-
Notifications
You must be signed in to change notification settings - Fork 0
/
app.R
1462 lines (1361 loc) · 63.4 KB
/
app.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
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
150
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
190
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
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
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
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
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
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
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
# libraries ----
remotes::install_github("deepanshu88/summaryBox")
remotes::install_github("matt-dray/badgr")
pacman::p_load(
"shiny", "shinyjs", "shinyBS",
"shinyWidgets", "DT", "readxl",
"writexl", "htmltools", "tidyverse",
"bslib"
)
library("summaryBox")
library("badgr")
# Read the data from the files ----
## file with the SO costs
datasetCosts <- read_csv("./data/eLTER-SO-costs_standard-observations-detailed-costs_V18.csv")
# Fixing some errors in the dataset before making it available for the app
datasetCosts <- datasetCosts %>%
# renaming terms
rename(type = method) %>%
# removing the SOHYD_065 as requested by Steffen Zacharias
filter(!code == "SOHYD_065") %>%
# fixing the issue on the flip information on the so_short_name from SOSOC_030 and SOSOC_031
mutate(so_short_name = replace(
so_short_name,
code == "SOSOC_030",
"Land-based income (SOSOC_030)"
)) %>%
mutate(so_short_name = replace(
so_short_name,
code == "SOSOC_031",
"Yield (SOSOC_031)"
)) %>%
# changing the upgradeInterval from SOHYD_168 to 10 years (requested by S. Zacharias on 2024-03-19)
mutate(upgradeInterval = replace(
upgradeInterval,
code == "SOHYD_168",
10
)) %>%
# changing the minimumSamplePerSite of SOHYD_006 to 1 (because the purchasePrice refers to a a bundle of 5)
mutate(minimumSamplePerSite = replace(
minimumSamplePerSite,
code == "SOHYD_006",
1
)) %>%
# updating the information on the Sociosphere V22 (Steffen Zacharias shares file in early April 2024)
### Sociosphere
#### SOSOC_029
# removing the SOSOC_029
filter(!c(type == "prime" & code == "SOSOC_029")) %>%
#### SOSOC_031
mutate(soBundles = replace(
soBundles,
type == "prime" & code == "SOSOC_031",
"Agricultural production (detailed sub-categories for cropland, grassland, forest, fishery, in t/ha and year)"
)) %>%
mutate(measurementInterval = replace(measurementInterval, type == "prime" & code == "SOSOC_031", 1)) %>%
mutate(samplingEffort = replace(samplingEffort, type == "prime" & code == "SOSOC_031", 4)) %>%
mutate(processingRawDataEffort = replace(processingRawDataEffort, type == "prime" & code == "SOSOC_031", 2)) %>%
#### SOSOC_030
mutate(soBundles = replace(soBundles, type == "prime" & code == "SOSOC_030", "FNVA, FNVA/AWU, FFI, FFI/FWU, Farm worker wages")) %>%
mutate(measurementInterval = replace(measurementInterval, type == "prime" & code == "SOSOC_030", 1)) %>%
mutate(sensorType = replace(sensorType, type == "prime" & code == "SOSOC_030", "national statistics")) %>%
mutate(samplingEffort = replace(samplingEffort, type == "prime" & code == "SOSOC_030", 4)) %>%
mutate(processingRawDataEffort = replace(processingRawDataEffort, type == "prime" & code == "SOSOC_030", 2)) %>%
#### SOSOC_114
mutate(soBundles = replace(soBundles, type == "prime" & code == "SOSOC_114", "Livestock numbers, breeds, + Feed/grazing management")) %>%
mutate(samplingType = replace(samplingType, type == "prime" & code == "SOSOC_114", "retrieval | sample")) %>%
mutate(measurementInterval = replace(measurementInterval, type == "prime" & code == "SOSOC_114", 1)) %>%
mutate(measurementIntervalUnit = replace(measurementIntervalUnit, type == "prime" & code == "SOSOC_114", "year")) %>%
mutate(measurementsPerYear = replace(measurementsPerYear, type == "prime" & code == "SOSOC_114", 1)) %>%
mutate(sensorType = replace(sensorType, type == "prime" & code == "SOSOC_114", "national statistics + site specific survey")) %>%
mutate(samplingEffort = replace(samplingEffort, type == "prime" & code == "SOSOC_114", 13)) %>%
mutate(processingRawDataEffort = replace(processingRawDataEffort, type == "prime" & code == "SOSOC_114", 11)) %>%
#### SOSOC_032
mutate(measurementInterval = replace(measurementInterval, type == "prime" & code == "SOSOC_032", 6)) %>%
mutate(measurementsPerYear = replace(measurementsPerYear, type == "prime" & code == "SOSOC_032", 1 / 6)) %>%
mutate(samplingEffort = replace(samplingEffort, type == "prime" & code == "SOSOC_032", 9)) %>%
mutate(processingRawDataEffort = replace(processingRawDataEffort, type == "prime" & code == "SOSOC_032", 9)) %>%
#### SOSOC_036
mutate(samplingEffort = replace(samplingEffort, type == "prime" & code == "SOSOC_036", 1)) %>%
mutate(processingRawDataEffort = replace(processingRawDataEffort, type == "prime" & code == "SOSOC_036", 1)) %>%
#### SOSOC_037
mutate(soBundles = replace(soBundles, type == "prime" & code == "SOSOC_037", "Farm structure / land management / area statistics (incl. conv./organic; conv./cons./no tillage)")) %>%
mutate(samplingType = replace(samplingType, type == "prime" & code == "SOSOC_037", "retrieval | sample")) %>%
mutate(sensorType = replace(sensorType, type == "prime" & code == "SOSOC_037", "national statistics + site-specific survey")) %>%
mutate(samplingEffort = replace(samplingEffort, type == "prime" & code == "SOSOC_037", 13)) %>%
mutate(processingRawDataEffort = replace(processingRawDataEffort, type == "prime" & code == "SOSOC_037", 11)) %>%
#### SOSOC_040
mutate(samplingType = replace(samplingType, type == "prime" & code == "SOSOC_040", "retrieval | sample")) %>%
mutate(measurementInterval = replace(measurementInterval, type == "prime" & code == "SOSOC_040", 6)) %>%
mutate(measurementsPerYear = replace(measurementsPerYear, type == "prime" & code == "SOSOC_040", 1 / 6)) %>%
mutate(sensorType = replace(sensorType, type == "prime" & code == "SOSOC_040", "quantitative + expert judgement + site-specific survey")) %>%
mutate(samplingEffort = replace(samplingEffort, type == "prime" & code == "SOSOC_040", 13)) %>%
mutate(processingRawDataEffort = replace(processingRawDataEffort, type == "prime" & code == "SOSOC_040", 11)) %>%
#### SOSOC_042
mutate(measurementInterval = replace(measurementInterval, type == "prime" & code == "SOSOC_042", 1)) %>%
mutate(samplingEffort = replace(samplingEffort, type == "prime" & code == "SOSOC_042", 4)) %>%
mutate(processingRawDataEffort = replace(processingRawDataEffort, type == "prime" & code == "SOSOC_042", 2)) %>%
#### SOSOC_043
mutate(samplingEffort = replace(samplingEffort, type == "prime" & code == "SOSOC_043", 4)) %>%
mutate(processingRawDataEffort = replace(processingRawDataEffort, type == "prime" & code == "SOSOC_043", 2)) %>%
#### SOSOC_044
mutate(samplingEffort = replace(samplingEffort, type == "prime" & code == "SOSOC_044", 4)) %>%
mutate(processingRawDataEffort = replace(processingRawDataEffort, type == "prime" & code == "SOSOC_044", 2)) %>%
#### SOSOC_045
mutate(sensorType = replace(sensorType, type == "prime" & code == "SOSOC_045", "national census data + site-specific survey")) %>%
mutate(samplingEffort = replace(samplingEffort, type == "prime" & code == "SOSOC_045", 13)) %>%
mutate(processingRawDataEffort = replace(processingRawDataEffort, type == "prime" & code == "SOSOC_045", 11)) %>%
#### SOSOC_183
mutate(so = replace(so, type == "prime" & code == "SOSOC_183", "Resource use (MFA)")) %>%
mutate(soBundles = replace(soBundles, type == "prime" & code == "SOSOC_183", "DE, IMP, EXP, DPO, BI, DMC, DMI, PTB, NAS: biomass, metal ores, non-metallic minerals, fossil energy carriers (other products, waste, emissions)")) %>%
mutate(sensorType = replace(sensorType, type == "prime" & code == "SOSOC_183", "national statistics")) %>%
mutate(samplingEffort = replace(samplingEffort, type == "prime" & code == "SOSOC_183", 4)) %>%
mutate(processingRawDataEffort = replace(processingRawDataEffort, type == "prime" & code == "SOSOC_183", 2)) %>%
#### SOSOC_184
mutate(soBundles = replace(soBundles, type == "prime" & code == "SOSOC_184", "CAP payments for direct support, rural development, market measures (€ total, avg. per beneficiary, avg. per ha, nr. of beneficiaries per total agric. holdings)")) %>%
mutate(sensorType = replace(sensorType, type == "prime" & code == "SOSOC_184", "national statistics")) %>%
mutate(samplingEffort = replace(samplingEffort, type == "prime" & code == "SOSOC_184", 4)) %>%
mutate(processingRawDataEffort = replace(processingRawDataEffort, type == "prime" & code == "SOSOC_184", 2)) %>%
## type basic -----
### Sociosphere ----
#### SOSOC_031
mutate(soBundles = replace(soBundles, type == "basic" & code == "SOSOC_031", "Agricultural production (detailed sub-categories for cropland, grassland, forest, fishery, in t/ha and year)")) %>%
#### SOSOC_030
mutate(soBundles = replace(soBundles, type == "basic" & code == "SOSOC_030", "FNVA, FNVA/AWU, FFI, FFI/FWU, Farm worker wages")) %>%
#### SOSOC_114
mutate(samplingType = replace(samplingType, type == "basic" & code == "SOSOC_114", "retrieval | sample")) %>%
mutate(sensorType = replace(sensorType, type == "basic" & code == "SOSOC_114", "official statistics/ + expert judgement")) %>%
mutate(samplingEffort = replace(samplingEffort, type == "basic" & code == "SOSOC_114", 4)) %>%
mutate(processingRawDataEffort = replace(processingRawDataEffort, type == "basic" & code == "SOSOC_114", 4)) %>%
#### SOSOC_032
mutate(measurementInterval = replace(measurementInterval, type == "basic" & code == "SOSOC_032", 3)) %>%
mutate(measurementsPerYear = replace(measurementsPerYear, type == "basic" & code == "SOSOC_032", 1 / 3)) %>%
mutate(sensorType = replace(sensorType, type == "basic" & code == "SOSOC_032", "expert judgement")) %>%
mutate(samplingEffort = replace(samplingEffort, type == "basic" & code == "SOSOC_032", 4)) %>%
mutate(processingRawDataEffort = replace(processingRawDataEffort, type == "basic" & code == "SOSOC_032", 4)) %>%
#### SOSOC_036
mutate(samplingEffort = replace(samplingEffort, type == "basic" & code == "SOSOC_036", 1)) %>%
mutate(processingRawDataEffort = replace(processingRawDataEffort, type == "basic" & code == "SOSOC_036", 1)) %>%
#### SOSOC_037
mutate(soBundles = replace(soBundles, type == "basic" & code == "SOSOC_037", "Farm structure / land management / area statistics (incl. conv./organic; conv./cons./no tillage)")) %>%
mutate(samplingType = replace(samplingType, type == "basic" & code == "SOSOC_037", "retrieval | sample")) %>%
mutate(measurementInterval = replace(measurementInterval, type == "basic" & code == "SOSOC_037", 1)) %>%
mutate(measurementsPerYear = replace(measurementsPerYear, type == "basic" & code == "SOSOC_037", 1)) %>%
mutate(samplingEffort = replace(samplingEffort, type == "basic" & code == "SOSOC_037", 4)) %>%
mutate(processingRawDataEffort = replace(processingRawDataEffort, type == "basic" & code == "SOSOC_037", 4)) %>%
#### SOSOC_040
mutate(samplingType = replace(samplingType, type == "basic" & code == "SOSOC_040", "retrieval | sample")) %>%
mutate(sensorType = replace(sensorType, type == "basic" & code == "SOSOC_040", "quantitative (statistics, measurements) + expert judgement")) %>%
mutate(samplingEffort = replace(samplingEffort, type == "basic" & code == "SOSOC_040", 4)) %>%
mutate(processingRawDataEffort = replace(processingRawDataEffort, type == "basic" & code == "SOSOC_040", 4)) %>%
#### SOSOC_042
#### SOSOC_043
mutate(measurementInterval = replace(measurementInterval, type == "basic" & code == "SOSOC_043", 1)) %>%
mutate(measurementsPerYear = replace(measurementsPerYear, type == "basic" & code == "SOSOC_043", 1)) %>%
#### SOSOC_044
mutate(measurementInterval = replace(measurementInterval, type == "basic" & code == "SOSOC_044", 1)) %>%
mutate(measurementsPerYear = replace(measurementsPerYear, type == "basic" & code == "SOSOC_044", 1)) %>%
#### SOSOC_045
mutate(samplingType = replace(samplingType, type == "basic" & code == "SOSOC_045", "retrieval | sample")) %>%
#### SOSOC_183
mutate(soBundles = replace(soBundles, type == "basic" & code == "SOSOC_183", "DE, IMP, EXP, DPO, BI, DMC, DMI, PTB, NAS: biomass, metal ores, non-metallic minerals, fossil energy carriers (other products, waste, emissions)")) %>%
#### SOSOC_184
mutate(soBundles = replace(soBundles, type == "basic" & code == "SOSOC_184", "CAP payments for direct support, rural development, market measures (€ total, avg. per beneficiary, avg. per ha, nr. of beneficiaries per total agric. holdings)")) %>%
# updating the totalHumanLabor after the changes
mutate(totalHumanLabor = installationEffort + maintenanceEffort + samplingEffort + processingRawDataEffort)
## file with the info on the SOs' spheres and method types
dataset <- readxl::read_excel("./data/eLTER-SO-costs_list-of-standard-observations.xlsx")
### Improving user friendliness on the names
dataset <- dataset %>%
mutate(habitat = gsub("_", " ", habitat)) %>%
# removing the eLTSER from the app
filter(!habitat == "e ltser platform") %>%
arrange(habitat)
# creating a table containing the relationship between code and so_short_name
codes_coding <- datasetCosts %>%
distinct(code, so_short_name)
# putting this information on the dataset
dataset <- inner_join(dataset, codes_coding) %>%
relocate(so_short_name, .after = code) # reorganizing the order of the terms
# Custom eLTER colors ----
color1 <- "#ED9632"
color2 <- "#0879C0"
color3 <- "#40907A"
color4 <- "#96CE58"
color5 <- "#BADFFD"
# Named vector for sphere colors
sphere_colors <- c(
"Atmosphere" = color5,
"Hydrosphere" = color2,
"Geosphere" = color3,
"Biosphere" = color4
)
# creating a fill scale function for the plots
scale_fill_elter <- function(...) {
scale_fill_manual(
values =
c( # color1,
color2,
color3,
color4,
color5
), ...
)
}
# Custom functions ----
costs_elter <- function(dataset, cat, hab) {
dataset %>%
filter(category == cat & habitat == hab & !is.na(type) == TRUE) %>%
distinct(sphere, code, so_short_name, standard_observation, type)
}
f_spheres <- function(dataset, spheres) {
df1 <- dataset %>%
filter(sphere %in% c(spheres))
df2 <- dataset %>%
filter(!sphere %in% c(spheres)) %>%
mutate(type = "basic")
bind_rows(df1, df2) %>%
arrange(sphere, code)
}
station_requirements <- function(dataset, cat, hab, spheres, site_or_platform) {
if (site_or_platform == "platform") {
dataset <- dataset %>% filter(sphere == "Sociosphere")
} else {
dataset <- dataset
}
df1 <- tryCatch(
{
costs_elter(dataset = dataset, cat = cat, hab = hab)
},
error = function(e) {
message("Error in costs_elter:", e)
data.frame() # return an empty data frame in case of an error
}
)
df2 <- f_spheres(dataset = df1, spheres = spheres)
inner_join(
df2,
dataset %>%
distinct(code, habitat, type) %>%
filter(!is.na(type)) %>%
arrange(code, type)
) %>%
filter(habitat == hab) %>%
dplyr::select(-habitat)
}
# Function to calculate the costs per year
SO_cost <- function(input_code, input_type) {
# Filter data for the specific code
filtered_data <- datasetCosts %>%
dplyr::filter(code == input_code, type == input_type)
# Check if the filtered data is empty
if (nrow(filtered_data) == 0) {
# Return an empty data frame or a data frame with default/NA values
return(data.frame(
code = input_code, type = input_type,
purchasePrice = NA, purchaseCostYear = NA,
maintenanceCostYear = NA, samplingCostYear = NA,
labAnalysisCostYear = NA, totalHumanLabor = NA,
totalCostYear = NA
))
}
# Calculating costs
results <- filtered_data %>%
dplyr::mutate(
# 2024-02-19; adding a column displaying the purchase costs independently from the upgrade interval
purchasePrice,
# purchase
purchaseCostYear = case_when(
purchasePrice == 0 ~ 0,
purchasePrice > 0 & upgradeInterval == 0 ~ round(((purchasePrice * minimumSamplePerSite)), 0),
purchasePrice > 0 & upgradeInterval > 0 ~ round(((purchasePrice * minimumSamplePerSite) / upgradeInterval), 0)
),
# # 2024-02-19: adding the upgrade interval on the table as requested by Steffen Zacharias (2024-02-16)
# maintenance
maintenanceCostYear = round(maintenancePrice, 0), # 2024-02-19: removed the effort (maintenanceEffort) from the calculation following the comments from Steffen Zacharias on 2014-02-16. # it should not include the price per site, as it is priced at for the entire bundle
# sampling
samplingCostYear = round(samplingPrice, 0), # 2024-02-19: Steffen Zacharias requested to remove the samples per year (measurementsPerYear) from the calculations and consider only the value on the sampling. Removed the sampling per sites as it shouldn't affect the calculations
# lab analysis
labAnalysisCostYear = round(labAnalysisPrice * minimumSamplePerSite * measurementsPerYear, 0),
# human labor
totalHumanLabor = round(totalHumanLabor, 2)
) %>%
# total cost
mutate(totalCostYear = sum(purchaseCostYear, maintenanceCostYear,
samplingCostYear, labAnalysisCostYear,
na.rm = TRUE
)) %>%
dplyr::select(
code, type, purchasePrice, purchaseCostYear,
maintenanceCostYear, samplingCostYear, labAnalysisCostYear,
totalHumanLabor, totalCostYear
)
return(results)
}
# Shiny app UI ----
ui <- fluidPage(
# specifying the title shown in the browser
tags$head(
tags$title("eLTER SO Costs")
),
# specifying the favicon for the web browser
tags$head(
tags$link(rel = "shortcut icon", type = "image/png", href = "eLTER-SO-costs_elter-logo.png")
),
# specifying the image with the eLTER SO Costs logo
titlePanel(div(
style = "text-align: center;",
tags$a(
href = "https://elter-ri.eu/", target = "_blank",
img(src = "eLTER-SO-costs_application-logo.jpg", height = "75px")
)
)),
tags$head(
tags$style(HTML("
.tabbable > .nav > li[class=active] > a { background-color: #F26522; color:white}
.navbar { background-color: #0073C2; }
.btn { background-color: #F26522; color: #fff; }
.btn:hover { background-color: darken(#F26522, 10%); }
.scrollable-checkbox {
height: 300px;
overflow-y: auto;
border: 1px solid #ccc;
padding: 10px;
border-radius: 5px;
background-color: #FFFFFF;
}
h1 { color: #0073C2; }
h2 { color: #0073C2; }
h3 { color: #0073C2; }
h4 { color: #000000; }
"))
),
tabsetPanel(
# Adding the Info tab ----
tabPanel(
HTML('Info <i class="fas fa-info-circle"></i>'),
fluidRow(
column(
6,
card(
fill = FALSE,
max_height = 1000,
card_body(
border_radius = "all",
width = 1000,
fillable = FALSE,
fill = FALSE,
plotOutput("id", width = "100%", height = "100%"),
class = "align-items-center",
HTML("<h1>Welcome</h1>"),
p(HTML("This interactive tool is designed to assist researchers and site managers associated with the <a href = 'https://elter-ri.eu/' target = '_blank'> <b>Integrated European Long-Term Ecosystem, critical zone and socio-ecological research (eLTER)</b></a> network in defining <a href = 'https://vocabs.lter-europe.net/so/en/' target = '_blank'> <b>Standard Observations (SOs)</b></a> and calculating the associated costs to upgrade and operate them at their sites. The outputs of this tool are subjected to changes, as modifications in the inputs might occur in the future based on the agreement of eLTER consortium. Additionally, the values presented here might differ slightly from precise calculations of the costs due to different reasons (e.g. differences in management among institutions, different costs associated with the sampling, maintenance, lab analysis and etc.).")),
HTML("<h2>Features</h2>"),
tags$ul(
tags$li(HTML("<b>Selecting parameters</b>")),
tags$ul(
tags$li(HTML("Begin by selecting the site category, habitat and focus spheres of your eLTER site on the <b>Set up</b> tab to tailor the SOs list to your specific needs."))
),
tags$li(HTML("<b>Customizing your SO list</b>")),
tags$ul(
tags$li(HTML("Deselect any SO that your site has already covered on the <i>side panel</i> of the <b>Set up</b> tab."))
),
tags$li(HTML("<b>Costs calculations</b>")),
tags$ul(
tags$li(HTML("Navigate to the <b>SO costs</b> tab to view a detailed breakdown of costs for running the selected SOs at your eLTER site. This includes purchase, maintenance, sampling, lab analysis costs, and the total human labor involved."))
),
tags$li(HTML("<b>Exporting the costs calculations</b>")),
tags$ul(
tags$li(HTML("On the <b>SO costs</b> tab navigate to the bottom of the table and add the name of your eLTER site and download your results using the download button."))
),
tags$li(HTML("<b>Informative visualizations</b>")),
tags$ul(
tags$li("Explore various plots providing insights into the number of SOs by sphere, annual cost breakdown by type, labor effort by sphere, and more. These visualizations aid in understanding the distribution and financial implications of the SOs required for your site.")
)
),
tags$h2("Step-by-step user guide"),
tags$p(HTML("For more detailed information, please access the document <a href='eLTER-SO-Costs_app-webinar_20240328_v1.0.0.pdf' target='_blank'>here</a>.")),
tags$h2("FAQ"),
tags$p(HTML("Click <a href = 'https://docs.google.com/document/d/1JchmwmXVXfhXL4iWhGFjD46rCNKTtfS6oaq68_nNZZA/edit?usp=sharing' target = '_blank'> <b>here</b></a> to access the Frequently Asked Questions.")),
tags$h2("Accessing the source code"),
tags$p(HTML("For those interested in exploring the underlying code, contributing to its development, or customizing the application for specific needs, the source code is available on <a href = 'https://github.com/allantsouza/eLTER-SO-Costs-web-application' target = '_blank'> <b>GitHub</b></a>.")),
tags$h2("Disclaimer"),
tags$p(HTML("This app is a <i>beta product</i>, and we are continuously working to improve its accuracy and functionality. <br /> If you encounter any issues or have suggestions for improvement, please contact the developer at: <a href='mailto:allan.souza@helsinki.fi'><b>allan.souza@helsinki.fi</b></a>. <br /> Your feedback is invaluable in helping us enhance this tool.")),
tags$h2("Citation"),
tags$p(HTML("Souza, A. T., Ashraful Alam, S., Rasilo, T., Zacharias, S., & Bäck, J. (2024). eLTER SO Costs web application (v0.1.0). Zenodo. https://doi.org/10.5281/zenodo.10948558")),
tags$a(
href = "https://doi.org/10.5281/zenodo.10948558",
target = "_blank", tags$img(src = "https://zenodo.org/badge/DOI/10.5281/zenodo.10948558.svg")
)
)
)
),
column(
6,
# Creating a card for the image
div(
class = "card rounded-lg",
div(
class = "card-body",
img(
src = "eLTER-SO-costs_landing-page-picture.jpg",
class = "img-fluid", alt = "Responsive image",
style = "max-width: 100%; height: auto; padding:5px; background-color:#F1F1F1;"
),
tags$h6(HTML("<i>Photo: Juho Aalto.</i>"), style = "padding:5px; text-align: right;")
)
)
)
)
# )
),
# Adding the Set up tab ----
tabPanel(
HTML('Set up <i class="fa-solid fa-gears"></i>'),
sidebarLayout(
sidebarPanel(
h2("Select parameters"),
# 2024-04-10: adding the selection button for sites or platforms
radioGroupButtons(
inputId = "site_or_platform",
label = "Site x Platform",
choices = c("Site" = "site", "Platform" = "platform"),
justified = TRUE,
width = "100%",
size = "normal",
checkIcon = list(
yes = icon(class = "fa-solid", "fa-check-to-slot"),
no = icon("square")
),
status = "info"
),
radioGroupButtons(
inputId = "cat",
label = "Category",
choices = c("1", "2"),
justified = TRUE,
width = "100%",
size = "normal",
checkIcon = list(
yes = icon(class = "fa-solid", "fa-check-to-slot"),
no = icon("square")
),
status = "info"
),
# conditional panel for habitat selection
conditionalPanel(
condition = "input.site_or_platform === 'site'",
selectInput("hab", "Site habitat", choices = c(Select = "", unique(dataset$habitat)))
),
# conditional panels for focus sphere selections
conditionalPanel(
condition = "input.site_or_platform === 'site' && input.cat === '1'",
selectInput("sphere1", "Focus sphere #1", choices = unique(dataset$sphere[dataset$sphere != "Sociosphere"])),
selectInput("sphere2", "Focus sphere #2", choices = unique(dataset$sphere[dataset$sphere != "Sociosphere"]))
),
# conditional panels for platforms
conditionalPanel(
condition = "input.site_or_platform === 'platform'",
selectInput("sphere1", "Sphere", choices = "Sociosphere")
),
# The SO Deselection Box as conditional of selecting sites
bsTooltip("codeSelect", "Remove the selection of SOs that are not pertinent in your case.", "right"), # Tooltips
conditionalPanel(
condition = "input.site_or_platform === 'site'",
uiOutput("codeSelect")
),
# help text for the selection boxes
bsTooltip("site_or_platform", "Choose either eLTER site or eLTER platform", "right"), # Tooltips
bsTooltip("cat", "Choose the category of your eLTER site", "right"), # Tooltips
bsTooltip("hab", "Choose the habitat of your eLTER site", "right"), # Tooltips
bsTooltip("sphere1", "Choose the focus sphere. Disabled to category 2 sites.", "right"), # Tooltips
bsTooltip("sphere2", "Choose the focus sphere. Disabled to category 2 sites.", "right"), # Tooltips
# download button
downloadButton("download", "Download",
class = "btn",
icon = icon(class = "fa-regular", name = "fa-circle-down")
),
),
mainPanel(
h2("List of the standard observations needed"),
DTOutput("updatedTable")
)
)
),
# Adding the SO costs tab ----
tabPanel(
HTML('SO costs <i class="fa-solid fa-square-poll-horizontal"></i>'),
fluidPage(
fluidRow(
column(
12,
summaryBox2(
title = "Selected", "Parameters", width = 6,
icon = "fa-solid fa-clipboard-list", style = "info"
),
summaryBox2(
title = "Estimated", "Costs", width = 6,
icon = "fa-solid fa-euro-sign", style = "info"
)
)
),
# SO costs tab: selected parameters -----
fluidRow(
column(
6,
h4(textOutput("selectedPlace"), style = "margin-left: 25px;"),
h4(textOutput("selectedCat"), style = "margin-left: 25px;"),
h4(textOutput("selectedHab"), style = "margin-left: 25px;"),
h4(textOutput("selectedSphere1"), style = "margin-left: 25px;"),
h4(textOutput("selectedSphere2"), style = "margin-left: 25px;")
),
column(
6,
h4(textOutput("totalCostsDisplay"), style = "margin-left: 15px;"),
bsTooltip("totalCostsDisplay", "This value sums the annual costs of replacement costs of equipment, maintenance, sampling, and lab analysis. The value used for the purchase costs takes into account the upgrade interval of the equipment (formula: purchase price / upgrade interval).", "left"), # Tooltips
h4(textOutput("UpfrontPurchaseCosts"), style = "margin-left: 15px;"),
bsTooltip("UpfrontPurchaseCosts", "This value considers the total initial purchase costs and should be used to estimate the costs of establishing an eLTER site from the beginning.", "left"), # Tooltips
h4(textOutput("totalHumanLaborDisplay"), style = "margin-left: 15px;"),
bsTooltip("totalHumanLaborDisplay", "Warning: The labor costs are not included in the estimated costs.", "left"), # Tooltips
p(HTML("<i>Note: The labor costs are not included in the costs. You must calculate the labor costs based on the labor needed at your site (indicated above) and the salary structure in your institution and/or country. Add this number to the estimated costs presented above to have the final cost for your eLTER site.</i>"),
style = "margin-left: 15px; margin-right: 25px; text-align: justify;"
)
)
),
# SO costs tab: table ----
# Double line break as a space
br(),
br(),
fluidRow(
column(
12,
summaryBox2(
title = "Detailed costs", "Upgrading and operating an eLTER site", width = 12,
icon = "fa-solid fa-table-list", style = "info"
),
h4(HTML("This table shows the costs (in €) of the standard observations (SOs) needed to upgrade and operate the eLTER site with the conditions selected at the <b>Set up</b> tab. The total cost is calculated by summing the different costs types (purchase, maintenance, sampling and lab analysis). Additionally, the table shows the human labor needed to operate the eLTER site, expressed as number of days needed to perform all tasks related to the specific SOs per year."),
style = "margin-left: 20px; margin-right: 20px; text-align: justify;"
),
h5(HTML("<br /> <i>Note #1: This table displays only the SOs which have costs associated to it (economic or human labor). <br /> Note #2: The orange bars displayed within each column visually represent the proportion of each SO's cost relative to the maximum cost found in that column. This graphical representation provides an intuitive understanding of how each SO's cost compares to the highest cost observed for that particular cost variable, allowing for quick visual assessment of cost distribution across SOs. <br /> </i>"),
style = "margin-left: 20px; margin-right: 20px; text-align: justify;"
),
# Double line break as a space
br(),
br(),
h5(DTOutput("costTable"),
style = "margin-left: 25px; margin-right: 25px; text-align: justify;"
)
),
fluidRow(
column(10, textInput("fileNameInput",
HTML("Site Name of as displayed in <a href = 'https://www.deims.org' target = '_blank'><b>deims.org</b></a>:"),
value = NA
),
style = "padding-right: 0; margin-left: 35px; margin-right: 45px; width: 100%"
),
column(2,
bsTooltip("downloadCosts", "Before downloading the table, please add the Site Name of your eLTER site in the box above.", "right"), # Tooltips
downloadButton("downloadCosts",
"Download",
style = "margin-right: 25px; text-align: center;",
icon = icon(class = "fa-regular", name = "fa-circle-down"),
),
style = "padding-right: 0; margin-left: 35px; margin-right: 25px;"
)
)
),
# Double line break as a space
br(),
br(),
# SO costs tab: plots ----
# fluidRow(
# column(
# 12,
# summaryBox2(
# title = "Data Visualization", "Insights", width = 12,
# icon = "fa-solid fa-magnifying-glass-chart", style = "info"
# ),
# )
# ),
conditionalPanel(
condition = "input.site_or_platform === 'site'",
fluidRow(
column(
12,
summaryBox2(
title = "Data Visualization", "Insights", width = 12,
icon = "fa-solid fa-magnifying-glass-chart", style = "info"
),
)
),
fluidRow(
column(
6,
h4("Number of standard observations breakdown by sphere",
style = "margin-left: 20px; margin-right: 20px; text-align: center;"
),
h5("This plot illustrates the total number of SOs needed to operate the eLTER site.",
style = "margin-left: 20px; margin-right: 20px; text-align: center;"
),
h5(plotOutput("updatedBarPlot"),
style = "margin-left: 25px; margin-right: 25px;"
),
downloadButton("downloadPlot", "Download",
style = "margin-left: 20px; margin-right: 20px;",
icon = icon(class = "fa-regular", name = "fa-circle-down")
),
# Double line break as a space
br(),
br(),
h4("Annual cost breakdown by type",
style = "margin-left: 20px; margin-right: 20px; text-align: center;"
),
h5("This plot depicts the total costs (by type) needed to operate the eLTER site.",
style = "margin-left: 20px; margin-right: 20px; text-align: center;"
),
h5(plotOutput("typeCostPlot"),
style = "margin-left: 25px; margin-right: 25px;"
),
downloadButton("downloadTypePlotCosts", "Download",
style = "margin-left: 20px; margin-right: 20px;",
icon = icon(class = "fa-regular", name = "fa-circle-down")
),
# Double line break as a space
br(),
br(),
),
column(
6,
h4("Annual labor effort by sphere",
style = "margin-left: 20px; margin-right: 20px; text-align: center;"
),
h5("This plot depicts the total working days by sphere needed to operate the eLTER site.",
style = "margin-left: 20px; margin-right: 20px; text-align: center;"
),
h5(plotOutput("sphereFTECostPlot"),
style = "margin-left: 20px; margin-right: 20px; text-align: center;"
),
downloadButton("downloadHumanCostPlot", "Download",
icon = icon(class = "fa-regular", name = "fa-circle-down")
),
# Double line break as a space
br(),
br(),
h4("Annual cost breakdown by sphere", style = "text-align: center;"),
h5("This plot illustrates how costs are distributed across different spheres to operate the eLTER site.",
style = "margin-left: 20px; margin-right: 20px; text-align: center;"
),
h5(plotOutput("sphereCostPlot"), style = "margin-left: 20px; margin-right: 20px; text-align: center;"),
downloadButton("downloadSpherePlot", "Download",
icon = icon(class = "fa-regular", name = "fa-circle-down")
),
# Double line break as a space
br(),
br(),
)
)
)
)
)
)
)
# Shiny app server ----
server <- function(input, output, session) {
# rendering the table with unique combinations of sphere, code, and standard_observation
output$completeTable <- renderDT({
unique_data <- dataset %>%
distinct(sphere, code, standard_observation)
datatable(unique_data, options = list(pageLength = 100))
})
# Reactive function to compute station requirements
station_result <- reactive({
req(input$cat, input$hab, input$sphere1, input$sphere2, input$site_or_platform)
station_requirements(
dataset = dataset,
cat = input$cat,
hab = input$hab,
spheres = c(input$sphere1, input$sphere2),
site_or_platform = input$site_or_platform
)
})
# Dynamic UI for code deselection
output$codeSelect <- renderUI({
data <- if (input$site_or_platform == "platform") {
# taking one habitat as an example, as for Sociosphere it doesn't matter which one
defaultHabitat <- "forests and other wooded land"
dataset %>%
filter(sphere == "Sociosphere" & habitat == defaultHabitat) %>%
distinct(code, so_short_name) %>%
arrange(so_short_name)
} else {
req(input$cat, input$hab, input$sphere1, input$sphere2)
station_requirements(dataset, input$cat, input$hab, c(input$sphere1, input$sphere2), "site")
}
# Prepare the choices for the SO selection box
choices <- setNames(data$code, data$so_short_name)
# Return the pickerInput for both sites and platforms
pickerInput(
inputId = "selectedCodes",
label = "Optionally, deselect the SOs that are not needed.",
choices = choices,
selected = data$code, # Preset all choices as selected by default
multiple = TRUE,
options = list(`actions-box` = TRUE)
)
})
# Total costs display
output$totalCostsDisplay <- renderText({
cost_data <- cost_calculated_data()
if (nrow(cost_data) > 0) {
total_cost <- sum(cost_data$totalCostYear, na.rm = TRUE)
paste("Total annual cost: €", format(total_cost, big.mark = ",", decimal.mark = ".", digits = 2))
} else {
"No costs calculated yet."
}
})
# For the upfront purchase costs display
output$UpfrontPurchaseCosts <- renderText({
cost_data <- cost_calculated_data()
if (nrow(cost_data) > 0) {
upfront_cost <- sum(cost_data$purchasePrice, na.rm = TRUE)
paste("Upfront purchase cost: €", format(upfront_cost,
big.mark = ",",
decimal.mark = ".", digits = 2
))
} else {
"No costs calculated yet."
}
})
# Reactive expression for the filtered data
filtered_data <- reactive({
# Handling platform selection
if (input$site_or_platform == "platform") {
platform_data <- dataset %>%
filter(sphere == "Sociosphere") %>%
select(-habitat, -category) %>%
distinct()
# Further filter based on selected category
if (input$cat == "1") {
platform_data <- platform_data %>%
mutate(type = "prime") # Assuming 'prime' observations for category 1
} else if (input$cat == "2") {
platform_data <- platform_data %>%
filter(type == "basic") # Assuming 'basic' observations for category 2
}
return(platform_data)
} else {
# Prepare data based on other inputs for sites
req(input$cat, input$hab, input$sphere1, input$sphere2)
site_data <- station_requirements(dataset, input$cat, input$hab, c(input$sphere1, input$sphere2), input$site_or_platform)
site_data <- site_data[site_data$code %in% input$selectedCodes, ]
return(site_data)
}
})
# Render the updated table based on the filtered data
output$updatedTable <- renderDT({
datatable(
filtered_data() %>%
select(-so_short_name) %>%
relocate(type, .after = code) %>%
rename(
"Sphere" = sphere,
"SO code" = code,
"Standard Observation" = standard_observation,
"Method type" = type
),
options = list(pageLength = 100),
rownames = FALSE,
selection = "none" # removing the option to highlight rows on the table
)
})
# Render the updated bar plot based on the filtered data
output$updatedBarPlot <- renderPlot({
req(filtered_data())
filtered_data() %>%
mutate(type = fct_recode(type,
"Prime" = "prime",
"Basic" = "basic"
)) %>%
ggplot(aes(x = type, fill = sphere)) +
geom_bar(position = "dodge", col = "black") +
labs(x = "Type of SO (Standard Observation)", y = "Count", fill = "") +
coord_flip() +
scale_fill_manual(values = sphere_colors) +
theme_bw() +
theme(text = element_text(size = 18), legend.position = "bottom")
})
# DownloadHandler for the table
output$download <- downloadHandler(
filename = function() {
paste0("eLTER-SO-Costs-requirements-table_", Sys.Date(), ".xlsx")
},
content = function(file) {
req(filtered_data())
filtered_data <- filtered_data() %>%
select(-so_short_name) %>%
relocate(type, .after = code) %>%
rename(
"Sphere" = sphere,
"SO code" = code,
"Standard Observation" = standard_observation,
"Method type" = type
)
writexl::write_xlsx(filtered_data, file)
}
)
# DownloadHandler for the bar plot
output$downloadPlot <- downloadHandler(
filename = function() {
paste0("eLTER-SO-Costs-SO-type-plot_", Sys.Date(), ".png")
},
content = function(file) {
req(filtered_data())
g <- filtered_data() %>%
mutate(type = fct_recode(type,
"Prime" = "prime",
"Basic" = "basic"
)) %>%
ggplot(aes(x = type, fill = sphere)) +
geom_bar(position = "dodge", col = "black") +
labs(x = "Type of SO (Standard Observation)", y = "Count", fill = "Sphere") +
coord_flip() +
scale_fill_manual(values = sphere_colors) +
theme_bw() +
theme(
text = element_text(size = 18),
legend.position = "bottom"
)
# Save the ggplot object to file
ggsave(file, plot = g, width = 11, height = 8, dpi = 300)
}
)
# Reactive expression for cost calculation
cost_calculated_data <- reactive({
req(filtered_data())
data <- filtered_data()
# Getting the unique combinations of code and type
unique_combinations <- unique(data[, c("code", "type")])
# Applying the SO_cost function to each unique combination of code and type
cost_data <- do.call(rbind, lapply(seq_len(nrow(unique_combinations)), function(i) {
SO_cost(unique_combinations$code[i], unique_combinations$type[i])
}))
# Join to include so_short_name and possibly sphere or any identifier for Sociosphere
cost_data <- cost_data %>%
left_join(codes_coding, by = "code") %>%
# Assuming `data` has a 'sphere' column or similar to identify Sociosphere. If not, adjust accordingly.
left_join(data[, c("code", "sphere")], by = "code") # Ensure `data` has the 'sphere' column
# Replace NA values with zero in selected columns
cost_data <- cost_data %>%
mutate(
purchasePrice = ifelse(is.na(purchasePrice), 0, purchasePrice),
purchaseCostYear = ifelse(is.na(purchaseCostYear), 0, purchaseCostYear),
maintenanceCostYear = ifelse(is.na(maintenanceCostYear), 0, maintenanceCostYear),
samplingCostYear = ifelse(is.na(samplingCostYear), 0, samplingCostYear),
labAnalysisCostYear = ifelse(is.na(labAnalysisCostYear), 0, labAnalysisCostYear),
totalHumanLabor = ifelse(is.na(totalHumanLabor), 0, totalHumanLabor),
totalCostYear = ifelse(is.na(totalCostYear), 0, totalCostYear)
)
# Conditionally adjust totalHumanLabor for Sociosphere when "Site" is selected
if (input$site_or_platform == "site") {
cost_data <- cost_data %>%
mutate(totalHumanLabor = ifelse(sphere == "Sociosphere", 0, totalHumanLabor))
}
# Remove rows where all cost fields are zero
cost_data <- cost_data %>%
filter(rowSums(select(., purchasePrice, purchaseCostYear, maintenanceCostYear, samplingCostYear, labAnalysisCostYear, totalHumanLabor, totalCostYear)) != 0) %>%
select(-sphere)
return(cost_data)
})
# Render the calculated costs table
output$costTable <- renderDT({
req(cost_calculated_data())
# Get the data from the reactive expression
cost_data <- cost_calculated_data()
# Calculate the sum for each column
summary_row <- cost_data %>%
summarise_if(is.numeric, sum, na.rm = TRUE) %>%
mutate(
so_short_name = "Total",
"type" = NA
) %>%
relocate(so_short_name, .before = purchaseCostYear) %>%
relocate(type, .after = so_short_name)
# Append the summary row to the original data
final_data <- bind_rows(cost_data, summary_row)
if (input$site_or_platform == "platform") {
datatable(
final_data %>%
relocate(totalHumanLabor, .after = totalCostYear) %>%
relocate(so_short_name, .after = code) %>%
select(-code) %>%
arrange(totalCostYear, totalHumanLabor) %>%
select(so_short_name, type, totalHumanLabor) %>%
rename(
"SO short name" = so_short_name,
"Method type" = type,
"Person days (per year)" = totalHumanLabor
),
options = list(
pageLength = 100,
columnDefs = list(
list(width = "500px", targets = c(0)),
list(width = "60px", targets = c(1))
),
autoWidth = TRUE
),
rownames = FALSE,
selection = "none" # removing the option to highlight rows on the table
) %>%
formatStyle(
"Method type",
fontWeight = styleEqual("prime", "bold") # making the font bold for prime method
) %>%
formatStyle("Person days (per year)",
background = styleColorBar(range(final_data$totalHumanLabor), color1),
backgroundSize = "100% 100%",
backgroundRepeat = "no-repeat",
backgroundPosition = "center"
) %>%
# Highlighting the summary row
formatStyle(
"SO short name",
target = "row",
backgroundColor = styleEqual("Total", "#F26522"),
backgroundSize = "100% 100%",
fontWeight = styleEqual("Total", "bold"), # making the font bold for Total
color = styleEqual("Total", "white")
)
} else {
# Render the DataTable with the final data
datatable(
final_data %>%
relocate(totalHumanLabor, .after = totalCostYear) %>%
relocate(so_short_name, .after = code) %>%
select(-code) %>%
arrange(totalCostYear, totalHumanLabor) %>%
rename(
"SO short name" = so_short_name,
"Method type" = type,
"Implementation" = purchasePrice,
"Replacement costs of equipment" = purchaseCostYear,
"Maintenance (per year)" = maintenanceCostYear,
"Sampling (per year)" = samplingCostYear,
"Lab analysis (per year)" = labAnalysisCostYear,