inst/UI.R
In lubrunn/DSP_App_Abgabe: What the Package Does (One Line, Title Case)
Sys.setlocale("LC_TIME", "English")
ui <- fluidPage(
#### this gets the dimension of the current window --> helps adjusting width and height of plots that
# dont do it automatically
tags$head(tags$script('
var dimension = [0, 0];
$(document).on("shiny:connected", function(e) {
dimension[0] = window.innerWidth;
dimension[1] = window.innerHeight;
Shiny.onInputChange("dimension", dimension);
});
$(window).resize(function(e) {
dimension[0] = window.innerWidth;
dimension[1] = window.innerHeight;
Shiny.onInputChange("dimension", dimension);
});
')),
#######
shinyjs::useShinyjs(),
shinyFeedback::useShinyFeedback(),
rintrojs::introjsUI(),
cicerone::use_cicerone(),
#### change color of instrucitons boxes
tags$style(HTML(".introjs-tooltip {
background-color: #1d465a;}"
)),
theme = shinythemes::shinytheme("superhero"),
### change button colors
tags$style(HTML('.btn-default {
background-color: #2b3e50;
border-color: #888888;
}')),
#shinythemes::themeSelector(),
#titlePanel("Sentiment_Covid_App"),
navbarPage("DSP",
##### directory tab
dir_setter_panel(),
##### twitter
twitter_main_panel(),
##### comparison tab
comp_tab,
####### model tabs
navbarMenu("Model",
tabPanel("Granger",
sidebarPanel(
tabs_custom_gra()
),
mainPanel(
tabsetPanel(
tabPanel("Information Granger",
tags$hr(),
htmlOutput("info_granger"),
withMathJax()),
tabPanel("Visualize",
tags$hr(),
dygraphs::dygraphOutput("stocks_granger"),
tags$hr(),
dygraphs::dygraphOutput("second_granger")),
tabPanel("Background-steps",
tags$hr(),
htmlOutput("grangertext1"),
verbatimTextOutput("optimallags"),
tags$hr(),
htmlOutput("grangertext2"),
verbatimTextOutput("dickey_fuller"),
verbatimTextOutput("dickey_fuller_second"),
tags$hr(),
htmlOutput("grangertext3"),
verbatimTextOutput("dickey_fuller_diff"),
verbatimTextOutput("dickey_fuller_second_diff")),
tabPanel("Results",
tags$hr(),
verbatimTextOutput("granger_result"),
htmlOutput("granger_satz"),
tags$head(tags$style(HTML("#granger_satz{
font-size: 22px;
font-style: bold;
color: white !important;}")))
)))),
tabPanel("Regression Analysis",
sidebarPanel(
tabs_custom()
),
mainPanel(
tabsetPanel(id = "regressiontabs",
tabPanel("Information Regression",
tags$hr(),
htmlOutput("info_regression"),
withMathJax()),
tabPanel("Summary Statistics",
tags$hr(),
"Summary Statistics",
tableOutput("reg_summary"),
tags$head(tags$style(HTML("#reg_summary{
color: black;
font-size: 18px;
font-style: bold;
color: white !important;
}"
)
)),
plotOutput("correlation_reg")
),
tabPanel("Linear Regression",
tags$hr(),
htmlOutput("regression_equation"),
tags$hr(),
tableOutput("regression_result")),
tabPanel("Quantile Regression",value=1,
tags$hr(),
tableOutput("regression_result_Qreg")
)
)
)
),
tabPanel("VAR-forecasting",
sidebarPanel(
tabs_custom_var()
),
mainPanel(
tabsetPanel(id = "vartabs",
tabPanel("Information VAR",
tags$hr(),
htmlOutput("info_var"),
withMathJax()),
tabPanel("Summary Statistics",
tags$hr(),
"Summary Statistics",
tableOutput("var_summary"),
plotOutput("correlation_var")
),
tabPanel("Validity",
tags$hr(),
dygraphs::dygraphOutput("plot_forecast"),
tags$hr(),
"Performance Metrics:",
tableOutput("var_metrics"),
tags$hr(),
verbatimTextOutput("serial_test"),
htmlOutput("var"),value=1,
),
tabPanel("Actual Forecast",
tags$hr(),
dygraphs::dygraphOutput("plot_forecast_real"),
tags$hr(),
verbatimTextOutput("serial_test_real"),
htmlOutput("var_real"))
))),#close tabpanel VAR forecasting
tabPanel("XGboost-forecasting",
sidebarPanel(
conditionalPanel(condition="input.tabs == 'Summary statistics'",
tabs_custom_xgb()),
conditionalPanel(condition="input.tabs == 'AR & MA structure'",
tags$h4("Generate features:"),
shinyWidgets::radioGroupButtons("lag_tabs","Choose type of feature generation?",choices = c("default","custom"),
selected = "default",status = "primary",checkIcon = list(yes = icon("ok",lib = "glyphicon"),
no = icon("remove",lib = "glyphicon")),size = "sm") %>% shinyhelper::helper(type = "markdown",
title = "Inline Help",
content = "default_lag_selection",
buttonLabel = "Got it!",
easyClose = FALSE,
fade = TRUE,
size = "s"),
custom_lag_tab()
),
conditionalPanel(condition="input.tabs == 'Validity'",
# numericInput("split_at","select training/test split",min = 0.1, value=0.7,max = 1,
# step = 0.1),
tags$h4("Set up model:"),
shinyWidgets::radioGroupButtons("model_spec","Choose model specification",choices = c("default","custom","hyperparameter tuning"="hyperparameter_tuning"),
selected = "default",status = "primary",checkIcon = list(yes = icon("ok",lib = "glyphicon"),
no = icon("remove",lib = "glyphicon")),size = "sm") %>% shinyhelper::helper(type = "markdown",
title = "Inline Help",
content = "model_selection",
buttonLabel = "Got it!",
easyClose = FALSE,
fade = TRUE,
size = "s"),
model_specification(),
tags$br(),
tags$h6("Choose role of features in forecast period:"),
shinyWidgets::radioGroupButtons("ftpye","Select usage of features",choices = c("no features"="no_features","past features"="past_features","forecasted features"="forecasted_features"),
selected = "no_features",status = "primary",checkIcon = list(yes = icon("ok",lib = "glyphicon"),
no = icon("remove",lib = "glyphicon")),size = "sm") %>% shinyhelper::helper(type = "markdown",
title = "Inline Help",
content = "features",
buttonLabel = "Got it!",
easyClose = FALSE,
fade = TRUE,
size = "s"),
numericInput("n_ahead","Select forecast horizon",min = 1, value=5,max = 20,
step = 1),
actionButton("run", "Run Model"),
shinyjs::hidden(p(id = "text1", "Processing...")),
actionButton("pred", "Predict"),
shinyjs::hidden(p(id = "text2", "Please run the model first")),
selectInput("forecast_plot_choice","Select plot to show:",
c("Forecasted","Full"),selected="Full")
),
conditionalPanel(condition="input.tabs == 'Actual forecast'",
tags$h4("Set up model:"),
shinyWidgets::radioGroupButtons("model_spec_for","Choose model specification",choices = c("default","custom","hyperparameter tuning"="hyperparameter_tuning"),
selected = "default",status = "primary",checkIcon = list(yes = icon("ok",lib = "glyphicon"),
no = icon("remove",lib = "glyphicon")),size = "sm"),
model_specification_for(),
tags$br(),
tags$h6("Choose role of features in forecast period:"),
shinyWidgets::radioGroupButtons("ftpye2","Select covariates for forecast",choices = c("no features"="no_features","past features"="past_features","forecasted features"="forecasted_features"),
selected = "no_features",status = "primary",checkIcon = list(yes = icon("ok",lib = "glyphicon"),no = icon("remove",lib = "glyphicon")),size = "sm"),
numericInput("n_ahead2","select forecast window (days):",min = 1, value=5,max = 20,
step = 1),
actionButton("run2", "Run Model on the full dataset"),
shinyjs::hidden(p(id = "text1_act", "Processing...")),
actionButton("pred2", "Predict")),
shinyjs::hidden(p(id = "text2_act", "Please run the model first"))
),
mainPanel(
tabsetPanel(type = "tabs", id = "tabs",
tabPanel("Information XGB",
tags$hr(),
htmlOutput("info_xgb_firstpage"),
withMathJax()),
tabPanel("Summary statistics",value="Summary statistics",
tableOutput("xgb_summary"),
tags$hr(style="border-color: white;"),
tags$br(),
tags$br(),
plotOutput("correlation_xgb")
),
tabPanel("AR & MA structure", value = "AR & MA structure",
conditionalPanel(
condition = "input.correlation_type == 'ACF' && input.lag_tabs == 'custom'",
plotOutput("acf_plot_xgb")),
conditionalPanel(
condition = "input.correlation_type == 'PACF' && input.lag_tabs == 'custom'",
plotOutput("pacf_plot_xgb")),
conditionalPanel("input.lag_tabs == 'custom'",
tags$br(),
tags$hr(),
textOutput("error_text"),
DT::dataTableOutput("tableCustom")),
conditionalPanel("input.lag_tabs == 'default'",
DT::dataTableOutput("df_xgb_default"))
),
tabPanel("Validity", value = "Validity",
#verbatimTextOutput("model_xgb"),
tableOutput("model_fit")%>% shinycssloaders::withSpinner(type = 5),
tableOutput("serial_out_xgb"),
htmlOutput("test_text_xgb"),
dygraphs::dygraphOutput("forecast_xgb")%>% shinycssloaders::withSpinner(type = 5),
tableOutput("xgb_metrics")
),
tabPanel("Actual forecast", value = "Actual forecast",
tableOutput("model_fit_act")%>% shinycssloaders::withSpinner(type = 5),
tableOutput("serial_out_xgb_for"),
htmlOutput("test_text_xgb_act"),
dygraphs::dygraphOutput("plot_1_xgb_actual")%>% shinycssloaders::withSpinner(type = 5)
)
)
)
)
)#close Navbarmenu
)#close Navbarpage
)#close fluidpage
lubrunn/DSP_App_Abgabe documentation built on Dec. 21, 2021, 11:51 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Sys.setlocale("LC_TIME", "English")
ui <- fluidPage(
#### this gets the dimension of the current window --> helps adjusting width and height of plots that
# dont do it automatically
tags$head(tags$script('
var dimension = [0, 0];
$(document).on("shiny:connected", function(e) {
dimension[0] = window.innerWidth;
dimension[1] = window.innerHeight;
Shiny.onInputChange("dimension", dimension);
});
$(window).resize(function(e) {
dimension[0] = window.innerWidth;
dimension[1] = window.innerHeight;
Shiny.onInputChange("dimension", dimension);
});
')),
#######
shinyjs::useShinyjs(),
shinyFeedback::useShinyFeedback(),
rintrojs::introjsUI(),
cicerone::use_cicerone(),
#### change color of instrucitons boxes
tags$style(HTML(".introjs-tooltip {
background-color: #1d465a;}"
)),
theme = shinythemes::shinytheme("superhero"),
### change button colors
tags$style(HTML('.btn-default {
background-color: #2b3e50;
border-color: #888888;
}')),
#shinythemes::themeSelector(),
#titlePanel("Sentiment_Covid_App"),
navbarPage("DSP",
##### directory tab
dir_setter_panel(),
##### twitter
twitter_main_panel(),
##### comparison tab
comp_tab,
####### model tabs
navbarMenu("Model",
tabPanel("Granger",
sidebarPanel(
tabs_custom_gra()
),
mainPanel(
tabsetPanel(
tabPanel("Information Granger",
tags$hr(),
htmlOutput("info_granger"),
withMathJax()),
tabPanel("Visualize",
tags$hr(),
dygraphs::dygraphOutput("stocks_granger"),
tags$hr(),
dygraphs::dygraphOutput("second_granger")),
tabPanel("Background-steps",
tags$hr(),
htmlOutput("grangertext1"),
verbatimTextOutput("optimallags"),
tags$hr(),
htmlOutput("grangertext2"),
verbatimTextOutput("dickey_fuller"),
verbatimTextOutput("dickey_fuller_second"),
tags$hr(),
htmlOutput("grangertext3"),
verbatimTextOutput("dickey_fuller_diff"),
verbatimTextOutput("dickey_fuller_second_diff")),
tabPanel("Results",
tags$hr(),
verbatimTextOutput("granger_result"),
htmlOutput("granger_satz"),
tags$head(tags$style(HTML("#granger_satz{
font-size: 22px;
font-style: bold;
color: white !important;}")))
)))),
tabPanel("Regression Analysis",
sidebarPanel(
tabs_custom()
),
mainPanel(
tabsetPanel(id = "regressiontabs",
tabPanel("Information Regression",
tags$hr(),
htmlOutput("info_regression"),
withMathJax()),
tabPanel("Summary Statistics",
tags$hr(),
"Summary Statistics",
tableOutput("reg_summary"),
tags$head(tags$style(HTML("#reg_summary{
color: black;
font-size: 18px;
font-style: bold;
color: white !important;
}"
)
)),
plotOutput("correlation_reg")
),
tabPanel("Linear Regression",
tags$hr(),
htmlOutput("regression_equation"),
tags$hr(),
tableOutput("regression_result")),
tabPanel("Quantile Regression",value=1,
tags$hr(),
tableOutput("regression_result_Qreg")
)
)
)
),
tabPanel("VAR-forecasting",
sidebarPanel(
tabs_custom_var()
),
mainPanel(
tabsetPanel(id = "vartabs",
tabPanel("Information VAR",
tags$hr(),
htmlOutput("info_var"),
withMathJax()),
tabPanel("Summary Statistics",
tags$hr(),
"Summary Statistics",
tableOutput("var_summary"),
plotOutput("correlation_var")
),
tabPanel("Validity",
tags$hr(),
dygraphs::dygraphOutput("plot_forecast"),
tags$hr(),
"Performance Metrics:",
tableOutput("var_metrics"),
tags$hr(),
verbatimTextOutput("serial_test"),
htmlOutput("var"),value=1,
),
tabPanel("Actual Forecast",
tags$hr(),
dygraphs::dygraphOutput("plot_forecast_real"),
tags$hr(),
verbatimTextOutput("serial_test_real"),
htmlOutput("var_real"))
))),#close tabpanel VAR forecasting
tabPanel("XGboost-forecasting",
sidebarPanel(
conditionalPanel(condition="input.tabs == 'Summary statistics'",
tabs_custom_xgb()),
conditionalPanel(condition="input.tabs == 'AR & MA structure'",
tags$h4("Generate features:"),
shinyWidgets::radioGroupButtons("lag_tabs","Choose type of feature generation?",choices = c("default","custom"),
selected = "default",status = "primary",checkIcon = list(yes = icon("ok",lib = "glyphicon"),
no = icon("remove",lib = "glyphicon")),size = "sm") %>% shinyhelper::helper(type = "markdown",
title = "Inline Help",
content = "default_lag_selection",
buttonLabel = "Got it!",
easyClose = FALSE,
fade = TRUE,
size = "s"),
custom_lag_tab()
),
conditionalPanel(condition="input.tabs == 'Validity'",
# numericInput("split_at","select training/test split",min = 0.1, value=0.7,max = 1,
# step = 0.1),
tags$h4("Set up model:"),
shinyWidgets::radioGroupButtons("model_spec","Choose model specification",choices = c("default","custom","hyperparameter tuning"="hyperparameter_tuning"),
selected = "default",status = "primary",checkIcon = list(yes = icon("ok",lib = "glyphicon"),
no = icon("remove",lib = "glyphicon")),size = "sm") %>% shinyhelper::helper(type = "markdown",
title = "Inline Help",
content = "model_selection",
buttonLabel = "Got it!",
easyClose = FALSE,
fade = TRUE,
size = "s"),
model_specification(),
tags$br(),
tags$h6("Choose role of features in forecast period:"),
shinyWidgets::radioGroupButtons("ftpye","Select usage of features",choices = c("no features"="no_features","past features"="past_features","forecasted features"="forecasted_features"),
selected = "no_features",status = "primary",checkIcon = list(yes = icon("ok",lib = "glyphicon"),
no = icon("remove",lib = "glyphicon")),size = "sm") %>% shinyhelper::helper(type = "markdown",
title = "Inline Help",
content = "features",
buttonLabel = "Got it!",
easyClose = FALSE,
fade = TRUE,
size = "s"),
numericInput("n_ahead","Select forecast horizon",min = 1, value=5,max = 20,
step = 1),
actionButton("run", "Run Model"),
shinyjs::hidden(p(id = "text1", "Processing...")),
actionButton("pred", "Predict"),
shinyjs::hidden(p(id = "text2", "Please run the model first")),
selectInput("forecast_plot_choice","Select plot to show:",
c("Forecasted","Full"),selected="Full")
),
conditionalPanel(condition="input.tabs == 'Actual forecast'",
tags$h4("Set up model:"),
shinyWidgets::radioGroupButtons("model_spec_for","Choose model specification",choices = c("default","custom","hyperparameter tuning"="hyperparameter_tuning"),
selected = "default",status = "primary",checkIcon = list(yes = icon("ok",lib = "glyphicon"),
no = icon("remove",lib = "glyphicon")),size = "sm"),
model_specification_for(),
tags$br(),
tags$h6("Choose role of features in forecast period:"),
shinyWidgets::radioGroupButtons("ftpye2","Select covariates for forecast",choices = c("no features"="no_features","past features"="past_features","forecasted features"="forecasted_features"),
selected = "no_features",status = "primary",checkIcon = list(yes = icon("ok",lib = "glyphicon"),no = icon("remove",lib = "glyphicon")),size = "sm"),
numericInput("n_ahead2","select forecast window (days):",min = 1, value=5,max = 20,
step = 1),
actionButton("run2", "Run Model on the full dataset"),
shinyjs::hidden(p(id = "text1_act", "Processing...")),
actionButton("pred2", "Predict")),
shinyjs::hidden(p(id = "text2_act", "Please run the model first"))
),
mainPanel(
tabsetPanel(type = "tabs", id = "tabs",
tabPanel("Information XGB",
tags$hr(),
htmlOutput("info_xgb_firstpage"),
withMathJax()),
tabPanel("Summary statistics",value="Summary statistics",
tableOutput("xgb_summary"),
tags$hr(style="border-color: white;"),
tags$br(),
tags$br(),
plotOutput("correlation_xgb")
),
tabPanel("AR & MA structure", value = "AR & MA structure",
conditionalPanel(
condition = "input.correlation_type == 'ACF' && input.lag_tabs == 'custom'",
plotOutput("acf_plot_xgb")),
conditionalPanel(
condition = "input.correlation_type == 'PACF' && input.lag_tabs == 'custom'",
plotOutput("pacf_plot_xgb")),
conditionalPanel("input.lag_tabs == 'custom'",
tags$br(),
tags$hr(),
textOutput("error_text"),
DT::dataTableOutput("tableCustom")),
conditionalPanel("input.lag_tabs == 'default'",
DT::dataTableOutput("df_xgb_default"))
),
tabPanel("Validity", value = "Validity",
#verbatimTextOutput("model_xgb"),
tableOutput("model_fit")%>% shinycssloaders::withSpinner(type = 5),
tableOutput("serial_out_xgb"),
htmlOutput("test_text_xgb"),
dygraphs::dygraphOutput("forecast_xgb")%>% shinycssloaders::withSpinner(type = 5),
tableOutput("xgb_metrics")
),
tabPanel("Actual forecast", value = "Actual forecast",
tableOutput("model_fit_act")%>% shinycssloaders::withSpinner(type = 5),
tableOutput("serial_out_xgb_for"),
htmlOutput("test_text_xgb_act"),
dygraphs::dygraphOutput("plot_1_xgb_actual")%>% shinycssloaders::withSpinner(type = 5)
)
)
)
)
)#close Navbarmenu
)#close Navbarpage
)#close fluidpage
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.