresold = NA } # Stores info needed to plot Periods_trend = append(Periods_trend, Periods) NPeriod_trend = append(NPeriod_trend, j) Var_trend = append(Var_trend, var) Code_trend = append(Code_trend, code) Pthresold_trend = append(Pthresold_trend, Pthresold) TrendMean_trend = append(TrendMean_trend, trendMean) DataMean_trend = append(DataMean_trend, dataMean) Fill_trend = append(Fill_trend, fill) Color_trend = append(Color_trend, color) } } } # If there is a 'mean_period' if (!is.null(mean_period)) { # Blank vectors to store info about breaking analysis Periods_mean = c() NPeriod_mean = c() Var_mean = list() Code_mean = c() DataMean_mean = c() BreakMean_mean = c() # Convert 'mean_period' to list
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mean_period = as.list(mean_period) # Number of mean period nPeriod_mean = length(mean_period) # Blank array to store difference of mean between two periods BreakMean_code = array(rep(1, nPeriod_mean*nbp*nCode), dim=c(nPeriod_mean, nbp, nCode)) # Blank array to store mean for a temporary period in order # to compute the difference of mean with a second period dataMeantmp = array(rep(NA, nbp*nCode), dim=c(nbp, nCode)) # For all period of breaking analysis for (j in 1:nPeriod_mean) { # For all the code for (k in 1:nCode) { # Gets the code code = Code[k] # For all variable for (i in 1:nbp) { # Extracts the data corresponding to # the current variable df_data = list_df2plot[[i]]$data # Extract the variable of the plot var = list_df2plot[[i]]$var # Extracts the data corresponding to the code df_data_code = df_data[df_data$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) # Creates a period name Periods = paste(Datemin, Datemax, sep=' / ') # 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 } # Normalises the break by the mean of the # initial period BreakMean = Break / dataMeantmp[i, k] # Stores the result BreakMean_code[j, i, k] = BreakMean # Stores temporarily the mean of the current period dataMeantmp[i, k] = dataMean # Stores info needed to plot Periods_mean = append(Periods_mean, Periods) NPeriod_mean = append(NPeriod_mean, j) Var_mean = append(Var_mean, var) Code_mean = append(Code_mean, code)
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DataMean_mean = append(DataMean_mean, dataMean) BreakMean_mean = append(BreakMean_mean, BreakMean) } } } # Computes the min and the max of the averaged trend for # all the station minBreakMean = apply(BreakMean_code, c(1, 2), min, na.rm=TRUE) maxBreakMean = apply(BreakMean_code, c(1, 2), max, na.rm=TRUE) # Blanks vector to store color info Fill_mean = c() Color_mean = c() # Index to count over all break computed ii = 1 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 averaged breaking BreakMean = BreakMean_mean[ii] # Gets the color associated color_res = get_color(BreakMean, minBreakMean[j, i], maxBreakMean[j, i], palette_name='perso', reverse=TRUE) # Gets the fill and contour color fill = color_res color = 'white' # Stores it Fill_mean = append(Fill_mean, fill) Color_mean = append(Color_mean, color) # Passes to the next index ii = ii + 1 } } } } # If the slice option is not specified, the info for all # stations will be draw on the same page if (is.null(slice)) { slice = nCode } # Extracts each possibilities of first letter of station code firstLetter = levels(factor(substr(Code, 1, 1))) # Number of different first letters nfL = length(firstLetter) # For all the available first letter for (ifL in 1:nfL) { # Gets the first letter fL = firstLetter[ifL] # Print the matrix name print(paste('Matrix for region : ', fL, " (", round(ifL/nfL*100, 1), " %)", sep='')) # Get only station code with the same first letter subCodefL = Code[substr(Code, 1, 1) == fL] # Counts the number of station in it nsubCodefL = length(subCodefL) # Computes the number of pages needed to plot all stations nMat = as.integer(nsubCodefL/slice) + 1
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# For all the pages for (imat in 1:nMat) { # Extracts the station for the current page subCode = subCodefL[(slice*(imat-1)+1):(slice*imat)] # Removes NA stations subCode = subCode[!is.na(subCode)] # Reverses verticale order of stations subCode = rev(subCode) # Gets the number of station for the page nsubCode = length(subCode) # Creates logical vector to select only info about # stations that will be plot on the page CodefL_trend = Code_trend %in% subCode # Extracts those info subPeriods_trend = Periods_trend[CodefL_trend] subNPeriod_trend = NPeriod_trend[CodefL_trend] subVar_trend = Var_trend[CodefL_trend] subCode_trend = Code_trend[CodefL_trend] subPthresold_trend = Pthresold_trend[CodefL_trend] subTrendMean_trend = TrendMean_trend[CodefL_trend] subDataMean_trend = DataMean_trend[CodefL_trend] subFill_trend = Fill_trend[CodefL_trend] subColor_trend = Color_trend[CodefL_trend] # Same for breaking analysis CodefL_mean = Code_mean %in% subCode # Extracts right info subPeriods_mean = Periods_mean[CodefL_mean] subNPeriod_mean = NPeriod_mean[CodefL_mean] subVar_mean = Var_mean[CodefL_mean] subCode_mean = Code_mean[CodefL_mean] subDataMean_mean = DataMean_mean[CodefL_mean] subBreakMean_mean = BreakMean_mean[CodefL_mean] subFill_mean = Fill_mean[CodefL_mean] subColor_mean = Color_mean[CodefL_mean] # Extracts the name of the currently hydrological # region plotted title = df_meta[df_meta$code == subCode[1],]$region_hydro ### Plot ### # Fixes the height and width of the table according to # the number of station and the number of column to draw height = nsubCode width = nbp * 2 * nPeriod_trend + nPeriod_trend + nPeriod_mean * nbp + nPeriod_mean + nbp # Fixes the size of the plot area to keep proportion right options(repr.plot.width=width, repr.plot.height=height) # Open a new plot with a personalise theme mat = ggplot() + theme_ash + # Modification of theme in order to remove axis theme( panel.border=element_blank(), axis.text.x=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank(), axis.ticks.x=element_blank(), axis.title.y=element_blank(), plot.margin=margin(t=5, r=5, b=5, l=5, unit="mm") ) # Postion and name of the title xt = 1 - 6 yt = height + 2 Title = bquote(bold(.(title))) # Writes the title mat = mat + annotate("text", x=xt, y=yt, label=Title,
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hjust=0, vjust=1, size=6, color="#00A3A8") ### Trend ### # For all the trend period for (j in 1:nPeriod_trend) { # Extracts the info to plot associated to the # right period Periods_trend_per = subPeriods_trend[subNPeriod_trend == j] NPeriods_trend_per = subNPeriod_trend[subNPeriod_trend == j] Var_trend_per = subVar_trend[subNPeriod_trend == j] Code_trend_per = subCode_trend[subNPeriod_trend == j] Pthresold_trend_per = subPthresold_trend[subNPeriod_trend == j] TrendMean_trend_per = subTrendMean_trend[subNPeriod_trend == j] DataMean_trend_per = subDataMean_trend[subNPeriod_trend == j] Fill_trend_per = subFill_trend[subNPeriod_trend == j] Color_trend_per = subColor_trend[subNPeriod_trend == j] # Converts the vector of hydrological variable to # a vector of integer associated to those variable Xtmp = as.integer(factor(as.character(Var_trend_per))) # Computes X position of the column for the period dates Xc = j + (j - 1)*nbp*2 # Computes X positions of columns for the mean of # variables Xm = Xtmp + (j - 1)*nbp*2 + j # Computes X positions of columns for the averaged trend X = Xtmp + (j - 1)*nbp*2 + nbp + j # Computes Y positions of each line for each station Y = as.integer(factor(Code_trend_per)) # Reverses vertical order of stations Y = rev(Y) # Position of a line to delimite periods x = Xc - 0.4 xend = X[length(X)] + 0.25 y = height + 1 yend = height + 1 # Drawing of the line mat = mat + annotate("segment", x=x, xend=xend, y=y, yend=yend, color="grey40", size=0.35) # Position of the name of the current period yt = y + 0.15 Start = trend_period[[j]][1] End = trend_period[[j]][2] # Name of the period periodName = bquote(bold('Période')~bold(.(as.character(j)))) # Naming the period mat = mat + annotate("text", x=x, y=yt, label=periodName, hjust=0, vjust=0.5, size=3, color='grey40') # For all the variable for (i in 1:length(X)) {
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mat = mat + # Plots circles for averaged trends gg_circle(r=0.45, xc=X[i], yc=Y[i], fill=Fill_trend_per[i], color=Color_trend_per[i]) + # Plots circles for averaged of variables gg_circle(r=0.45, xc=Xm[i], yc=Y[i], fill='white', color='grey40') + # Plots circles for the column of period dates gg_circle(r=0.45, xc=Xc, yc=Y[i], fill='white', color='grey40') } # For all averaged trends on this periods for (i in 1:length(TrendMean_trend_per)) { # Extracts the value of the averaged trend trendMean = TrendMean_trend_per[i] # Converts it to the right format with two # significant figures trendMeanC = signif(trendMean*100, 2) # If it is significative if (!is.na(Pthresold_trend_per[i])) { # The text color is white Tcolor = 'white' # Otherwise } else { # The text is grey Tcolor = 'grey85' } # Same for averaged variables over # the current period dataMean = DataMean_trend_per[i] dataMeanC = signif(dataMean, 2) mat = mat + # Writes the mean trend annotate('text', x=X[i], y=Y[i], label=trendMeanC, hjust=0.5, vjust=0.5, size=3, color=Tcolor) + # Writes the mean of the associated variable annotate('text', x=Xm[i], y=Y[i], label=dataMeanC, hjust=0.5, vjust=0.5, size=3, color='grey40') } # Writes a name for the period dates column mat = mat + annotate('text', x=Xc, y=max(Y) + 0.85, label=bquote(bold('Début')), hjust=0.5, vjust=0.5, size=3, color='grey20') + annotate('text', x=Xc, y=max(Y) + 0.6, label=bquote(bold('Fin')), hjust=0.5, vjust=0.5, size=3, color='grey20') # For all variable for (i in 1:nbp) { # Extract the variable of the plot var = list_df2plot[[i]]$var mat = mat + # Writes the type of the variable annotate('text', x=X[i], y=max(Y) + 0.82, label=bquote(.(var)), hjust=0.5, vjust=0.5, size=3.25, color='grey20') + # Writes the unit of the variable
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annotate('text', x=X[i], y=max(Y) + 0.6, label=bquote('[%.'*ans^{-1}*']'), hjust=0.5, vjust=0.5, size=2, color='grey40') + # Writes the type of the averaged variable annotate('text', x=Xm[i], y=max(Y) + 0.82, label=bquote('µ'*.(var)), hjust=0.5, vjust=0.5, size=3.25, color='grey20') + # Writes the unit of the averaged variable annotate('text', x=Xm[i], y=max(Y) + 0.6, label=bquote('['*m^3*'.'*s^{-1}*']'), hjust=0.5, vjust=0.5, size=2, color='grey40') } # For all the station on the page for (k in 1:nsubCode) { # Gets the code code = subCode[k] # Extracts label for the period dates label = Periods_trend_per[Code_trend_per == code][1] # Gets the start and end of the period # for the station periodStart = substr(label, 1, 4) periodEnd = substr(label, 14, 17) mat = mat + # Writes the starting value annotate('text', x=Xc, y=k + 0.13, label=bquote(bold(.(periodStart))), hjust=0.5, vjust=0.5, size=3, color='grey40') + # Writes the ending value annotate('text', x=Xc, y=k - 0.13, label=bquote(bold(.(periodEnd))), hjust=0.5, vjust=0.5, size=3, color='grey40') } } ### Mean ### # For all the trend period for (j in 1:nPeriod_mean) { # Extracts the info to plot associated to the # right period Periods_mean_per = subPeriods_mean[subNPeriod_mean == j] NPeriods_mean_per = subNPeriod_mean[subNPeriod_mean == j] Var_mean_per = subVar_mean[subNPeriod_mean == j] Code_mean_per = subCode_mean[subNPeriod_mean == j] DataMean_mean_per = subDataMean_mean[subNPeriod_mean == j] BreakMean_mean_per = subBreakMean_mean[subNPeriod_mean == j] Fill_mean_per = subFill_mean[subNPeriod_mean == j] Color_mean_per = subColor_mean[subNPeriod_mean == j] # Converts the vector of hydrological variable to # a vector of integer associated to those variable Xtmp_mean = as.integer(factor(as.character(Var_mean_per))) # Computes X position of the column for the period dates Xc_mean = j + (j - 1)*nbp + X[length(X)] # Computes X positions of columns for the mean of # variables
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Xm_mean = Xtmp_mean + (j - 1)*nbp + j + X[length(X)] # Computes X positions of columns for the difference of # mean between periods (break) Xr_mean = Xtmp_mean + (j - 1)*nbp*2 + j + X[length(X)] # Computes Y positions of each line for each station Y_mean = as.integer(factor(Code_mean_per)) # Reverses vertical order of stations Y_mean = rev(Y_mean) # Position of a line to delimite periods x = Xc_mean - 0.4 xend = Xm_mean[length(Xm_mean)] + 0.25 y = height + 1 yend = height + 1 # Drawing of the line mat = mat + annotate("segment", x=x, xend=xend, y=y, yend=yend, color="grey40", size=0.35) # Position of the name of the current period yt = y + 0.15 Start = mean_period[[j]][1] End = mean_period[[j]][2] # Name of the period periodName = bquote(bold('Période')~bold(.(as.character(j+nPeriod_trend)))) # Naming the period mat = mat + annotate("text", x=x, y=yt, label=periodName, hjust=0, vjust=0.5, size=3, color='grey40') # If this is not the first period if (j > 1) { # Position of a line to delimite results of # difference of mean bewteen periods x = Xr_mean[1] - 0.4 xend = Xr_mean[length(Xr_mean)] + 0.25 # Drawing of the line mat = mat + annotate("segment", x=x, xend=xend, y=y, yend=yend, color="grey40", size=0.35) # Naming the breaking columns breakName = bquote(bold('Écart')~bold(.(as.character(j-1+nPeriod_trend)))*bold('-')*bold(.(as.character(j+nPeriod_trend)))) # Writes the name mat = mat + annotate("text", x=x, y=yt, label=breakName, hjust=0, vjust=0.5, size=3, color='grey40') } # For all the variable for (i in 1:length(Xm_mean)) { mat = mat + # Plots circles for averaged variables gg_circle(r=0.45, xc=Xm_mean[i], yc=Y[i], fill='white', color='grey40') + # Plots circles for the column of period dates gg_circle(r=0.45, xc=Xc_mean, yc=Y[i], fill='white', color='grey40') # If this is not the first period if (j > 1) { mat = mat +
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# Plots circles for breaking results gg_circle(r=0.45, xc=Xr_mean[i], yc=Y[i], fill=Fill_mean_per[i], color=Color_mean_per[i]) } } # For all averaged variables on this period for (i in 1:length(DataMean_mean_per)) { # Extracts values of averaged variables dataMean = DataMean_mean_per[i] # Converts it to the right format with two # significant figures dataMeanC = signif(dataMean, 2) # Writes averaged variables values mat = mat + annotate('text', x=Xm_mean[i], y=Y[i], label=dataMeanC, hjust=0.5, vjust=0.5, size=3, color='grey40') # If this is not the first period if (j > 1) { # Extracts values of breaking between periods BreakMean = BreakMean_mean_per[i] # Converts it to the right format with two # significant figures BreakMeanC = signif(BreakMean*100, 2) # Writes breaking values mat = mat + annotate('text', x=Xr_mean[i], y=Y[i], label=BreakMeanC, hjust=0.5, vjust=0.5, size=3, color='white') } } # Writes a name for the period dates column mat = mat + annotate('text', x=Xc_mean, y=max(Y) + 0.85, label=bquote(bold('Début')), hjust=0.5, vjust=0.5, size=3, color='grey20') + annotate('text', x=Xc_mean, y=max(Y) + 0.6, label=bquote(bold('Fin')), hjust=0.5, vjust=0.5, size=3, color='grey20') # For all variables for (i in 1:nbp) { # Extract the variable of the plot var = list_df2plot[[i]]$var mat = mat + # Writes the type of the averaged variable annotate('text', x=Xm_mean[i], y=max(Y) + 0.82, label=bquote('µ'*.(var)), hjust=0.5, vjust=0.5, size=3.25, color='grey20') + # Writes the unit of the averaged variable annotate('text', x=Xm_mean[i], y=max(Y) + 0.6, label=bquote('['*m^3*'.'*s^{-1}*']'), hjust=0.5, vjust=0.5, size=2, color='grey40') # If this is not the first period if (j > 1) { mat = mat + # Writes the type of the breaking variable annotate('text', x=Xr_mean[i], y=max(Y) + 0.82, label=bquote('d'*.(var)),
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hjust=0.5, vjust=0.5, size=3.25, color='grey20') + # Writes the unit of the breaking variable annotate('text', x=Xr_mean[i], y=max(Y) + 0.6, label=bquote('[%]'), hjust=0.5, vjust=0.5, size=2, color='grey40') } } # For all the station on the page for (k in 1:nsubCode) { # Gets the code code = subCode[k] # Extracts label for the period dates label = Periods_mean_per[Code_mean_per == code][1] # Gets the start and end of the period # for the station periodStart = substr(label, 1, 4) periodEnd = substr(label, 14, 17) mat = mat + # # Writes the starting value annotate('text', x=Xc_mean, y=k + 0.13, label=bquote(bold(.(periodStart))), hjust=0.5, vjust=0.5, size=3, color='grey40') + # Writes the ending value annotate('text', x=Xc_mean, y=k - 0.13, label=bquote(bold(.(periodEnd))), hjust=0.5, vjust=0.5, size=3, color='grey40') } } ### Code ### # For all the station for (k in 1:nsubCode) { # Gets the code code = subCode[k] # Gets the name of the station name = df_meta[df_meta$code == code,]$nom # Fixes a limit for the max number # of characters available ncharMax = 38 # If the number of character of the name is greater # than the limit if (nchar(name) > ncharMax) { # Cuts the name and add '...' name = paste(substr(name, 1, ncharMax), '...', sep='') } mat = mat + # Writes the code of the station annotate('text', x=0.3, y=k + 0.14, label=bquote(bold(.(code))), hjust=1, vjust=0.5, size=3.5, color="#00A3A8") + # Writes the name of the station annotate('text', x=0.3, y=k - 0.14, label=name, hjust=1, vjust=0.5, size=3.5, color="#00A3A8") } ### Environment ### mat = mat + # Fixed coordinate system
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coord_fixed() + # X axis scale_x_continuous(limits=c(1 - rel(6), width + rel(0.5)), expand=c(0, 0)) + # Y axis scale_y_continuous(limits=c(1 - rel(0.5), height + rel(2)), expand=c(0, 0)) # Paper format in A3 if needed if (A3) { width = 42 height = 29.7 dpi = 300 # Otherwise in A4 } else { width = 29.7 height = 21 dpi = 100 } # Saving ggsave(plot=mat, path=outdirTmp, filename=paste(outnameTmp, '_', fL, imat, '.pdf', sep=''), width=width, height=height, units='cm', dpi=dpi) } } }