User Interface

1<button type="button" value="Love" onclick="fetchValue(this)"> Primary </button>
2
3function fetchValue(_button){  
4  alert(_button.value);
5}
6

1<button type="button" value="Love" onclick="fetchValue(this)"> Primary </button>
2
3function fetchValue(_button){  
4  alert(_button.value);
5}
6<button type="button" value="Love", value2="Love", 
7value3="Love" onclick="fetchValue(this)"> Primary </button>
8

1def predictImage(request):
2    # print(request)
3    # print(request.POST.dict())
4    fileObj = request.FILES['filePath']
5    fs = FileSystemStorage()
6    filePathName = fs.save(fileObj.name, fileObj)
7    filePathName = fs.url(filePathName)
8    testimage = '.'+filePathName
9    # img = image.load_img(testimage, target_size=(img_height, img_width))
10    img = detect_image(testimage)
11    filePathName = fs.save(fileObj.name + "_result", img) # -> HERE IS THE ERROR
12    filePathName = fs.url(filePathName)
13

1def predictImage(request):
2    # print(request)
3    # print(request.POST.dict())
4    fileObj = request.FILES['filePath']
5    fs = FileSystemStorage()
6    filePathName = fs.save(fileObj.name, fileObj)
7    filePathName = fs.url(filePathName)
8    testimage = '.'+filePathName
9    # img = image.load_img(testimage, target_size=(img_height, img_width))
10    img = detect_image(testimage)
11    filePathName = fs.save(fileObj.name + "_result", img) # -> HERE IS THE ERROR
12    filePathName = fs.url(filePathName)
13import numpy as np
14import cv2
15
16def detect_image(img_path):
17
18    confidenceThreshold = 0.5
19    NMSThreshold = 0.3
20
21    modelConfiguration = 'cfg/yolov3.cfg'
22    modelWeights = 'yolov3.weights'
23
24    labelsPath = 'coco.names'
25    labels = open(labelsPath).read().strip().split('\n')
26
27    np.random.seed(10)
28    COLORS = np.random.randint(0, 255, size=(len(labels), 3), dtype="uint8")
29
30    net = cv2.dnn.readNetFromDarknet(modelConfiguration, modelWeights)
31
32    image = cv2.imread(img_path)
33    (H, W) = image.shape[:2]
34
35    #Determine output layer names
36    layerName = net.getLayerNames()
37    layerName = [layerName[i - 1] for i in net.getUnconnectedOutLayers()]
38
39    blob = cv2.dnn.blobFromImage(image, 1 / 255.0, (416, 416), swapRB = True, crop = False)
40    net.setInput(blob)
41    layersOutputs = net.forward(layerName)
42
43    boxes = []
44    confidences = []
45    classIDs = []
46
47    for output in layersOutputs:
48        for detection in output:
49            scores = detection[5:]
50            classID = np.argmax(scores)
51            confidence = scores[classID]
52            if confidence > confidenceThreshold:
53                box = detection[0:4] * np.array([W, H, W, H])
54                (centerX, centerY,  width, height) = box.astype('int')
55                x = int(centerX - (width/2))
56                y = int(centerY - (height/2))
57
58                boxes.append([x, y, int(width), int(height)])
59                confidences.append(float(confidence))
60                classIDs.append(classID)
61
62    #Apply Non Maxima Suppression
63    detectionNMS = cv2.dnn.NMSBoxes(boxes, confidences, confidenceThreshold, NMSThreshold)
64
65    if(len(detectionNMS) > 0):
66        for i in detectionNMS.flatten():
67            (x, y) = (boxes[i][0], boxes[i][1])
68            (w, h) = (boxes[i][2], boxes[i][3])
69
70            color = [int(c) for c in COLORS[classIDs[i]]]
71            cv2.rectangle(image, (x, y), (x + w, y + h), color, 2)
72            text = '{}: {:.4f}'.format(labels[classIDs[i]], confidences[i])
73            cv2.putText(image, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
74    
75    return image 
76    
77    #cv2.imshow('Image', image)
78    #cv2.waitKey(0)
79

1def predictImage(request):
2    # print(request)
3    # print(request.POST.dict())
4    fileObj = request.FILES['filePath']
5    fs = FileSystemStorage()
6    filePathName = fs.save(fileObj.name, fileObj)
7    filePathName = fs.url(filePathName)
8    testimage = '.'+filePathName
9    # img = image.load_img(testimage, target_size=(img_height, img_width))
10    img = detect_image(testimage)
11    filePathName = fs.save(fileObj.name + "_result", img) # -> HERE IS THE ERROR
12    filePathName = fs.url(filePathName)
13import numpy as np
14import cv2
15
16def detect_image(img_path):
17
18    confidenceThreshold = 0.5
19    NMSThreshold = 0.3
20
21    modelConfiguration = 'cfg/yolov3.cfg'
22    modelWeights = 'yolov3.weights'
23
24    labelsPath = 'coco.names'
25    labels = open(labelsPath).read().strip().split('\n')
26
27    np.random.seed(10)
28    COLORS = np.random.randint(0, 255, size=(len(labels), 3), dtype="uint8")
29
30    net = cv2.dnn.readNetFromDarknet(modelConfiguration, modelWeights)
31
32    image = cv2.imread(img_path)
33    (H, W) = image.shape[:2]
34
35    #Determine output layer names
36    layerName = net.getLayerNames()
37    layerName = [layerName[i - 1] for i in net.getUnconnectedOutLayers()]
38
39    blob = cv2.dnn.blobFromImage(image, 1 / 255.0, (416, 416), swapRB = True, crop = False)
40    net.setInput(blob)
41    layersOutputs = net.forward(layerName)
42
43    boxes = []
44    confidences = []
45    classIDs = []
46
47    for output in layersOutputs:
48        for detection in output:
49            scores = detection[5:]
50            classID = np.argmax(scores)
51            confidence = scores[classID]
52            if confidence > confidenceThreshold:
53                box = detection[0:4] * np.array([W, H, W, H])
54                (centerX, centerY,  width, height) = box.astype('int')
55                x = int(centerX - (width/2))
56                y = int(centerY - (height/2))
57
58                boxes.append([x, y, int(width), int(height)])
59                confidences.append(float(confidence))
60                classIDs.append(classID)
61
62    #Apply Non Maxima Suppression
63    detectionNMS = cv2.dnn.NMSBoxes(boxes, confidences, confidenceThreshold, NMSThreshold)
64
65    if(len(detectionNMS) > 0):
66        for i in detectionNMS.flatten():
67            (x, y) = (boxes[i][0], boxes[i][1])
68            (w, h) = (boxes[i][2], boxes[i][3])
69
70            color = [int(c) for c in COLORS[classIDs[i]]]
71            cv2.rectangle(image, (x, y), (x + w, y + h), color, 2)
72            text = '{}: {:.4f}'.format(labels[classIDs[i]], confidences[i])
73            cv2.putText(image, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
74    
75    return image 
76    
77    #cv2.imshow('Image', image)
78    #cv2.waitKey(0)
79filePathName = fs.save(fileObj.name + "_result", img)
80

1def predictImage(request):
2    # print(request)
3    # print(request.POST.dict())
4    fileObj = request.FILES['filePath']
5    fs = FileSystemStorage()
6    filePathName = fs.save(fileObj.name, fileObj)
7    filePathName = fs.url(filePathName)
8    testimage = '.'+filePathName
9    # img = image.load_img(testimage, target_size=(img_height, img_width))
10    img = detect_image(testimage)
11    filePathName = fs.save(fileObj.name + "_result", img) # -> HERE IS THE ERROR
12    filePathName = fs.url(filePathName)
13import numpy as np
14import cv2
15
16def detect_image(img_path):
17
18    confidenceThreshold = 0.5
19    NMSThreshold = 0.3
20
21    modelConfiguration = 'cfg/yolov3.cfg'
22    modelWeights = 'yolov3.weights'
23
24    labelsPath = 'coco.names'
25    labels = open(labelsPath).read().strip().split('\n')
26
27    np.random.seed(10)
28    COLORS = np.random.randint(0, 255, size=(len(labels), 3), dtype="uint8")
29
30    net = cv2.dnn.readNetFromDarknet(modelConfiguration, modelWeights)
31
32    image = cv2.imread(img_path)
33    (H, W) = image.shape[:2]
34
35    #Determine output layer names
36    layerName = net.getLayerNames()
37    layerName = [layerName[i - 1] for i in net.getUnconnectedOutLayers()]
38
39    blob = cv2.dnn.blobFromImage(image, 1 / 255.0, (416, 416), swapRB = True, crop = False)
40    net.setInput(blob)
41    layersOutputs = net.forward(layerName)
42
43    boxes = []
44    confidences = []
45    classIDs = []
46
47    for output in layersOutputs:
48        for detection in output:
49            scores = detection[5:]
50            classID = np.argmax(scores)
51            confidence = scores[classID]
52            if confidence > confidenceThreshold:
53                box = detection[0:4] * np.array([W, H, W, H])
54                (centerX, centerY,  width, height) = box.astype('int')
55                x = int(centerX - (width/2))
56                y = int(centerY - (height/2))
57
58                boxes.append([x, y, int(width), int(height)])
59                confidences.append(float(confidence))
60                classIDs.append(classID)
61
62    #Apply Non Maxima Suppression
63    detectionNMS = cv2.dnn.NMSBoxes(boxes, confidences, confidenceThreshold, NMSThreshold)
64
65    if(len(detectionNMS) > 0):
66        for i in detectionNMS.flatten():
67            (x, y) = (boxes[i][0], boxes[i][1])
68            (w, h) = (boxes[i][2], boxes[i][3])
69
70            color = [int(c) for c in COLORS[classIDs[i]]]
71            cv2.rectangle(image, (x, y), (x + w, y + h), color, 2)
72            text = '{}: {:.4f}'.format(labels[classIDs[i]], confidences[i])
73            cv2.putText(image, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
74    
75    return image 
76    
77    #cv2.imshow('Image', image)
78    #cv2.waitKey(0)
79filePathName = fs.save(fileObj.name + "_result", img)
80'numpy.ndarray' object has no attribute 'read'
81

1bool
2CANavigationView::UpdateHostView( void )
3{
4    TNavigationItemPtr item;
5    TIndex index;
6
7    if( _hostViewUpdateNeeded == false )
8        return false;
9
10    Windows::UI::Xaml::Controls::NavigationViewItemBase hItem( nullptr );
11    Windows::UI::Xaml::Controls::TextBlock hText( nullptr );
12    winrt::hstring hTag;
13
14    // Remove all navigation items from the current host view:
15    _hostNavigationView.MenuItems().Clear();
16    _hostNavigationView.IsSettingsVisible( false );
17
18    // Build the navigation menu items:
19    for( index = 0; index < _navigationItems.CountOfItems(); index++ )
20    {
21        item = * _navigationItems.GetItemAtIndex( index );
22
23        if( item->identifier == kSettingsItem )
24        {
25            _hostNavigationView.IsSettingsVisible( true );
26            hText = Windows::UI::Xaml::Controls::TextBlock();
27            CSString::ConvertToUIString( item->title->GetString( gAppLanguageCode ), & hTag );
28            hText.Text( hTag );
29//          Issue #1 : cannot access to the Settings item
30//          _hostNavigationView.SettingsItem().as< Windows::UI::Xaml::Controls::NavigationViewItem >().Content( hText );
31//          SettingsItem() returns nullptr...
32        }
33        else
34        {
35            switch( item->type )
36            {
37                case eNavigationHeader:
38                    hItem = Windows::UI::Xaml::Controls::NavigationViewItemHeader();
39                    hText = Windows::UI::Xaml::Controls::TextBlock();
40                    CSString::ConvertToUIString( item->title->GetString( gAppLanguageCode ), & hTag );
41                    hText.Text( hTag );
42//                  Issue #2: The header's title is not displayed
43                    hItem.Content( hText );
44                    _hostNavigationView.MenuItems().Append( hItem );
45                    break;
46
47                case eNavigationSeparator:
48                    hItem = Windows::UI::Xaml::Controls::NavigationViewItemSeparator();
49                    _hostNavigationView.MenuItems().Append( hItem );
50                    break;
51
52                case eNavigationItem:
53                    hItem = Windows::UI::Xaml::Controls::NavigationViewItem();
54                    CSString::ConvertToUIString( CAUIElement::GetStringFromUIIdentifier( item->identifier ), & hTag );
55                    hItem.Tag( winrt::box_value( hTag ) );
56                    hText = Windows::UI::Xaml::Controls::TextBlock();
57                    CSString::ConvertToUIString( item->title->GetString( gAppLanguageCode ), & hTag );
58                    hText.Text( hTag );
59                    hItem.Content( hText );
60                    hItem.as< Windows::UI::Xaml::Controls::NavigationViewItem>().Icon( GetHostIcon( item->icon ) );
61                    _hostNavigationView.MenuItems().Append( hItem );
62                    break;
63
64                default:
65                    break;
66            }
67        }
68    }
69
70    _hostViewUpdateNeeded = false;
71
72    return true;
73}
74

1bool
2CANavigationView::UpdateHostView( void )
3{
4    TNavigationItemPtr item;
5    TIndex index;
6
7    if( _hostViewUpdateNeeded == false )
8        return false;
9
10    Windows::UI::Xaml::Controls::NavigationViewItemBase hItem( nullptr );
11    Windows::UI::Xaml::Controls::TextBlock hText( nullptr );
12    winrt::hstring hTag;
13
14    // Remove all navigation items from the current host view:
15    _hostNavigationView.MenuItems().Clear();
16    _hostNavigationView.IsSettingsVisible( false );
17
18    // Build the navigation menu items:
19    for( index = 0; index < _navigationItems.CountOfItems(); index++ )
20    {
21        item = * _navigationItems.GetItemAtIndex( index );
22
23        if( item->identifier == kSettingsItem )
24        {
25            _hostNavigationView.IsSettingsVisible( true );
26            hText = Windows::UI::Xaml::Controls::TextBlock();
27            CSString::ConvertToUIString( item->title->GetString( gAppLanguageCode ), & hTag );
28            hText.Text( hTag );
29//          Issue #1 : cannot access to the Settings item
30//          _hostNavigationView.SettingsItem().as< Windows::UI::Xaml::Controls::NavigationViewItem >().Content( hText );
31//          SettingsItem() returns nullptr...
32        }
33        else
34        {
35            switch( item->type )
36            {
37                case eNavigationHeader:
38                    hItem = Windows::UI::Xaml::Controls::NavigationViewItemHeader();
39                    hText = Windows::UI::Xaml::Controls::TextBlock();
40                    CSString::ConvertToUIString( item->title->GetString( gAppLanguageCode ), & hTag );
41                    hText.Text( hTag );
42//                  Issue #2: The header's title is not displayed
43                    hItem.Content( hText );
44                    _hostNavigationView.MenuItems().Append( hItem );
45                    break;
46
47                case eNavigationSeparator:
48                    hItem = Windows::UI::Xaml::Controls::NavigationViewItemSeparator();
49                    _hostNavigationView.MenuItems().Append( hItem );
50                    break;
51
52                case eNavigationItem:
53                    hItem = Windows::UI::Xaml::Controls::NavigationViewItem();
54                    CSString::ConvertToUIString( CAUIElement::GetStringFromUIIdentifier( item->identifier ), & hTag );
55                    hItem.Tag( winrt::box_value( hTag ) );
56                    hText = Windows::UI::Xaml::Controls::TextBlock();
57                    CSString::ConvertToUIString( item->title->GetString( gAppLanguageCode ), & hTag );
58                    hText.Text( hTag );
59                    hItem.Content( hText );
60                    hItem.as< Windows::UI::Xaml::Controls::NavigationViewItem>().Icon( GetHostIcon( item->icon ) );
61                    _hostNavigationView.MenuItems().Append( hItem );
62                    break;
63
64                default:
65                    break;
66            }
67        }
68    }
69
70    _hostViewUpdateNeeded = false;
71
72    return true;
73}
74DBG-[000002686A230710]CANavigationView::UpdateDisplayedLanguage() {
75    DBG-[000002686A230710]CANavigationView::UpdateHostView() {
76        DBG-[000002686A230710]CANavigationView::UpdateHostView() Navigation item 0, type 2
77        DBG-[000002686A230710]CANavigationView::UpdateHostView() Header case: Reference Library
78        DBG-[000002686A230710]CANavigationView::UpdateHostView() Navigation item 1, type 1
79        DBG-[000002686A230710]CANavigationView::UpdateHostView() Navigation item case
80        DBG-[000002686A230710]CANavigationView::UpdateHostView() Navigation item 2, type 1
81        DBG-[000002686A230710]CANavigationView::UpdateHostView() Navigation item case
82        DBG-[000002686A230710]CANavigationView::UpdateHostView() Navigation item 3, type 1
83        DBG-[000002686A230710]CANavigationView::UpdateHostView() Navigation item case
84        DBG-[000002686A230710]CANavigationView::UpdateHostView() Navigation item 4, type 3
85        DBG-[000002686A230710]CANavigationView::UpdateHostView() Separator case
86        DBG-[000002686A230710]CANavigationView::UpdateHostView() Navigation item 5, type 2
87        DBG-[000002686A230710]CANavigationView::UpdateHostView() Header case: Project Library
88        DBG-[000002686A230710]CANavigationView::UpdateHostView() Navigation item 6, type 1
89        DBG-[000002686A230710]CANavigationView::UpdateHostView() Navigation item case
90        DBG-[000002686A230710]CANavigationView::UpdateHostView() Navigation item 7, type 1
91        DBG-[000002686A230710]CANavigationView::UpdateHostView() Settings case
92        DBG-[000002686A230710]CANavigationView::UpdateHostView() Value of SettingsItem(): 0000000000000000
93        DBG-[000002686A230710]CANavigationView::UpdateHostView() Count of menu items for the navigation view: 7 (8)
94    DBG-}
95DBG-}
96

1<html>
2<body>
3<script>
4/* Silly implementation of the "Concentration" memory match game.
5   This is just a S/O example but it should actually work =P
6 */
7
8class SymbolMatchGame {
9
10  constructor(symbolsArray) {
11    this.symbols = symbolsArray.concat(symbolsArray); // we want two of every item
12    this.allButtons = [];
13    this.lastButtonClicked = null;
14  }
15
16  shuffle() {
17    for (let i = this.symbols.length - 1; i > 0; i--) {
18      const j = Math.floor(Math.random() * (i + 1));
19      const temp = this.symbols[i];
20      this.symbols[i] = this.symbols[j];
21      this.symbols[j] = temp;
22    }
23  }
24
25  build(parentElement) {
26    document.body.innerHTML = '';
27    this.shuffle();
28    const rowSize = Math.floor(Math.sqrt(this.symbols.length));
29    for (let i = 0; i < this.symbols.length; i++) {
30      const button = document.createElement('input');
31      button.type = 'button';
32      button.setAttribute('secret-value', this.symbols[i]);
33      button.value = ' ';
34      button.onclick = (event) => this.turnOver(event);
35      this.allButtons.push(button);
36      document.body.appendChild(button);
37      if ((i+1) % rowSize === 0) {
38        const lineBreak = document.createElement('br');
39        document.body.appendChild(lineBreak);
40      }
41    }
42  }
43  
44  turnOver(event) {
45    const button = event.target;
46    if (this.lastButtonClicked === null) {
47        this.allButtons.forEach(button => button.value = button.disabled ? button.value : ' ');
48        this.lastButtonClicked = button;
49    } else if (button === this.lastButtonClicked) {
50        button.value = ' ';
51        this.lastButtonClicked = null;
52    } else {
53        if (button.getAttribute('secret-value') === this.lastButtonClicked.getAttribute('secret-value')) {
54            console.log('Match found!');
55            button.disabled = true;
56            this.lastButtonClicked.disabled = true;
57        } else {
58            console.log('No match!');
59        }
60        this.lastButtonClicked = null;
61    }
62    button.value = button.getAttribute('secret-value');
63    if (this.gameIsSolved()) {
64        alert('You did it! Game will reset.')
65        this.build();
66    }
67  }
68  
69  gameIsSolved() {
70    const remainingButtons = game.allButtons.filter(button => !button.disabled)
71    return remainingButtons.length === 0;
72  }
73
74}
75
76const alphabetArray = Array.from(Array(8).keys()).map(k => String.fromCharCode(k+65));
77game = new SymbolMatchGame(alphabetArray);
78game.build();
79</script>
80</body> 
81</html>
82

1$(document).ready(function() {
2    
3  let testSelect = document.createElement('select');
4  let firstOption = document.createElement('option');
5  firstOption.text = 'blablablabla...';
6  firstOption.defaultSelected = true;
7  testSelect.appendChild(firstOption);
8  
9  document.getElementById('testDiv').appendChild(testSelect);
10  $(testSelect).selectmenu({height: '50%'}); // no matter what value of height I use, the menu does not change
11
12  testButton = document.createElement('button');
13  testButton.innerHTML = 'Like this example button';
14  $(testButton).button();
15  document.getElementById('testDiv').appendChild(testButton); // I want the select menu to look similar to a button like this
16    
17});

1$(document).ready(function() {
2    
3  let testSelect = document.createElement('select');
4  let firstOption = document.createElement('option');
5  firstOption.text = 'blablablabla...';
6  firstOption.defaultSelected = true;
7  testSelect.appendChild(firstOption);
8  
9  document.getElementById('testDiv').appendChild(testSelect);
10  $(testSelect).selectmenu({height: '50%'}); // no matter what value of height I use, the menu does not change
11
12  testButton = document.createElement('button');
13  testButton.innerHTML = 'Like this example button';
14  $(testButton).button();
15  document.getElementById('testDiv').appendChild(testButton); // I want the select menu to look similar to a button like this
16    
17});<script type="text/javascript" src="https://ajax.googleapis.com/ajax/libs/jquery/3.5.1/jquery.min.js"></script>
18<script type="text/javascript" src="https://code.jquery.com/ui/1.13.0/jquery-ui.min.js"></script>
19<link rel="stylesheet" href="http://code.jquery.com/ui/1.13.0/themes/redmond/jquery-ui.css"> 
20
21<div id="testDiv" style="width:100px; height:100px;"></div>

1library(shiny)
2library(DT)
3
4shinyInput <- function (FUN, id_base, suffix, label = "", ...) 
5{
6  inputId <- paste0(id_base, suffix)
7  args <- list(...)
8  args <- c(list(label = label), args)
9  args <- lapply(args, function(a) rep(a, length.out = length(inputId)))
10  rv <- character(length(inputId))
11  for (i in seq_along(rv)) {
12    this_arg <- lapply(args, `[`, i)
13    ctrl <- do.call(FUN, c(list(inputId = inputId[i]), this_arg))
14    rv[i] <- as.character(ctrl)
15  }
16  rv
17}
18
19X <- data.frame(id = LETTERS, 
20                selected = sample(c(TRUE, FALSE), 
21                                  size = length(LETTERS), 
22                                  replace = TRUE))
23
24X$IsSelected <- 
25  shinyInput(
26    shiny::checkboxInput, 
27    id_base = "new_input_", 
28    suffix = X$id, 
29    value = X$selected
30  )
31
32shinyApp(
33  ui = fluidPage(
34    verbatimTextOutput("value_check"),
35    textOutput("input_a_value"),
36    DT::dataTableOutput("dt")
37  ), 
38  
39  server = shinyServer(function(input, output, session){
40    
41    Data <- reactiveValues(
42      X = X
43    )
44    
45    output$value_check <- 
46      renderPrint({
47        sort(names(input))
48      })
49    
50    output$dt <- 
51      DT::renderDataTable({
52        
53        
54        DT::datatable(X, 
55                      selection = "none", 
56                      escape = FALSE, 
57                      filter = "top", 
58                      #rownames = FALSE, 
59                      class = "compact cell-border", 
60                      options = list(preDrawCallback = JS('function() { Shiny.unbindAll(this.api().table().node()); }'),
61                                     drawCallback = JS('function() { Shiny.bindAll(this.api().table().node()); } ')))
62      })
63  })
64)
65

1library(shiny)
2library(DT)
3
4shinyInput <- function (FUN, id_base, suffix, label = "", ...) 
5{
6  inputId <- paste0(id_base, suffix)
7  args <- list(...)
8  args <- c(list(label = label), args)
9  args <- lapply(args, function(a) rep(a, length.out = length(inputId)))
10  rv <- character(length(inputId))
11  for (i in seq_along(rv)) {
12    this_arg <- lapply(args, `[`, i)
13    ctrl <- do.call(FUN, c(list(inputId = inputId[i]), this_arg))
14    rv[i] <- as.character(ctrl)
15  }
16  rv
17}
18
19X <- data.frame(id = LETTERS, 
20                selected = sample(c(TRUE, FALSE), 
21                                  size = length(LETTERS), 
22                                  replace = TRUE))
23
24X$IsSelected <- 
25  shinyInput(
26    shiny::checkboxInput, 
27    id_base = "new_input_", 
28    suffix = X$id, 
29    value = X$selected
30  )
31
32shinyApp(
33  ui = fluidPage(
34    verbatimTextOutput("value_check"),
35    textOutput("input_a_value"),
36    DT::dataTableOutput("dt")
37  ), 
38  
39  server = shinyServer(function(input, output, session){
40    
41    Data <- reactiveValues(
42      X = X
43    )
44    
45    output$value_check <- 
46      renderPrint({
47        sort(names(input))
48      })
49    
50    output$dt <- 
51      DT::renderDataTable({
52        
53        
54        DT::datatable(X, 
55                      selection = "none", 
56                      escape = FALSE, 
57                      filter = "top", 
58                      #rownames = FALSE, 
59                      class = "compact cell-border", 
60                      options = list(preDrawCallback = JS('function() { Shiny.unbindAll(this.api().table().node()); }'),
61                                     drawCallback = JS('function() { Shiny.bindAll(this.api().table().node()); } ')))
62      })
63  })
64)
65# Set up environment ------------------------------------------------
66library(shiny)
67library(DT)
68library(magrittr)
69
70# Example of data coming from the database. -------------------------
71
72set.seed(pi^2)
73
74SourceData <- 
75  data.frame(sample_id = 1:25, 
76             is_selected = sample(c(TRUE, FALSE), 25, replace = TRUE))
77
78
79# Support Functions -------------------------------------------------
80# These would exist, for example, in an internal package
81
82shinyInput <- function (FUN, id_base, suffix, label = "", ...) 
83{
84  inputId <- paste0(id_base, suffix)
85  args <- list(...)
86  args <- c(list(label = label), args)
87  args <- lapply(args, function(a) rep(a, length.out = length(inputId)))
88  rv <- character(length(inputId))
89  for (i in seq_along(rv)) {
90    this_arg <- lapply(args, `[`, i)
91    ctrl <- do.call(FUN, c(list(inputId = inputId[i]), this_arg))
92    rv[i] <- as.character(ctrl)
93  }
94  rv
95}
96
97prepareDataForDisplay <- function(Data){
98  Data$is_selected <- 
99    shinyInput(shiny::checkboxInput, 
100               id_base = "is_selected_", 
101               suffix = Data$sample_id, 
102               value = Data$is_selected)
103  
104  Data
105}
106
107# User Interface ----------------------------------------------------
108
109ui <- 
110  fluidPage(
111    verbatimTextOutput("value_check"), 
112    
113    actionButton(inputId = "btn_saveSelection", 
114                 label = "Save Selection"), 
115    actionButton(inputId = "btn_selectAll", 
116                 label = "Select All"),
117    actionButton(inputId = "btn_unselectAll", 
118                 label = "Unselect All"),
119    actionButton(inputId = "btn_restoreDefault", 
120                 label = "Restore Default (select odd only)"),
121    
122    DT::dataTableOutput("dt")
123  )
124
125# Server ------------------------------------------------------------
126
127server <- 
128  shinyServer(function(input, output, session){
129    
130    # Event Observers -----------------------------------------------
131    
132    observeEvent(
133      input$btn_selectAll, 
134      {
135        check_input <- names(input)[grepl("is_selected_", names(input))]
136        
137        lapply(check_input, 
138               function(ci){
139                 updateCheckboxInput(session = session, 
140                                     inputId = ci, 
141                                     value = TRUE)
142               })
143      }
144    )
145    
146    observeEvent(
147      input$btn_unselectAll, 
148      {
149        check_input <- names(input)[grepl("is_selected_", names(input))]
150        
151        lapply(check_input, 
152               function(ci){
153                 updateCheckboxInput(session = session, 
154                                     inputId = ci, 
155                                     value = FALSE)
156               })
157      }
158    )
159    
160    observeEvent(
161      input$btn_restoreDefault,
162      {
163        check_input <- names(input)[grepl("is_selected_", names(input))]
164
165        lapply(check_input, 
166               function(ci){
167                 id <- as.numeric(sub("is_selected_", "", ci))
168                 
169                 updateCheckboxInput(session = session, 
170                                     inputId = ci, 
171                                     value = id %% 2 == 1)
172               })
173      }
174    )
175    
176    observeEvent(
177      input$btn_saveSelection,
178      {
179        check_input <- names(input)[grepl("is_selected_", names(input))]
180        
181        id <- as.numeric(sub("is_selected_", "", check_input))
182
183        for (i in seq_along(check_input)){
184          SourceData$is_selected[SourceData$sample_id == id[i]] <- 
185            input[[check_input[i]]]
186        }
187
188        # At this point, I would also save changes to the remote database.
189        
190        DT::replaceData(proxy = dt_proxy, 
191                        data = prepareDataForDisplay(SourceData))
192      }
193    )
194    
195    # Output elements -----------------------------------------------
196    
197    output$value_check <- 
198      renderPrint({
199        sort(names(input))
200      })
201    
202    output$dt <- 
203      DT::renderDataTable({
204        SourceData %>% 
205          prepareDataForDisplay() %>% 
206          DT::datatable(selection = "none", 
207                        escape = FALSE, 
208                        filter = "top", 
209                        class = "compact cell-border", 
210                        options = list(preDrawCallback = JS('function() { Shiny.unbindAll(this.api().table().node()); }'),
211                                       drawCallback = JS('function() { Shiny.bindAll(this.api().table().node()); } ')))
212      })
213    
214    dt_proxy <- DT::dataTableProxy("dt")
215    
216  })
217
218# Run the application -----------------------------------------------
219
220shinyApp(
221  ui = ui, 
222  server = server
223)
224