inst/bn/ui.R
In SAFE-ICU/ShinyBN: Enabling Decisions with Bayesian Networks Artificial Intelligence
library('bnlearn')
library('shiny')
library('shinydashboard')
library('visNetwork')
library('shinyWidgets')
library("shinyBS")
library('shinyalert')
library('rintrojs')
source('error.bar.R')
source('graph.custom.R')
source('graph.custom.assoc.R')
source('custom.discretize.R')
source('check.NA.R')
source('check.discrete.R')
source('custom.association.R')
source('custom.Modules.R')
source('dashboardthemes.R')
source('dependency.R')
myDashboardHeader <- function (..., title = NULL, titleWidth = NULL, disable = FALSE,
.list = NULL) {
items <- c(list(...), .list)
# lapply(items, tagAssert, type = "li", class = "dropdown")
titleWidth <- validateCssUnit(titleWidth)
custom_css <- NULL
if (!is.null(titleWidth)) {
custom_css <- tags$head(tags$style(HTML(gsub("_WIDTH_",
titleWidth, fixed = TRUE, "\n @media (min-width: 768px) {\n .main-header > .navbar {\n margin-left: _WIDTH_;text-align: left;\n }\n .main-header .logo {\n width: _WIDTH_;\n }\n }\n "))))
}
tags$header(class = "main-header", custom_css, style = if (disable)
"display: none;", span(class = "logo", title), tags$nav(class = "navbar navbar-static-top",
role = "navigation", span(shiny::icon("bars"), style = "display:none;"),
# a(href = "#", class = "sidebar-toggle", `data-toggle` = "offcanvas",
# role = "button", span(class = "sr-only", "Toggle navigation")),
div(class = "navbar-custom-menu", tags$ul(class = "nav navbar-nav",
items))))
}
dashboardPage(skin = "blue",
myDashboardHeader(title = "wiseR",
titleWidth = "400"
#,tags$li(class = "dropdown", bsButton("homeIntro", label = NULL, icon = icon("question-circle", lib="font-awesome"), style = "primary", size = "large"))
),
dashboardSidebar(width = 50,
sidebarMenu(id = "sidebarMenu",
menuItem(text = "",
tabName = "Home",
icon = icon("home")
),
menuItem(text = "",
icon = shiny::icon("globe"),
tabName = "Structure"
),
menuItem(text = "",
icon = shiny::icon("github"),
href = "https://github.com/SAFE-ICU/ShinyBN"),
menuItem(text = "",
icon = shiny::icon("info"),
tabName = "About")
)
),
dashboardBody(id ="dashboardBody",
# Include shinyalert Ui
shinyalert::useShinyalert(),
shinyDashboardThemes(
theme = "grey_light"
),
# Include introjs UI
rintrojs::introjsUI(),
#shinythemes::themeSelector(),
#theme = shinytheme("united"),
tags$script(HTML("$('body').addClass('fixed');")),
shinydashboard::tabItems(
shinydashboard::tabItem(tabName = "Home",
fluidRow(box(#title = "",
status = "primary",
width = 12,
div(style="text-align:center",
shiny::img(src = "wiseR_HomePage.png",height = 400,width = 1250)
),
fluidRow(
style = "margin-left:40px;padding:10px;",
column(width=3, align = "center", h4('Discover Dark Knowledge')),
column(width=1, align = "center", img(src = "arrow.png",height = 40,width = 60)),
column(width=3, align = "center", h4('Assess Impact')),
column(width=1, align = "center", img(src = "arrow.png",height = 40,width = 60)),
column(width=3, align = "center", h4('Take Decisions'))
),
hr(),
div(style="text-align:center",
actionButton("start", "Start Analyzing", style = "background-color:#2E86C1;color:white;height:50px;font-size:20px", width = '300px', align = "center")
)
)
)),
shinydashboard::tabItem(tabName = "Structure",
tabBox(id = "visula_tabs",
width = 12,
tabPanel("App Settings",
shiny::fluidRow(
column(3,materialSwitch(inputId = "parallel", label = "Enable Parallel Computing", status = "primary", right = TRUE), style="margin:30px;"),
column(3,selectInput("clusters",choices = c(1:20),label = "Number of clusters")))
),
tabPanel('Data',
shiny::fluidRow(
shiny::column(3,
shinyWidgets::radioGroupButtons(inputId = "dataoption",
choices = c("Dataset","Explore"),
selected = "Dataset",
justified = FALSE
)
)
),
conditionalPanel(
"input.dataoption =='Dataset'",
fluidRow(style="padding:0px",
shiny::column(2, dropdownButton(
h5('Choose default dataset'),
fluidRow(column(9,selectInput('defData',label = NULL,choices = c("Alarm","Asia","Coronary","Lizards","Marks","Insurance","Hailfinder"))),column(3,actionButton('loadDef','load', class = "butt"))),
h5('Data Format:'),
shiny::selectInput('format',label = NULL,c(".CSV",".RData","Comma Seperated","Semicolon Seperated","Tab Seperated","Space Seperated")),
h5('Input Possible variables as Factor:'),
checkboxInput("factorCheck", label = NULL, value = FALSE, width = NULL),
h5('File Input:'),
shiny::fileInput('dataFile',
label = NULL,
accept = c('.RData','.csv')
),
label = "upload",circle = F, status = "primary", icon = icon("upload"), width = "500px",tooltip = tooltipOptions(title = "upload data as csv or RData")
)),
shiny::column(2, dropdownButton(
div(id="dataImpute",
shiny::h4("Impute Missing Data:"),
actionButton('impute','Impute', class = "butt")),
div(id="dataDiscretize",
shiny::h4('Discretize Data'),
h5('Discretization Type:'),
shiny::fluidRow(column(9,shiny::selectInput('dtype',label = NULL,c("hybrid discretization(Recommended)"="hybrid","hartemink discretization(Recommended)"="hartemink","interval discretization"="interval","quantile discretization"="quantile","frequency discretization"="frequency","K-means clustering"="cluster"))),column(3,actionButton('discretize',"Discretize", class = "butt"))),
h5('Hartemink Discritization Parameters:'),
shiny::fluidRow(column(4,shiny::textInput('breakH',label = 'breaks',placeholder = 5)),column(4,shiny::textInput('ibreakH',label = 'ibreaks',placeholder = 5)))
),
div(id="dataTranspose",
shiny::h4("Transpose data frame:"),
actionButton('transpose','Transpose', class = "butt")),
div(id="dataSort",
shiny::h4("Sort data frame:"),
actionButton('sort','Arrange Columns', class = "butt")),
div(id="dataDelete",
shiny::h4("Delete variables"),
shiny::fluidRow(shiny::column(6,selectInput('delSelect',label = NULL,"",multiple = T)),shiny::column(3,actionButton('delete','Delete', class = "butt")),shiny::column(3,actionButton('reset','Reset', class = "butt")))
),
div(id="dataIntervention",
shiny::h4("Adjust Interventional Data"),
shiny::fluidRow(shiny::column(4,selectInput('intSelect',label = NULL,"")),shiny::column(8,actionButton('intervention','Adjust', class = "butt")))
),
label = "Pre-Process",circle = F, status = "primary", icon = icon("edit"), width = "500px",tooltip = tooltipOptions(title = "prepare data for bayesian network analysis")
)),
shiny::column(2, downloadButton("downloadDataset", "Download", class = "butt"))),
tags$head(tags$style(".butt{background-color:#2E86C1;} .butt{color:white;} .butt{border:#2E86C1;}")
),
hr(),
shinycssloaders::withSpinner(DT::dataTableOutput("datasetTable"),color = "#2E86C1")
),
conditionalPanel(
"input.dataoption=='Explore'",
selectInput("freqSelect",label = "Variable",""),
shinycssloaders::withSpinner(plotOutput("freqPlot",height = "600px"),color="#2E86C1")
)
),
tabPanel("Association Network",
shiny::fluidRow(
column(5,h5("")),
column(4,shinyWidgets::radioGroupButtons(inputId = "assocOption",
choices = c("Association Network","Export Table"),
selected = "Association Network",
justified = FALSE
))
),
conditionalPanel(
"input.assocOption=='Association Network'",
shiny::fluidRow(
shiny::column(1,
div(style="width: 500px;",
dropdownButton(
h5("Association Network"),
shiny::fluidRow(column(8,shiny::selectInput('assocType',label = NULL,c("cramer's V (Recommended)"="cramer's V","Cohen's D","Goodman Kruskal lambda","Tschuprow's T"))),column(4,actionButton('association',"Build", class = "butt"))),
sliderInput("threshold", label = "Association Threshold",min = 0, max = 1,value = 0.75),
label = "` Build",circle = F, status = "primary", icon = icon("glyphicon glyphicon-wrench",lib = "glyphicon"), width = "400px",tooltip = tooltipOptions(title = "Build association Network")
))
),
shiny::column(2, style='margin-right:0px;',
dropdownButton(
div(id="Agraph",
h4('Highlight Variables'),
shiny::fluidRow(shiny::column(6,selectInput('Avarselect',label = "Variables Names","",multiple = T)),
shiny::column(3,selectInput('Avarshape',label = "Shape","")),
shiny::column(3,actionButton('Agroup','Group', style="margin-top:25px;", class = "butt"))
),
h4("Or"),
shiny::fluidRow(shiny::column(6,textInput('Avarselectvector',label = "Column indices")),
shiny::column(3,selectInput('Avarshape2',label = "Shape","")),
shiny::column(3,actionButton('Agroup2','Group', style="margin-top:25px;", class = "butt"))
),
h3("Or"),
shiny::fluidRow(shiny::column(6,selectInput('AmodGroup',label = "Module",choices = "")),
shiny::column(3,selectInput('Avarshape3',label = "Shape","")),
shiny::column(3, actionButton('Agroup3','Group', style="margin-top:25px;", class = "butt"))
),
hr(),
shiny::fluidRow(shiny::column(6,h4('Visible Neighbors chain'),div(id = "AgraphChain",
sliderInput("Adegree", label = NULL,
min = 1, max = 10,
value = 1
))),
shiny::column(6,h4('Nth Order Neighbors'), div(id = "ANChain",
sliderInput("AdegreeN", label = NULL,
min = 1, max = 10,
value = 1
)))
),
hr(),
div(id="AgraphLayout",
h4("Select Graph Layout"),
shiny::selectInput('Agraph_layout',label = NULL,"layout_nicely"))
),
label = "Visual Settings",circle = F, status = "primary", icon = icon("gear"), width = "500px",tooltip = tooltipOptions(title = "graph visualization settings")
)
),
shiny::column(1,bsButton('graphBtn2', 'Refresh', icon = icon("refresh"),style = "primary")),
shiny::column(2,
div(style = "position:absolute;right:0.1em;",
h5("Nth neighbors(of selection):"))),
shiny::column(2,
shiny::selectInput("Aneighbornodes",label = NULL,choices = "")
),
shiny::column(1,style="padding-right:0px",
shiny::selectInput("AmoduleSelection",label = NULL,"graph")),
shiny::column(2,style="margin-right:20px",dropdownButton(
shiny::fluidRow(shiny::column(6,selectInput('AmoduleAlgo',label = NULL,choices = c("ward.D","ward.D2", "single", "complete", "average", "mcquitty", "median","centroid"))),shiny::column(1,bsButton("Acommunities","Build Modules", style="primary"))),
label = "Detect Modules",circle = F, status = "primary", width = "300px",tooltip = tooltipOptions(title = "Build modules in the graph")
))
),
br(),
shinycssloaders::withSpinner(visNetworkOutput("assocPlot",height = "550px"), color= "#2E86C1")
),
conditionalPanel(
"input.assocOption=='Export Table'",
downloadButton('assocDownload','Download', class = "butt"),
shinycssloaders::withSpinner(DT::dataTableOutput("assocTable"),color = "#2E86C1")
)
),
tabPanel("Bayesian Network",
fluidPage(
shiny::fluidRow(
shiny::column(2,dropdownButton(
shinyWidgets::radioGroupButtons(inputId = "structureOption",
choices = c("Specify Known Edges (optional)","Learn Network From Data","Upload Saved Network","Edit Learnt Network (optional)","Validate Network"),
selected = "Specify Known Edges (optional)",
justified = FALSE
),
shiny::conditionalPanel(
"input.structureOption=='Upload Saved Network'",
h5("parameter learning algorithm"),
selectizeInput('paramMethod',label = NULL,choices = c("Bayesian parameter estimation" = "bayes","Maximum Likelihood parameter estimation" = "mle")),
hr(),
shinyWidgets::radioGroupButtons(inputId = "uploadOption",
choices = c("Averaged Network","Bootstrapped Network"),
selected = "Averaged Network",
justified = FALSE
),
shiny::conditionalPanel(
"input.uploadOption=='Averaged Network'",
div(
# File input
shiny::p("Note: Upload .RData file"),
shiny::fileInput(
'structFile',
strong('File Input:'),
accept = c('.RData')
)
)
),
shiny::conditionalPanel(
"input.uploadOption=='Bootstrapped Network'",
div(
# File input
shiny::p("Note: Upload .RData file"),
shiny::fileInput(
'bootFile',
strong('File Input:'),
accept = c('.RData')
),
fluidRow(
column(6,h5("Edge Strength"),
sliderInput("edgeStrengthU", label = NULL,
min = 0, max = 1,
value = 0.5)),
column(6,h5("Direction Confidence:"),
sliderInput("directionStrengthU", label = NULL,
min = 0, max = 1,
value = 0.5))
)
)
),
actionButton("parameterTuningU","Parameter Tuning", class = "butt")
),
shiny::conditionalPanel(
"input.structureOption=='Specify Known Edges (optional)'",
shiny::fluidRow(column(5,h5("Upload list of prior known edges (as .CSV)"))),
shiny::fluidRow(column(5,shiny::fileInput('priorFile',label = NULL,accept = c('.RData')))),
shiny::fluidRow(shiny::column(3,h5("from")),shiny::column(3,h5("to")),shiny::column(3,h5("")),shiny::column(3,h5("Select from table"))),
shiny::fluidRow(shiny::column(3,selectInput("fromarc1",label = NULL,choices=c())),shiny::column(3,selectInput("toarc1",label = NULL,choices=c())),column(3,actionButton("addarc1","Add", class = "butt")),actionButton("RemoveArc","Remove", class = "butt"),actionButton("ReverseArc","Reverse", class = "butt")),
shinycssloaders::withSpinner(DT::dataTableOutput("priorout"),color = "#2E86C1")
),
shiny::conditionalPanel(
"input.structureOption=='Learn Network From Data'",
div(style ='overflow-y:scroll;height:600px;padding-right:20px;',
# Structural learning algorithm input select
shiny::fluidRow(
shiny::column(6,
shiny::selectizeInput(
inputId = "alg",
label="Learning Algorithm",
choices = list(
"Score-based Learning" =
c("Hill Climbing (Recommended)" = "hc",
"Tabu (Recommended)" = "tabu"),
"Constraint-based Learning" =
c("Grow-Shrink" = "gs",
"Incremental Association" = "iamb",
"Fast IAMB" = "fast.iamb",
"Inter IAMB" = "inter.iamb",
"PC" = "pc.stable"
),
"Hybrid Learning" =
c("Max-Min Hill Climbing" = "mmhc",
"2-phase Restricted Maximization" = 'rsmax2'
),
"Local Discovery Learning" =
c("Max-Min Parents and Children" = 'mmpc',
"Semi-Interleaved HITON-PC" = "si.hiton.pc",
"ARACNE" = "aracne",
"Chow-Liu" = "chow.liu"
)
)
)
),
shiny::column(6,
selectizeInput("paramMethod2",label = "Parameter fitting algorithm",choices = c("Bayesian parameter estimation" = "bayes","Maximum Likelihood parameter estimation" = "mle"))
)
),
shiny::fluidRow(
shiny::column(6,selectInput("algoscore",label = "Network Score",choices = c("modified Bayesian Dirichlet equivalent"="mbde","log-likelihood"="loglik","Akaike Information Criterion"="aic","Bayesian Information Criterion"="bic","Bayesian Dirichlet equivalent"="bde","Bayesian Dirichlet sparse"="bds","locally averaged Bayesian Dirichlet"="bdla"))),
shiny::column(6,sliderInput("iss", label = "Imaginary sample size",
min = 1, max = 1000,
value = 10))
),
h5("Use Expert Knowledge by Forcing/Prohibiting Edges"),
shiny::fluidRow(shiny::column(6,selectInput("listType",label = NULL,choices = c("Blacklist","Whitelist"))),shiny::column(6,shiny::fileInput('listFile',label = NULL,accept = c('.csv')))),
fluidRow(
column(6, h5("Bootstrap replicates"),
sliderInput("boot", label = NULL,
min = 1, max = 1000,
value = 10)),
column(6, h5("Proportion of sample for Bootstrap:"),
sliderInput("SampleSize", label = NULL,
min = 0, max = 1,
value = 0.7))
),
hr(),
fluidRow(
column(6,h5("Edge Strength"),
sliderInput("edgeStrength", label = NULL,
min = 0, max = 1,
value = 0.5)),
column(6,h5("Direction Confidence:"),
sliderInput("directionStrength", label = NULL,
min = 0, max = 1,
value = 0.5))
),
actionButton('learnBtn', 'Bootstrap', class = "butt"),
actionButton('learnSBtn','Direct', class = "butt"),
actionButton('PruneBtn','Parameter Tuning', class = "butt"),
hr(),
shiny::h5("Save learned structure"),
downloadButton('saveBtn','Save', class = "butt")
)
),
shiny::conditionalPanel(
"input.structureOption=='Edit Learnt Network (optional)'",
shiny::fluidRow(shiny::column(3,h5("from")),shiny::column(3,h5("to")),shiny::column(3,h5("")),shiny::column(3,h5("Select from table"))),
shiny::fluidRow(shiny::column(3,selectInput("fromarc",label = NULL,choices=c())),shiny::column(3,selectInput("toarc",label = NULL,choices=c())),column(3,actionButton("addarc","Add", class = "butt")),actionButton("RemoveArc2","Remove", class = "butt"),actionButton("ReverseArc2","Reverse", class = "butt")),
shinycssloaders::withSpinner(DT::dataTableOutput("postout"),color = "#2E86C1")
),
shiny::conditionalPanel(
"input.structureOption=='Validate Network'",
shiny::fluidRow(shiny::column(6,shiny::selectInput('crossFunc',label = "Validation Method",choices = c("10-fold"="k-fold","hold-out"))),shiny::column(6,shiny::selectInput('lossFunc',label = "Loss Function",choices = c("pred","pred-lw")))),
h5("Parameter Fitting Method"),
shiny::fluidRow(shiny::column(8,shiny::selectInput('paramMethod3',label = NULL,choices = c("Bayesian parameter estimation" = "bayes","Maximum Likelihood parameter estimation" = "mle"))),shiny::column(4,shiny::actionButton("calLoss","Cross Validate", class = "butt"))),
h5("Log-Likelihood Loss of the learned model"),
shiny::verbatimTextOutput("valLoss"),
h5("Network Score"),
shiny::fluidRow(shiny::column(6,selectInput("scoreAlgo",label = NULL,choices = c("modified Bayesian Dirichlet equivalent"="mbde","log-likelihood"="loglik","Akaike Information Criterion"="aic","Bayesian Information Criterion"="bic","Bayesian Dirichlet equivalent"="bde","Bayesian Dirichlet sparse"="bds","locally averaged Bayesian Dirichlet"="bdla"))),shiny::column(2,actionButton("getScore","Score", class = "butt")),shiny::column(4,shiny::verbatimTextOutput("netScore")))
),
label = "Structure Learning",circle = F, status = "primary", icon = icon("wrench"), width = "800px",tooltip = tooltipOptions(title = "Upload structure")
)),
shiny::column(2, dropdownButton(
fluidRow(column(6,actionButton("exactInference","Learn Exact Inference",class="butt")),column(6,materialSwitch(inputId = "exact", label = "Enable Exact Inferences", status = "primary", right = TRUE))),
hr(),
h4("Select evidence to add to the model"),
shiny::fluidRow(shiny::column(6,actionButton('insertBtn', 'Insert', class = "butt")),
shiny::column(6,actionButton('removeBtn', 'Remove', class = "butt"))
),
shiny::fluidRow(shiny::column(6,tags$div(id = 'placeholder1')),
shiny::column(6,tags$div(id = 'placeholder2'))
),
hr(),
h4("Select an event of interest"),
shiny::h5("Event Node:"),
shiny::selectInput("event",
label = NULL,
""),
shiny::h4("Display inference plot"),
shiny::fluidRow(shiny::column(5,actionButton('plotBtn', 'without error bars', class = "butt")),shiny::column(4,actionButton('plotStrengthBtn', 'with error bars', class = "butt"))),
hr(),
shiny::h4("No. of resampling iterations for error bars"),
textInput("numInterval", label = NULL,placeholder = 25),
label = "Inference Learning",circle = F, status = "primary", icon = icon("bar-chart-o"), width = "500px",tooltip = tooltipOptions(title = "Learn Inferences")
)),
shiny::column(7,shinyWidgets::radioGroupButtons(inputId = "bayesianOption",
choices = c("Bayesian Network","Fitted Local Distributions", "Infer Decisions","Export Tables"),
selected = "Bayesian Network",
justified = FALSE
))
),
shiny::conditionalPanel(
"input.bayesianOption=='Bayesian Network'",
shiny::column(12,
shiny::fluidRow(
shiny::column(2,
div(
h5("Nth Neighbors(of selection):"))),
shiny::column(2,style="padding-right:0px",
shiny::selectInput("neighbornodes",label = NULL,choices = "")),
shiny::column(1,
div(style = "position:absolute;right:0em;",
h5("Modules:"))),
shiny::column(1,style="padding-right:0px",
shiny::selectInput("moduleSelection",label = NULL,"graph")),
shiny::column(2,style="margin-right:20px",dropdownButton(
shiny::fluidRow(shiny::column(6,selectInput('moduleAlgo',label = NULL,choices = c("ward.D","ward.D2", "single", "complete", "average", "mcquitty", "median","centroid"))),shiny::column(1,bsButton("Bcommunities","Build Modules", style="primary"))),
label = "Detect Modules",circle = F, status = "primary", width = "300px",tooltip = tooltipOptions(title = "Build modules in the graph")
)),
shiny::column(2,style = "margin-right:8px",
dropdownButton(
div(id="Bgraph",
h4('Highlight variables:'),
shiny::fluidRow(shiny::column(6,selectizeInput('varselect',label = "Variable names","",multiple = T)),
shiny::column(3,selectInput('varshape',label = "Shape","")),
shiny::column(3, actionButton('group','Group', style="margin-top:25px;", class = "butt"))
),
h3("Or"),
shiny::fluidRow(shiny::column(6,textInput('varselectvector',label = "Column indices")),
shiny::column(3,selectInput('varshape2',label = "Shape","")),
shiny::column(3, actionButton('group2','Group', style="margin-top:25px;", class = "butt"))
),
h3("Or"),
shiny::fluidRow(shiny::column(6,selectInput('modGroup',label = "Module",choices = "")),
shiny::column(3,selectInput('varshape3',label = "Shape","")),
shiny::column(3, actionButton('group3','Group', style="margin-top:25px;", class = "butt"))
),
shiny::fluidRow(shiny::column(6,h4('Visible Neighbors'),div(id = "graphChain",
sliderInput("degree", label = NULL,
min = 1, max = 10,
value = 1
))),
shiny::column(6,h4('Nth Neighbors'), div(id = "NChain",
sliderInput("degreeN", label = NULL,
min = 1, max = 10,
value = 1
)))
),
hr(),
div(id="graphLayout",
h4("Select Graph Layout"),
shiny::selectInput('graph_layout',label = NULL,"layout_nicely"))
),
label = "Visual Settings",circle = F, status = "primary", icon = icon("gear"), width = "400px",tooltip = tooltipOptions(title = "graph settings")
)
),
shiny::column(1, bsButton('graphBtn', 'Refresh', icon = icon("refresh"),style = "primary"))),
shinycssloaders::withSpinner(visNetworkOutput("netPlot",height = "480px"), color= "#2E86C1")
)
),
shiny::conditionalPanel(
"input.bayesianOption=='Infer Decisions'",
dropdownButton(
sliderInput("NumBar", label = "No. of bars",min = 0, max = 1,value = 1,step=1),
actionButton("sortPlot","Sort X-axis", class = "butt"),
label = "Plot",circle = F, status = "primary", icon = icon("gear"), width = "400px",tooltip = tooltipOptions(title = "plot settings")
),
shinycssloaders::withSpinner(plotOutput("distPlot",height = "450px"), color="#2E86C1")
),
shiny::conditionalPanel(
"input.bayesianOption=='Fitted Local Distributions'",
selectInput("paramSelect",label = "Variable",""),
shinycssloaders::withSpinner(plotOutput("parameterPlot",height = "450px"),color="#2E86C1")
),
conditionalPanel(
"input.bayesianOption=='Export Tables'",
shiny::fluidRow(shiny::column(4,selectInput("tableName",label = NULL,"")),shiny::column(1,downloadButton("downloadData", "Download", class = "butt"))),
shinycssloaders::withSpinner(DT::dataTableOutput("tableOut"),color = "#2E86C1")
)
)
),
tabPanel("Publish your dashboard",
shiny::fluidRow(
column(3,h5("Name")),
column(3,h5("Theme"))),
shiny::fluidRow(
column(3,textInput("name",placeholder = NULL,label = NULL)),
column(3,selectInput("theme",label = NULL,choices = c("Blue gradient"="blue_gradient","BoE website"="boe_website","Grey light"="grey_light","Grey dark"="grey_dark","OneNote"="onenote","Poor man's Flatly"="poor_mans_flatly","Purple gradient"="purple_gradient"))),
column(2,actionButton("build",'build', class = "butt")),
column(3,downloadButton('dashboard','Download', class = "butt")))
)
)
),
tabItem(tabName = "About",
fluidRow(box(
status = "primary",
width = 12,
div(style="text-align:center",
h2('Team')
),
fluidRow(
style = "margin-left:50px;padding:10px;",
column(width=3, align = "center",
#img(src = "tps.jpg", style = "max-width: 50%; width: 50%; height: auto;"),
h4('Tavpritesh Sethi'),
h5('Assistant Professor, IIIT-Delhi'),
h5('Visiting Assistant Professor, Stanford Medicine'),
h5('tavpriteshsethi@iiitd.ac.in | tavsethi@stanford.edu'),
fluidRow(width = 12,
column(width=2, a(img(src = "email.png", style = "margin:5px; width: 20px; height: 20px"), href = "mailto:tavpriteshsethi@iiitd.ac.in"), target = "_blank"),
column(width=2, a(img(src = "email.png", style = "margin:5px; width: 20px; height: 20px"), href = "mailto:tavsethi@stanford.edu"), target = "_blank"),
column(width=2, a(img(src = "github.png", width = '30px', height = '30px'), href = "https://github.com/SAFE-ICU?tab=repositories"), target = "_blank"),
column(width=2, a(img(src = "facebook.png", style = "margin:5px; width: 20px; height: 20px"), href = "https://www.facebook.com/tavpritesh.sethi"), target = "_blank"),
column(width=2, a(img(src = "linkedin.png", style = "margin:5px; width: 20px; height: 20px"), href = "https://in.linkedin.com/in/tavpritesh"), target = "_blank"),
column(width=2, a(img(src = "twitter.png", style = "margin:6px; width: 18px; height: 18px"), href = "https://twitter.com/tavpritesh"), target = "_blank")
)),
column(width=1, align = "center", img(src = "vertical-line.png",style = "max-width: 100%; width: 100%; height: auto;")),
column(width=3, align = "center",
#img(src = "shubham.jpg",style = "max-width: 50%; width: 50%; height: auto"),
h4('Shubham Maheshwari'),
h5('B.Tech Computer Science, IIIT-Delhi'),
h5('shubham14101@iiitd.ac.in'),
br(),
br(),
br(),
fluidRow(width = 12,
column(width=2, a(img(src = "email.png", style = "margin:5px; width: 20px; height: 20px"), href = "mailto:shubham14101@iiitd.ac.in"), target = "_blank"),
column(width=2, a(img(src = "github.png", width = '30px', height = '30px'), href = "https://github.com/shubham14101"),target = "_blank"),
column(width=2, a(img(src = "facebook.png", style = "margin:5px; width: 20px; height: 20px"), href = "https://www.facebook.com/shubham.maheshwari3"),target = "_blank"),
column(width=2, a(img(src = "linkedin.png", style = "margin:5px; width: 20px; height: 20px"), href = "https://www.linkedin.com/in/shubham-maheshwari-93a35b108/"),target = "_blank"),
column(width=2, a(img(src = "twitter.png", style = "margin:6px; width: 18px; height: 18px"), href = "https://twitter.com/real_SM96"),target = "_blank")
)),
column(width=1, align = "center", img(src = "vertical-line.png",style = "max-width: 100%; width: 100%; height: 100%;")),
column(width=3, align = "center",
#img(src = "anant.jpg", style = "max-width: 50%; width: 50%; height: auto"),
h4('Anant Mittal'),
h5('B.Tech Computer Science, IIIT-Delhi'),
h5('anant14015@iiitd.ac.in'),
br(),
br(),
br(),
fluidRow(width = 12,
column(width=2, a(img(src = "email.png", style = "margin:5px; width: 20px; height: 20px"), href = "mailto:anant14015@iiitd.ac.in"), target = "_blank"),
column(width=2, a(img(src = "github.png", width = '30px', height = '30px'), href = "https://github.com/anant15"), target = "_blank"),
column(width=2, a(img(src = "facebook.png", style = "margin:5px; width: 20px; height: 20px"), href = "https://www.facebook.com/shubham.maheshwari3"), target = "_blank"),
column(width=2, a(img(src = "linkedin.png", style = "margin:5px; width: 20px; height: 20px"), href = "https://www.linkedin.com/in/shubham-maheshwari-93a35b108/"), target = "_blank"),
column(width=2, a(img(src = "twitter.png", style = "margin:6px; width: 18px; height: 18px"), href = "https://twitter.com/real_SM96"),target = "_blank")
))
),
hr(),
div(style="text-align:center",
h3('Reference'),
h5('Placeholder'),
hr(),
h3('Acknowlegments'),
h5("Rakesh Lodha, Professor (Pediatrics), All India Institute of Medical Sciences, New Delhi, India"),
h5("Nigam Shah, Associate Professor (Biomedical Informatics), Stanford University, USA"),
h5('Funding Support: The Wellcome Trust/DBT India Alliance Early Career Award IA/CPHE/14/1/501504')
)
)
)
)
)
)
)
SAFE-ICU/ShinyBN documentation built on May 28, 2019, 3:57 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
library('bnlearn')
library('shiny')
library('shinydashboard')
library('visNetwork')
library('shinyWidgets')
library("shinyBS")
library('shinyalert')
library('rintrojs')
source('error.bar.R')
source('graph.custom.R')
source('graph.custom.assoc.R')
source('custom.discretize.R')
source('check.NA.R')
source('check.discrete.R')
source('custom.association.R')
source('custom.Modules.R')
source('dashboardthemes.R')
source('dependency.R')
myDashboardHeader <- function (..., title = NULL, titleWidth = NULL, disable = FALSE,
.list = NULL) {
items <- c(list(...), .list)
# lapply(items, tagAssert, type = "li", class = "dropdown")
titleWidth <- validateCssUnit(titleWidth)
custom_css <- NULL
if (!is.null(titleWidth)) {
custom_css <- tags$head(tags$style(HTML(gsub("_WIDTH_",
titleWidth, fixed = TRUE, "\n @media (min-width: 768px) {\n .main-header > .navbar {\n margin-left: _WIDTH_;text-align: left;\n }\n .main-header .logo {\n width: _WIDTH_;\n }\n }\n "))))
}
tags$header(class = "main-header", custom_css, style = if (disable)
"display: none;", span(class = "logo", title), tags$nav(class = "navbar navbar-static-top",
role = "navigation", span(shiny::icon("bars"), style = "display:none;"),
# a(href = "#", class = "sidebar-toggle", `data-toggle` = "offcanvas",
# role = "button", span(class = "sr-only", "Toggle navigation")),
div(class = "navbar-custom-menu", tags$ul(class = "nav navbar-nav",
items))))
}
dashboardPage(skin = "blue",
myDashboardHeader(title = "wiseR",
titleWidth = "400"
#,tags$li(class = "dropdown", bsButton("homeIntro", label = NULL, icon = icon("question-circle", lib="font-awesome"), style = "primary", size = "large"))
),
dashboardSidebar(width = 50,
sidebarMenu(id = "sidebarMenu",
menuItem(text = "",
tabName = "Home",
icon = icon("home")
),
menuItem(text = "",
icon = shiny::icon("globe"),
tabName = "Structure"
),
menuItem(text = "",
icon = shiny::icon("github"),
href = "https://github.com/SAFE-ICU/ShinyBN"),
menuItem(text = "",
icon = shiny::icon("info"),
tabName = "About")
)
),
dashboardBody(id ="dashboardBody",
# Include shinyalert Ui
shinyalert::useShinyalert(),
shinyDashboardThemes(
theme = "grey_light"
),
# Include introjs UI
rintrojs::introjsUI(),
#shinythemes::themeSelector(),
#theme = shinytheme("united"),
tags$script(HTML("$('body').addClass('fixed');")),
shinydashboard::tabItems(
shinydashboard::tabItem(tabName = "Home",
fluidRow(box(#title = "",
status = "primary",
width = 12,
div(style="text-align:center",
shiny::img(src = "wiseR_HomePage.png",height = 400,width = 1250)
),
fluidRow(
style = "margin-left:40px;padding:10px;",
column(width=3, align = "center", h4('Discover Dark Knowledge')),
column(width=1, align = "center", img(src = "arrow.png",height = 40,width = 60)),
column(width=3, align = "center", h4('Assess Impact')),
column(width=1, align = "center", img(src = "arrow.png",height = 40,width = 60)),
column(width=3, align = "center", h4('Take Decisions'))
),
hr(),
div(style="text-align:center",
actionButton("start", "Start Analyzing", style = "background-color:#2E86C1;color:white;height:50px;font-size:20px", width = '300px', align = "center")
)
)
)),
shinydashboard::tabItem(tabName = "Structure",
tabBox(id = "visula_tabs",
width = 12,
tabPanel("App Settings",
shiny::fluidRow(
column(3,materialSwitch(inputId = "parallel", label = "Enable Parallel Computing", status = "primary", right = TRUE), style="margin:30px;"),
column(3,selectInput("clusters",choices = c(1:20),label = "Number of clusters")))
),
tabPanel('Data',
shiny::fluidRow(
shiny::column(3,
shinyWidgets::radioGroupButtons(inputId = "dataoption",
choices = c("Dataset","Explore"),
selected = "Dataset",
justified = FALSE
)
)
),
conditionalPanel(
"input.dataoption =='Dataset'",
fluidRow(style="padding:0px",
shiny::column(2, dropdownButton(
h5('Choose default dataset'),
fluidRow(column(9,selectInput('defData',label = NULL,choices = c("Alarm","Asia","Coronary","Lizards","Marks","Insurance","Hailfinder"))),column(3,actionButton('loadDef','load', class = "butt"))),
h5('Data Format:'),
shiny::selectInput('format',label = NULL,c(".CSV",".RData","Comma Seperated","Semicolon Seperated","Tab Seperated","Space Seperated")),
h5('Input Possible variables as Factor:'),
checkboxInput("factorCheck", label = NULL, value = FALSE, width = NULL),
h5('File Input:'),
shiny::fileInput('dataFile',
label = NULL,
accept = c('.RData','.csv')
),
label = "upload",circle = F, status = "primary", icon = icon("upload"), width = "500px",tooltip = tooltipOptions(title = "upload data as csv or RData")
)),
shiny::column(2, dropdownButton(
div(id="dataImpute",
shiny::h4("Impute Missing Data:"),
actionButton('impute','Impute', class = "butt")),
div(id="dataDiscretize",
shiny::h4('Discretize Data'),
h5('Discretization Type:'),
shiny::fluidRow(column(9,shiny::selectInput('dtype',label = NULL,c("hybrid discretization(Recommended)"="hybrid","hartemink discretization(Recommended)"="hartemink","interval discretization"="interval","quantile discretization"="quantile","frequency discretization"="frequency","K-means clustering"="cluster"))),column(3,actionButton('discretize',"Discretize", class = "butt"))),
h5('Hartemink Discritization Parameters:'),
shiny::fluidRow(column(4,shiny::textInput('breakH',label = 'breaks',placeholder = 5)),column(4,shiny::textInput('ibreakH',label = 'ibreaks',placeholder = 5)))
),
div(id="dataTranspose",
shiny::h4("Transpose data frame:"),
actionButton('transpose','Transpose', class = "butt")),
div(id="dataSort",
shiny::h4("Sort data frame:"),
actionButton('sort','Arrange Columns', class = "butt")),
div(id="dataDelete",
shiny::h4("Delete variables"),
shiny::fluidRow(shiny::column(6,selectInput('delSelect',label = NULL,"",multiple = T)),shiny::column(3,actionButton('delete','Delete', class = "butt")),shiny::column(3,actionButton('reset','Reset', class = "butt")))
),
div(id="dataIntervention",
shiny::h4("Adjust Interventional Data"),
shiny::fluidRow(shiny::column(4,selectInput('intSelect',label = NULL,"")),shiny::column(8,actionButton('intervention','Adjust', class = "butt")))
),
label = "Pre-Process",circle = F, status = "primary", icon = icon("edit"), width = "500px",tooltip = tooltipOptions(title = "prepare data for bayesian network analysis")
)),
shiny::column(2, downloadButton("downloadDataset", "Download", class = "butt"))),
tags$head(tags$style(".butt{background-color:#2E86C1;} .butt{color:white;} .butt{border:#2E86C1;}")
),
hr(),
shinycssloaders::withSpinner(DT::dataTableOutput("datasetTable"),color = "#2E86C1")
),
conditionalPanel(
"input.dataoption=='Explore'",
selectInput("freqSelect",label = "Variable",""),
shinycssloaders::withSpinner(plotOutput("freqPlot",height = "600px"),color="#2E86C1")
)
),
tabPanel("Association Network",
shiny::fluidRow(
column(5,h5("")),
column(4,shinyWidgets::radioGroupButtons(inputId = "assocOption",
choices = c("Association Network","Export Table"),
selected = "Association Network",
justified = FALSE
))
),
conditionalPanel(
"input.assocOption=='Association Network'",
shiny::fluidRow(
shiny::column(1,
div(style="width: 500px;",
dropdownButton(
h5("Association Network"),
shiny::fluidRow(column(8,shiny::selectInput('assocType',label = NULL,c("cramer's V (Recommended)"="cramer's V","Cohen's D","Goodman Kruskal lambda","Tschuprow's T"))),column(4,actionButton('association',"Build", class = "butt"))),
sliderInput("threshold", label = "Association Threshold",min = 0, max = 1,value = 0.75),
label = "` Build",circle = F, status = "primary", icon = icon("glyphicon glyphicon-wrench",lib = "glyphicon"), width = "400px",tooltip = tooltipOptions(title = "Build association Network")
))
),
shiny::column(2, style='margin-right:0px;',
dropdownButton(
div(id="Agraph",
h4('Highlight Variables'),
shiny::fluidRow(shiny::column(6,selectInput('Avarselect',label = "Variables Names","",multiple = T)),
shiny::column(3,selectInput('Avarshape',label = "Shape","")),
shiny::column(3,actionButton('Agroup','Group', style="margin-top:25px;", class = "butt"))
),
h4("Or"),
shiny::fluidRow(shiny::column(6,textInput('Avarselectvector',label = "Column indices")),
shiny::column(3,selectInput('Avarshape2',label = "Shape","")),
shiny::column(3,actionButton('Agroup2','Group', style="margin-top:25px;", class = "butt"))
),
h3("Or"),
shiny::fluidRow(shiny::column(6,selectInput('AmodGroup',label = "Module",choices = "")),
shiny::column(3,selectInput('Avarshape3',label = "Shape","")),
shiny::column(3, actionButton('Agroup3','Group', style="margin-top:25px;", class = "butt"))
),
hr(),
shiny::fluidRow(shiny::column(6,h4('Visible Neighbors chain'),div(id = "AgraphChain",
sliderInput("Adegree", label = NULL,
min = 1, max = 10,
value = 1
))),
shiny::column(6,h4('Nth Order Neighbors'), div(id = "ANChain",
sliderInput("AdegreeN", label = NULL,
min = 1, max = 10,
value = 1
)))
),
hr(),
div(id="AgraphLayout",
h4("Select Graph Layout"),
shiny::selectInput('Agraph_layout',label = NULL,"layout_nicely"))
),
label = "Visual Settings",circle = F, status = "primary", icon = icon("gear"), width = "500px",tooltip = tooltipOptions(title = "graph visualization settings")
)
),
shiny::column(1,bsButton('graphBtn2', 'Refresh', icon = icon("refresh"),style = "primary")),
shiny::column(2,
div(style = "position:absolute;right:0.1em;",
h5("Nth neighbors(of selection):"))),
shiny::column(2,
shiny::selectInput("Aneighbornodes",label = NULL,choices = "")
),
shiny::column(1,style="padding-right:0px",
shiny::selectInput("AmoduleSelection",label = NULL,"graph")),
shiny::column(2,style="margin-right:20px",dropdownButton(
shiny::fluidRow(shiny::column(6,selectInput('AmoduleAlgo',label = NULL,choices = c("ward.D","ward.D2", "single", "complete", "average", "mcquitty", "median","centroid"))),shiny::column(1,bsButton("Acommunities","Build Modules", style="primary"))),
label = "Detect Modules",circle = F, status = "primary", width = "300px",tooltip = tooltipOptions(title = "Build modules in the graph")
))
),
br(),
shinycssloaders::withSpinner(visNetworkOutput("assocPlot",height = "550px"), color= "#2E86C1")
),
conditionalPanel(
"input.assocOption=='Export Table'",
downloadButton('assocDownload','Download', class = "butt"),
shinycssloaders::withSpinner(DT::dataTableOutput("assocTable"),color = "#2E86C1")
)
),
tabPanel("Bayesian Network",
fluidPage(
shiny::fluidRow(
shiny::column(2,dropdownButton(
shinyWidgets::radioGroupButtons(inputId = "structureOption",
choices = c("Specify Known Edges (optional)","Learn Network From Data","Upload Saved Network","Edit Learnt Network (optional)","Validate Network"),
selected = "Specify Known Edges (optional)",
justified = FALSE
),
shiny::conditionalPanel(
"input.structureOption=='Upload Saved Network'",
h5("parameter learning algorithm"),
selectizeInput('paramMethod',label = NULL,choices = c("Bayesian parameter estimation" = "bayes","Maximum Likelihood parameter estimation" = "mle")),
hr(),
shinyWidgets::radioGroupButtons(inputId = "uploadOption",
choices = c("Averaged Network","Bootstrapped Network"),
selected = "Averaged Network",
justified = FALSE
),
shiny::conditionalPanel(
"input.uploadOption=='Averaged Network'",
div(
# File input
shiny::p("Note: Upload .RData file"),
shiny::fileInput(
'structFile',
strong('File Input:'),
accept = c('.RData')
)
)
),
shiny::conditionalPanel(
"input.uploadOption=='Bootstrapped Network'",
div(
# File input
shiny::p("Note: Upload .RData file"),
shiny::fileInput(
'bootFile',
strong('File Input:'),
accept = c('.RData')
),
fluidRow(
column(6,h5("Edge Strength"),
sliderInput("edgeStrengthU", label = NULL,
min = 0, max = 1,
value = 0.5)),
column(6,h5("Direction Confidence:"),
sliderInput("directionStrengthU", label = NULL,
min = 0, max = 1,
value = 0.5))
)
)
),
actionButton("parameterTuningU","Parameter Tuning", class = "butt")
),
shiny::conditionalPanel(
"input.structureOption=='Specify Known Edges (optional)'",
shiny::fluidRow(column(5,h5("Upload list of prior known edges (as .CSV)"))),
shiny::fluidRow(column(5,shiny::fileInput('priorFile',label = NULL,accept = c('.RData')))),
shiny::fluidRow(shiny::column(3,h5("from")),shiny::column(3,h5("to")),shiny::column(3,h5("")),shiny::column(3,h5("Select from table"))),
shiny::fluidRow(shiny::column(3,selectInput("fromarc1",label = NULL,choices=c())),shiny::column(3,selectInput("toarc1",label = NULL,choices=c())),column(3,actionButton("addarc1","Add", class = "butt")),actionButton("RemoveArc","Remove", class = "butt"),actionButton("ReverseArc","Reverse", class = "butt")),
shinycssloaders::withSpinner(DT::dataTableOutput("priorout"),color = "#2E86C1")
),
shiny::conditionalPanel(
"input.structureOption=='Learn Network From Data'",
div(style ='overflow-y:scroll;height:600px;padding-right:20px;',
# Structural learning algorithm input select
shiny::fluidRow(
shiny::column(6,
shiny::selectizeInput(
inputId = "alg",
label="Learning Algorithm",
choices = list(
"Score-based Learning" =
c("Hill Climbing (Recommended)" = "hc",
"Tabu (Recommended)" = "tabu"),
"Constraint-based Learning" =
c("Grow-Shrink" = "gs",
"Incremental Association" = "iamb",
"Fast IAMB" = "fast.iamb",
"Inter IAMB" = "inter.iamb",
"PC" = "pc.stable"
),
"Hybrid Learning" =
c("Max-Min Hill Climbing" = "mmhc",
"2-phase Restricted Maximization" = 'rsmax2'
),
"Local Discovery Learning" =
c("Max-Min Parents and Children" = 'mmpc',
"Semi-Interleaved HITON-PC" = "si.hiton.pc",
"ARACNE" = "aracne",
"Chow-Liu" = "chow.liu"
)
)
)
),
shiny::column(6,
selectizeInput("paramMethod2",label = "Parameter fitting algorithm",choices = c("Bayesian parameter estimation" = "bayes","Maximum Likelihood parameter estimation" = "mle"))
)
),
shiny::fluidRow(
shiny::column(6,selectInput("algoscore",label = "Network Score",choices = c("modified Bayesian Dirichlet equivalent"="mbde","log-likelihood"="loglik","Akaike Information Criterion"="aic","Bayesian Information Criterion"="bic","Bayesian Dirichlet equivalent"="bde","Bayesian Dirichlet sparse"="bds","locally averaged Bayesian Dirichlet"="bdla"))),
shiny::column(6,sliderInput("iss", label = "Imaginary sample size",
min = 1, max = 1000,
value = 10))
),
h5("Use Expert Knowledge by Forcing/Prohibiting Edges"),
shiny::fluidRow(shiny::column(6,selectInput("listType",label = NULL,choices = c("Blacklist","Whitelist"))),shiny::column(6,shiny::fileInput('listFile',label = NULL,accept = c('.csv')))),
fluidRow(
column(6, h5("Bootstrap replicates"),
sliderInput("boot", label = NULL,
min = 1, max = 1000,
value = 10)),
column(6, h5("Proportion of sample for Bootstrap:"),
sliderInput("SampleSize", label = NULL,
min = 0, max = 1,
value = 0.7))
),
hr(),
fluidRow(
column(6,h5("Edge Strength"),
sliderInput("edgeStrength", label = NULL,
min = 0, max = 1,
value = 0.5)),
column(6,h5("Direction Confidence:"),
sliderInput("directionStrength", label = NULL,
min = 0, max = 1,
value = 0.5))
),
actionButton('learnBtn', 'Bootstrap', class = "butt"),
actionButton('learnSBtn','Direct', class = "butt"),
actionButton('PruneBtn','Parameter Tuning', class = "butt"),
hr(),
shiny::h5("Save learned structure"),
downloadButton('saveBtn','Save', class = "butt")
)
),
shiny::conditionalPanel(
"input.structureOption=='Edit Learnt Network (optional)'",
shiny::fluidRow(shiny::column(3,h5("from")),shiny::column(3,h5("to")),shiny::column(3,h5("")),shiny::column(3,h5("Select from table"))),
shiny::fluidRow(shiny::column(3,selectInput("fromarc",label = NULL,choices=c())),shiny::column(3,selectInput("toarc",label = NULL,choices=c())),column(3,actionButton("addarc","Add", class = "butt")),actionButton("RemoveArc2","Remove", class = "butt"),actionButton("ReverseArc2","Reverse", class = "butt")),
shinycssloaders::withSpinner(DT::dataTableOutput("postout"),color = "#2E86C1")
),
shiny::conditionalPanel(
"input.structureOption=='Validate Network'",
shiny::fluidRow(shiny::column(6,shiny::selectInput('crossFunc',label = "Validation Method",choices = c("10-fold"="k-fold","hold-out"))),shiny::column(6,shiny::selectInput('lossFunc',label = "Loss Function",choices = c("pred","pred-lw")))),
h5("Parameter Fitting Method"),
shiny::fluidRow(shiny::column(8,shiny::selectInput('paramMethod3',label = NULL,choices = c("Bayesian parameter estimation" = "bayes","Maximum Likelihood parameter estimation" = "mle"))),shiny::column(4,shiny::actionButton("calLoss","Cross Validate", class = "butt"))),
h5("Log-Likelihood Loss of the learned model"),
shiny::verbatimTextOutput("valLoss"),
h5("Network Score"),
shiny::fluidRow(shiny::column(6,selectInput("scoreAlgo",label = NULL,choices = c("modified Bayesian Dirichlet equivalent"="mbde","log-likelihood"="loglik","Akaike Information Criterion"="aic","Bayesian Information Criterion"="bic","Bayesian Dirichlet equivalent"="bde","Bayesian Dirichlet sparse"="bds","locally averaged Bayesian Dirichlet"="bdla"))),shiny::column(2,actionButton("getScore","Score", class = "butt")),shiny::column(4,shiny::verbatimTextOutput("netScore")))
),
label = "Structure Learning",circle = F, status = "primary", icon = icon("wrench"), width = "800px",tooltip = tooltipOptions(title = "Upload structure")
)),
shiny::column(2, dropdownButton(
fluidRow(column(6,actionButton("exactInference","Learn Exact Inference",class="butt")),column(6,materialSwitch(inputId = "exact", label = "Enable Exact Inferences", status = "primary", right = TRUE))),
hr(),
h4("Select evidence to add to the model"),
shiny::fluidRow(shiny::column(6,actionButton('insertBtn', 'Insert', class = "butt")),
shiny::column(6,actionButton('removeBtn', 'Remove', class = "butt"))
),
shiny::fluidRow(shiny::column(6,tags$div(id = 'placeholder1')),
shiny::column(6,tags$div(id = 'placeholder2'))
),
hr(),
h4("Select an event of interest"),
shiny::h5("Event Node:"),
shiny::selectInput("event",
label = NULL,
""),
shiny::h4("Display inference plot"),
shiny::fluidRow(shiny::column(5,actionButton('plotBtn', 'without error bars', class = "butt")),shiny::column(4,actionButton('plotStrengthBtn', 'with error bars', class = "butt"))),
hr(),
shiny::h4("No. of resampling iterations for error bars"),
textInput("numInterval", label = NULL,placeholder = 25),
label = "Inference Learning",circle = F, status = "primary", icon = icon("bar-chart-o"), width = "500px",tooltip = tooltipOptions(title = "Learn Inferences")
)),
shiny::column(7,shinyWidgets::radioGroupButtons(inputId = "bayesianOption",
choices = c("Bayesian Network","Fitted Local Distributions", "Infer Decisions","Export Tables"),
selected = "Bayesian Network",
justified = FALSE
))
),
shiny::conditionalPanel(
"input.bayesianOption=='Bayesian Network'",
shiny::column(12,
shiny::fluidRow(
shiny::column(2,
div(
h5("Nth Neighbors(of selection):"))),
shiny::column(2,style="padding-right:0px",
shiny::selectInput("neighbornodes",label = NULL,choices = "")),
shiny::column(1,
div(style = "position:absolute;right:0em;",
h5("Modules:"))),
shiny::column(1,style="padding-right:0px",
shiny::selectInput("moduleSelection",label = NULL,"graph")),
shiny::column(2,style="margin-right:20px",dropdownButton(
shiny::fluidRow(shiny::column(6,selectInput('moduleAlgo',label = NULL,choices = c("ward.D","ward.D2", "single", "complete", "average", "mcquitty", "median","centroid"))),shiny::column(1,bsButton("Bcommunities","Build Modules", style="primary"))),
label = "Detect Modules",circle = F, status = "primary", width = "300px",tooltip = tooltipOptions(title = "Build modules in the graph")
)),
shiny::column(2,style = "margin-right:8px",
dropdownButton(
div(id="Bgraph",
h4('Highlight variables:'),
shiny::fluidRow(shiny::column(6,selectizeInput('varselect',label = "Variable names","",multiple = T)),
shiny::column(3,selectInput('varshape',label = "Shape","")),
shiny::column(3, actionButton('group','Group', style="margin-top:25px;", class = "butt"))
),
h3("Or"),
shiny::fluidRow(shiny::column(6,textInput('varselectvector',label = "Column indices")),
shiny::column(3,selectInput('varshape2',label = "Shape","")),
shiny::column(3, actionButton('group2','Group', style="margin-top:25px;", class = "butt"))
),
h3("Or"),
shiny::fluidRow(shiny::column(6,selectInput('modGroup',label = "Module",choices = "")),
shiny::column(3,selectInput('varshape3',label = "Shape","")),
shiny::column(3, actionButton('group3','Group', style="margin-top:25px;", class = "butt"))
),
shiny::fluidRow(shiny::column(6,h4('Visible Neighbors'),div(id = "graphChain",
sliderInput("degree", label = NULL,
min = 1, max = 10,
value = 1
))),
shiny::column(6,h4('Nth Neighbors'), div(id = "NChain",
sliderInput("degreeN", label = NULL,
min = 1, max = 10,
value = 1
)))
),
hr(),
div(id="graphLayout",
h4("Select Graph Layout"),
shiny::selectInput('graph_layout',label = NULL,"layout_nicely"))
),
label = "Visual Settings",circle = F, status = "primary", icon = icon("gear"), width = "400px",tooltip = tooltipOptions(title = "graph settings")
)
),
shiny::column(1, bsButton('graphBtn', 'Refresh', icon = icon("refresh"),style = "primary"))),
shinycssloaders::withSpinner(visNetworkOutput("netPlot",height = "480px"), color= "#2E86C1")
)
),
shiny::conditionalPanel(
"input.bayesianOption=='Infer Decisions'",
dropdownButton(
sliderInput("NumBar", label = "No. of bars",min = 0, max = 1,value = 1,step=1),
actionButton("sortPlot","Sort X-axis", class = "butt"),
label = "Plot",circle = F, status = "primary", icon = icon("gear"), width = "400px",tooltip = tooltipOptions(title = "plot settings")
),
shinycssloaders::withSpinner(plotOutput("distPlot",height = "450px"), color="#2E86C1")
),
shiny::conditionalPanel(
"input.bayesianOption=='Fitted Local Distributions'",
selectInput("paramSelect",label = "Variable",""),
shinycssloaders::withSpinner(plotOutput("parameterPlot",height = "450px"),color="#2E86C1")
),
conditionalPanel(
"input.bayesianOption=='Export Tables'",
shiny::fluidRow(shiny::column(4,selectInput("tableName",label = NULL,"")),shiny::column(1,downloadButton("downloadData", "Download", class = "butt"))),
shinycssloaders::withSpinner(DT::dataTableOutput("tableOut"),color = "#2E86C1")
)
)
),
tabPanel("Publish your dashboard",
shiny::fluidRow(
column(3,h5("Name")),
column(3,h5("Theme"))),
shiny::fluidRow(
column(3,textInput("name",placeholder = NULL,label = NULL)),
column(3,selectInput("theme",label = NULL,choices = c("Blue gradient"="blue_gradient","BoE website"="boe_website","Grey light"="grey_light","Grey dark"="grey_dark","OneNote"="onenote","Poor man's Flatly"="poor_mans_flatly","Purple gradient"="purple_gradient"))),
column(2,actionButton("build",'build', class = "butt")),
column(3,downloadButton('dashboard','Download', class = "butt")))
)
)
),
tabItem(tabName = "About",
fluidRow(box(
status = "primary",
width = 12,
div(style="text-align:center",
h2('Team')
),
fluidRow(
style = "margin-left:50px;padding:10px;",
column(width=3, align = "center",
#img(src = "tps.jpg", style = "max-width: 50%; width: 50%; height: auto;"),
h4('Tavpritesh Sethi'),
h5('Assistant Professor, IIIT-Delhi'),
h5('Visiting Assistant Professor, Stanford Medicine'),
h5('tavpriteshsethi@iiitd.ac.in | tavsethi@stanford.edu'),
fluidRow(width = 12,
column(width=2, a(img(src = "email.png", style = "margin:5px; width: 20px; height: 20px"), href = "mailto:tavpriteshsethi@iiitd.ac.in"), target = "_blank"),
column(width=2, a(img(src = "email.png", style = "margin:5px; width: 20px; height: 20px"), href = "mailto:tavsethi@stanford.edu"), target = "_blank"),
column(width=2, a(img(src = "github.png", width = '30px', height = '30px'), href = "https://github.com/SAFE-ICU?tab=repositories"), target = "_blank"),
column(width=2, a(img(src = "facebook.png", style = "margin:5px; width: 20px; height: 20px"), href = "https://www.facebook.com/tavpritesh.sethi"), target = "_blank"),
column(width=2, a(img(src = "linkedin.png", style = "margin:5px; width: 20px; height: 20px"), href = "https://in.linkedin.com/in/tavpritesh"), target = "_blank"),
column(width=2, a(img(src = "twitter.png", style = "margin:6px; width: 18px; height: 18px"), href = "https://twitter.com/tavpritesh"), target = "_blank")
)),
column(width=1, align = "center", img(src = "vertical-line.png",style = "max-width: 100%; width: 100%; height: auto;")),
column(width=3, align = "center",
#img(src = "shubham.jpg",style = "max-width: 50%; width: 50%; height: auto"),
h4('Shubham Maheshwari'),
h5('B.Tech Computer Science, IIIT-Delhi'),
h5('shubham14101@iiitd.ac.in'),
br(),
br(),
br(),
fluidRow(width = 12,
column(width=2, a(img(src = "email.png", style = "margin:5px; width: 20px; height: 20px"), href = "mailto:shubham14101@iiitd.ac.in"), target = "_blank"),
column(width=2, a(img(src = "github.png", width = '30px', height = '30px'), href = "https://github.com/shubham14101"),target = "_blank"),
column(width=2, a(img(src = "facebook.png", style = "margin:5px; width: 20px; height: 20px"), href = "https://www.facebook.com/shubham.maheshwari3"),target = "_blank"),
column(width=2, a(img(src = "linkedin.png", style = "margin:5px; width: 20px; height: 20px"), href = "https://www.linkedin.com/in/shubham-maheshwari-93a35b108/"),target = "_blank"),
column(width=2, a(img(src = "twitter.png", style = "margin:6px; width: 18px; height: 18px"), href = "https://twitter.com/real_SM96"),target = "_blank")
)),
column(width=1, align = "center", img(src = "vertical-line.png",style = "max-width: 100%; width: 100%; height: 100%;")),
column(width=3, align = "center",
#img(src = "anant.jpg", style = "max-width: 50%; width: 50%; height: auto"),
h4('Anant Mittal'),
h5('B.Tech Computer Science, IIIT-Delhi'),
h5('anant14015@iiitd.ac.in'),
br(),
br(),
br(),
fluidRow(width = 12,
column(width=2, a(img(src = "email.png", style = "margin:5px; width: 20px; height: 20px"), href = "mailto:anant14015@iiitd.ac.in"), target = "_blank"),
column(width=2, a(img(src = "github.png", width = '30px', height = '30px'), href = "https://github.com/anant15"), target = "_blank"),
column(width=2, a(img(src = "facebook.png", style = "margin:5px; width: 20px; height: 20px"), href = "https://www.facebook.com/shubham.maheshwari3"), target = "_blank"),
column(width=2, a(img(src = "linkedin.png", style = "margin:5px; width: 20px; height: 20px"), href = "https://www.linkedin.com/in/shubham-maheshwari-93a35b108/"), target = "_blank"),
column(width=2, a(img(src = "twitter.png", style = "margin:6px; width: 18px; height: 18px"), href = "https://twitter.com/real_SM96"),target = "_blank")
))
),
hr(),
div(style="text-align:center",
h3('Reference'),
h5('Placeholder'),
hr(),
h3('Acknowlegments'),
h5("Rakesh Lodha, Professor (Pediatrics), All India Institute of Medical Sciences, New Delhi, India"),
h5("Nigam Shah, Associate Professor (Biomedical Informatics), Stanford University, USA"),
h5('Funding Support: The Wellcome Trust/DBT India Alliance Early Career Award IA/CPHE/14/1/501504')
)
)
)
)
)
)
)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.