Newer
Older
# \\\
# Copyright 2021-2022 Louis Héraut*1
#
# *1 INRAE, France
# louis.heraut@inrae.fr
#
# This file is part of ash R toolbox.
#
# ash R toolbox is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or (at
# your option) any later version.
#
# ash R toolbox is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with ash R toolbox. If not, see <https://www.gnu.org/licenses/>.
# ///
#
#
# plotting/datasheet.R
#
# 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.
# Sourcing R file
source('processing/analyse.R', encoding='UTF-8')
## 1. DATASHEET PANEL
# Manages datasheets creations for all stations. Makes the call to
# the different headers, trend analysis graphs and realises arranging
# every plots.
datasheet_panel = function (list_df2plot, df_meta, trend_period, info_header, time_header, foot_note, layout_matrix, info_ratio, time_ratio, var_ratio, foot_height, resources_path, logo_dir, AEAGlogo_file, INRAElogo_file, FRlogo_file, outdirTmp, df_page=NULL) {
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
# The percentage of augmentation and diminution of the min
# and max limits for y axis
lim_pct = 10
# Number of variable/plot
nbp = length(list_df2plot)
# Get all different stations code
Code = levels(factor(df_meta$code))
nCode = length(Code)
# Gets a trend example
df_trend = list_df2plot[[1]]$trend
# Convert 'trend_period' to list
trend_period = as.list(trend_period)
# Number of trend period
nPeriod_trend = length(trend_period)
nPeriod_max = 0
for (code in Code) {
# Extracts the trend corresponding to the code
df_trend_code = df_trend[df_trend$code == code,]
# Extract start and end of trend periods
Start = df_trend_code$period_start
End = df_trend_code$period_end
# Get the name of the different period
UStart = levels(factor(Start))
UEnd = levels(factor(End))
# Compute the max of different start and end
# so the number of different period
nPeriod = max(length(UStart), length(UEnd))
# If the number of period for the trend is greater
# than the current max period, stocks it
if (nPeriod > nPeriod_max) {
nPeriod_max = nPeriod
}
}
# Blank array to store time info
tab_Start = array(rep('', nCode*nbp*nPeriod_max),
dim=c(nCode, nbp, nPeriod_max))
tab_End = array(rep('', nCode*nbp*nPeriod_max),
dim=c(nCode, nbp, nPeriod_max))
tab_Code = array(rep('', nCode*nbp*nPeriod_max),
dim=c(nCode, nbp, nPeriod_max))
tab_Periods = array(rep('', nCode*nbp*nPeriod_max),
dim=c(nCode, nbp, nPeriod_max))
# For all code
for (k in 1:nCode) {
# Gets the code
code = Code[k]
# For all the variable
for (i in 1:nbp) {
df_trend = list_df2plot[[i]]$trend
# 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$period_end
# Get the name of the different period
UStart = levels(factor(Start))
UEnd = levels(factor(End))
# Compute the max of different start and end
# so the number of different period
nPeriod = max(length(UStart), length(UEnd))
# For all the period
for (j in 1:nPeriod_max) {
# Stocks period
Periods = paste(Start[j],
End[j],
sep=' / ')
# Saves the time info
tab_Start[k, i, j] = as.character(Start[j])
tab_End[k, i, j] = as.character(End[j])
tab_Code[k, i, j] = code
tab_Periods[k, i, j] = Periods
}
}
}
# Blank array to store mean of the trend for each
# station, perdiod and variable
TrendValue_code = array(rep(1, nPeriod_trend*nbp*nCode),
dim=c(nPeriod_trend, nbp, nCode))
# For all the period
for (j in 1:nPeriod_max) {
# For all the code
for (k in 1:nCode) {
# Gets the code
code = Code[k]
for (i in 1:nbp) {
# Extracts the data corresponding to the
# current variable
df_data = list_df2plot[[i]]$data
# Extracts the trend corresponding to the
# current variable
df_trend = list_df2plot[[i]]$trend
# Extracts the type of the variable
type = list_df2plot[[i]]$type
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
# Extracts the data corresponding to the code
df_data_code = df_data[df_data$code == code,]
df_trend_code = df_trend[df_trend$code == code,]
# Gets the associated time info
Start = tab_Start[k, i, j]
End = tab_End[k, i, j]
Periods = tab_Periods[k, i, 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
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') {
trendValue = df_trend_code_per$trend
}
# If the p value is under the threshold
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
# Stores the mean trend
TrendValue_code[j, i, k] = trendValue
# Otherwise
} else {
# Do not stocks it
TrendValue_code[j, i, k] = NA
}
}
}
}
# Compute the min and the max of the mean trend for all the station
minTrendValue = apply(TrendValue_code, c(1, 2), min, na.rm=TRUE)
maxTrendValue = apply(TrendValue_code, c(1, 2), max, na.rm=TRUE)
# Blank vector to store the max number of digit of label for
# each station
NspaceMax = c()
# For all the station
for (code in Code) {
# Default max digit
NspaceMax_code = 0
# If the time header is given
if (!is.null(time_header)) {
nbpMod = nbp + 1
}
# For all type of graph
for (i in 1:nbpMod) {
if (i > nbp) {
# Extracts the data serie corresponding to the code
df_data_code = time_header[time_header$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$code == code,]
}
# If variable type is date
if (type == 'saisonnalité') {
# The number of digit is 6 because months are display
# with 3 characters
Nspace = 6
# If it is a flow variable
} else if (type == 'sévérité') {
# Gets the max number of digit on the label
maxtmp = max(df_data_code$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
}
}
# 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(info_header) + as.numeric(!is.null(time_header))
# 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)
# If the info header is needed
if (info_header) {
# Extracts the data serie corresponding to the code
time_header_code = time_header[time_header$code == code,]
# Gets the info plot
Hinfo = info_panel(list_df2plot,
df_meta,
period=mean_period[[1]],
df_shapefile=df_shapefile,
codeLight=code,
df_data_code=time_header_code)
# Stores it
P[[1]] = Hinfo
}
# If the time header is given
if (!is.null(time_header)) {
# Extracts the data serie corresponding to the code
time_header_code = time_header[time_header$code == code,]
# Gets the limits of the time serie
axis_xlim = c(min(time_header_code$Date),
max(time_header_code$Date))
# Gets the time serie plot
Htime = time_panel(time_header_code, df_trend_code=NULL,
trend_period=trend_period, 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
}
# Computes the number of column of plot asked on the datasheet
nbcol = ncol(as.matrix(layout_matrix))
# 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
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()
# # Default grey color for not significant trend
# grey = 85
# For all the period
for (j in 1:nPeriod_max) {
# If the trend is significant
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
# Gets the associated time info
Start = tab_Start[k, i, j]
End = tab_End[k, i, j]
Periods = tab_Periods[k, i, 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
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') {
trendValue = df_trend_code_per$trend
}
# Gets the color corresponding to the mean trend
color_res = get_color(trendValue,
minTrendValue[j, i],
maxTrendValue[j, i],
palette_name='perso',
reverse=TRUE)
# Stores it temporarily
colortmp = color_res
# Otherwise
} else {
# # Stores the default grey color
# colortmp = paste('grey', grey, sep='')
# # And gets a new shade of grey if there is
# # an other not significant trend
# grey = grey - 10
# Stores the default grey color
colortmp = paste('grey85', sep='')
}
# Stores the color
color = append(color, colortmp)
}
# Computes the time panel associated to the current variable
p = time_panel(df_data_code, df_trend_code, var=var,
missRect=missRect, trend_period=trend_period,
mean_period=mean_period, axis_xlim=axis_xlim,
unit2day=unit2day, grid=FALSE, color=color,
NspaceMax=NspaceMax[k], last=(i == nbp),
lim_pct=lim_pct)
# Stores the plot
subsection = code
n_page = df_page$n[nrow(df_page)] + 1
df_page = bind_rows(
df_page,
tibble(section=section,
subsection=subsection,
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,
AEAGlogo_file, INRAElogo_file,
FRlogo_file, foot_height)
P[[nbg]] = foot
}
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
# 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 the 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)) {
LM = rbind(2, LM)
} else {
time_ratio = 0
}
if (info_header) {
LM = rbind(1, LM)
} else {
info_ratio = 0
}
if (foot_note) {
id_foot = length(LM) + 1
LM = rbind(LM, id_foot)
} else {
foot_height = 0
}
LMcol = ncol(LM)
LMrow = nrow(LM)
LM = rbind(rep(99, times=LMcol), LM, rep(99, times=LMcol))
LMrow = nrow(LM)
LM = cbind(rep(99, times=LMrow), LM, rep(99, times=LMrow))
LMcol = ncol(LM)
Norm_ratio = height * (info_ratio + time_ratio + var_ratio*nbp) / (height - 2*margin_size - foot_height)
info_height = height * info_ratio / Norm_ratio
time_height = height * time_ratio / Norm_ratio
var_height = height * var_ratio / Norm_ratio
Hcut = LM[, 2]
heightLM = rep(0, times=LMrow)
heightLM[Hcut == 1] = info_height
heightLM[Hcut == 2] = time_height
heightLM[Hcut > 2 & Hcut < id_foot] = var_height
heightLM[Hcut == id_foot] = foot_height
Wcut = LM[(nrow(LM)-1),]
widthLM = rep(col_width, times=LMcol)
# Plot the graph as the layout
plot = grid.arrange(grobs=P, layout_matrix=LM,
heights=heightLM, widths=widthLM)
# Saving
ggsave(plot=plot,
path=outdirTmp,
filename=paste(as.character(code), '.pdf', sep=''),
width=width, height=height, units='cm', dpi=100)
}
}
## 2. OTHER PANEL FOR THE DATASHEET
### 2.1. Time panel
time_panel = function (df_data_code, df_trend_code, var, type, alpha=0.1, 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) {
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
1507
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593
1594
1595
1596
1597
1598
# If 'type' is square root apply it to data
if (var == 'sqrt(Q)') {
df_data_code$Value = sqrt(df_data_code$Value)
}
# 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
}
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)) {
minor_minDatetmp_lim = as.Date(axis_xlim[1])
minor_maxDatetmp_lim = as.Date(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))]
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(minor_minDate_lim,
'-01-01', sep=''))
minor_maxDate_lim = as.Date(paste(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)) {
# trend_period = as.list(trend_period)
# Imin = 10^99
# for (per in trend_period) {
# I = interval(per[1], per[2])
# if (I < Imin) {
# Imin = I
# trend_period_min = as.Date(per)
# }
# }
# p = p +
# geom_rect(aes(xmin=min(df_data_code$Date),
# ymin=0,
# xmax=trend_period_min[1],
# ymax= maxQ*1.1),
# linetype=0, fill='grey97') +
# geom_rect(aes(xmin=trend_period_min[2],
# ymin=0,
# xmax=max(df_data_code$Date),
# ymax= maxQ*1.1),
# linetype=0, fill='grey97')
# 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]
# If it is not a flow or sqrt of flow time serie
if (var != 'sqrt(Q)' & var != 'Q') {
# 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 it is not a flow or sqrt of flow time serie
if (var != 'sqrt(Q)' & var != 'Q') {
# If there is an 'axis_lim'
if (!is.null(axis_xlim)) {
# 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]
}
# Add one year the the temporary end of period
# if there is no 'axis_lim'
} else {
maxPer = maxPer + years(1)
}
}
# 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
# 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)
}
# 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$Date)
} else {
# Min and max is set with the limit axis parameter
xmin = axis_xlim[1]
xmax = axis_xlim[2]
}
# Create a vector for all the y grid position
ygrid = seq(minQ_win, maxQ_win, breakQ)
# Blank vector to store position
ord = c()
abs = c()
# For all the grid element
for (i in 1:length(ygrid)) {
# Store grid position
ord = c(ord, rep(ygrid[i], times=2))
abs = c(abs, xmin, xmax)
}
# Create a tibble to store all the position
plot_grid = tibble(abs=as.Date(abs), ord=ord)
# Plot the y grid
p = p +
geom_line(data=plot_grid,
aes(x=abs, y=ord, group=ord),
color='grey85',
size=0.15)
}
### Data ###
# If it is a square root flow or flow
if (var == 'sqrt(Q)' | var == 'Q') {
# Plot the data as line
p = p +
geom_line(aes(x=df_data_code$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$period_end
# Get the name of the different period
UStart = levels(factor(Start))
UEnd = levels(factor(End))
# Compute the max of different start and end
# so the number of different period
nPeriod_trend = max(length(UStart), length(UEnd))
# Blank tibble to store trend data and legend data
plot_trend = tibble()
leg_trend = tibble()
# For all the different period
for (i in 1:nPeriod_trend) {
# Extracts the corresponding data for the period
df_data_code_per =
df_data_code[df_data_code$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
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$Date
}
# The y limit is stored in a vector
codeValue = c(minQ_win, maxQ_win)
# Position of the x beginning and end of the legend symbol
x = gpct(1.5, codeDate, shift=TRUE)
xend = x + gpct(3, codeDate)
# Spacing between legend symbols
dy = gpct(9, codeValue, min_lim=ymin_lim)
# Position of the y beginning and end of the legend symbol
y = gpct(92, codeValue,
min_lim=ymin_lim, shift=TRUE) - (i-1)*dy
yend = y
# Position of x for the beginning of the associated text
xt = xend + gpct(1, codeDate)
# Position of the background rectangle of the legend
xminR = x - gpct(1, codeDate)
yminR = y - gpct(5, codeValue, min_lim=ymin_lim)
# If it is a flow variable
if (type == 'sévérité') {
xmaxR = x + gpct(32.5, codeDate)
# If it is a date variable
} else if (type == 'saisonnalité') {
xmaxR = x + gpct(20.5, codeDate)
}
ymaxR = y + gpct(5, codeValue, min_lim=ymin_lim)
# Gets the trend
trend = df_trend_code_per$trend
# Gets the p value
pVal = df_trend_code_per$p
# Converts it to character
pValC = as.character(format(round(pVal, 2),
nsmall=2))
# Computes the mean trend
trendMean = trend/dataMean
# Computes the magnitude of the trend
power = get_power(trend)
# Converts it to character
powerC = as.character(power)
# If the power is positive
if (powerC >= 0) {
# Adds a space in order to compensate for the minus
# sign that sometimes is present for the other periods
spaceC = ' '
# Otherwise
} else {
# No space is added
spaceC = ''
}
# Gets the power of ten of magnitude
brk = 10^power
# Converts trend to character for sientific expression
trendC = as.character(format(round(trend / brk, 2),
nsmall=2))
# If the trend is positive
if (trendC >= 0) {
# Adds two space in order to compensate for the minus
# sign that sometimes is present for the other periods
trendC = paste(' ', trendC, sep='')
}
# Converts mean trend to character
trendMeanC = as.character(format(round(trendMean*100, 2),
nsmall=2))
if (trendMeanC >= 0) {
# Adds two space in order to compensate for the minus
# sign that sometimes is present for the other periods
trendMeanC = paste(' ', trendMeanC, sep='')
}
# Create temporary tibble with variable to plot legend
leg_trendtmp = tibble(x=x, xend=xend,
y=y, yend=yend,
xt=xt,
trendC=trendC,
powerC=powerC,
spaceC=spaceC,
trendMeanC=trendMeanC,
pValC=pValC,
xminR=xminR, yminR=yminR,
xmaxR=xmaxR, ymaxR=ymaxR,
period=i)
# Bind it to the main tibble to store it with other period
leg_trend = bind_rows(leg_trend, leg_trendtmp)
}
# For all periods
for (i in 1:nPeriod_trend) {
# Extract the trend of the current sub period
leg_trend_per = leg_trend[leg_trend$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=0.5)
}
# For all periods
for (i in 1:nPeriod_trend) {
# Extract the trend of the current sub period
leg_trend_per = leg_trend[leg_trend$period == i,]
# Get the character variable for naming the trend
trendC = leg_trend_per$trendC
powerC = leg_trend_per$powerC
spaceC = leg_trend_per$spaceC
trendMeanC = leg_trend_per$trendMeanC
pValC = leg_trend_per$pValC
# If it is a flow variable
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}*']')
}
# 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=color[i],
linetype='solid',
lwd=0.8) +
annotate("text",
label=label, size=2.8,
x=leg_trend_per$xt, y=leg_trend_per$y,
hjust=0, vjust=0.5,
color=color[i])
}
# 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='solid',
size=0.75,
lineend="round")
}
}
# Y axis title
# If it is a flow variable
if (type == 'sévérité') {
p = p +
ylab(bquote(bold(.(var))~~'['*m^{3}*'.'*s^{-1}*']'))
# If it is a date variable
} else if (type == 'saisonnalité') {
p = p +
ylab(bquote(bold(.(var))~~"[jour de l'année]"))
}
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
}
# Parameters of the x axis contain the limit of the date data
p = p +
scale_x_date(breaks=seq(minDate_lim, maxDate_lim,
by=paste(breakDate, 'years')),
minor_breaks=seq(minor_minDate_lim,
minor_maxDate_lim,
by=paste(minor_breakDate,
'years')),
guide='axis_minor',
date_labels="%Y",
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') {
# 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)
# 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') {
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"))
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, period, df_shapefile, codeLight, df_data_code=NULL) {
# 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=period,
margin=margin(t=0, r=0, b=0, l=5,
unit="mm"))
# Otherwise
} else {
# Puts it blank
hyd = void
}
# Computes the map associated to the station
map = map_panel(list_df2plot,
df_meta,
df_shapefile=df_shapefile,
codeLight=codeLight,
margin=margin(t=0, r=-12, b=0, l=0, unit="mm"),
showSea=FALSE,
verbose=FALSE)
# Gets the metadata about the station
df_meta_code = df_meta[df_meta$code == codeLight,]
# Extracts the name
nom = df_meta_code$nom
# Corrects some errors about the formatting of title with dash
nom = gsub("-", "- ", nom)
# Computes the time span of data, the start and the end
duration = as.numeric(format(as.Date(df_meta_code$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$fin), "%d/%m/%Y")
# Name of the datasheet
text1 = paste(
"<b>", codeLight, '</b> - ', nom,
sep='')
# Subitle info
text2 = paste(
"<b>",
"Gestionnaire : ", df_meta_code$gestionnaire, "<br>",
"Région hydro : ", df_meta_code$region_hydro,
"</b>",
sep='')
# Spatial info about station
text3 = paste(
"<b>",
"Superficie : ", df_meta_code$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 ; Lambert 93]<br>",
"Y = ", df_meta_code$L93Y_m_BH, " [m ; Lambert 93]",
"</b>",
sep='')
# Time info about station
text4 = paste(
"<b>",
"Date de début : ", debut, "<br>",
"Date de fin : ", fin, "<br>",
"Nombre d'années : ", duration, " [ans]", "<br>",
"Taux de lacunes : ", signif(df_meta_code$tLac100, 2), " [%]",
"</b>",
sep='')
# 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))
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))
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))
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))
# 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'),
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)),
expand=c(0, 0))
# Returns the plot
return (hyd)
}