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datasheet.R 59.17 KiB
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# \\\
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# Copyright 2021-2022 Louis Héraut*1,
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#                     Éric Sauquet*2,
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#                     Valentin Mansanarez
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#
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# *1   INRAE, France
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#      louis.heraut@inrae.fr
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# *2   INRAE, France
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#      eric.sauquet@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
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# modify it under the terms of the GNU General Public License as
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# published by the Free Software Foundation, either version 3 of the
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# License, or (at 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.
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# If not, see <https://www.gnu.org/licenses/>.
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# ///
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#
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#
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# Rcode/plotting/datasheet.R
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#
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# Regroups all the graphical tools to generates the datasheets. More precisely, the 'datasheet_panel' function manages all the call for each station of the different graphical functions that generates info header, time serie visualisation and trend analysis graphs for every variable. It also deals with the arranging of all the plots in a single PDF page.
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# Sourcing R file
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source(file.path('Rcode', 'processing', 'analyse.R'), encoding='UTF-8') # hydrograph
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source(file.path('Rcode', 'plotting', 'shortcut.R'), encoding='UTF-8')
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## 1. DATASHEET PANEL MANAGER ________________________________________
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# Manages datasheets creations for all stations. Makes the call to
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# the different headers, trend analysis graphs and realises arranging
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# every plots.
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datasheet_panel = function (list_df2plot, df_meta, trend_period,
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                            mean_period, linetype_per, axis_xlim,
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                            colorForce, info_header, time_header,
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                            foot_note, layout_matrix, info_height,
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                            time_ratio, var_ratio, foot_height,
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                            paper_size, resources_path, df_shapefile,
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                            logo_dir, PRlogo_file, AEAGlogo_file,
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                            INRAElogo_file, FRlogo_file,
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                            outdirTmp_pdf, outdirTmp_png,
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                            df_page=NULL) {
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    # The percentage of augmentation and diminution of the min
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    # and max limits for y axis
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    lim_pct = 10
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    # Number of variable/plot
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    nbVar = length(list_df2plot)
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    # Get all different stations code
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    Code = levels(factor(df_meta$code))
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    nCode = length(Code)
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    if (!is.null(trend_period)) {
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        # Convert 'trend_period' to list
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        trend_period = as.list(trend_period)
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        # Number of trend period
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        nPeriod_trend = length(trend_period)
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# Extracts the min and the max of the mean trend for all the station res = short_trendExtremes(list_df2plot, Code, nPeriod_trend, nbVar, nCode, colorForce) minTrendValue = res$min maxTrendValue = res$max } # Blank vector to store the max number of digit of label for # each station NspaceMax = c() # For all the station for (code in Code) { # Default max digit NspaceMax_code = 0 # If the time header is given it adds one to the number of plot nbVarMod = nbVar + as.numeric(!is.null(time_header)) # For all type of graph for (i in 1:nbVarMod) { if (i > nbVar) { # Extracts the data serie corresponding to the code df_data_code = time_header[time_header$code == code,] type = 'sévérité' } else { # Extracts the data corresponding to the current variable df_data = list_df2plot[[i]]$data # Extracts the type corresponding to the current variable type = list_df2plot[[i]]$type # Extracts the data corresponding to the code df_data_code = df_data[df_data$code == code,] } # If variable type is date if (type == 'saisonnalité') { # The number of digit is 6 because months are display # with 3 characters Nspace = 6 # If it is a flow variable } else if (type == 'sévérité' | type == 'data' | type == 'pluviométrie' | type == 'température' | type == 'évapotranspiration') { # Gets the max number of digit on the label maxtmp = max(df_data_code$Value, na.rm=TRUE) # Taking into account of the augmentation of # max for the window maxtmp = maxtmp * (1 + lim_pct/100) # If the max is greater than 10 if (maxtmp >= 10) { # The number of digit is the magnitude plus # the first number times 2 Nspace = (get_power(maxtmp) + 1)*2 # Plus spaces between thousands hence every 8 digits Nspace = Nspace + as.integer(Nspace/8) # If the max is less than 10 and greater than 1 } else if (maxtmp < 10 & maxtmp >= 1) { # The number of digit is the magnitude plus # the first number times 2 plus 1 for the dot # and 2 for the first decimal Nspace = (get_power(maxtmp) + 1)*2 + 3 # If the max is less than 1 (and obviously more than 0) } else if (maxtmp < 1) { # Fixes the number of significant decimals to 3 maxtmp = signif(maxtmp, 3) # The number of digit is the number of character # of the max times 2 minus 1 for the dots that # count just 1 space Nspace = nchar(as.character(maxtmp))*2 - 1 } }
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# If it is the temporary max number if (Nspace > NspaceMax_code) { # Stores it NspaceMax_code = Nspace } } # Stores the max digit number for labels of a station NspaceMax = c(NspaceMax, NspaceMax_code) } # For all the station for (k in 1:nCode) { # Gets the code code = Code[k] # Print code of the station for the current plotting print(paste("Datasheet for station : ", code, " (", round(k/nCode*100, 0), " %)", sep='')) # Number of header (is info and time serie are needed) nbh = as.numeric(!is.null(info_header)) + as.numeric(!is.null(time_header)) nbp = max(layout_matrix, na.rm=TRUE) # Actualises the number of plot nbg = nbp + nbh + as.numeric(foot_note) # Opens a blank list to store plot P = vector(mode='list', length=nbg) for (id in 1:nbg) { P[[id]] = void } # If the info header is needed if (!is.null(info_header)) { if ("data.frame" %in% class(info_header)) { # Extracts the data serie corresponding to the code info_header_code = info_header[info_header$code == code,] to_do = 'all' } else { info_header_code = NULL to_do = info_header } # Gets the info plot Hinfo = info_panel(list_df2plot, df_meta, trend_period=trend_period, mean_period=mean_period, periodHyd=mean_period[[1]], df_shapefile=df_shapefile, codeLight=code, df_data_code=info_header_code, to_do=to_do) # Stores it P[[1]] = Hinfo } # If the time header is given if (!is.null(time_header)) { # Extracts the data serie corresponding to the code time_header_code = time_header[time_header$code == code,] if (is.null(axis_xlim)) { # Gets the limits of the time serie axis_xlim_code = c(min(time_header_code$Date), max(time_header_code$Date)) } else { axis_xlim_code = axis_xlim
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} # Gets the time serie plot Htime = time_panel(time_header_code, df_trend_code=NULL, trend_period=trend_period, axis_xlim=axis_xlim_code, missRect=TRUE, unit2day=365.25, var='Q', type='sévérité', grid=TRUE, ymin_lim=0, NspaceMax=NspaceMax[k], first=TRUE, lim_pct=lim_pct) # Stores it P[[2]] = Htime } else { axis_xlim_code = axis_xlim } # Computes the number of column of plot asked on the datasheet nbcol = ncol(as.matrix(layout_matrix)) # For all variable for (i in 1:nbVar) { # 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 unit2day = list_df2plot[[i]]$unit2day missRect = list_df2plot[[i]]$missRect # Extract the variable of the plot var = list_df2plot[[i]]$var type = list_df2plot[[i]]$type # 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,] # Blank vector to store color color = c() if (!is.null(trend_period)) { # For all the period for (j in 1:nPeriod_trend) { # If the trend is significant # if (df_trend_code$p[j] <= alpha | colorForce){ if (df_trend_code$p[j] <= alpha){ # Extract start and end of trend periods Start = df_trend_code$period_start[j] End = df_trend_code$period_end[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
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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' | type == 'pluviométrie' | type == 'température' | type == 'évapotranspiration') { trendValue = df_trend_code_per$trend } # gets the color corresponding to the mean trend color_res = get_color(trendValue, minTrendValue[j, i], maxTrendValue[j, i], palette_name='perso', reverse=TRUE) # Stores it temporarily colortmp = color_res # Otherwise } else { # Stores the default grey color colortmp = 'grey85' } # Stores the color color = append(color, colortmp) grid = FALSE } } if (var != 'sqrt(Q)' & var != 'Q') { grid = FALSE ymin_lim = NULL } else { grid = TRUE ymin_lim = 0 } if (is.null(time_header) & i == 1) { first = TRUE } else { first = FALSE } # Computes the time panel associated to the current variable p = time_panel(df_data_code, df_trend_code, var=var, type=type, linetype_per=linetype_per, alpha=alpha, colorForce=colorForce, missRect=missRect, trend_period=trend_period, mean_period=mean_period, axis_xlim=axis_xlim_code, unit2day=unit2day, grid=grid, ymin_lim=ymin_lim, color=color, NspaceMax=NspaceMax[k], first=first, last=(i == nbVar), lim_pct=lim_pct) # Stores the plot P[[i+nbh]] = p } if (!is.null(df_page)) { section = 'Fiche station' subsection = code n_page = df_page$n[nrow(df_page)] + 1 df_page = bind_rows( df_page, tibble(section=section, subsection=subsection,
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n=n_page)) } if (foot_note) { footName = 'fiche station' if (is.null(df_page)) { n_page = k } foot = foot_panel(footName, n_page, resources_path, logo_dir, PRlogo_file, AEAGlogo_file, INRAElogo_file, FRlogo_file, foot_height) P[[nbg]] = foot } # Convert the 'layout_matrix' to a matrix if it is not already layout_matrix = as.matrix(layout_matrix) # Number of element of the matrix nel = nrow(layout_matrix)*ncol(layout_matrix) # Gets the place where there is NA value idNA = which(is.na(layout_matrix), arr.ind=TRUE) LM = layout_matrix # Adds non existing plot is where there is NA LM[idNA] = seq(max(layout_matrix, na.rm=TRUE) + 1, max(layout_matrix, na.rm=TRUE) + 1 + nel) # Shifts all plots to be coherent with the adding of header LM = LM + nbh if (!is.null(time_header)) { id_time = nbh LM = rbind(nbh, LM) } else { id_time = NA time_ratio = 0 } if (!is.null(info_header)) { id_info = nbh - as.numeric(!is.null(time_header)) LM = rbind(nbh - as.numeric(!is.null(time_header)), LM) } else { id_info = NA info_ratio = 0 } if (foot_note) { id_foot = length(LM) + 1 LM = rbind(LM, id_foot) } else { id_foot = max(LM) + 1 foot_height = 0 } # If paper format is A4 if (paper_size == 'A4') { width = 21 height = 29.7 } else if (is.vector(paper_size) & length(paper_size) > 1) { width = paper_size[1] height = paper_size[2] } 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))
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LMcol = ncol(LM) margin_size = 0.5 Norm_ratio = height * (time_ratio + var_ratio*nbp) / (height - 2*margin_size - foot_height - info_height) time_height = height * time_ratio / Norm_ratio var_height = height * var_ratio / Norm_ratio Hcut = LM[, 2] heightLM = rep(0, times=LMrow) heightLM[Hcut == id_info] = info_height heightLM[Hcut == id_time] = time_height heightLM[Hcut > nbh & Hcut < id_foot] = var_height heightLM[Hcut == id_foot] = foot_height 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 # Plot the graph as the layout plot = grid.arrange(grobs=P, layout_matrix=LM, heights=heightLM, widths=widthLM) # Saving ggsave(plot=plot, path=outdirTmp_pdf, filename=paste(as.character(code), '.pdf', sep=''), width=width, height=height, units='cm', dpi=100) # Saving ggsave(plot=plot, path=outdirTmp_png, filename=paste(as.character(code), '.png', sep=''), width=width, height=height, units='cm', dpi=400) } return (df_page) } ## 2. PANEL FOR THE DATASHEET __________________________________ ### 2.1. Time panel __________________________________________________ time_panel = function (df_data_code, df_trend_code, var, type, linetype_per='solid', alpha=0.1, colorForce=FALSE, missRect=FALSE, unit2day=365.25, trend_period=NULL, mean_period=NULL, axis_xlim=NULL, grid=TRUE, ymin_lim=NULL, color=NULL, NspaceMax=NULL, first=FALSE, last=FALSE, lim_pct=10) { # Compute max and min of flow maxQ = max(df_data_code$Value, na.rm=TRUE) minQ = min(df_data_code$Value, na.rm=TRUE) spread = maxQ - minQ nTick = 6 maxQ_win = maxQ + spread*lim_pct/100 minQ_win = minQ - spread*lim_pct/100 if (minQ_win < 0) { minQtmp_lim = 0 } else { minQtmp_lim = minQ_win }
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if (!is.null(ymin_lim)) { minQ_win = ymin_lim } spreadtmp = maxQ_win - minQtmp_lim breakQtmp = spreadtmp / (nTick - 1) GradQ_10 = c(0, 1, 1.5, 2, 2.5, 3, 4, 5, 10) Grad = GradQ_10 * 10^get_power(breakQtmp) dist = abs(Grad - breakQtmp) idGrad = which.min(dist) breakQ = Grad[idGrad] if (is.null(ymin_lim)) { Grad = GradQ_10 * 10^get_power(minQtmp_lim) Grad[Grad > minQtmp_lim] = NA dist = abs(Grad - minQtmp_lim) idGrad = which.min(dist) minQ_lim = Grad[idGrad] } else { minQ_lim = ymin_lim } maxQ_list = c() i = 1 maxQtmp = minQ_lim while (maxQtmp <= maxQ_win) { maxQtmp = minQ_lim + i*breakQ i = i + 1 } maxQ_lim = maxQtmp # If x axis limits are specified if (!is.null(axis_xlim)) { axis_xlim = as.Date(axis_xlim) minor_minDatetmp_lim = axis_xlim[1] minor_maxDatetmp_lim = axis_xlim[2] # Otherwise } else { minor_minDatetmp_lim = as.Date(df_data_code$Date[1]) minor_maxDatetmp_lim = as.Date(df_data_code$Date[length(df_data_code$Date)]) } minor_minDatetmp_lim = as.numeric(format(minor_minDatetmp_lim, "%Y")) minor_maxDatetmp_lim = as.numeric(format(minor_maxDatetmp_lim, "%Y")) minDatetmp_lim = minor_minDatetmp_lim maxDatetmp_lim = minor_maxDatetmp_lim nTick = 8 spreadtmp = minor_maxDatetmp_lim - minor_minDatetmp_lim breakDatetmp = spreadtmp / (nTick - 1) GradDate_10 = c(1, 2.5, 5, 10) Grad = GradDate_10 * 10^get_power(breakDatetmp) dist = abs(Grad - breakDatetmp) idGrad = which.min(dist) breakDate = Grad[idGrad] listDate = seq(round(minDatetmp_lim, -1)-10^(get_power(breakDate)+1), round(maxDatetmp_lim, -1)+10^(get_power(breakDate)+1), by=breakDate) minDate_lim = listDate[which.min(abs(listDate - minDatetmp_lim))]
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maxDate_lim = listDate[which.min(abs(listDate - maxDatetmp_lim))] minDate_lim = as.Date(paste(minDate_lim, '-01-01', sep='')) maxDate_lim = as.Date(paste(maxDate_lim, '-01-01', sep='')) minor_breakDatetmp = breakDate / 5 GradMinorDate_10 = c(1, 2, 5, 10) Grad = GradMinorDate_10 * 10^get_power(minor_breakDatetmp) dist = abs(Grad - minor_breakDatetmp) idGrad = which.min(dist) minor_breakDate = Grad[idGrad] listDate = seq(round(minor_minDatetmp_lim, -1) - 10^(get_power(minor_breakDate)+1), round(minor_maxDatetmp_lim, -1) + 10^(get_power(minor_breakDate)+1), by=minor_breakDate) minor_minDate_lim = listDate[which.min(abs(listDate - minor_minDatetmp_lim))] minor_maxDate_lim = listDate[which.min(abs(listDate - minor_maxDatetmp_lim))] minor_minDate_lim = as.Date(paste(round(minor_minDate_lim), '-01-01', sep='')) minor_maxDate_lim = as.Date(paste(round(minor_maxDate_lim), '-01-01', sep='')) # Open new plot p = ggplot() + theme_ash # Margins if (first) { p = p + theme(plot.margin=margin(t=2.5, r=0, b=3, l=0, unit="mm")) } else if (last) { p = p + theme(plot.margin=margin(t=2, r=0, b=0, l=0, unit="mm")) } else if (first & last) { p = p + theme(plot.margin=margin(t=2.5, r=0, b=0, l=0, unit="mm")) } else { p = p + theme(plot.margin=margin(t=2, r=0, b=2, l=0, unit="mm")) } ## Sub period background ## if (!is.null(trend_period)) { # Convert trend period to list if it is not trend_period = as.list(trend_period) # Fix a disproportionate minimum for period Imin = 10^99 # For all the sub period of analysis in 'trend_period' for (per in trend_period) { # Compute time interval of period I = interval(per[1], per[2]) # If it is the smallest interval if (I < Imin) { # Store it Imin = I # Fix min period of analysis trend_period_min = as.Date(per) } } minPer = trend_period_min[1] maxPer = trend_period_min[2]
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# If there is an 'axis_lim' if (!is.null(axis_xlim)) { # If the temporary start of period is smaller # than the fix start of x axis limit if (minPer < axis_xlim[1]) { # Set the start of the period to the start of # the x axis limit minPer = axis_xlim[1] } # If the temporary end of period plus one year # is smaller than the fix end of x axis limit if (maxPer + years(1) < axis_xlim[2]) { # Add one year the the temporary end of period maxPer = maxPer + years(1) } else { # Set the start of the period to the start of # the x axis limit maxPer = axis_xlim[2] } # If there is no 'axis_lim' } else { if (minPer < min(df_data_code$Date)) { minPer = min(df_data_code$Date) } if (maxPer > max(df_data_code$Date)) { maxPer = max(df_data_code$Date) } } # Draw rectangle to delimiting the sub period p = p + geom_rect(aes(xmin=minPer, ymin=minQ_win, xmax=maxPer, ymax= maxQ_win), linetype=0, fill='grey97') } ## Mean step ## # If there is a 'mean_period' if (!is.null(mean_period)) { # Convert 'mean_period' to list mean_period = as.list(mean_period) # Number of mean period nPeriod_mean = length(mean_period) # Blank tibble to store variable in order to plot # rectangle for mean period plot_mean = tibble() # Blank tibble to store variable in order to plot # upper limit of rectangle for mean period plot_line = tibble() # For all mean period for (j in 1:nPeriod_mean) { # Get the current start and end of the sub period xmin = as.Date(mean_period[[j]][1]) xmax = as.Date(mean_period[[j]][2]) # Extract the data corresponding to this sub period df_data_code_per = df_data_code[df_data_code$Date >= xmin & df_data_code$Date <= xmax,] # If the min over the sub period is greater # than the min of the entier period and # it is not the first sub period if (xmin > min(df_data_code$Date) & j != 1) { # Substract 6 months to be in the middle of
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# the previous year xmin = add_months(xmin, -6) } # If it is not a flow or sqrt of flow time serie and # it is the first period if (var != 'sqrt(Q)' & var != 'Q' & j == 1) { # If there is an x axis limit if (!is.null(axis_xlim)) { # If the min of the period is before the x axis min if (xmin < axis_xlim[1]) { # The min for the sub period is the x axis xmin = axis_xlim[1] } } } # If the max over the sub period is smaller # than the max of the entier period and # it is not the last sub period if (xmax < max(df_data_code$Date) & j != nPeriod_mean) { # Add 6 months to be in the middle of # the following year xmax = add_months(xmax, 6) } # If it is not a flow or sqrt of flow time serie and # it is the last period if (var != 'sqrt(Q)' & var != 'Q' & j == nPeriod_mean) { # If there is an x axis limit if (!is.null(axis_xlim)) { # If the max of the period plus 1 year # is smaller thant the max of the x axis limit if (xmax + years(1) < axis_xlim[2]) { # Add one year to the max to include # the entire last year graphically xmax = xmax + years(1) } else { # The max of this sub period is the max # of the x axis limit xmax = axis_xlim[2] } } # If there is no axis limit # } else { # # Add one year to the max to include # # the entire last year graphically # xmax = xmax + years(1) # } } # Mean of the flow over the sub period ymax = mean(df_data_code_per$Value, na.rm=TRUE) # Create temporary tibble with variable # to create rectangle for mean step plot_meantmp = tibble(xmin=xmin, xmax=xmax, ymin=minQ_win, ymax=ymax, period=j) # Bind it to the main tibble to store it with other period plot_mean = bind_rows(plot_mean, plot_meantmp) # Create vector for the upper limit of the rectangle abs = c(xmin, xmax) ord = c(ymax, ymax) # Create temporary tibble with variable # to create upper limit for rectangle plot_linetmp = tibble(abs=abs, ord=ord, period=j) # Bind it to the main tibble to store it with other period plot_line = bind_rows(plot_line, plot_linetmp) }
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# Plot rectangles p = p + geom_rect(data=plot_mean, aes(xmin=xmin, ymin=ymin, xmax=xmax, ymax=ymax), linetype=0, fill='grey93') # Plot upper line for rectangle p = p + geom_line(data=plot_line, aes(x=abs, y=ord, group=period), color='grey85', size=0.15) # for all the sub periods except the last one for (i in 1:(nPeriod_mean - 1)) { # Computes the time difference in days between periods dPeriod = abs(as.Date(mean_period[[i+1]][1]) - as.Date(mean_period[[i]][2])) if (dPeriod < 10) { # The x limit is the x max of the ith rectangle xLim = plot_mean$xmax[i] # The y limit of rectangle is the max of # the two neighboring mean step rectangle yLim = max(c(plot_mean$ymax[i], plot_mean$ymax[i+1])) # Make a tibble to store data plot_lim = tibble(x=c(xLim, xLim), y=c(minQ_win, yLim)) # Plot the limit of rectangles p = p + geom_line(data=plot_lim, aes(x=x, y=y), linetype='dashed', size=0.15, color='grey85') } else { # Takes the x and y limits for the ith rectangle xLim_i = plot_mean$xmax[i] yLim_i = plot_mean$ymax[i] # Takes the x and y limits for the i+1th rectangle xLim_i1 = plot_mean$xmin[i+1] yLim_i1 = plot_mean$ymax[i+1] # Make a tibble to store data plot_lim = tibble(x_i=c(xLim_i, xLim_i), y_i=c(minQ_win, yLim_i), x_i1=c(xLim_i1, xLim_i1), y_i1=c(minQ_win, yLim_i1)) # Plot the limit of rectangles p = p + geom_line(data=plot_lim, aes(x=x_i, y=y_i), linetype='dashed', size=0.15, color='grey85') + geom_line(data=plot_lim, aes(x=x_i1, y=y_i1), linetype='dashed', size=0.15, color='grey85') } } } ### Grid ### if (grid) { # If there is no axis limit if (is.null(axis_xlim)) { # The min and the max is set by # the min and the max of the date data xmin = min(df_data_code$Date) xmax = max(df_data_code$Date) } else { # Min and max is set with the limit axis parameter xmin = axis_xlim[1] xmax = axis_xlim[2]
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} # Create a vector for all the y grid position ygrid = seq(minQ_win, maxQ_win, breakQ) # Blank vector to store position ord = c() abs = c() # For all the grid element for (i in 1:length(ygrid)) { # Store grid position ord = c(ord, rep(ygrid[i], times=2)) abs = c(abs, xmin, xmax) } # Create a tibble to store all the position plot_grid = tibble(abs=as.Date(abs), ord=ord) # Plot the y grid p = p + geom_line(data=plot_grid, aes(x=abs, y=ord, group=ord), color='grey85', size=0.15) } ### Data ### # If it is a square root flow or flow if (var == 'sqrt(Q)' | var == 'Q') { # Plot the data as line p = p + geom_line(aes(x=df_data_code$Date, y=df_data_code$Value), color='grey20', size=0.3, lineend="round") } else { # Plot the data as point p = p + geom_point(aes(x=df_data_code$Date, y=df_data_code$Value), shape=19, color='grey50', alpha=1, stroke=0, size=1) } ### Missing data ### # If the option is TRUE if (missRect) { # Remove NA data NAdate = df_data_code$Date[is.na(df_data_code$Value)] # Get the difference between each point of date data without NA dNAdate = diff(NAdate) # If difference of day is not 1 then # it is TRUE for the beginning of each missing data period NAdate_Down = NAdate[append(Inf, dNAdate) != 1] # If difference of day is not 1 then # it is TRUE for the ending of each missing data period NAdate_Up = NAdate[append(dNAdate, Inf) != 1] # Plot the missing data period p = p + geom_rect(aes(xmin=NAdate_Down, ymin=minQ_win, xmax=NAdate_Up, ymax=maxQ_win), linetype=0, fill='Wheat', alpha=0.4) } ### Trend ### # If there is trends if (!is.null(df_trend_code)) { # Extract start and end of trend periods Start = df_trend_code$period_start End = df_trend_code$period_end # Get the name of the different period
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UStart = levels(factor(Start)) UEnd = levels(factor(End)) # Compute the max of different start and end # so the number of different period nPeriod_trend = max(length(UStart), length(UEnd)) # Blank tibble to store trend data and legend data plot_trend = tibble() leg_trend = tibble() # For all the different period for (i in 1:nPeriod_trend) { # Extracts the corresponding data for the period df_data_code_per = df_data_code[df_data_code$Date >= Start[i] & df_data_code$Date <= End[i],] # Computes the mean of the data on the period dataMean = mean(df_data_code_per$Value, na.rm=TRUE) # Get the trend associated to the first period df_trend_code_per = df_trend_code[df_trend_code$period_start == Start[i] & df_trend_code$period_end == End[i],] # Number of trend selected Ntrend = nrow(df_trend_code_per) # If the number of trend is greater than a unique one if (Ntrend > 1) { # Extract only the first hence it is the same period df_trend_code_per = df_trend_code_per[1,] } # Search for the index of the closest existing date # to the start of the trend period of analysis iStart = which.min(abs(df_data_code$Date - Start[i])) # Same for the end iEnd = which.min(abs(df_data_code$Date - End[i])) # Get the start and end date associated xmin = df_data_code$Date[iStart] xmax = df_data_code$Date[iEnd] # If there is a x axis limit if (!is.null(axis_xlim)) { # If the min of the current period # is smaller than the min of the x axis limit if (xmin < axis_xlim[1]) { # The min of the period is the min # of the x axis limit xmin = axis_xlim[1] } # Same for end if (xmax > axis_xlim[2]) { xmax = axis_xlim[2] } } # Create vector to store x data abs = c(xmin, xmax) # Convert the number of day to the unit of the period abs_num = as.numeric(abs) / unit2day # Compute the y of the trend ord = abs_num * df_trend_code_per$trend + df_trend_code_per$intercept # Create temporary tibble with variable to plot trend # for each period
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plot_trendtmp = tibble(abs=abs, ord=ord, period=i) # Bind it to the main tibble to store it with other period plot_trend = bind_rows(plot_trend, plot_trendtmp) # If there is a x axis limit if (!is.null(axis_xlim)) { # The x axis limit is selected codeDate = axis_xlim } else { # The entire date data is selected codeDate = df_data_code$Date } # The y limit is stored in a vector codeValue = c(minQ_win, maxQ_win) # Position of the x beginning and end of the legend symbol x = gpct(1.5, codeDate, shift=TRUE) xend = x + gpct(3, codeDate) # Spacing between legend symbols dy = gpct(9, codeValue, min_lim=ymin_lim) # Position of the y beginning and end of the legend symbol y = gpct(92, codeValue, min_lim=ymin_lim, shift=TRUE) - (i-1)*dy yend = y # Position of x for the beginning of the associated text xt = xend + gpct(1, codeDate) # Position of the background rectangle of the legend xminR = x - gpct(1, codeDate) yminR = y - gpct(5, codeValue, min_lim=ymin_lim) # If it is a flow variable if (type == 'sévérité') { xmaxR = x + gpct(32.5, codeDate) # If it is a date variable } else if (type == 'saisonnalité' | type == 'pluviométrie' | type == 'température' | type == 'évapotranspiration') { xmaxR = x + gpct(20.5, codeDate) } ymaxR = y + gpct(5, codeValue, min_lim=ymin_lim) # Gets the trend trend = df_trend_code_per$trend # Gets the p value pVal = df_trend_code_per$p if (pVal <= alpha) { colorLine = color[i] colorLabel = color[i] } else { colorLine = 'grey85' colorLabel = 'grey85' } # Computes the mean trend trendMean = trend/dataMean # Computes the magnitude of the trend power = get_power(trend) # Converts it to character powerC = as.character(power) # If the power is positive if (powerC >= 0) { # Adds a space in order to compensate for the minus # sign that sometimes is present for the other periods spaceC = ' ' # Otherwise } else { # No space is added spaceC = '' }
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# Gets the power of ten of magnitude brk = 10^power # Converts trend to character for sientific expression trendC = as.character(format(round(trend / brk, 2), nsmall=2)) # If the trend is positive if (trendC >= 0) { # Adds two space in order to compensate for the minus # sign that sometimes is present for the other periods trendC = paste(' ', trendC, sep='') } # Converts mean trend to character trendMeanC = as.character(format(round(trendMean*100, 2), nsmall=2)) if (trendMeanC >= 0) { # Adds two space in order to compensate for the minus # sign that sometimes is present for the other periods trendMeanC = paste(' ', trendMeanC, sep='') } # Create temporary tibble with variable to plot legend leg_trendtmp = tibble(x=x, xend=xend, y=y, yend=yend, xt=xt, colorLine=colorLine, colorLabel=colorLabel, trendC=trendC, powerC=powerC, spaceC=spaceC, trendMeanC=trendMeanC, xminR=xminR, yminR=yminR, xmaxR=xmaxR, ymaxR=ymaxR, period=i) # Bind it to the main tibble to store it with other period leg_trend = bind_rows(leg_trend, leg_trendtmp) } if (length(linetype_per) < nPeriod_trend) { linetype_per = rep(linetype_per, times=nPeriod_trend) } linetypeLeg_per = linetype_per linetypeLeg_per[linetype_per == 'longdash'] = '33' linetypeLeg_per[linetype_per == 'dashed'] = '22' linetypeLeg_per[linetype_per == 'dotted'] = '11' # For all periods for (i in 1:nPeriod_trend) { # Extract the trend of the current sub period leg_trend_per = leg_trend[leg_trend$period == i,] # Plot the background for legend p = p + geom_rect(data=leg_trend_per, aes(xmin=xminR, ymin=yminR, xmax=xmaxR, ymax=ymaxR), linetype=0, fill='white', alpha=0.3) # Get the character variable for naming the trend colorLine = leg_trend_per$colorLine colorLabel = leg_trend_per$colorLabel trendC = leg_trend_per$trendC powerC = leg_trend_per$powerC spaceC = leg_trend_per$spaceC trendMeanC = leg_trend_per$trendMeanC # If it is a flow variable
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if (type == 'sévérité') { # Create the name of the trend label = bquote(bold(.(trendC)~'x'~'10'^{.(powerC)}*.(spaceC))~'['*m^{3}*'.'*s^{-1}*'.'*an^{-1}*']'~~bold(.(trendMeanC))~'[%.'*an^{-1}*']') # If it is a date variable } else if (type == 'saisonnalité') { # Create the name of the trend label = bquote(bold(.(trendC)~'x'~'10'^{.(powerC)}*.(spaceC))~'[jour.'*an^{-1}*']') } else if (type == 'pluviométrie' | type == 'évapotranspiration') { # Create the name of the trend label = bquote(bold(.(trendC)~'x'~'10'^{.(powerC)}*.(spaceC))~'[mm.'*an^{-1}*']') } else if (type == 'température') { # Create the name of the trend label = bquote(bold(.(trendC)~'x'~'10'^{.(powerC)}*.(spaceC))~'[°C.'*an^{-1}*']') } # Plot the trend symbole and value of the legend p = p + annotate("segment", x=leg_trend_per$x, xend=leg_trend_per$xend, y=leg_trend_per$y, yend=leg_trend_per$yend, color=colorLine, linetype=linetypeLeg_per[i], lwd=0.8, lineend="round") + annotate("text", label=label, size=2.8, x=leg_trend_per$xt, y=leg_trend_per$y, hjust=0, vjust=0.5, color=colorLabel) } # For all periods for (i in 1:nPeriod_trend) { # Extract the trend of the current sub period plot_trend_per = plot_trend[plot_trend$period == i,] # Plot the line of white background of each trend p = p + geom_line(data=plot_trend_per, aes(x=abs, y=ord), color='white', linetype='solid', size=1.5, lineend="round") } # For all periods for (i in 1:nPeriod_trend) { # Extract the trend of the current sub period plot_trend_per = plot_trend[plot_trend$period == i,] # Plot the line of trend p = p + geom_line(data=plot_trend_per, aes(x=abs, y=ord), color=color[i], linetype=linetype_per[i], size=0.75, lineend="round") } } # Y axis title # If it is a flow variable if (type == 'sévérité' | var == 'Q') { p = p + ylab(bquote(bold(.(var))~~'['*m^{3}*'.'*s^{-1}*']')) } else if (var == 'sqrt(Q)') {
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p = p + ylab(bquote(bold(.(var))~~'['*m^{3/2}*'.'*s^{-1/2}*']')) # If it is a date variable } else if (type == 'saisonnalité') { p = p + ylab(bquote(bold(.(var))~~"[jour de l'année]")) } else if (type == 'pluviométrie' | type == 'évapotranspiration') { p = p + ylab(bquote(bold(.(var))~~'[mm]')) } else if (type == 'température') { p = p + ylab(bquote(bold(.(var))~~"[°C]")) } if (!last & !first) { p = p + theme(axis.text.x=element_blank()) } if (first) { position = 'top' } else { position = 'bottom' } if (is.null(axis_xlim)) { limits = c(min(df_data_code$Date), max(df_data_code$Date)) } else { limits = axis_xlim } if (breakDate < 1) { breaks = waiver() minor_breaks = waiver() date_labels = waiver() } else { breaks = seq(minDate_lim, maxDate_lim, by=paste(breakDate, 'years')) minor_breaks = seq(minor_minDate_lim, minor_maxDate_lim, by=paste(minor_breakDate, 'years')) date_labels = "%Y" } # Parameters of the x axis contain the limit of the date data p = p + scale_x_date(breaks=breaks, minor_breaks=minor_breaks, guide='axis_minor', date_labels=date_labels, limits=limits, position=position, expand=c(0, 0)) # If it is a date variable if (type == 'saisonnalité') { # The number of digit is 6 because months are display # with 3 characters Nspace = 6 prefix = strrep(' ', times=NspaceMax-Nspace) accuracy = NULL # If it is a flow variable } else if (type == 'sévérité' | type == 'data' | type == 'pluviométrie' | type == 'température' | type == 'évapotranspiration') { # Gets the max number of digit on the label maxtmp = max(df_data_code$Value, na.rm=TRUE) # Taking into account of the augmentation of # max for the window
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maxtmp = maxtmp * (1 + lim_pct/100) # If the max is greater than 10 if (maxtmp >= 10) { # The number of digit is the magnitude plus # the first number times 2 Nspace = (get_power(maxtmp) + 1)*2 # Plus spaces between thousands hence every 8 digits Nspace = Nspace + as.integer(Nspace/8) # Gets the associated number of white space prefix = strrep(' ', times=NspaceMax-Nspace) # The accuracy is 1 accuracy = 1 # If the max is less than 10 and greater than 1 } else if (maxtmp < 10 & maxtmp >= 1) { # The number of digit is the magnitude plus # the first number times 2 plus 1 for the dot # and 2 for the first decimal Nspace = (get_power(maxtmp) + 1)*2 + 3 # Gets the associated number of white space prefix = strrep(' ', times=NspaceMax-Nspace) # The accuracy is 0.1 accuracy = 0.1 # If the max is less than 1 (and obviously more than 0) } else if (maxtmp < 1) { # Fixes the number of significant decimals to 3 maxtmp = signif(maxtmp, 3) # The number of digit is the number of character # of the max times 2 minus 1 for the dots that # count just 1 space Nspace = nchar(as.character(maxtmp))*2 - 1 # Gets the associated number of white space prefix = strrep(' ', times=NspaceMax-Nspace) # Computes the accuracy accuracy = 10^(-nchar(as.character(maxtmp))+2) } } # Parameters of the y axis # If it is a flow variable if (type == 'sévérité' | type == 'data' | type == 'pluviométrie' | type == 'température' | type == 'évapotranspiration') { p = p + scale_y_continuous(breaks=seq(minQ_lim, maxQ_lim, breakQ), limits=c(minQ_win, maxQ_win), expand=c(0, 0), labels=number_format(accuracy=accuracy, prefix=prefix)) # If it is a date variable } else if (type == 'saisonnalité') { # monthNum = as.numeric(format(seq(as.Date(minQ_lim), # as.Date(maxQ_lim), # by=paste(breakQ, 'days')), # "%m")) monthStart = as.Date(paste(substr(as.Date(minQ_lim), 1, 7), '-01', sep='')) monthEnd = as.Date(paste(substr(as.Date(maxQ_lim), 1, 7), '-01', sep='')) byMonth = round(breakQ/30.4, 0) if (byMonth == 0) { byMonth = 1 } breaksDate = seq(monthStart, monthEnd, by=paste(byMonth, 'months')) breaksNum = as.numeric(breaksDate) breaksMonth = as.numeric(format(breaksDate, "%m"))
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monthName = c('Jan', 'Fév', 'Mar', 'Avr', 'Mai', 'Jui', 'Jui', 'Aou', 'Sep', 'Oct', 'Nov', 'Déc') monthName = paste(prefix, monthName, sep='') labels = monthName[breaksMonth] p = p + scale_y_continuous(breaks=breaksNum, limits=c(minQ_win, maxQ_win), labels=labels, expand=c(0, 0)) } return(p) } ### 2.2. Info panel __________________________________________________ # Plots the header that regroups all the info on the station info_panel = function(list_df2plot, df_meta, trend_period=NULL, mean_period=NULL, periodHyd=NULL, df_shapefile=NULL, codeLight=NULL, df_data_code=NULL, to_do='all') { # If there is a data serie for the given code if (!is.null(df_data_code)) { # Computes the hydrograph hyd = hydrograph_panel(df_data_code, period=periodHyd, margin=margin(t=0, r=0, b=0, l=5, unit="mm")) # Otherwise } else { # Puts it blank hyd = void } if (!is.null(df_shapefile)) { # Computes the map associated to the station map = map_panel(list_df2plot, df_meta, trend_period=trend_period, mean_period=mean_period, df_shapefile=df_shapefile, codeLight=codeLight, mapType='mini', margin=margin(t=0, r=-12, b=0, l=0, unit="mm"), showSea=FALSE, verbose=FALSE) # Otherwise } else { # Puts it blank map = void } # Gets the metadata about the station df_meta_code = df_meta[df_meta$code == codeLight,] if ('name' %in% to_do | 'all' %in% to_do) { # Extracts the name nom = df_meta_code$nom # Corrects some errors about the formatting of title with dash nom = gsub("-", "-&nbsp;", nom) # Name of the datasheet text1 = paste( "<b>", codeLight, '</b> - ', nom, sep='') # Converts all texts to graphical object in the right position gtext1 = richtext_grob(text1, x=0, y=1,
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margin=unit(c(t=0, r=5, b=0, l=0), "mm"), hjust=0, vjust=1, gp=gpar(col="#00A3A8", fontsize=14)) } else if ('code' %in% to_do) { # Name of the datasheet text1 = paste( "<b>", codeLight, '</b>', sep='') # Converts all texts to graphical object in the right position gtext1 = richtext_grob(text1, x=0, y=1, margin=unit(c(t=0, r=5, b=0, l=0), "mm"), hjust=0, vjust=1, gp=gpar(col="#00A3A8", fontsize=14)) } else { gtext1 = void } # Subitle info if ('loc' %in% to_do | 'all' %in% to_do) { text2 = paste( "<b>", "Gestionnaire : ", df_meta_code$gestionnaire, "<br>", "Bassin hydrographique : ", df_meta_code$region_hydro, "</b>", sep='') gtext2 = richtext_grob(text2, x=0, y=1.25, margin=unit(c(t=0, r=0, b=0, l=0), "mm"), hjust=0, vjust=1, gp=gpar(col="grey20", fontsize=8)) } else { gtext2 = void } # Spatial info about station if ('spatial' %in% to_do | 'all' %in% to_do) { text3 = paste( "<b>", "Superficie : ", df_meta_code$surface_km2_BH, " [km<sup>2</sup>] <br>", "Altitude : ", df_meta_code$altitude_m_BH, " [m]<br>", "X = ", df_meta_code$L93X_m_BH, " [m ; Lambert93]<br>", "Y = ", df_meta_code$L93Y_m_BH, " [m ; Lambert93]", "</b>", sep='') gtext3 = richtext_grob(text3, x=0, y=1, margin=unit(c(t=0, r=0, b=0, l=0), "mm"), hjust=0, vjust=1, gp=gpar(col="grey20", fontsize=9)) } else { gtext3 = void } # Time info about station if ('temporal' %in% to_do | 'all' %in% to_do) { # Computes the time span of data, the start and the end duration = as.numeric(format(as.Date(df_meta_code$fin), "%Y")) - as.numeric(format(as.Date(df_meta_code$debut), "%Y")) debut = format(as.Date(df_meta_code$debut), "%d/%m/%Y") fin = format(as.Date(df_meta_code$fin), "%d/%m/%Y") text4 = paste( "<b>",
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"Date de début : ", debut, "<br>", "Date de fin : ", fin, "<br>", "Nombre d'années : ", duration, " [ans]", "<br>", "Taux de lacunes : ", signif(df_meta_code$tLac100, 2), " [%]", "</b>", sep='') gtext4 = richtext_grob(text4, x=0, y=1, margin=unit(c(t=0, r=0, b=0, l=0), "mm"), hjust=0, vjust=1, gp=gpar(col="grey20", fontsize=9)) } else { gtext4 = void } # Makes a list of all plots P = list(gtext1, gtext2, gtext3, gtext4, hyd, map) # P = list(void, void, void, void, void, void, void) # Creates the matrix layout LM = matrix(c(1, 1, 1, 6, 2, 2, 5, 6, 3, 4, 5, 6, 3, 4, 5, 6), nrow=4, byrow=TRUE) # And sets the relative height of each plot heights = rep(1, times=nrow(LM)) # heights[2] = 0.1 heights[2] = 0.8 # Arranges all the graphical objetcs plot = grid.arrange(grobs=P, layout_matrix=LM, heights=heights) # Return the plot object return(plot) } ### 2.3. Hydrograph panel ____________________________________________ # Creates a hydrograph for a station with the data serie of flow hydrograph_panel = function (df_data_code, period, margin=NULL) { # Computes the hydrograph res_hydrograph = get_hydrograph(df_data_code, period=period) # Extracts the results monthMean = res_hydrograph$QM regime_hydro = res_hydrograph$meta # Vector of month index monthNum = 1:12 # Vector of month name abbreviation monthName = c("J", "F", "M", "A", "M", "J", "J", "A", "S", "O", "N", "D") # Open a new plot with the personalise theme hyd = ggplot() + theme_ash + # Theme modification theme( # plot.background=element_rect(fill=NA, color="#EC4899"), panel.border=element_blank(), axis.text.x=element_text(margin=unit(c(0, 0, 0, 0), "mm"), vjust=1, hjust=0.5), axis.ticks.x=element_blank(), axis.line.y=element_line(color='grey85', size=0.3), plot.title=element_text(size=8, vjust=-0.5, hjust=-1E-3, color='grey40'),
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axis.title.y=element_text(size=8, vjust=0, hjust=0.5, color='grey40')) + # Adds a title to the y axis ggtitle(regime_hydro) + # Y axis title ylab(bquote(bold('QM')~~'['*m^{3}*'.'*s^{-1}*']')) # If there is no margins specified if (is.null(margin)) { # Sets all margins to 0 hyd = hyd + theme(plot.margin=margin(t=0, r=0, b=0, l=0, unit="mm")) # Otherwise } else { # Sets margins to the given ones hyd = hyd + theme(plot.margin=margin) } hyd = hyd + # Plots the bar geom_bar(aes(x=monthNum, y=monthMean), stat='identity', fill="grey70", width=0.75, size=0.2) + # X axis scale_x_continuous(breaks=monthNum, labels=monthName, limits=c(0, max(monthNum)+0.5), expand=c(0, 0)) + # Y axis scale_y_continuous(limits=c(0, max(monthMean)), n.breaks=4, expand=c(0, 0)) # Returns the plot return (hyd) }