inst/Shiny/ui.R
In AlexChristensen/SemNeT: Methods and Measures for Semantic Network Analysis
suppressMessages(library(shiny))
suppressMessages(library(shinyalert))
suppressMessages(library(shinyjs))
suppressMessages(library(shinyMatrix))
suppressMessages(library(shinyBS))
suppressMessages(library(SemNeT))
# Interface for SemNeT----
ui <- (
navbarPage(title = "Semantic Network Analysis with SemNeT", id = "tabs",
# Load Data Panel
tabPanel(
"Load Data",
# Input
sidebarPanel(
# Environment objects
uiOutput("clean_ui"), uiOutput("group_ui"),
# Data upload
tags$div(fileInput("data", label = "Upload Preprocessed (Binary) Response Matrix",
accept = c(".rds", ".csv", ".xls", ".xlsx", ".txt")), id = "data"),
# Group variable upload
tags$div(fileInput("group", label = "Upload Group Variable"), id = "group"),
# Data Example
actionButton("data_example", label = "See Response Matrix Example", inline = TRUE),
# Group Example
actionButton("group_example", label = "See Group Variable Example", inline = TRUE),
br(), br(),
actionButton("load_data", label = "Load Data", inline = TRUE),
actionButton("save_data", label = "Save Data", inline = TRUE)
),
# Output
tagList(
mainPanel(
HTML('<center><img src = "SemNeT.png" width = "500", ></center>'),
tableOutput("example_data_response"),
tableOutput("example_data_binary"),
tableOutput("example_group")
),
tags$footer(htmlOutput("SEMNA_cite"), align = "left", style = "position:absolute; top:0; width:30%; height:50px; color: black; margin-left: 20px; margin-top: 700px; z-index: 1000;")
)
),
# Network Estimation Panel
tabPanel(
"Network Estimation",
# Input
sidebarPanel(
# Network estimation method
selectInput("estimation", "Network Estimation Method", c("Community Network (CN)",
"Naive Random Walk (NRW)",
"Pathfinder Network (PF)",
"Triangulated Maximally Filtered Graph (TMFG)"
)),
uiOutput("network_options_1"),
uiOutput("network_options_2"),
uiOutput("network_options_3"),
actionButton("run_est", label = "Estimate Networks"),
actionButton("save_nets", label = "Save Networks", inline = TRUE)
),
# Output
tagList(
mainPanel(
plotOutput("viz"),
tableOutput("measures")
),
tags$footer(htmlOutput("net_cite"), align = "left", style = "position:absolute; top:0; width:30%; height:50px; color: black; margin-left: 20px; margin-top: 700px; z-index: 1000;")
)
),
# Random Network Analyses Panel
tabPanel(
"Random Network Analyses",
# Input
sidebarPanel(
numericInput("iters_rand", label = "Number of Iterations", value = 1000, min = 0, step = 100),
uiOutput("cores_rand"),
actionButton("run_rand", label = "Perform Random Network Analyses"),
actionButton("save_rand", label = "Save Random Network Results", inline = TRUE)
),
# Output
tagList(
mainPanel(
tableOutput("randnet")
),
tags$footer(htmlOutput("randnet_cite"), align = "left", style = "position:absolute; top:0; width:30%; height:50px; color: black; margin-left: 20px; margin-top: 500px; z-index: 1000;")
)
),
# Bootstrap Analyses Panel
tabPanel(
"Bootstrap Analyses",
# Input
sidebarPanel(
selectInput("type_select", "Type of Bootstrap",
c("Case", "Node"),
selected = "Case"),
uiOutput("type"),
uiOutput("test"),
numericInput("iters_boot", label = "Number of Iterations", value = 1000, min = 0, step = 100),
uiOutput("cores_boot"),
actionButton("run_boot", label = "Perform Bootstrap Analyses"),
actionButton("run_plot", label = "Generate Bootstrap Plots"),
actionButton("save_boot", label = "Save Bootstrap Analyses", inline = TRUE)
),
# Output
tagList(
mainPanel(
tableOutput("aspl"),
tableOutput("cc"),
tableOutput("q"),
tableOutput("tab"),
plotOutput("asplPlot"),
plotOutput("ccPlot"),
plotOutput("qPlot")
),
tags$footer(htmlOutput("partial_cite"), align = "left", style = "position:absolute; top:0; width:30%; height:50px; color: black; margin-left: 20px; margin-top: 700px; z-index: 1000;")
)
),
# Permutation Analyses Panel
#tabPanel(
# "Permutation Analyses",
#
# # Input
# sidebarPanel(
#
# uiOutput("group1"), uiOutput("group2"), uiOutput("alter"),
#
# selectInput("meas_perm", label = "Network Measure",
# choices = c("Average Shortest Path Length (ASPL)",
# "Clustering Coefficient (CC)",
# "Modularity (Q)"
# )
# ),
#
# numericInput("iters_perm", label = "Number of Iterations", value = 1000, min = 0, step = 100),
#
# uiOutput("cores_perm"),
#
# actionButton("run_perm", label = "Perform Permutation Analyses")
#
# ),
#
# # Output
# tagList(
#
# mainPanel(
# tableOutput("perm_table")
# ),
#
# )
#
# ),
# Random Walk Analyses Panel
tabPanel(
"Random Walk Analyses",
# Input
sidebarPanel(
uiOutput("A"),
uiOutput("B"),
numericInput("steps", label = "Starting Number of Steps", value = 10, min = 0, max = Inf, step = 1),
numericInput("reps", label = "Number of Repetitions (Each Repetition Increases Number of Steps by 10)", value = 20, min = 0, max = Inf, step = 5),
numericInput("iters_walk", label = "Number of Iterations", value = 10000, min = 0, max = Inf, step = 1000),
uiOutput("cores_walk"),
actionButton("run_walk", label = "Perform Random Walk Analyses"),
actionButton("save_walk", label = "Save Random Walk Analyses", inline = TRUE)
),
# Output
tagList(
mainPanel(
tableOutput("walk_rand")
),
tags$footer(htmlOutput("randwalk_cite"), align = "left", style = "position:absolute; top:0; width:30%; height:50px; color: black; margin-left: 20px; margin-top: 800px; z-index: 1000;")
)
),
# Spreading Activation Analyses Panel
tabPanel(
"Spreading Activation Analyses",
# Input
sidebarPanel(
uiOutput("network_select"),
numericInput("retention", label = "Retention (proportion of activation that remains in spreading node)", value = 0.5, min = 0, max = 1, step = .05),
numericInput("time", label = "Number of Time Steps", value = 10, min = 0, max = Inf, step = 1),
numericInput("decay", label = "Decay (activation lost at each time step)", value = 0, min = 0, max = 1, step = .05),
numericInput("suppress", label = "Suppress (activation less than value is set to zero)", value = 0, min = 0, max = 1, step = .001),
actionButton("set_act", label = "Set Activations"),
shinyBS::bsModal("node_pop", title = "Node Activation Table", trigger = "set_act", size = "large",
uiOutput("node_select")
),
actionButton("run_spr_act", label = "Perform Spreading Activation Analyses"),
selectInput("animate_size", label = "Plot Size",
choices = c("Small (500 x 500)", "Medium (900 x 900)", "Large (1400 x 1400)"),
selected = "Medium (900 x 900)"),
actionButton("animate", label = "Generate Spreading Activation Plot"),
actionButton("save_act", label = "Save Spreading Activation Analyses", inline = TRUE),
actionButton("reset_act", label = "Reset Spreading Activation")
),
# Output
tagList(
mainPanel(
uiOutput("animate_slider"),
plotOutput("spreadr_animate")
),
tags$footer(htmlOutput("spreadr_cite"), align = "left", style = "position:absolute; top:0; width:30%; height:50px; color: black; margin-left: 20px; margin-top: 700px; z-index: 1000;")
)
),
# Save and Reset Results Panel
tabPanel(
"Save and Reset All Results",
# Input
sidebarPanel(
actionButton("save_master", label = "Save All Results"),
br(), br(),
actionButton("reset", label = "Clear Results")
)
),
# Use shinyalert
shinyalert::useShinyalert(),
# Use shinyjs
shinyjs::useShinyjs()
)
)
AlexChristensen/SemNeT documentation built on Aug. 21, 2023, 11:01 p.m.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
suppressMessages(library(shiny))
suppressMessages(library(shinyalert))
suppressMessages(library(shinyjs))
suppressMessages(library(shinyMatrix))
suppressMessages(library(shinyBS))
suppressMessages(library(SemNeT))
# Interface for SemNeT----
ui <- (
navbarPage(title = "Semantic Network Analysis with SemNeT", id = "tabs",
# Load Data Panel
tabPanel(
"Load Data",
# Input
sidebarPanel(
# Environment objects
uiOutput("clean_ui"), uiOutput("group_ui"),
# Data upload
tags$div(fileInput("data", label = "Upload Preprocessed (Binary) Response Matrix",
accept = c(".rds", ".csv", ".xls", ".xlsx", ".txt")), id = "data"),
# Group variable upload
tags$div(fileInput("group", label = "Upload Group Variable"), id = "group"),
# Data Example
actionButton("data_example", label = "See Response Matrix Example", inline = TRUE),
# Group Example
actionButton("group_example", label = "See Group Variable Example", inline = TRUE),
br(), br(),
actionButton("load_data", label = "Load Data", inline = TRUE),
actionButton("save_data", label = "Save Data", inline = TRUE)
),
# Output
tagList(
mainPanel(
HTML('<center><img src = "SemNeT.png" width = "500", ></center>'),
tableOutput("example_data_response"),
tableOutput("example_data_binary"),
tableOutput("example_group")
),
tags$footer(htmlOutput("SEMNA_cite"), align = "left", style = "position:absolute; top:0; width:30%; height:50px; color: black; margin-left: 20px; margin-top: 700px; z-index: 1000;")
)
),
# Network Estimation Panel
tabPanel(
"Network Estimation",
# Input
sidebarPanel(
# Network estimation method
selectInput("estimation", "Network Estimation Method", c("Community Network (CN)",
"Naive Random Walk (NRW)",
"Pathfinder Network (PF)",
"Triangulated Maximally Filtered Graph (TMFG)"
)),
uiOutput("network_options_1"),
uiOutput("network_options_2"),
uiOutput("network_options_3"),
actionButton("run_est", label = "Estimate Networks"),
actionButton("save_nets", label = "Save Networks", inline = TRUE)
),
# Output
tagList(
mainPanel(
plotOutput("viz"),
tableOutput("measures")
),
tags$footer(htmlOutput("net_cite"), align = "left", style = "position:absolute; top:0; width:30%; height:50px; color: black; margin-left: 20px; margin-top: 700px; z-index: 1000;")
)
),
# Random Network Analyses Panel
tabPanel(
"Random Network Analyses",
# Input
sidebarPanel(
numericInput("iters_rand", label = "Number of Iterations", value = 1000, min = 0, step = 100),
uiOutput("cores_rand"),
actionButton("run_rand", label = "Perform Random Network Analyses"),
actionButton("save_rand", label = "Save Random Network Results", inline = TRUE)
),
# Output
tagList(
mainPanel(
tableOutput("randnet")
),
tags$footer(htmlOutput("randnet_cite"), align = "left", style = "position:absolute; top:0; width:30%; height:50px; color: black; margin-left: 20px; margin-top: 500px; z-index: 1000;")
)
),
# Bootstrap Analyses Panel
tabPanel(
"Bootstrap Analyses",
# Input
sidebarPanel(
selectInput("type_select", "Type of Bootstrap",
c("Case", "Node"),
selected = "Case"),
uiOutput("type"),
uiOutput("test"),
numericInput("iters_boot", label = "Number of Iterations", value = 1000, min = 0, step = 100),
uiOutput("cores_boot"),
actionButton("run_boot", label = "Perform Bootstrap Analyses"),
actionButton("run_plot", label = "Generate Bootstrap Plots"),
actionButton("save_boot", label = "Save Bootstrap Analyses", inline = TRUE)
),
# Output
tagList(
mainPanel(
tableOutput("aspl"),
tableOutput("cc"),
tableOutput("q"),
tableOutput("tab"),
plotOutput("asplPlot"),
plotOutput("ccPlot"),
plotOutput("qPlot")
),
tags$footer(htmlOutput("partial_cite"), align = "left", style = "position:absolute; top:0; width:30%; height:50px; color: black; margin-left: 20px; margin-top: 700px; z-index: 1000;")
)
),
# Permutation Analyses Panel
#tabPanel(
# "Permutation Analyses",
#
# # Input
# sidebarPanel(
#
# uiOutput("group1"), uiOutput("group2"), uiOutput("alter"),
#
# selectInput("meas_perm", label = "Network Measure",
# choices = c("Average Shortest Path Length (ASPL)",
# "Clustering Coefficient (CC)",
# "Modularity (Q)"
# )
# ),
#
# numericInput("iters_perm", label = "Number of Iterations", value = 1000, min = 0, step = 100),
#
# uiOutput("cores_perm"),
#
# actionButton("run_perm", label = "Perform Permutation Analyses")
#
# ),
#
# # Output
# tagList(
#
# mainPanel(
# tableOutput("perm_table")
# ),
#
# )
#
# ),
# Random Walk Analyses Panel
tabPanel(
"Random Walk Analyses",
# Input
sidebarPanel(
uiOutput("A"),
uiOutput("B"),
numericInput("steps", label = "Starting Number of Steps", value = 10, min = 0, max = Inf, step = 1),
numericInput("reps", label = "Number of Repetitions (Each Repetition Increases Number of Steps by 10)", value = 20, min = 0, max = Inf, step = 5),
numericInput("iters_walk", label = "Number of Iterations", value = 10000, min = 0, max = Inf, step = 1000),
uiOutput("cores_walk"),
actionButton("run_walk", label = "Perform Random Walk Analyses"),
actionButton("save_walk", label = "Save Random Walk Analyses", inline = TRUE)
),
# Output
tagList(
mainPanel(
tableOutput("walk_rand")
),
tags$footer(htmlOutput("randwalk_cite"), align = "left", style = "position:absolute; top:0; width:30%; height:50px; color: black; margin-left: 20px; margin-top: 800px; z-index: 1000;")
)
),
# Spreading Activation Analyses Panel
tabPanel(
"Spreading Activation Analyses",
# Input
sidebarPanel(
uiOutput("network_select"),
numericInput("retention", label = "Retention (proportion of activation that remains in spreading node)", value = 0.5, min = 0, max = 1, step = .05),
numericInput("time", label = "Number of Time Steps", value = 10, min = 0, max = Inf, step = 1),
numericInput("decay", label = "Decay (activation lost at each time step)", value = 0, min = 0, max = 1, step = .05),
numericInput("suppress", label = "Suppress (activation less than value is set to zero)", value = 0, min = 0, max = 1, step = .001),
actionButton("set_act", label = "Set Activations"),
shinyBS::bsModal("node_pop", title = "Node Activation Table", trigger = "set_act", size = "large",
uiOutput("node_select")
),
actionButton("run_spr_act", label = "Perform Spreading Activation Analyses"),
selectInput("animate_size", label = "Plot Size",
choices = c("Small (500 x 500)", "Medium (900 x 900)", "Large (1400 x 1400)"),
selected = "Medium (900 x 900)"),
actionButton("animate", label = "Generate Spreading Activation Plot"),
actionButton("save_act", label = "Save Spreading Activation Analyses", inline = TRUE),
actionButton("reset_act", label = "Reset Spreading Activation")
),
# Output
tagList(
mainPanel(
uiOutput("animate_slider"),
plotOutput("spreadr_animate")
),
tags$footer(htmlOutput("spreadr_cite"), align = "left", style = "position:absolute; top:0; width:30%; height:50px; color: black; margin-left: 20px; margin-top: 700px; z-index: 1000;")
)
),
# Save and Reset Results Panel
tabPanel(
"Save and Reset All Results",
# Input
sidebarPanel(
actionButton("save_master", label = "Save All Results"),
br(), br(),
actionButton("reset", label = "Clear Results")
)
),
# Use shinyalert
shinyalert::useShinyalert(),
# Use shinyjs
shinyjs::useShinyjs()
)
)
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.
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