diff --git a/plotting/layout.R b/plotting/layout.R
index afb19ca3b3e442a51b6971665b23536730a7a216..44a84f98694cbbeeb6bb3c9952d901a4ba0c005b 100644
--- a/plotting/layout.R
+++ b/plotting/layout.R
@@ -130,11 +130,12 @@ datasheet_layout = function (df_data, df_meta, layout_matrix,
isplot=c('datasheet', 'matrix', 'map'),
figdir='', filedir_opt='', filename_opt='',
variable='', df_trend=NULL,
- p_threshold=0.1, unit2day=365.25, type='',
- trend_period=NULL, mean_period=NULL,
- axis_xlim=NULL, missRect=FALSE,
- time_header=NULL, info_header=TRUE,
- info_ratio=1, time_ratio=2, var_ratio=3,
+ p_threshold=0.1, unit2day=365.25, var='',
+ type='', trend_period=NULL,
+ mean_period=NULL, axis_xlim=NULL,
+ missRect=FALSE, time_header=NULL,
+ info_header=TRUE, info_ratio=1,
+ time_ratio=2, var_ratio=3,
df_shapefile=NULL) {
# Name of the document
@@ -195,6 +196,12 @@ datasheet_layout = function (df_data, df_meta, layout_matrix,
unit2day = replicate(nbp, unit2day)
}}
+ if (all(class(var) != 'list')) {
+ var = list(var)
+ if (length(var) == 1) {
+ var = replicate(nbp, var)
+ }}
+
if (all(class(type) != 'list')) {
type = list(type)
if (length(type) == 1) {
@@ -217,7 +224,7 @@ datasheet_layout = function (df_data, df_meta, layout_matrix,
trend=df_trend[[i]],
p_threshold=p_threshold[[i]],
unit2day=unit2day[[i]],
- type=type[[i]],
+ var=var[[i]], type=type[[i]],
missRect=missRect[[i]])
# Stores it
list_df2plot[[i]] = df2plot
diff --git a/plotting/map.R b/plotting/map.R
index 5dc1e2c830b9a27d83c453877e2717cd7141536b..9e6eb743a094ef8cc65f89defc3fd88ad07a86fc 100644
--- a/plotting/map.R
+++ b/plotting/map.R
@@ -156,7 +156,7 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer=1, outdirTmp=''
}
# Computes the mean of the data on the period
- dataMean = mean(df_data_code_per$Qm3s, na.rm=TRUE)
+ dataMean = mean(df_data_code_per$Value, na.rm=TRUE)
# Normalises the trend value by the mean of the data
trendMean = df_trend_code_per$trend / dataMean
@@ -187,13 +187,13 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer=1, outdirTmp=''
break
}
# Extracts the variable of the plot
- type = list_df2plot[[i]]$type
+ var = list_df2plot[[i]]$var
# Createsa name for the map
- outname = paste('map_', type, sep='')
+ outname = paste('map_', var, sep='')
# If there is the verbose option
if (verbose) {
# Prints the name of the map
- print(paste('Map for variable : ', type,
+ print(paste('Map for variable : ', var,
" (", round(i/nbp*100, 1), " %)",
sep=''))
}
@@ -368,7 +368,7 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer=1, outdirTmp=''
}
# Computes the mean of the data on the period
- dataMean = mean(df_data_code_per$Qm3s, na.rm=TRUE)
+ 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
@@ -485,7 +485,7 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer=1, outdirTmp=''
size=0.6, color="#00A3A8") +
# Writes title
geom_shadowtext(data=tibble(x=-0.3, y=0.2,
- label=type),
+ label=var),
aes(x=x, y=y, label=label),
fontface="bold",
color="#00A3A8",
diff --git a/plotting/matrix.R b/plotting/matrix.R
index 0bdf77343edfc8ea321e7a43e6d01bf9436a8df0..f8454d5175d045ddb94a539bef6b3f8c3bf1f434 100644
--- a/plotting/matrix.R
+++ b/plotting/matrix.R
@@ -156,7 +156,7 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
}
# Computes the mean of the data on the period
- dataMean = mean(df_data_code_per$Qm3s, na.rm=TRUE)
+ dataMean = mean(df_data_code_per$Value, na.rm=TRUE)
# Normalises the trend value by the mean of the data
trendMean = df_trend_code_per$trend / dataMean
@@ -180,7 +180,7 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
# Blank vectors to store info about trend analyses
Periods_trend = c()
NPeriod_trend = c()
- Type_trend = list()
+ Var_trend = list()
Code_trend = c()
Pthresold_trend = c()
TrendMean_trend = c()
@@ -200,7 +200,7 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
df_trend = list_df2plot[[i]]$trend
p_threshold = list_df2plot[[i]]$p_threshold
# Extract the variable of the plot
- type = list_df2plot[[i]]$type
+ 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
@@ -229,7 +229,7 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
}
# Computes the mean of the data on the period
- dataMean = mean(df_data_code_per$Qm3s, na.rm=TRUE)
+ dataMean = mean(df_data_code_per$Value, na.rm=TRUE)
# Normalises the trend value by the mean of the data
trendMean = df_trend_code_per$trend / dataMean
@@ -256,7 +256,7 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
# Stores info needed to plot
Periods_trend = append(Periods_trend, Periods)
NPeriod_trend = append(NPeriod_trend, j)
- Type_trend = append(Type_trend, type)
+ Var_trend = append(Var_trend, var)
Code_trend = append(Code_trend, code)
Pthresold_trend = append(Pthresold_trend, Pthresold)
TrendMean_trend = append(TrendMean_trend, trendMean)
@@ -272,7 +272,7 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
# Blank vectors to store info about breaking analysis
Periods_mean = c()
NPeriod_mean = c()
- Type_mean = list()
+ Var_mean = list()
Code_mean = c()
DataMean_mean = c()
BreakMean_mean = c()
@@ -302,7 +302,7 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
# the current variable
df_data = list_df2plot[[i]]$data
# Extract the variable of the plot
- type = list_df2plot[[i]]$type
+ var = list_df2plot[[i]]$var
# Extracts the data corresponding to the code
df_data_code = df_data[df_data$code == code,]
@@ -323,7 +323,7 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
sep=' / ')
# Mean of the flow over the sub period
- dataMean = mean(df_data_code_per$Qm3s,
+ dataMean = mean(df_data_code_per$Value,
na.rm=TRUE)
# If this in not the first period
@@ -346,7 +346,7 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
# Stores info needed to plot
Periods_mean = append(Periods_mean, Periods)
NPeriod_mean = append(NPeriod_mean, j)
- Type_mean = append(Type_mean, type)
+ Var_mean = append(Var_mean, var)
Code_mean = append(Code_mean, code)
DataMean_mean = append(DataMean_mean, dataMean)
BreakMean_mean = append(BreakMean_mean,
@@ -435,7 +435,7 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
# Extracts those info
subPeriods_trend = Periods_trend[CodefL_trend]
subNPeriod_trend = NPeriod_trend[CodefL_trend]
- subType_trend = Type_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]
@@ -448,7 +448,7 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
# Extracts right info
subPeriods_mean = Periods_mean[CodefL_mean]
subNPeriod_mean = NPeriod_mean[CodefL_mean]
- subType_mean = Type_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]
@@ -500,8 +500,8 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
subPeriods_trend[subNPeriod_trend == j]
NPeriods_trend_per =
subNPeriod_trend[subNPeriod_trend == j]
- Type_trend_per =
- subType_trend[subNPeriod_trend == j]
+ Var_trend_per =
+ subVar_trend[subNPeriod_trend == j]
Code_trend_per =
subCode_trend[subNPeriod_trend == j]
Pthresold_trend_per =
@@ -517,7 +517,7 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
# Converts the vector of hydrological variable to
# a vector of integer associated to those variable
- Xtmp = as.integer(factor(as.character(Type_trend_per)))
+ 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
@@ -620,11 +620,11 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
# For all variable
for (i in 1:nbp) {
# Extract the variable of the plot
- type = list_df2plot[[i]]$type
+ var = list_df2plot[[i]]$var
mat = mat +
# Writes the type of the variable
annotate('text', x=X[i], y=max(Y) + 0.82,
- label=bquote(.(type)),
+ label=bquote(.(var)),
hjust=0.5, vjust=0.5,
size=3.25, color='grey20') +
# Writes the unit of the variable
@@ -634,7 +634,7 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
size=2, color='grey40') +
# Writes the type of the averaged variable
annotate('text', x=Xm[i], y=max(Y) + 0.82,
- label=bquote('µ'*.(type)),
+ label=bquote('µ'*.(var)),
hjust=0.5, vjust=0.5,
size=3.25, color='grey20') +
# Writes the unit of the averaged variable
@@ -678,8 +678,8 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
subPeriods_mean[subNPeriod_mean == j]
NPeriods_mean_per =
subNPeriod_mean[subNPeriod_mean == j]
- Type_mean_per =
- subType_mean[subNPeriod_mean == j]
+ Var_mean_per =
+ subVar_mean[subNPeriod_mean == j]
Code_mean_per =
subCode_mean[subNPeriod_mean == j]
DataMean_mean_per =
@@ -693,7 +693,7 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
# Converts the vector of hydrological variable to
# a vector of integer associated to those variable
- Xtmp_mean = as.integer(factor(as.character(Type_mean_per)))
+ Xtmp_mean = as.integer(factor(as.character(Var_mean_per)))
# Computes X position of the column for the period dates
Xc_mean = j + (j - 1)*nbp + X[length(X)]
# Computes X positions of columns for the mean of
@@ -818,11 +818,11 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
# For all variables
for (i in 1:nbp) {
# Extract the variable of the plot
- type = list_df2plot[[i]]$type
+ var = list_df2plot[[i]]$var
mat = mat +
# Writes the type of the averaged variable
annotate('text', x=Xm_mean[i], y=max(Y) + 0.82,
- label=bquote('µ'*.(type)),
+ label=bquote('µ'*.(var)),
hjust=0.5, vjust=0.5,
size=3.25, color='grey20') +
# Writes the unit of the averaged variable
@@ -837,7 +837,7 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
# Writes the type of the breaking variable
annotate('text', x=Xr_mean[i],
y=max(Y) + 0.82,
- label=bquote('d'*.(type)),
+ label=bquote('d'*.(var)),
hjust=0.5, vjust=0.5,
size=3.25, color='grey20') +
# Writes the unit of the breaking variable
diff --git a/processing/analyse.R b/processing/analyse.R
index a56cf5741fd4a204db398c3a6b17e29c12790dcc..9e8eb65ba0f4292107c600aeffc4e26d389bf05e 100644
--- a/processing/analyse.R
+++ b/processing/analyse.R
@@ -80,7 +80,7 @@ get_intercept = function (df_Xtrend, df_Xlist, unit2day=365.25) {
& df_Xtrend$period_end == End[i])
# Compute mean of flow and time period
- mu_X = mean(df_data_code_per$Qm3s, na.rm=TRUE)
+ mu_X = mean(df_data_code_per$Value, na.rm=TRUE)
mu_t = as.numeric(mean(c(Start[i],
End[i]),
na.rm=TRUE)) / unit2day
@@ -223,7 +223,7 @@ get_VCN10trend = function (df_data, df_meta, period, p_thresold) {
df_data_code = tibble(Date=rollmean(df_data_code$Date,
10,
fill=NA),
- Qm3s=rollmean(df_data_code$Qm3s,
+ Value=rollmean(df_data_code$Value,
10,
fill=NA),
code=c)
@@ -291,7 +291,7 @@ get_dateVCN10trend = function (df_data, df_meta, period, p_thresold) {
df_data_code = df_data[df_data$code == c,]
# Perform the roll mean of the flow over 10 days
df_data_code = tibble(Date=df_data_code$Date,
- Qm3s=rollmean(df_data_code$Qm3s,
+ Value=rollmean(df_data_code$Value,
10,
fill=NA),
code=c)
@@ -365,10 +365,10 @@ get_break = function (df_data, df_meta, p_thresold=0.05) {
# Get the associated data
df_data_code = df_data[df_data$code == code,]
# Remove NA data
- df_data_codeNoNA = df_data_code[!is.na(df_data_code$Qm3s),]
+ df_data_codeNoNA = df_data_code[!is.na(df_data_code$Value),]
# Perform the break analysis thanks to the Pettitt test
- res_break = pettitt.test(df_data_codeNoNA$Qm3s)
+ res_break = pettitt.test(df_data_codeNoNA$Value)
# Extract p value
p_value = res_break$p
@@ -386,8 +386,8 @@ get_break = function (df_data, df_meta, p_thresold=0.05) {
df_data_codeNoNA$Date[ibreak])
Code_break = append(Code_break, code)
}
- # step1 = mean(df_data_codeNoNA$Qm3s[1:ibreak])
- # step2 = mean(df_data_codeNoNA$Qm3s[(ibreak+1):nbreak])
+ # step1 = mean(df_data_codeNoNA$Value[1:ibreak])
+ # step2 = mean(df_data_codeNoNA$Value[(ibreak+1):nbreak])
}
# Create a tibble with the break analysis results
df_break = tibble(code=Code_break, Date=as.Date(date_break))
diff --git a/processing/extract.R b/processing/extract.R
index 20e36435b13e4a3dc92690fdf83b3d02f2d928aa..1207e0457ae43615c4421044d1ac6b086e1a4b01 100644
--- a/processing/extract.R
+++ b/processing/extract.R
@@ -457,7 +457,7 @@ extract_data = function (computer_data_path, filedir, filename,
# of the station
df_data = tibble(Date=as.Date(as.character(df_data$Date),
format="%Y%m%d"),
- Qm3s=df_data$Qls * 1E-3,
+ Value=df_data$Qls * 1E-3,
df_data[-1:-2],
code=code)
return (df_data)
diff --git a/processing/format.R b/processing/format.R
index c0a5de02aa873d96a79d68142185d1bcacab00b2..1068f9857d513ab6622dba3f5fd08c4028455845 100644
--- a/processing/format.R
+++ b/processing/format.R
@@ -51,7 +51,7 @@ prepare = function(df_data, colnamegroup=NULL) {
# Creates a new tibble of data with a group column
data = tibble(Date=df_data$Date,
group=group_indices(df_data),
- Qm3s=df_data$Qm3s)
+ Value=df_data$Value)
# Gets the different value of the group
Gkey = group_keys(df_data)
@@ -73,7 +73,7 @@ reprepare = function(df_XEx, df_Xlist, colnamegroup=NULL) {
# Changes the column name of the results of the
# 'extract.Var' function
- colnames(df_XEx) = c('Date', 'group', 'Qm3s')
+ colnames(df_XEx) = c('Date', 'group', 'Value')
# Converts Date column as character
df_XEx$Date = as.character(df_XEx$Date)
diff --git a/script.R b/script.R
index e4f30aa9b3a5f25a7ff3e5d77c03fdf4df424403..037d0c2d9886bf4fc372eb4a888455720c1d376c 100644
--- a/script.R
+++ b/script.R
@@ -57,19 +57,19 @@ filedir =
filename =
# ""
- c(
- "S2235610_HYDRO_QJM.txt",
- "P1712910_HYDRO_QJM.txt",
- "P0885010_HYDRO_QJM.txt",
- "O5055010_HYDRO_QJM.txt",
- "O0384010_HYDRO_QJM.txt"
- )
-
# c(
+ # "S2235610_HYDRO_QJM.txt",
+ # "P1712910_HYDRO_QJM.txt",
+ # "P0885010_HYDRO_QJM.txt",
+ # "O5055010_HYDRO_QJM.txt",
+ # "O0384010_HYDRO_QJM.txt"
+ # )
+
+ c(
# "S4214010_HYDRO_QJM.txt"
# "O0384010_HYDRO_QJM.txt",
- # "Q7002910_HYDRO_QJM.txt"
- # )
+ "Q7002910_HYDRO_QJM.txt"
+ )
@@ -274,7 +274,7 @@ df_shapefile = ini_shapefile(computer_data_path,
# layout_matrix=c(1, 2),
# df_meta=df_meta,
# missRect=list(TRUE, TRUE),
- # type=list('Q', 'sqrt(Q)'),
+ # var=list('Q', 'sqrt(Q)'),
# info_header=TRUE,
# time_header=NULL,
# var_ratio=3,
@@ -296,7 +296,8 @@ datasheet_layout(isplot=c(
res_QMNAtrend$trend,
res_VCN10trend$trend,
res_dateVCN10trend$trend),
- type=list('QA', 'QMNA', 'VCN10', 'VCN10 date'),
+ var=list('QA', 'QMNA', 'VCN10', 'date du VCN10'),
+ type=list('flow', 'flow', 'flow', 'date'),
layout_matrix=matrix(c(1, 2, 3, 4), ncol=1),