-
Notifications
You must be signed in to change notification settings - Fork 1
/
ui.R
879 lines (821 loc) · 56.4 KB
/
ui.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
# Shiny Caret is an interactive interface for using various machine learning methods from the caret package ----
# (c) Nick Ward (University of Canterbury) 2018-2020 ----
# UI code -------------------------------------------------------------------------------------------------
# header ----
header <- shinydashboardPlus::dashboardHeaderPlus(
title = tagList(span(class = "logo-lg", "Shiny Caret Laboratory"), icon("diamond")),
left_menu = tagList(
shinyWidgets::dropdownButton(inputId = "Submenu",
tags$h3("Parameters"),
checkboxInput(inputId = "Header", label = "Has header", value = TRUE),
textInput(inputId = "DateFormat", label = "Date format", value = "%Y-%m-%d"),
radioButtons(inputId = "Sep", label = "Separator", choices = c(Comma = ",", Semicolon = ";", Tab = "\t"), selected = ","),
radioButtons(inputId = "Quote", label = "Quote character", choices = c(None = "", "Double Quote" = '"', "Single Quote" = "'"), selected = '"'),
radioButtons(inputId = "Decimal", label = "Decimal separator", choices = c("Dot" = ".", "Comma" = ","), selected = '.'),
selectizeInput(inputId = "MissingStrings", label = "Missing placeholders (text)", options = list(create = TRUE), multiple = TRUE, choices = missingStrings, selected = missingStrings),
sliderInput(inputId = "MaxRows", label = "Maximum initial sample size", min = 500, max = 3000, step = 100, value = maxRows),
checkboxInput(inputId = "HideIrrelevant", label = "Hide Irrelevant", value = FALSE ),
sliderInput(inputId = "Continuous", label = "Discont.-Continuous", min = 2, max = 100, step = 1, value = c(15,50)),
checkboxInput(inputId = "ScaleChart", label = "Standarise charts", value = TRUE),
checkboxInput(inputId = "UseYJ", label = "Reshape numeric variables?", value = TRUE),
checkboxInput(inputId = "UseDummy", label = "Convert everything to numeric?", value = TRUE),
selectizeInput(inputId = "SortOrder", label = "Display Order", choices = "", selected = "", multiple = FALSE, options = list(placeholder = 'Ensure an ID column is available')),
sliderInput(inputId = "Cols", label = "Number of variables to chart", min = 5, max = 30, step = 1, value = 20),
sliderInput(inputId = "Multiplier", label = "IQR Multiplier", min = 0, max = 5, step = 0.1, value = 1.5), #debounced up to here
checkboxInput(inputId = "NullNormalise", label = "Make relative chart", value = TRUE),
checkboxInput(inputId = "HideWorse", label = "Hide worse models", value = TRUE),
checkboxInput(inputId = "ExcludeNull", label = "Exclude Null model", value = FALSE),
checkboxInput(inputId = "HideTimings", label = "Hide prediction timing metrics", value = TRUE),
checkboxInput(inputId = "ShowNotches", label = "ShowNotches", value = FALSE),
sliderInput(inputId = "Probability", label = "Probability", min = 68, max = 99, step = 1, value = 95, post = "%"),
sliderInput(inputId = "ObserveMvOut", label = "Rate multivar outliers", min = 0, max = 3, step = 0.1, value = 1),
sliderInput(inputId = "Observe1DOut", label = "Rate univar outliers", min = 0, max = 3, step = 0.1, value = 1),
sliderInput(inputId = "ObserveMiss", label = "Rate missingness", min = 0, max = 3, step = 0.1, value = 1),
checkboxInput(inputId = "MergeWeighting", label = "Scale by observation weighting", value = FALSE),
sliderInput(inputId = "ConsiderCont", label = "Rate continuous levels", min = 0, max = 3, step = 0.1, value = 1),
sliderInput(inputId = "ConsiderNom", label = "Rate nominal levels", min = 0, max = 3, step = 0.1, value = 1),
# sliderInput(inputId = "ConsiderImbal", label = "Rate nominal imbalance", min = 0, max = 3, step = 0.1, value = 1),
sliderInput(inputId = "ConsiderOut", label = "Rate continuous outliers", min = 0, max = 3, step = 0.1, value = 1),
sliderInput(inputId = "ConsiderNZV", label = "Rate near-zero-variance", min = 0, max = 3, step = 0.1, value = 1),
checkboxInput(inputId = "MergeVarImp", label = "Scale by variable importance", value = FALSE),
selectizeInput(inputId = "CorMethod", label = "Correlation method", choices = c("Pearson", "Spearman" , "Kendal", "Distance" , "Predictive Power"), selected = "pearson"),
checkboxInput(inputId = "CorAbs", label = "Use absolute correlation?", value = TRUE),
selectizeInput(inputId = "CorGrouping", label = "Correlation grouping", choices = c("ward.D", "ward.D2", "single", "complete", "average", "mcquitty", "median", "centroid", "none"), selected = "none"),
sliderInput(inputId = "NumTerms", label = "Number of encoded features", min = 1, max = 10, value = 5, step = 1),
#sliderInput(inputId = "MissRatio", label = "Missingness ratio", min = 10, max = 100, value = 50, step = 5),
sliderInput(inputId = "VarThresh", label = "Proportion of explained variance", min = 0.8, max = 0.99, step = 0.01, value = 0.95),
sliderInput(inputId = "CorrThresh", label = "Correlation threshold", min = 0.8, max = 0.99, step = 0.01, value = 0.95),
sliderInput(inputId = "NumComp", label = "Number of latent components", min = 2, max = 50, step = 1, value = 15),
sliderInput(inputId = "PolyDegree", label = "Polynomial degree", min = 1, max = 3, value = 2, step = 1),
checkboxInput(inputId = "OneHot", label = "One-Hot style encoding", value = FALSE),
sliderInput(inputId = "OtherThreshold", label = "Threshhold for \"Other\" level", min = 1, max = 100, step = 1, value = 5, post = "%"),
sliderInput(inputId = "Eps", label = "Neighbourhood radius", min = 0.0, max = 1, value = 0),
sliderInput(inputId = "MinPts", label = "Minimum number of points", min = 2, max = 30, step = 1, value = 4),
sliderInput(inputId = "Klof", label = "Size of neighbourhood", min = 2, max = 30, step = 1, value = 4),
checkboxInput(inputId = "Parallel", label = "Use parallel processing", value = TRUE),
checkboxInput(inputId = "NullModel", label = "Add a null model", value = TRUE),
selectizeInput(inputId = "Positive", label = "Positive class", choices = c(), selected = NULL),
selectizeInput(inputId = "DimReductType", label = "Chart-based dimensional reduction", choices = c(), selected = NULL),
circle = FALSE, status = "primary", icon = icon("wrench"), width = "600px"
),
actionLink(inputId = "Save", label = "", icon = icon("save")),
actionLink(inputId = "Help", label = "", icon = icon("question-circle")),
shinyBS::bsTooltip(id = "Help", title = "Activate/Deactivate tool-tips"),
actionLink(inputId = "Next", label = "", icon = icon("step-forward"))
#actionLink(inputId = "HelpExport", label = "", icon = icon("file-export"))
)
)
# sidebar ----
sidebar <- shinydashboard::dashboardSidebar(
sidebarMenu(id = "Navbar",
menuItem(text = "Raw Data", icon = icon("table"), startExpanded = TRUE, expandedName = "RawData",
menuSubItem(text = "Load", tabName = "DataLoad", selected = TRUE, icon = icon("file-download")),
menuSubItem(text = "Roles", tabName = "DataColumns", icon = icon("user-secret")),
menuSubItem(text = "Chart", tabName = "RolesChart", icon = icon("chart-area")),
menuSubItem(text = "Summary", tabName = "DataSummary", icon = icon("newspaper")),
menuSubItem(text = "Table", tabName = "DataTable", icon = icon("table"))
),
menuItem(text = "Exploration", icon = icon("search"), expandedName = "Exploration",
menuItem(text = "Homogeneity", icon = icon("chess-board"),
menuSubItem(text = "Sequence chart", tabName = "SequenceChart"),
menuSubItem(text = "Missing pattern", tabName = "MissingPattern"),
menuSubItem(text = "Outliers", tabName = "Outliers"),
menuSubItem(text = "Continuity", tabName = "Continuity")
),
menuItem(text = "Novelties", icon = icon("eye"),
menuSubItem(text = "Observations", tabName = "Observations"),
menuSubItem(text = "Variables", tabName = "Variables"),
menuSubItem(text = "Box plots", tabName = "Boxplots"),
menuSubItem(text = "Bag plots", tabName = "Bagplot")
),
menuItem(text = "Charts", icon = icon("bar-chart-o"),
menuSubItem(text = "Correlation", tabName = "Correlation"),
menuSubItem(text = "Pairs", tabName = "Pairs")
),
menuItem(text = "Diversity", icon = icon("fingerprint"),
menuSubItem(text = "DistPlot", tabName = "DistPlot"),
menuSubItem(text = "Clusters", tabName = "Clusters")
),
menuItem(text = "Missingness", icon = icon("braille"),
menuSubItem(text = "Summary", tabName = "MissSummary"),
menuSubItem(text = "Correlation", tabName = "MissCorrelation"),
menuSubItem(text = "Pattern", tabName = "MissPattern"),
menuSubItem(text = "Explanation", tabName = "MissExplain"),
menuSubItem(text = "Variables", tabName = "MissVariables")
)
),
menuItem(text = "Sampling", icon = icon("random"), tabName = "Sampling",
menuSubItem(text = "Test Cases", tabName = "TestCases", icon = icon("vials")),
menuSubItem(text = "Resampling", tabName = "Resampling", icon = icon("redo"))
),
menuItem(text = "Data Processing", icon = icon("cogs"), expandedName = "DataProcessing",
menuSubItem(text = "Steps", tabName = "ProcessingSteps", icon = icon("shoe-prints")),
menuSubItem(text = "Recipe", tabName = "ProcessingRecipe", icon = icon("list-ol")),
menuSubItem(text = "Summary", tabName = "ProcessingSummary", icon = icon("clipboard-list")),
menuSubItem(text = "Processed", tabName = "ProcessingTable", icon = icon("table"))
),
menuItem(text = "Methods", tabName = "Methods", icon = icon("check-double"),
menuSubItem(text = "Criteria", tabName = "MethodCriteria", icon = icon("sliders")),
menuSubItem(text = "Map", tabName = "MethodSimilarity", icon = icon("map-marked-alt")),
menuSubItem(text = "List", tabName = "MethodTable", icon = icon("check-square")),
menuSubItem(text = "Summary", tabName = "MethodSummary", icon = icon("table"))
),
menuItem(text = "Train Models", tabName = "Train", icon = icon("dumbbell")),
menuItem(text = "Model Selection", tabName = "Select", icon = icon("balance-scale"),
menuSubItem(text = "Metrics", tabName = "ModelMetrics", icon = icon("tachometer-alt")),
menuSubItem(text = "Correlation", tabName = "ModelCorrelation", icon = icon("th")),
menuSubItem(text = "Hierarchy", tabName = "ModelHierarchy", icon = icon("sitemap")),
menuSubItem(text = "Selection", tabName = "ModelSelection", icon = icon("clipboard-check"))
),
menuItem(text = "Graduate", tabName = "Graduate", icon = icon("graduation-cap"),
menuSubItem(text = "Fit", tabName = "GradFit1", icon = icon("th")),
menuSubItem(text = "Summary", tabName = "GradSummary", icon = icon("tachometer-alt")),
menuSubItem(text = "Fit", tabName = "GradFit2", icon = icon("th")),
menuSubItem(text = "Hyperparameters", tabName = "GradParameters", icon = icon("sitemap")),
menuSubItem(text = "Residuals", tabName = "GradResiduals", icon = icon("clipboard-check"))
),
menuItem(text = "Analyse", tabName = "Analyse", icon = icon("stethoscope"))
))
# body ----
body <- shinydashboard::dashboardBody(
tagList(
tags$style(type = "text/css", value = "#value{ width: 200px; word-break: break-all; white-space: normal;}"),
ifelse(DEV, shinythemes::themeSelector(), ""),
chooseSliderSkin("Modern", color = NULL),
# The CSS below configures the tabBox colour scheme
tags$style("
.nav-tabs { background-color: lightblue }
.sidebar-menu .treeview-menu { padding: 0 0 0 30px; }
")
),
shinyjs::useShinyjs(),
shinyalert::useShinyalert(),
tabItems(
tabItem(tabName = "DataLoad",
tabBox(id = "DataSource", title = "Load", width = 12, side = "right", height = "300px",
tabPanel(title = "Server Data File", icon = icon("file-excel"),
fluidRow(
column(
width = 6, offset = 3,
shinyFiles::shinyFilesButton(id = "ServerFile", label = "Choose a server file", title = "Choose a data file that resides where R is running", multiple = FALSE),
disabled(shiny::textInput(inputId = "ServerFileName", label = "", value = ""))
)
)
),
tabPanel(title = "Local Data File", icon = icon("file-alt"),
fluidRow(
column(
width = 4, offset = 3,
tags$div(id = "LocalFileDiv", # the div takes a tool tip
fileInput(inputId = "LocalFile", label = "Choose a local file", multiple = FALSE),
hidden(shiny::textInput(inputId = "LocalFileName", label = "", value = ""))
)
)
)
),
tabPanel(title = "Remote Data Resource", icon = icon("cloud-download-alt"),
fluidRow(
column(
width = 10, offset = 1,
textInput(inputId = "URL", label = "URL", value = "http://"))
)
),
tabPanel(title = "Package Dataset", icon = icon("r-project"),
fluidRow(
column(
width = 10, offset = 1,
selectizeInput(inputId = "Package", label = "Choose an available package containing data", choices = c(.packages(all.available = TRUE)), selected = "caret"),
selectizeInput(inputId = "DataSet", label = "Choose a data set", choices = c(), selected = NULL, width = "100%")
)
)
)
),
fluidRow(
column(
width = 9, offset = 3,
box(title = "Project", background = DATAColour,
selectizeInput(inputId = "Project", label = "Input a project name", choices = c(), multiple = FALSE, selected = NULL, options = list(create = TRUE)),
actionButton(inputId = "LoadProjectReq", label = "Load Project", icon = icon("play"))
)
)
),
fluidRow(
infoBoxOutput(outputId = "ObservationCount", width = 6),
infoBoxOutput(outputId = "VariableCount", width = 6)
)
),
tabItem(tabName = "DataColumns",
box(title = "Data Roles", solidHeader = TRUE, background = DATAColour, width = 12, collapsible = TRUE,
fluidRow(
column(
width = 2,
selectizeInput(inputId = "Target", label = "Outcome", choices = "", selected = "", multiple = FALSE),
plotOutput("ClassPieChart", height = "50px")
),
column(
width = 2,
selectizeInput(inputId = "ID", label = "Identifier", choices = c(), selected = c(), options = list(placeholder = 'Assign an ID column')),
checkboxInput(inputId = "AddIds", label = "Convert row names to a variable", value = FALSE)
),
column(
width = 2,
selectizeInput(inputId = "HideCol", label = "Unused", choices = "", selected = "", multiple = TRUE)
),
column(
width = 2,
selectizeInput(inputId = "PreSplit", label = "Train-Test Partition", choices = "", selected = "", multiple = FALSE),
plotOutput("SplitPieChart", height = "50px")
),
column(
width = 2,
selectizeInput(inputId = "Weights", label = "Observation Weighting", choices = "", selected = "", multiple = FALSE),
checkboxInput(inputId = "AddWeights", label = "Auto Balance", value = FALSE),
selectizeInput(inputId = "BalanceFactors", label = "Balance Factors", choices = "", selected = "", multiple = TRUE)
),
column(
width = 2,
selectizeInput(inputId = "Groups", label = "Observation Stratifier", choices = c(), selected = c(), multiple = FALSE),
plotOutput(outputId = "GroupPieChart", height = "50px")
)
)
),
fluidRow(
infoBoxOutput(outputId = "StringCount", width = 4),
infoBoxOutput(outputId = "NZVCount", width = 4),
infoBoxOutput(outputId = "ContCount", width = 4)
),
fluidRow(
infoBoxOutput(outputId = "MissVar", width = 4),
infoBoxOutput(outputId = "MissObs", width = 4),
infoBoxOutput(outputId = "MissTarget", width = 4)
),
fluidRow(
infoBoxOutput(outputId = "TargetCheck", width = 6),
infoBoxOutput(outputId = "RatioObs", width = 6)
)
),
tabItem(tabName = "RolesChart",
box(title = "Data Roles", solidHeader = TRUE, collapsible = FALSE, width = 12, background = DATAColour,
withSpinner(ui_element = plotlyOutput(outputId = "RoleTypeChart", height = FullHeight),
color = SPINNER_COLOUR, type = SPINNER_TYPE, size = SPINNER_SIZE
)
)
),
tabItem(tabName = "DataSummary",
box(title = "Raw Data Summary", solidHeader = TRUE, collapsible = FALSE, width = 12, background = DATAColour,
withSpinner(ui_element = htmlOutput(outputId = "DataSummary", style = "color:black"),
color = SPINNER_COLOUR, type = SPINNER_TYPE, size = SPINNER_SIZE
)
)
),
tabItem(tabName = "DataTable",
box(title = "Raw Data Table", solidHeader = TRUE, collapsible = FALSE, width = 12, background = DATAColour,
withSpinner(ui_element = DT::dataTableOutput(outputId = "Data"),
color = SPINNER_COLOUR, type = SPINNER_TYPE, size = SPINNER_SIZE
)
)
),
tabItem(tabName = "SequenceChart",
box(title = "Sequence chart", solidHeader = TRUE, background = EDAColour, width = 12,
withSpinner(ui_element = plotOutput(outputId = "SequenceChart1", height = FullHeight),
color = SPINNER_COLOUR, type = SPINNER_TYPE, size = SPINNER_SIZE
),
withSpinner(ui_element = plotOutput(outputId = "SequenceChart2", height = FullHeight),
color = SPINNER_COLOUR, type = SPINNER_TYPE, size = SPINNER_SIZE
),
withSpinner(ui_element = plotOutput(outputId = "SequenceChart3", height = FullHeight),
color = SPINNER_COLOUR, type = SPINNER_TYPE, size = SPINNER_SIZE
),
withSpinner(ui_element = plotOutput(outputId = "SequenceChart4", height = FullHeight),
color = SPINNER_COLOUR, type = SPINNER_TYPE, size = SPINNER_SIZE
),
withSpinner(ui_element = plotOutput(outputId = "SequenceChart5", height = FullHeight),
color = SPINNER_COLOUR, type = SPINNER_TYPE, size = SPINNER_SIZE
)
)
),
tabItem(tabName = "MissingPattern",
box(title = "Missing pattern", solidHeader = TRUE, background = EDAColour, width = 12,
withSpinner(ui_element = plotlyOutput(outputId = "MissingChart1", height = FullHeight),
color = SPINNER_COLOUR, type = SPINNER_TYPE, size = SPINNER_SIZE
)
)
),
tabItem(tabName = "Outliers",
box(title = "Outliers", solidHeader = TRUE, background = EDAColour, width = 12,
withSpinner(ui_element = plotlyOutput(outputId = "OutlierPlots", height = FullHeight),
color = SPINNER_COLOUR, type = SPINNER_TYPE, size = SPINNER_SIZE
)
)
),
tabItem(tabName = "Continuity",
box(title = "Continuity", solidHeader = TRUE, background = EDAColour, width = 12,
withSpinner(ui_element = plotlyOutput(outputId = "RisingChart", height = FullHeight),
color = SPINNER_COLOUR, type = SPINNER_TYPE, size = SPINNER_SIZE
)
)
),
tabItem(tabName = "Observations",
box(title = "Observations", solidHeader = TRUE, background = EDAColour, width = 12,
withSpinner(ui_element = plotlyOutput(outputId = "NoveltyObservations", height = FullHeight),
color = SPINNER_COLOUR, type = SPINNER_TYPE, size = SPINNER_SIZE
)
)
),
tabItem(tabName = "Variables",
box(title = "Variables", solidHeader = TRUE, background = EDAColour, width = 12,
withSpinner(ui_element = plotlyOutput(outputId = "NoveltyColumns", height = FullHeight),
color = SPINNER_COLOUR, type = SPINNER_TYPE, size = SPINNER_SIZE
)
)
),
tabItem(tabName = "Boxplots", width = 12,
box(title = "Box Plots", solidHeader = TRUE, background = EDAColour, width = 12,
withSpinner(ui_element = plotOutput(outputId = "NumericNovelties", height = FullHeight),
color = SPINNER_COLOUR, type = SPINNER_TYPE, size = SPINNER_SIZE
)
)
),
tabItem(tabName = "Bagplot",
box(title = "Bag Plot", solidHeader = TRUE, background = EDAColour, width = 12,
fluidRow(
column(width = 10,
plotOutput(outputId = "MultiDimNovelties", height = FullHeight),
box(title = "Parameters", collapsible = TRUE, width = 12, solidHeader = TRUE, background = EDAColour, collapsed = TRUE,
fluidRow(
column(width = 6,
selectizeInput(inputId = "DimReductType", label = "Style of Dimensional Reduction", choices = c(""), selected = "")
),
column(width = 6,
selectizeInput(inputId = "DistanceMethod", label = "Distance metric", choices = c("euclidean","manhattan"), selected = "euclidean")
)
)
)
),
column(width = 2,
box(title = "Outliers", width = 12, collapsible = TRUE, solidHeader = TRUE, background = EDAColour,
textOutput(outputId = "MultiDimNovText"),
DT::dataTableOutput(outputId = "MultiDimNovIDs", height = "500px")
)
)
)
)
),
tabItem(tabName = "Correlation",
box(title = "Variable Correlation", solidHeader = TRUE, background = EDAColour, width = 12,
withSpinner(ui_element = plotlyOutput(outputId = "Correlation", height = FullHeight),
color = SPINNER_COLOUR, type = SPINNER_TYPE, size = SPINNER_SIZE
)
)
),
tabItem(tabName = "Pairs",
box(title = "Pairs", solidHeader = TRUE, background = EDAColour, width = 12,
withSpinner(ui_element = plotOutput(outputId = "Pairs", height = FullHeight),
color = SPINNER_COLOUR, type = SPINNER_TYPE, size = SPINNER_SIZE
)
)
),
tabItem(tabName = "DistPlot",
box(title = "Observation spread", solidHeader = TRUE, background = EDAColour, width = 12,
withSpinner(ui_element = plotOutput("KnnDistPlot", height = FullHeight),
color = SPINNER_COLOUR, type = SPINNER_TYPE, size = SPINNER_SIZE
)
)
),
tabItem(tabName = "Clusters",
panel(
box(title = "Clusters", solidHeader = TRUE, background = EDAColour, width = 12, collapsible = TRUE,
withSpinner(ui_element = plotOutput("ClusterPlot", height = FullHeight),
color = SPINNER_COLOUR, type = SPINNER_TYPE, size = SPINNER_SIZE
)
),
valueBoxOutput(outputId = "ClusterCount", width = 6),
valueBoxOutput(outputId = "Outliers", width = 6),
box(title = "Cluster outliers", solidHeader = TRUE, background = EDAColour, width = 12, collapsible = TRUE, collapsed = TRUE,
verbatimTextOutput(outputId = "ClusterPrint")
)
)
),
tabItem(tabName = "MissSummary", width = 12,
panel(heading = "Summary",
infoBox(title = "Missing Observations", width = 4, color = EDAColour, fill = TRUE, icon = icon("align-justify"), textOutput(outputId = "MissObservations")),
infoBox(title = "Missing Variables", width = 4, color = EDAColour, fill = TRUE, icon = icon("columns"), textOutput(outputId = "MissVariables")),
infoBox(title = "Missing Values", width = 4, color = EDAColour, fill = TRUE, icon = icon("border-all"), textOutput(outputId = "MissValues")),
box(title = "Variables", solidHeader = TRUE, background = EDAColour, width = 6,
sliderInput(inputId = "MissVarThreshold", label = "Missing variable threshold", min = 0, max = 100, step = 1, value = 50, post = "%", width = "100%")
),
box(title = "Observations", solidHeader = TRUE, background = EDAColour, width = 6,
sliderInput(inputId = "MissObsThreshold", label = "Missing observation threshold", min = 0, max = 100, step = 1, value = 50, post = "%", width = "100%")
),
infoBox(title = "Heavily Missing Variables", width = 6, color = EDAColour, fill = TRUE, icon = icon("columns"), textOutput(outputId = "HeavyMissVar")),
infoBox(title = "Heavily Missing Observations", width = 6, color = EDAColour, fill = TRUE, icon = icon("align-justify"), textOutput(outputId = "HeavyMissObs"))
)
),
tabItem(tabName = "MissCorrelation",
box(title = "Missingness correlation", solidHeader = TRUE, background = EDAColour, width = 12,
withSpinner(ui_element = plotlyOutput(outputId = "MissCorr", height = FullHeight, width = FullHeight), # make square
color = SPINNER_COLOUR, type = SPINNER_TYPE, size = SPINNER_SIZE
)
)
),
tabItem(tabName = "MissPattern",
box(title = "Missingness pattern", solidHeader = TRUE, background = EDAColour, width = 12,
withSpinner(ui_element = plotOutput(outputId = "MissingChart2", height = FullHeight),
color = SPINNER_COLOUR, type = SPINNER_TYPE, size = SPINNER_SIZE
)
)
),
tabItem(tabName = "MissExplain", width = 12,
box(title = "Missingness explanation", solidHeader = TRUE, background = EDAColour, width = 12,
withSpinner(ui_element = plotOutput(outputId = "MissingnessInformationPlot", height = FullHeight),
color = SPINNER_COLOUR, type = SPINNER_TYPE, size = SPINNER_SIZE
)
)
),
tabItem(tabName = "MissVariables", width = 12,
box(title = "Missingness variables", solidHeader = TRUE, background = EDAColour, width = 12,
withSpinner(ui_element = plotlyOutput(outputId = "MissingnessVariables", height = FullHeight),
color = SPINNER_COLOUR, type = SPINNER_TYPE, size = SPINNER_SIZE
)
)
),
tabItem(tabName = "TestCases",
box(title = "Partitioning", solidHeader = TRUE, width = 6, background = SAMPColour,
fluidRow(
column(width = 6,
sliderInput(inputId = "Ratio", label = "Partition ratio", min = 0.3, max = 1.0, value = 0.8, step = 0.01)
),
column(width = 6,
textOutput(outputId = "Splitter"),
actionButton(inputId = "Evaluate", label = "Evaluate Fairness", icon = icon("play"))
)
)
),
infoBox(title = "Train Count", width = 4, icon = icon("plus"), color = SAMPColour, fill = TRUE,
value = textOutput(outputId = "SplitCountTrain")
),
infoBox(title = "Test Count", width = 4, icon = icon("minus"), color = SAMPColour, fill = TRUE,
value = textOutput(outputId = "SplitCountTest")
),
infoBoxOutput(outputId = "TestUncertainty", width = 8),
infoBoxOutput(outputId = "SplitTest", width = 10)
),
tabItem(tabName = "Resampling",
box(title = "Train Resampling", solidHeader = TRUE, width = 6, background = SAMPColour,
fluidRow(
column(width = 6,
# hiding methods "oob" & "timeslice"
selectizeInput(inputId = "Method", label = "Method", choices = c("boot", "boot632", "optimism_boot", "boot_all", "cv", "repeatedcv", "LOOCV", "adaptive_cv", "adaptive_boot", "LGOCV", "adaptive_LGOCV"), selected = "boot")
),
column(width = 6,
sliderInput(inputId = "Number", label = "Number of folds or iterations", min = 1, max = 30, step = 1, value = 10, ticks = FALSE),
sliderInput(inputId = "Repeats", label = "Number of CV repeats", min = 1, max = 10, step = 1, value = 1) #For repeated k-fold cross-validation only
)
)
),
infoBoxOutput(outputId = "NearZeroVarTest", width = 8),
box(title = "Resampling map", width = 12, collapsible = TRUE, background = SAMPColour, solidHeader = TRUE,
withSpinner(ui_element = plotlyOutput(outputId = "ResampleChart", height = FullHeight),
color = SPINNER_COLOUR, type = SPINNER_TYPE, size = SPINNER_SIZE
)
)
),
tabItem(tabName = "ProcessingSteps",
box(title = tagList("Processing Steps", actionButton(inputId = "Restart", label = "Restart", icon = icon("repeat"))),
width = 12,
collapsible = TRUE,
fluidRow(
column(
width = 3,
box(title = "Missing values", width = 12, solidHeader = TRUE, background = PROCColour, collapsible = TRUE,
checkboxInput(inputId = "MissingVar", label = "Remove heavily missing variables?", value = FALSE),
checkboxInput(inputId = "MissingObs", label = "Remove heavily missing observations?", value = FALSE),
checkboxInput(inputId = "Unknown", label = "Assign missing nominal levels to Unknown?", value = FALSE),
checkboxInput(inputId = "Shadow", label = "Add shadow variables?", value = FALSE),
selectizeInput(inputId = "Impute", label = "Missing value processing?", choices = missChoices, selected = "none")
)
),
column(
width = 3,
box(title = "Non-numeric", width = 12, solidHeader = TRUE, background = PROCColour, collapsible = TRUE,
selectizeInput(inputId = "Balance", label = "Balance outcome processing?", choices = c("None" = "none", "Up" = "up", "Down" = "down", "Up-Down" = "up-down"), selected = "none"),
checkboxInput(inputId = "text", label = "Expand text into text features?", value = FALSE),
selectizeInput(inputId = "String", label = "High Cardinality processing?",
choices = c("None" = "none", "Omit" = "omit", "Weight-of-evidence encoding" = "woe", "Mean encoding" = "mean", "Hash encoding" = "hash", "Embedded encoding" = "embed", "Binary encoding" = "binary"),
selected = "none"),
checkboxInput(inputId = "Other", label = "Pool rare levels?", value = FALSE),
selectizeInput(inputId = "DateFeatures", label = "Expand dates into features", multiple = TRUE,
choices = c("day of week" = "dow", "day of year" = "doy", "week" = "week", "month" = "month", "decimal", "quarter" = "quarter", "semester", "year"),
selected = c()),
checkboxInput(inputId = "Cyclic", label = "Treat dow, month as cyclic?", value = FALSE),
checkboxInput(inputId = "Convert", label = "Convert nominal predictors to binary?", value = FALSE)
)
),
column(
width = 3,
box(title = "Numeric", width = 12, solidHeader = TRUE, background = PROCColour, collapsible = TRUE,
checkboxInput(inputId = "YJ", label = "Reshape numeric distributions?", value = FALSE),
checkboxInput(inputId = "Center", label = "Apply mean centering?", value = FALSE),
checkboxInput(inputId = "Scale", label = "Apply SD scaling?", value = FALSE),
selectizeInput(inputId = "DimReduce", label = "Dimensionally reduce?", choices = c("None" = "none", "Correlation" = "corr", "PCA" = "pca", "ICA" = "ica", "IsoMap" = "isomap", "PLS (sup)" = "pls", "UMAP (sup)" = "umap", "kPLS" = "kpls"), selected = "none"),
checkboxInput(inputId = "Poly", label = "Apply polynomial expansion?", value = FALSE)
)
),
column(
width = 3,
box(title = "Late steps", width = 12, solidHeader = TRUE, background = PROCColour, collapsible = TRUE,
checkboxInput(inputId = "Clusters", label = "Add clusters?", value = FALSE),
checkboxInput(inputId = "Centers", label = "Add Distances to class centers?", value = FALSE),
checkboxInput(inputId = "FeatureSelection", label = "Apply Feature Selection?", value = FALSE),
selectizeInput(inputId = "Variance", label = "Variance processing?", choices = c("None" = "none", "Zero Variance" = "zv", "Near-zero variance" = "nzv"), selected = "none"),
checkboxInput(inputId = "LinComb", label = "Remove variables to make linearly independent?", value = FALSE)
)
)
)
),
box(title = "Issues to resolve", width = 12, collapsible = TRUE,
infoBoxOutput(outputId = "MissPresent", width = 4),
infoBoxOutput(outputId = "NominalPresent", width = 4),
infoBoxOutput(outputId = "StringPresent", width = 4),
infoBoxOutput(outputId = "NZVPresent", width = 4),
#infoBoxOutput(outputId = "VariablesPresent", width = 4),
infoBoxOutput(outputId = "VariablesRatio", width = 4),
infoBoxOutput(outputId = "LinCombPresent", width = 4),
infoBoxOutput(outputId = "Imbalanced", width = 4),
infoBoxOutput(outputId = "RecipeErr", width = 8)
)
),
tabItem(tabName = "ProcessingRecipe",
box(title = "Recipe Ingredients", width = 12, solidHeader = TRUE, background = PROCColour, collapsible = TRUE,
verbatimTextOutput(outputId = "PreProcSummary")
)
),
tabItem(tabName = "ProcessingSummary",
box(title = "Processed Data Summary", solidHeader = TRUE, collapsible = TRUE, width = 12, background = PROCColour,
withSpinner(ui_element = htmlOutput(outputId = "ProcessedDataSummary", style = "color:black"),
color = SPINNER_COLOUR, type = SPINNER_TYPE, size = SPINNER_SIZE
)
)
),
tabItem(tabName = "ProcessingTable",
box(title = "Processed Train-Data Table", solidHeader = TRUE, collapsible = FALSE, width = 12, background = PROCColour,
withSpinner(ui_element = DT::dataTableOutput("PreProcTable"),
color = SPINNER_COLOUR, type = SPINNER_TYPE, size = SPINNER_SIZE
)
)
),
tabItem(tabName = "MethodCriteria",
box(title = "Criteria by which to restrict the choice of methods", width = 12, background = METHColour,
box(title = "Basic criteria", width = 3, background = METHColour,
checkboxInput(inputId = "AvailPackages", label = "Available packages only", value = TRUE),
actionButton(inputId = "RefreshModels", label = "Reset", icon = icon("refresh"))
),
box(title = "Implied criteria", width = 6, background = METHColour,
fluidRow(
radioButtons(inputId = "ProbType", label = "Problem Type:", choices = c("Any", "Classification","Regression"), selected = "Any", inline = TRUE),
radioButtons(inputId = "PredictorsTag", label = "Special predictors", choices = c("All Nominal", "All Binary", "Mixture"), selected = "Mixture", inline = TRUE),
column(width = 6,
checkboxInput(inputId = "TwoClassTag", label = "Two class outcome?", value = TRUE),
checkboxInput(inputId = "WeightingTag", label = "Weightings present?", value = FALSE),
checkboxInput(inputId = "OutliersTag", label = "Outliers still present?", value = FALSE),
checkboxInput(inputId = "MissingTag", label = "Missing data still present?", value = FALSE)
),
column(width = 6,
checkboxInput(inputId = "ProbabilityTag", label = "Class probabilities required?", value = FALSE),
checkboxInput(inputId = "NominalsTag", label = "Nominal data still present?", value = FALSE),
checkboxInput(inputId = "NZVTag", label = "Near-Zero Variance still present?", value = FALSE),
checkboxInput(inputId = "LinCombTag", label = "Linear Combinations still present?", value = FALSE),
checkboxInput(inputId = "RatioTag", label = "Excessive variables cf observations?", value = FALSE)
)
)
),
box(title = "Subjective criteria", width = 3, background = METHColour,
selectizeInput(inputId = "IncFeatures", label = "Essential characteristics", choices = c(), multiple = TRUE, selected = NULL),
selectizeInput(inputId = "ExcFeatures", label = "Excluded characteristics", choices = c(), multiple = TRUE, selected = NULL)
)
),
infoBoxOutput(outputId = "Methods")
),
tabItem(tabName = "MethodSimilarity",
box(title = "Map of methods that meet the criteria", width = 12, background = METHColour, solidHeader = TRUE,
withSpinner(ui_element = plotOutput(outputId = "D2plot", height = "600px", hover = "MapHover", dblclick = "MapDblClick"),
color = SPINNER_COLOUR, type = SPINNER_TYPE, size = SPINNER_SIZE
),
uiOutput("MapInfo"),
box(title = "", width = 11, background = METHColour, solidHeader = TRUE,
actionButton(inputId = "Suggest", label = "Suggest dissimilar", icon = icon(name = "plus-circle"))
)
)
),
tabItem(tabName = "MethodTable",
box(title = "List of Methods that meet the criteria", width = 12, background = METHColour, solidHeader = TRUE,
actionButton(inputId = "ChooseAll", label = "All", icon = icon("align-justify")),
DT::dataTableOutput(outputId = "MethodsTable")
)
),
tabItem(tabName = "MethodSummary",
box(title = "Chosen methods", width = 6, solidHeader = TRUE, background = METHColour,
actionButton(inputId = "Install", label = "Install missing packages", icon = icon(name = "download")),
tableOutput(outputId = "SelectedMethodsTable")
),
infoBoxOutput(outputId = "SelWarn")
),
tabItem(tabName = "Train",
# "oob" (only for random forest, bagged trees, bagged earth, bagged flexible discriminant analysis, or conditional tree forest models)
# "timeslice" left out because it needs lots of extra parameters
fluidRow(
column(width = 2,
box(title = "Actions", width = 12, background = TRAINColour, collapsible = TRUE,
actionButton(inputId = "Reset", label = "Reset", icon = icon("fast-backward"), width = "100px"),
hr(),
actionButton(inputId = "Train", label = "Train", icon = icon("play"), width = "100px"),
hr(),
shinyBS::bsButton(inputId = "Pause", label = "Pause", icon = icon("pause"), type = "toggle", value = FALSE, width = "100px")
),
box(title = "Select a model", background = TRAINColour, width = 12,
radioButtons(inputId = "ResultsFor", label = "", choices = "", selected = ""),
actionButton(inputId = "UndoModel", label = "Untrain", icon = icon("undo-alt"), width = "100px")
)
),
column(width = 10,
box(title = "Optimisation", width = 12, background = TRAINColour, collapsible = TRUE,
fluidRow(
column(width = 3, selectizeInput(inputId = "HypMetric", label = "Performance metric", regChoices, selected = "RMSE", width = "140px")),
column(width = 3, selectizeInput(inputId = "SelectionFunc", label = "Optimum selection", choices = selectionChoices, selected = selectionChoices[1], width = "140px")),
column(width = 3, selectizeInput(inputId = "Search", label = "Search type", choices = searchChoices, selected = searchChoices[1], width = "140px")),
column(width = 3, sliderInput(inputId = "TuneLength", label = "Tune attempts", min = 1, max = 15, step = 1, value = 4, ticks = FALSE, width = "140px"))
)
),
shinydashboard::tabBox(
id = "TrainTab",
side = "left",
width = 12,
tabPanel(title = "Summary",
icon = icon("file"),
panel(
box(title = "Errors and Warnings", width = 12, verbatimTextOutput(outputId = "TrainWarn"), solidHeader = TRUE, background = TRAINColour, collapsible = TRUE),
box(title = "Resampled performance", width = 12, div(style = 'overflow-x: auto', tableOutput(outputId = "Resamples")), solidHeader = TRUE, background = TRAINColour, collapsible = TRUE),
box(title = "Optimum hyper-parameters", width = 6, tableOutput(outputId = "Hyperparams"), solidHeader = TRUE, background = TRAINColour, collapsible = TRUE),
box(title = "Method characteristics", width = 6, tableOutput(outputId = "Characteristics"), solidHeader = TRUE, background = TRAINColour, collapsible = TRUE)
)
),
tabPanel(title = "Fit",
icon = icon("tachometer-alt"),
panel(
box(title = "Model performance", width = 6, solidHeader = TRUE, background = TRAINColour,
withSpinner(ui_element = plotOutput(outputId = "ModelTrainPlot", height = "400px"),
color = SPINNER_COLOUR, type = SPINNER_TYPE, size = SPINNER_SIZE
)
),
box(id = "ROCPlot", title = "Response Operating Characteristics", width = 6, solidHeader = TRUE, background = TRAINColour,
withSpinner(ui_element = plotOutput(outputId = "RocCurvePlot", height = "400px"),
color = SPINNER_COLOUR, type = SPINNER_TYPE, size = SPINNER_SIZE
)
),
box(title = "Classification Statistics", width = 6, verbatimTextOutput(outputId = "ClassStats"), solidHeader = TRUE, background = TRAINColour, collapsible = TRUE)
)
),
tabPanel(title = "Tuning",
icon = icon("chart-line"),
withSpinner(ui_element = plotOutput(outputId = "Optimize", height = "400px"),
color = SPINNER_COLOUR, type = SPINNER_TYPE, size = SPINNER_SIZE
)
),
tabPanel(
title = "Recipe",
icon = icon("book-open"),
panel(
box(title = "Recipe Definition", width = 12, verbatimTextOutput(outputId = "TrainedRecipe"), solidHeader = TRUE, background = TRAINColour, collapsible = TRUE),
box(title = "Variable Roles", width = 12, tableOutput(outputId = "RecipeSummary"), solidHeader = TRUE, background = TRAINColour, collapsible = TRUE)
)
),
# tabPanel(
# title = "Breakdown",
# icon = icon("columns"),
# withSpinner(color = SPINNER_COLOUR, type = SPINNER_TYPE, size = SPINNER_SIZE,
# plotOutput(outputId = "Breakdown", height = "400px")
# ),
# ),
tabPanel(
title = "Variables",
icon = icon("columns"),
panel(
box(title = "Importance", solidHeader = TRUE, background = TRAINColour, collapsible = TRUE, width = 12,
plotOutput(outputId = "VarImp", height = "400px")
),
box(title = "Coefficents", width = 12, verbatimTextOutput(outputId = "ModelCoef"), solidHeader = TRUE, background = TRAINColour, collapsible = TRUE)
)
),
tabPanel(
title = "Log",
icon = icon("quote-left"),
panel(
box(title = "Output", width = 12, verbatimTextOutput(outputId = "TrainLog"), solidHeader = TRUE, background = TRAINColour, collapsible = TRUE)
)
)
)
#)
)
)
),
tabItem(tabName = "ModelMetrics",
box(title = "Model Metrics", icon = icon("tachometer-alt"), solidHeader = TRUE, background = MODELSColour, width = 12,
withSpinner(ui_element = plotOutput(outputId = "Selection", height = FullHeight),
color = SPINNER_COLOUR, type = SPINNER_TYPE, size = SPINNER_SIZE
)
)
),
tabItem(tabName = "ModelCorrelation",
box(title = "Model Correlation", icon = icon("th"), solidHeader = TRUE, background = MODELSColour, width = 12,
withSpinner(ui_element = plotOutput(outputId = "ResidualCorr", height = FullHeight),
color = SPINNER_COLOUR, type = SPINNER_TYPE, size = SPINNER_SIZE
)
)
),
tabItem(tabName = "ModelHierarchy",
box(title = "Model Hierarchy", icon = icon("sitemap"), solidHeader = TRUE, background = MODELSColour, width = 12,
withSpinner(ui_element = plotOutput(outputId = "ModelTree", height = FullHeight),
color = SPINNER_COLOUR, type = SPINNER_TYPE, size = SPINNER_SIZE
)
)
),
tabItem(tabName = "ModelSelection",
box(title = "Model Selection", icon = icon("clipboard-check"), solidHeader = TRUE, background = MODELSColour, width = 12,
fluidRow(
column(width = 8,
radioButtons(inputId = "SelectedModel", label = "Choose the best performing model", choices = "", selected = "", inline = TRUE, width = "100%")
),
column(width = 4,
actionButton(inputId = "AddModel", label = "Ensemble", icon = icon("play")),
selectizeInput(inputId = "Ensemble", label = "Choose a set to ensemble", multiple = TRUE, choices = "", selected = "")
)
)
)
),
tabItem(tabName = "GradFit1", title = "Fit", icon = icon("heartbeat"),
panel(
box(title = "Projected Model performance on unseen data", solidHeader = TRUE, background = GRADColour, width = 12, collapsible = TRUE,
withSpinner(ui_element = plotOutput(outputId = "GradModelPlot1", height = "400px"),
color = SPINNER_COLOUR, type = SPINNER_TYPE, size = SPINNER_SIZE
)
),
box(title = "Statistics", solidHeader = TRUE, background = GRADColour, width = 12, collapsible = TRUE,
div(style = 'overflow-x: auto', tableOutput(outputId = "Performance1Plus"))
),
box(title = "Model Summary", solidHeader = TRUE, background = GRADColour, width = 12, collapsible = TRUE, collapsed = TRUE,
verbatimTextOutput(outputId = "Performance1")
)
)
),
tabItem(tabName = "GradSummary", icon = icon("file"),
box(title = "Graduation Summary", solidHeader = TRUE, width = 12,
box(fluidRow(column(offset = 1, width = 5, checkboxInput(inputId = "LockModel", label = "Lock Model based upon the training data", value = TRUE))),
fluidRow(column(offset = 1, width = 4, checkboxInput(inputId = "LockHyper", label = "Lock existing hyper-parameters", value = TRUE)),
column(width = 4, checkboxInput(inputId = "LockRecipe", label = "Lock existing recipe", value = TRUE)),
column(width = 1, actionButton(inputId = "GradTrain", label = "Train", icon = icon("play"), width = "100px"))),
title = textOutput(outputId = "GradLabel"), width = 12, solidHeader = TRUE, background = GRADColour, collapsible = TRUE
),
box(title = "Initial Resampled performance", width = 12, div(style = 'overflow-x: auto', tableOutput(outputId = "Resamples2")), solidHeader = TRUE, background = TRAINColour, collapsible = TRUE),
box(title = "Final Resampled performance", width = 12, div(style = 'overflow-x: auto', tableOutput(outputId = "FinalResamples")), solidHeader = TRUE, background = GRADColour, collapsible = TRUE),
box(title = "Initial hyper-parameters", width = 6, tableOutput(outputId = "Hyperparams2"), solidHeader = TRUE, background = TRAINColour, collapsible = TRUE),
box(title = "Final hyper-parameters", width = 6, tableOutput(outputId = "FinalHyperparams"), solidHeader = TRUE, background = GRADColour, collapsible = TRUE)
)
),
tabItem(tabName = "GradFit2", title = "Fit", icon = icon("heartbeat"),
box(title = "Finalised Model Fit", solidHeader = TRUE, background = GRADColour, width = 12,
withSpinner(ui_element = plotOutput(outputId = "GradModelPlot2", height = "400px"),
color = SPINNER_COLOUR, type = SPINNER_TYPE, size = SPINNER_SIZE
),
verbatimTextOutput(outputId = "Performance2")
)
),
tabItem(tabName = "GradParameters", icon = icon("chart-line"),
box(title = "Finalised Hyperparameters", solidHeader = TRUE, background = GRADColour, width = 12,
withSpinner(ui_element = plotOutput(outputId = "GradOptimize", height = "400px"),
color = SPINNER_COLOUR, type = SPINNER_TYPE, size = SPINNER_SIZE
)
)
),
tabItem(tabName = "GradResiduals", icon = icon("chart-area"),
box(title = "Finalised Residuals", solidHeader = TRUE, background = GRADColour, width = 12,
withSpinner(ui_element = plotOutput(outputId = "GradResiduals", height = "400px"),
color = SPINNER_COLOUR, type = SPINNER_TYPE, size = SPINNER_SIZE
)
)
),
tabItem(tabName = "Analyse",
box(title = "Graduation Analysis", solidHeader = TRUE, width = 12,
infoBoxOutput(outputId = "NullHandling", width = 6),
infoBoxOutput(outputId = "PredInterval", width = 6)
)
)
)
)
# shinyUI ----
shinyUI(
shinydashboardPlus::dashboardPagePlus(
header = header,
sidebar = sidebar,
body = body
)
)