diff --git a/plotting/datasheet.R b/plotting/datasheet.R
index 53d670d9753c848890db0e5ebe3ea19f3ef42007..551f3e2da0edd0d4dfdf75a1a1a66984a7399226 100644
--- a/plotting/datasheet.R
+++ b/plotting/datasheet.R
@@ -174,7 +174,7 @@ datasheet_panel = function (list_df2plot, df_meta, trend_period, mean_period, ax
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),
@@ -182,7 +182,7 @@ datasheet_panel = function (list_df2plot, df_meta, trend_period, mean_period, ax
} else {
axis_xlim_code = axis_xlim
}
-
+
# Gets the time serie plot
Htime = time_panel(time_header_code, df_trend_code=NULL,
trend_period=trend_period,
@@ -193,6 +193,8 @@ datasheet_panel = function (list_df2plot, df_meta, trend_period, mean_period, ax
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
@@ -307,7 +309,7 @@ datasheet_panel = function (list_df2plot, df_meta, trend_period, mean_period, ax
lim_pct=lim_pct)
# Stores the plot
- P[[i+nbh]] = p
+ P[[i+nbh]] = p
}
if (!is.null(df_page)) {
@@ -1064,7 +1066,7 @@ time_panel = function (df_data_code, df_trend_code, var, type, alpha=0.1, colorF
ymin=yminR,
xmax=xmaxR,
ymax=ymaxR),
- linetype=0, fill='white', alpha=0.5)
+ linetype=0, fill='white', alpha=0.3)
# Get the character variable for naming the trend
colorLine = leg_trend_per$colorLine
diff --git a/plotting/layout.R b/plotting/layout.R
index b1da9cf972f30815f331e7a7f281e06c0b48919e..1c8dc15a02e7bfc23297ff443a0dbd94b8c6898c 100644
--- a/plotting/layout.R
+++ b/plotting/layout.R
@@ -126,7 +126,7 @@ contour = void +
## 3. LAYOUT _________________________________________________________
# Generates a PDF that gather datasheets, map and summarize matrix about the trend analyses realised on selected stations
layout_panel = function (df_data, df_meta, layout_matrix,
- toplot=c('datasheet', 'matrix', 'map'),
+ what_plot=c('datasheet', 'matrix', 'map'),
figdir='', filedir_opt='', filename_opt='',
variable='', df_trend=NULL,
alpha=0.1, unit2day=365.25, var='',
@@ -246,7 +246,7 @@ layout_panel = function (df_data, df_meta, layout_matrix,
df_page = tibble(section='Sommaire', subsection=NA, n=1)
# If map needs to be plot
- if ('map' %in% toplot) {
+ if ('map' %in% what_plot) {
df_page = map_panel(list_df2plot,
df_meta,
idPer_trend=length(trend_period),
@@ -266,7 +266,7 @@ layout_panel = function (df_data, df_meta, layout_matrix,
}
# If summarize matrix needs to be plot
- if ('matrix' %in% toplot) {
+ if ('matrix' %in% what_plot) {
df_page = matrix_panel(list_df2plot,
df_meta,
trend_period,
@@ -286,7 +286,7 @@ layout_panel = function (df_data, df_meta, layout_matrix,
}
# If datasheets needs to be plot
- if ('datasheet' %in% toplot) {
+ if ('datasheet' %in% what_plot) {
df_page = datasheet_panel(list_df2plot,
df_meta,
trend_period=trend_period,
diff --git a/plotting/map.R b/plotting/map.R
index 94d89b5f563086fa35e692d31433a89042fa3962..d6261e7696643364224e88ee53dfc4a8bce4adba 100644
--- a/plotting/map.R
+++ b/plotting/map.R
@@ -738,6 +738,7 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
xValue = c()
yValue = c()
color = c()
+ shape = c()
# Start X position of the distribution
start_hist = 1
@@ -775,6 +776,20 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
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
@@ -784,7 +799,7 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
# Plots the point of the distribution
geom_point(data=plot_value,
aes(x=xValue, y=yValue),
- shape=21,
+ shape=shape,
color=color,
fill=color, stroke=0.4,
alpha=1)
diff --git a/processing/analyse.R b/processing/analyse.R
index 42540eee35309f58ff4a47e32964aeeac1d98696..99f56da5eed68ad7f45005cdcaf09236514f65e0 100644
--- a/processing/analyse.R
+++ b/processing/analyse.R
@@ -97,11 +97,19 @@ get_intercept = function (df_Xtrend, df_Xlist, unit2day=365.25) {
### 1.1. QA __________________________________________________________
# Realise the trend analysis of the average annual flow (QA)
# hydrological variable
-get_QAtrend = function (df_data, df_meta, period, alpha, yearLac_day, df_mod=tibble()) {
+get_QAtrend = function (df_data, df_meta, period, alpha, dayLac_lim, yearNA_lim, df_flag, df_mod=tibble()) {
+ # Local corrections if needed
+ res = flag_data(df_data, df_meta,
+ df_flag=df_flag,
+ df_mod=df_mod)
+ df_data = res$data
+ df_mod = res$mod
+
# Removes incomplete data from time series
res = missing_data(df_data, df_meta,
- yearLac_day=yearLac_day,
+ dayLac_lim=dayLac_lim,
+ yearNA_lim=yearNA_lim,
df_mod=df_mod)
df_data = res$data
df_mod = res$mod
@@ -156,11 +164,19 @@ get_QAtrend = function (df_data, df_meta, period, alpha, yearLac_day, df_mod=tib
### 1.2. QMNA ________________________________________________________
# Realise the trend analysis of the monthly minimum flow in the
# year (QMNA) hydrological variable
-get_QMNAtrend = function (df_data, df_meta, period, alpha, sampleSpan, yearLac_day, df_mod=tibble()) {
+get_QMNAtrend = function (df_data, df_meta, period, alpha, sampleSpan, dayLac_lim, yearNA_lim, df_flag, df_mod=tibble()) {
+ # Local corrections if needed
+ res = flag_data(df_data, df_meta,
+ df_flag=df_flag,
+ df_mod=df_mod)
+ df_data = res$data
+ df_mod = res$mod
+
# Removes incomplete data from time series
res = missing_data(df_data, df_meta,
- yearLac_day=yearLac_day,
+ dayLac_lim=dayLac_lim,
+ yearNA_lim=yearNA_lim,
df_mod=df_mod)
df_data = res$data
df_mod = res$mod
@@ -266,11 +282,18 @@ rollmean_code = function (df_data, Code, nroll=10, df_mod=NULL) {
# Realises the trend analysis of the minimum 10 day average flow
# over the year (VCN10) hydrological variable
-get_VCN10trend = function (df_data, df_meta, period, alpha, sampleSpan, yearLac_day, df_mod=tibble()) {
+get_VCN10trend = function (df_data, df_meta, period, alpha, sampleSpan, dayLac_lim, yearNA_lim, df_flag, df_mod=tibble()) {
# Get all different stations code
Code = levels(factor(df_meta$code))
+ # Local corrections if needed
+ res = flag_data(df_data, df_meta,
+ df_flag=df_flag,
+ df_mod=df_mod)
+ df_data = res$data
+ df_mod = res$mod
+
# Computes the rolling average by 10 days over the data
res = rollmean_code(df_data, Code, 10, df_mod=df_mod)
df_data_roll = res$data
@@ -278,7 +301,8 @@ get_VCN10trend = function (df_data, df_meta, period, alpha, sampleSpan, yearLac_
# Removes incomplete data from time series
res = missing_data(df_data_roll, df_meta,
- yearLac_day=yearLac_day,
+ dayLac_lim=dayLac_lim,
+ yearNA_lim=yearNA_lim,
df_mod=df_mod)
df_data_roll = res$data
df_mod = res$mod
@@ -373,12 +397,19 @@ which_underfirst = function (L, UpLim, select_longest=TRUE) {
return (id)
}
-get_tDEBtrend = function (df_data, df_meta, period, alpha, sampleSpan, yearLac_day, thresold_type='VCN10', select_longest=TRUE, df_mod=tibble()) {
+get_tDEBtrend = function (df_data, df_meta, period, alpha, sampleSpan, dayLac_lim, yearNA_lim, df_flag, thresold_type='VCN10', select_longest=TRUE, df_mod=tibble()) {
# Get all different stations code
Code = levels(factor(df_meta$code))
# Gets the number of station
nCode = length(Code)
+
+ # Local corrections if needed
+ res = flag_data(df_data, df_meta,
+ df_flag=df_flag,
+ df_mod=df_mod)
+ df_data = res$data
+ df_mod = res$mod
# Computes the rolling average by 10 days over the data
res = rollmean_code(df_data, Code, 10, df_mod=df_mod)
@@ -388,7 +419,8 @@ get_tDEBtrend = function (df_data, df_meta, period, alpha, sampleSpan, yearLac_d
# Removes incomplete data from time series
df_data = missing_data(df_data,
df_meta=df_meta,
- yearLac_day=yearLac_day)
+ dayLac_lim=dayLac_lim,
+ yearNA_lim=yearNA_lim)
# Samples the data
df_data = sampling_data(df_data,
@@ -398,7 +430,8 @@ get_tDEBtrend = function (df_data, df_meta, period, alpha, sampleSpan, yearLac_d
# Removes incomplete data from the averaged time series
res = missing_data(df_data_roll,
df_meta=df_meta,
- yearLac_day=yearLac_day,
+ dayLac_lim=dayLac_lim,
+ yearNA_lim=yearNA_lim,
df_mod=df_mod)
df_data_roll = res$data
df_mod = res$mod
@@ -527,21 +560,29 @@ get_tDEBtrend = function (df_data, df_meta, period, alpha, sampleSpan, yearLac_d
### 1.5. tCEN date ___________________________________________________
# Realises the trend analysis of the date of the minimum 10 day
# average flow over the year (VCN10) hydrological variable
-get_tCENtrend = function (df_data, df_meta, period, alpha, sampleSpan, yearLac_day, df_mod=tibble()) {
+get_tCENtrend = function (df_data, df_meta, period, alpha, sampleSpan, dayLac_lim, yearNA_lim, df_flag, df_mod=tibble()) {
# Get all different stations code
Code = levels(factor(df_meta$code))
# Blank tibble to store the data averaged
df_data_roll = tibble()
+ # Local corrections if needed
+ res = flag_data(df_data, df_meta,
+ df_flag=df_flag,
+ df_mod=df_mod)
+ df_data = res$data
+ df_mod = res$mod
+
# Computes the rolling average by 10 days over the data
res = rollmean_code(df_data, Code, 10, df_mod=df_mod)
df_data_roll = res$data
df_mod = res$mod
-
+
# Removes incomplete data from time series
res = missing_data(df_data_roll, df_meta,
- yearLac_day=yearLac_day,
+ dayLac_lim=dayLac_lim,
+ yearNA_lim=yearNA_lim,
df_mod=df_mod)
df_data_roll = res$data
df_mod = res$mod
diff --git a/processing/extract.R b/processing/extract.R
index d52307a35fe9488f63e7a4ad8bf99ca99124da57..4f51828d01620661016fb1fd105151e3c7b8960c 100644
--- a/processing/extract.R
+++ b/processing/extract.R
@@ -510,7 +510,7 @@ extract_data = function (computer_data_path, filedir, filename,
## 4. SHAPEFILE MANAGEMENT ___________________________________________
# Generates a list of shapefiles to draw a hydrological map of
# the France
-ini_shapefile = function (resources_path, fr_shpdir, fr_shpname, bs_shpdir, bs_shpname, sbs_shpdir, sbs_shpname, rv_shpdir, rv_shpname, is_river=TRUE) {
+ini_shapefile = function (resources_path, fr_shpdir, fr_shpname, bs_shpdir, bs_shpname, sbs_shpdir, sbs_shpname, rv_shpdir, rv_shpname, show_river=TRUE) {
# Path for shapefile
fr_shppath = file.path(resources_path, fr_shpdir, fr_shpname)
@@ -534,7 +534,7 @@ ini_shapefile = function (resources_path, fr_shpdir, fr_shpname, bs_shpdir, bs_s
df_subbassin = tibble(fortify(subbassin))
# If the river shapefile needs to be load
- if (is_river) {
+ if (show_river) {
# Hydrographic network
river = readOGR(dsn=rv_shppath, verbose=FALSE) ### trop long ###
river = river[which(river$Classe == 1),]
diff --git a/processing/format.R b/processing/format.R
index 88e390b6081094257c0a7d998f5c69f3cf8372f8..7805a11ba1b6a0b29b371ac7b6595140990f2e65 100644
--- a/processing/format.R
+++ b/processing/format.R
@@ -81,8 +81,54 @@ join_selection = function (df_data_AEAG, df_data_INRAE, df_meta_AEAG, df_meta_IN
return (list(data=df_data, meta=df_meta))
}
-### 1.2. Manages missing data ________________________________________
-missing_data = function (df_data, df_meta, yearLac_day=3, yearStart='01-01', Code=NULL, df_mod=NULL) {
+### 1.2. Local correction of data ____________________________________
+flag_data = function (df_data, df_meta, df_flag, Code=NULL, df_mod=NULL) {
+
+ if (is.null(Code)) {
+ # Get all different stations code
+ Code = levels(factor(df_meta$code))
+ nCode = length(Code)
+ } else {
+ nCode = length(Code)
+ }
+
+ for (code in Code) {
+ if (code %in% df_flag$code) {
+
+ df_flag_code = df_flag[df_flag$code == code,]
+ nbFlag = nrow(df_flag_code)
+
+ for (i in 1:nbFlag) {
+ newValue = df_flag_code$newValue[i]
+ date = df_flag_code$Date[i]
+ OKcor = df_data$code == code & df_data$Date == date
+ oldValue = df_data$Value[OKcor]
+ df_data$Value[OKcor] = newValue
+
+ if (!is.null(df_mod)) {
+ df_mod =
+ add_mod(df_mod, code,
+ type='Value correction',
+ fun_name='Manual new value assignment',
+ comment=paste('At ', date,
+ ' the value ', oldValue,
+ ' becomes ', newValue,
+ sep=''))
+ }
+ }
+ }
+ }
+
+ if (!is.null(df_mod)) {
+ res = list(data=df_data, mod=df_mod)
+ return (res)
+ } else {
+ return (df_data)
+ }
+}
+
+### 1.3. Manages missing data ________________________________________
+missing_data = function (df_data, df_meta, dayLac_lim=3, yearNA_lim=10, yearStart='01-01', Code=NULL, df_mod=NULL) {
if (is.null(Code)) {
# Get all different stations code
@@ -95,17 +141,45 @@ missing_data = function (df_data, df_meta, yearLac_day=3, yearStart='01-01', Cod
for (code in Code) {
# Extracts the data corresponding to the code
df_data_code = df_data[df_data$code == code,]
+
+ DateNA = df_data_code$Date[is.na(df_data_code$Value)]
+ dDateNA = diff(DateNA)
+ if (any(dDateNA != 1)) {
+
+ dDateNA = c(10, dDateNA)
+ idJump = which(dDateNA != 1)
+ NJump = length(idJump)
-
- Date_NA = df_data_code$Date[is.na(df_data_code$Value)]
- # print(Date_NA)
- # Start_NA
-
-
-
+ for (i in 1:NJump) {
+ idStart = idJump[i]
+
+ if (i < NJump) {
+ idEnd = idJump[i+1] - 1
+ } else {
+ idEnd = length(DateNA)
+ }
+
+ Start = DateNA[idStart]
+ End = DateNA[idEnd]
+
+ duration = (End - Start)/365.25
+ if (duration >= yearNA_lim) {
+ df_data_code$Value[df_data_code$Date <= End] = NA
+
+ if (!is.null(df_mod)) {
+ df_mod =
+ add_mod(df_mod, code,
+ type='Missing data management',
+ fun_name='NA assignment',
+ comment=paste('From the start of measurements',
+ ' to ', End, sep=''))
+ }
+ }
+ }
+ }
+
DateMD = substr(df_data_code$Date, 6, 10)
-
idyearStart = which(DateMD == yearStart)
if (DateMD[1] != yearStart) {
idyearStart = c(1, idyearStart)
@@ -139,7 +213,7 @@ missing_data = function (df_data, df_meta, yearLac_day=3, yearStart='01-01', Cod
yearLacMiss_pct = nbNA/nbDate * 100
- if (nbNA > yearLac_day) {
+ if (nbNA > dayLac_lim) {
df_data_code_year$Value = NA
df_data_code[OkYear,] = df_data_code_year
@@ -151,7 +225,7 @@ missing_data = function (df_data, df_meta, yearLac_day=3, yearStart='01-01', Cod
' to ', End, sep=''))
}
- } else if (nbNA <= yearLac_day & nbNA > 1) {
+ } else if (nbNA <= dayLac_lim & nbNA > 1) {
DateJ = as.numeric(df_data_code_year$Date)
Value = df_data_code_year$Value
@@ -182,7 +256,7 @@ missing_data = function (df_data, df_meta, yearLac_day=3, yearStart='01-01', Cod
}
}
-### 1.3. Sampling of the data ________________________________________
+### 1.4. Sampling of the data ________________________________________
sampling_data = function (df_data, df_meta, sampleSpan=c('05-01', '11-30'), Code=NULL, df_mod=NULL) {
if (is.null(Code)) {
diff --git a/script.R b/script.R
index 18d5b49edacd6083148266b23c62ad842a68c082..7934621a712344c0b1189fa5a69da915b032b775 100644
--- a/script.R
+++ b/script.R
@@ -58,13 +58,13 @@ filename =
# ""
# "all"
c(
- # "S2235610_HYDRO_QJM.txt",
- # "P0885010_HYDRO_QJM.txt"
- # "O3006710_HYDRO_QJM.txt",
- # "O4704030_HYDRO_QJM.txt"
- # "O0384010_HYDRO_QJM.txt",
- "O0362510_HYDRO_QJM.txt"
- # "Q7002910_HYDRO_QJM.txt"
+ "S2235610_HYDRO_QJM.txt",
+ "P0885010_HYDRO_QJM.txt",
+ "P0364010_HYDRO_QJM.txt",
+ "O7635010_HYDRO_QJM.txt",
+ "O3141010_HYDRO_QJM.txt",
+ "Q6332510_HYDRO_QJM.txt",
+ "Q7002910_HYDRO_QJM.txt"
# "Q0214010_HYDRO_QJM.txt",
# "O3035210_HYDRO_QJM.txt",
# "O0554010_HYDRO_QJM.txt",
@@ -104,7 +104,7 @@ which_layout =
# Selection
axis_xlim =
NULL
- # c("2002-01-01", "2003-01-01")
+ # c("1982-01-01", "1983-01-01")
## ANALYSIS
# Time period to analyse
@@ -121,15 +121,41 @@ mean_period = list(period1, period2)
alpha = 0.1
# Number of missing days per year before remove the year
-yearLac_day = 3
+dayLac_lim = 3
+
+# Maximum continuously missing years before removing everything
+# before it
+yearNA_lim = 10
+
+# Local corrections of the data
+df_flag = tibble(
+ code=c('O3141010',
+ 'O7635010',
+ 'O7635010',
+ 'O7635010',
+ 'O7635010'
+ ),
+ Date=c('1974-07-04',
+ '1948-09-06',
+ '1949-02-08',
+ '1950-07-20',
+ '1953-07-22'
+ ),
+ newValue=c(9.5,
+ 4,
+ 3,
+ 1,
+ 3) # /!\ Unit
+)
# Sampling span of the data
sampleSpan = c('05-01', '11-30')
-
-## MAP
# Is the hydrological network needs to be plot
-is_river = FALSE
+show_river = FALSE
+
+# If results and data used in the analysis needs to be written
+saving = FALSE
############### END OF REGION TO MODIFY (without risk) ###############
@@ -289,7 +315,9 @@ if ('analyse' %in% which_layout) {
res = get_QAtrend(df_data, df_meta,
period=trend_period,
alpha=alpha,
- yearLac_day=yearLac_day)
+ dayLac_lim=dayLac_lim,
+ yearNA_lim=yearNA_lim,
+ df_flag=df_flag)
df_QAdata = res$data
df_QAmod = res$mod
res_QAtrend = res$analyse
@@ -299,7 +327,9 @@ if ('analyse' %in% which_layout) {
period=trend_period,
alpha=alpha,
sampleSpan=sampleSpan,
- yearLac_day=yearLac_day)
+ dayLac_lim=dayLac_lim,
+ yearNA_lim=yearNA_lim,
+ df_flag=df_flag)
df_QMNAdata = res$data
df_QMNAmod = res$mod
res_QMNAtrend = res$analyse
@@ -309,7 +339,9 @@ if ('analyse' %in% which_layout) {
period=trend_period,
alpha=alpha,
sampleSpan=sampleSpan,
- yearLac_day=yearLac_day)
+ dayLac_lim=dayLac_lim,
+ yearNA_lim=yearNA_lim,
+ df_flag=df_flag)
df_VCN10data = res$data
df_VCN10mod = res$mod
res_VCN10trend = res$analyse
@@ -321,7 +353,9 @@ if ('analyse' %in% which_layout) {
sampleSpan=sampleSpan,
thresold_type='VCN10',
select_longest=TRUE,
- yearLac_day=yearLac_day)
+ dayLac_lim=dayLac_lim,
+ yearNA_lim=yearNA_lim,
+ df_flag=df_flag)
df_tDEBdata = res$data
df_tDEBmod = res$mod
res_tDEBtrend = res$analyse
@@ -331,7 +365,9 @@ if ('analyse' %in% which_layout) {
period=trend_period,
alpha=alpha,
sampleSpan=sampleSpan,
- yearLac_day=yearLac_day)
+ dayLac_lim=dayLac_lim,
+ yearNA_lim=yearNA_lim,
+ df_flag=df_flag)
df_tCENdata = res$data
df_tCENmod = res$mod
res_tCENtrend = res$analyse
@@ -350,17 +386,19 @@ if ('analyse' %in% which_layout) {
## 4. SAVING _________________________________________________________
-# for (v in var) {
-# df_datatmp = get(paste('df_', v, 'data', sep=''))
-# df_modtmp = get(paste('df_', v, 'mod', sep=''))
-# res_trendtmp = get(paste('res_', v, 'trend', sep=''))
-# # Modified data saving
-# write_data(df_datatmp, df_modtmp, resdir, optdir='modified_data',
-# filedir=v)
-# # Trend analysis saving
-# write_analyse(res_trendtmp, resdir, optdir='trend_analyse',
-# filedir=v)
-# }
+if (saving) {
+ for (v in var) {
+ df_datatmp = get(paste('df_', v, 'data', sep=''))
+ df_modtmp = get(paste('df_', v, 'mod', sep=''))
+ res_trendtmp = get(paste('res_', v, 'trend', sep=''))
+ # Modified data saving
+ write_data(df_datatmp, df_modtmp, resdir, optdir='modified_data',
+ filedir=v)
+ # Trend analysis saving
+ write_analyse(res_trendtmp, resdir, optdir='trend_analyse',
+ filedir=v)
+ }
+}
# res_tDEBtrend = read_listofdf(resdir, 'res_tDEBtrend')
@@ -370,12 +408,12 @@ df_shapefile = ini_shapefile(resources_path,
fr_shpdir, fr_shpname,
bs_shpdir, bs_shpname,
sbs_shpdir, sbs_shpname,
- rv_shpdir, rv_shpname, is_river=is_river)
+ rv_shpdir, rv_shpname, show_river=show_river)
### 5.1. Simple time panel to criticize station data _________________
# Plot time panel of debit by stations
if ('serie' %in% which_layout) {
- layout_panel(toplot=c('datasheet'),
+ layout_panel(what_plot=c('datasheet'),
df_meta=df_meta,
df_data=list(df_data,
df_sqrt),
@@ -395,10 +433,10 @@ if ('serie' %in% which_layout) {
### 5.2. Analysis layout _____________________________________________
if ('analyse' %in% which_layout) {
- layout_panel(toplot=c(
- 'datasheet'
+ layout_panel(what_plot=c(
+ # 'datasheet'
# 'matrix',
- # 'map'
+ 'map'
),
df_meta=df_meta,