ui.R
In NVE/FlomKart_ShinyApp: A list a functions to support the FlomKart Shiny app
# User interface file for Shiny App
ui <- navbarPage("Flood frequency analysis", # cut off: id = "nav",
tabPanel("About", icon = icon("info"),
fluidRow(
column(8, wellPanel(
HTML('
<p style="margin-left:1em; style="text-align:justify"> This app lets you explore various frequency analysis options for fitting flood data in Norway </p>
<p style="margin-left:1em; style="text-align:justify"> <b> Research questions </b> </p>
<p style="margin-left:1em; style="text-align:justify"> Which distribution gives the best fit to the data? </p>
<p style="margin-left:1em; style="text-align:justify"> Which estimation method provides the best fit to the data? </p>
<p style="margin-left:1em; style="text-align:justify"> Does the answer depend on local data availability? </p>
<p style="margin-left:1em; style="text-align:justify"> <big> <b> Probability distributions </b> </big> </p>
<div> <dl>
<p style="margin-left:1em; style="text-align:justify"> Gamma </p>
<p style="margin-left:1em; style="text-align:justify"> Gumbel </p>
<p style="margin-left:1em; style="text-align:justify"> GEV: Generalized extreme value </p>
<p style="margin-left:1em; style="text-align:justify"> GL: Generalized logistics </p>
<p style="margin-left:1em; style="text-align:justify"> Pearson III </p>
</div> </dl>
<p style="margin-left:1em; style="text-align:justify"> <b> Estimations methods </b> </p>
<p style="margin-left:1em; style="text-align:justify"> MLE : maximum likelihood estimation </p>
<p style="margin-left:1em; style="text-align:justify"> Lmom: Linear moments </p>
<p style="margin-left:1em; style="text-align:justify"> MOM: Ordinary moments </p>
<p style="margin-left:1em; style="text-align:justify"> Bayesian estimation </p>'
))
),
column(4, leafletOutput('map.groups_first.tab'))
),
fluidRow(
column(3, wellPanel(
selectInput(inputId='min_years', selected = '30', label='Select a minimum number of years',
choices = c(30,60,90,120))
)
),
column(3, wellPanel(
selectInput(inputId='max_years', selected = '150', label='Select a maximum number of years',
choices = c(60,90,120,150))
)
),
column(3, wellPanel(
selectInput(inputId='min_height', selected = '0', label='Select the catchment minimum elevation',
choices = seq(0,2500,500))
)
),
column(3, wellPanel(
selectInput(inputId='max_height', selected = '2500', label='Select the catchment maximum elevation',
choices = seq(0,2500,500))
)
)
),
fluidRow(
column(3,plotlyOutput('qdata_boxplot')),
column(3,plotlyOutput('qdata_barplot')),
column(3, wellPanel(
selectInput(inputId='station2rawplot', selected = station$number[37],
label = "Pick a station for the plots", choices = station$number)
)
)
# ,
# column(3, wellPanel(
# selectInput(inputId='station4region', selected = '1', label='Select a specific station',
# choices = c("0001", "0002"))
# )
# )
)
),
# tagList(
navbarMenu("Single station analysis", icon = icon("compress"),
tabPanel("Main plots",
fluidRow(
column(2, wellPanel(
selectInput(inputId='station', selected = station$number[37],
label = "Pick a station", choices = station$number)
)
),
bsTooltip("station", "A station can also be selected by clicking on its marker in the map below", "bottom", options = list(container="body")),
column(2, wellPanel(
selectInput(inputId='distr', selected = 'gamma', label='Probability distribution',
choices = distr.name)
)
),
bsTooltip("distr", "Gamma and gumbel are 2-parameter distributions, the others use 3", "top", options = list(container="body")),
column(2, wellPanel(
selectInput(inputId='method', label = "Estimation method", choices = method.name)
)
),
column(2, wellPanel(
selectInput(inputId='length', label = "Choose a record length", selected = 30,
choices = c('FULL RECORD', sampling_years)) # Sampling years should be recalculated by the app
)
),
column(2, wellPanel(
uiOutput("random.panel")
)
),
column(2, wellPanel(actionButton("help_loc_btn", "About this tab", class="btn-block"), br())),
bsModal("modal_loc", "About", "help_loc_btn", size="large",
HTML('
<p style="text-align:justify"> This app lets you explore various frequency analysis options for fitting flood data in Norway </p>
<p style="text-align:justify"> Our research questions are </p>
<p style="text-align:justify"> Which distribution gives the best fit to the data? </p>
<p style="text-align:justify"> Which estimation method provides the best fit to the data? </p>
<p style="text-align:justify"> Does the answer depend on local data availability? </p>'
))
),
fluidRow(
column(3, plotOutput('main.plot')),
column(3, plotOutput('rlevels.plot')), # plot4server_rlevel
column(3, plotOutput('cdf.plot')),
column(3, plotOutput('qq.plot')) # plot4server_rlevel
),
fluidRow(
column(5,
h4("Goodness of fit summary"),
formattableOutput("gof.table"),
# uiOutput("gof.table"), # Switch to this if plotting the interactive table (highlight)
h4("Summary for return levels, QS or BS"),
formattableOutput("gof.table2")
),
column(3, wellPanel(
selectInput(inputId='gof2table', selected = 'KS', label='Select a goodness of fit measure to summarize',
choices = c('CS', 'KS', 'AD'))),
wellPanel(),
wellPanel(
selectInput(inputId='gof2table2', selected = 'r.levels', label='Select a goodness of fit measure to summarize',
choices = c('r.levels', 'QS', 'BS')),
# selectInput(inputId='r.period4table', selected = 100, label='Choose a return period for the table',
# choices = return.periods)
uiOutput("r.period4table")
)
),
column(4, leafletOutput('map'))
) # closing last fluidrow
), # closing the first tab
tabPanel("Return levels",
fluidRow(
column(3, wellPanel(
selectInput(inputId='station4rlevels', selected = station$number[37],
label = "Pick a station", choices = station$number)
)),
column(3, wellPanel(
selectInput(inputId='r.period', selected = 100, label='Which return period to plot?',
choices = return.periods)
)
),
column(3, wellPanel(
selectInput(inputId='quantile', selected = 1, label='Mean, sd, min, max, q25, q50, q75',
choices = c(1, 2, 3, 4, 5, 6, 7))
)
)
),
fluidRow(plotOutput('plot.rlevels', width = "100%", height = "900px"))
), # closing tab
tabPanel("Quantile score",
fluidRow(
column(3, wellPanel(
selectInput(inputId='station4qs', selected = station$number[37],
label = "Pick a station", choices = station$number)
)),
column(3, wellPanel(
selectInput(inputId='r.period4qs', selected = 10, label='Which return period to plot?',
choices = rperiods.bs)
)
),
column(3, wellPanel(
selectInput(inputId='quantile4qs', selected = 1, label='Mean, sd, min, max, q25, q50, q75',
choices = c(1, 2, 3, 4, 5, 6, 7))
)
)
),
fluidRow(plotOutput('plot.qs', width = "100%", height = "900px"))
), # closing tab
tabPanel("Brier score",
fluidRow(
column(3, wellPanel(
selectInput(inputId='station4bs', selected = station$number[37],
label = "Pick a station", choices = station$number)
)),
column(3, wellPanel(
selectInput(inputId='r.period4bs', selected = 10, label='Which return period to plot?',
choices = rperiods.bs)
)
),
column(3, wellPanel(
selectInput(inputId='quantile4bs', selected = 1, label='Mean, sd, min, max, q25, q50, q75',
choices = c(1, 2, 3, 4, 5, 6, 7))
)
)
),
fluidRow(plotOutput('plot.bs', width = "100%", height = "900px"))
), # closing tab
tabPanel("NT",
fluidRow(
column(3, wellPanel(
selectInput(inputId='station4nt', selected = station$number[37],
label = "Pick a station", choices = station$number)
)),
column(3, wellPanel(
selectInput(inputId='r.period4nt', selected = 10, label='Which return period to plot?',
choices = rperiods.bs)
)
)
),
fluidRow(plotOutput('plot.nt', width = "100%", height = "900px"))
), # closing tab
tabPanel("Coefficients of variation",
fluidRow(
column(3, wellPanel(
selectInput(inputId='station4cv', selected = station$number[37],
label = "Pick a station", choices = station$number)
)),
column(3, wellPanel(
uiOutput("r.period4coefvar")
)
),
column(3, wellPanel(
selectInput(inputId='coeffvar2plot', selected = 'r.levels', label='Select a goodness of fit measure to summarize',
choices = c('r.levels', 'QS', 'BS'))
))
),
fluidRow(plotOutput('plot.rlevels_coeff', width = "100%", height = "900px"))
), # closing tab
tabPanel("Kolmogorov Smirnoff",
fluidRow(
column(3, wellPanel(
selectInput(inputId='station4ks', selected = station$number[37],
label = "Pick a station", choices = station$number)
)
)
),
fluidRow(
plotOutput('plot.ks', width = "100%", height = "900px")
)
), # closing tab
tabPanel("Anderson Darling",
fluidRow(
column(3, wellPanel(
selectInput(inputId='station4ad', selected = station$number[37],
label = "Pick a station", choices = station$number)
)
)
),
fluidRow(
plotOutput('plot.ad', width = "100%", height = "900px")
)
), # closing tab
tabPanel("Chi Square",
fluidRow(
column(3, wellPanel(
selectInput(inputId='station4cs', selected = station$number[37],
label = "Pick a station", choices = station$number)
)
)
),
fluidRow(
plotOutput('plot.cs', width = "100%", height = "900px")
)
) # closing tab
), # Closing navbarMenu
navbarMenu("Station averaged analysis", icon = icon("expand"),
tabPanel("Station-averaged CV",
fluidRow(
column(3, wellPanel(
uiOutput("r.period4coefvar_ave")
)
),
column(3, wellPanel(
selectInput(inputId='coeffvar2plot_ave', selected = 'r.levels', label='Select a goodness of fit measure to summarize',
choices = c('r.levels', 'QS', 'BS', 'NT'))
)),
column(3, wellPanel(
selectInput(inputId='min_years4coeff_ave', selected = '30', label='Select a minimum number of years',
choices = c(30,60,90,120))
)
),
column(3, wellPanel(
selectInput(inputId='max_years4coeff_ave', selected = '150', label='Select a maximum number of years',
choices = c(60,90,120,150))
)
)
),
fluidRow(
plotOutput('plot.rlevels_coeff_averaged', width = "100%", height = "800px")
)
), # closing tab
tabPanel("Station-averaged GOF",
fluidRow(
column(3, wellPanel(
selectInput(inputId='gof4ave', selected = 'KS', label='Select a goodness of fit measure',
choices = c('CS', 'KS', 'AD'))
)
),
column(4, wellPanel(
selectInput(inputId='min_years4ave', selected = '30', label='Select a minimum number of years',
choices = c(30,60,90,120))
)
),
column(4, wellPanel(
selectInput(inputId='max_years4ave', selected = '150', label='Select a maximum number of years',
choices = c(60,90,120,150))
)
)
),
fluidRow(
plotOutput('plot.gof_averaged', width = "100%", height = "900px")
)
) # closing tab
), # Closing navbarMenu
navbarMenu("Mapping tools",
tabPanel("Mapping GOF-based groups of stations",
fluidRow(
column(3, wellPanel(
selectInput(inputId='gof4st_groups', selected = 'KS', label='Select a goodness of fit measure',
choices = c('CS', 'KS', 'AD'))
)
),
column(3, wellPanel(
selectInput(inputId='minmax', label = "Want to get min GOF (best perf) or max GOF", choices = c("min", "max")) )
),
column(3, wellPanel(
selectInput(inputId='distr4st_groups', selected = 'gamma', label='Select a probability distribution',
choices = c(distr.name, "ALL"))
)
),
column(3, wellPanel(
selectInput(inputId='method4st_groups', label = "Select an estimation method", choices = c(method.name, "ALL")) )
)
),
fluidRow(
column(9, DT::dataTableOutput('group.table')
),
column(3,
h4("Maps of stations with common best method and distribution"),
leafletOutput('map.groups_from_gof', height = "800px")
)
)
),
tabPanel("Mapping parameter values",
fluidRow(
column(3, wellPanel(
selectInput(inputId='dist4param_maps', selected = 'GEV', label='Select a distribution',
choices = distr.name)
)
),
column(3, wellPanel(
selectInput(inputId='method4param_maps', selected = 'mle', label = "Select an estimation method", choices = method.name )
)
),
column(3, wellPanel(
selectInput(inputId='param4param_maps', selected = 1, label = "Which parameter to map", choices = c(1, 2, 3) )
)
)
),
fluidRow(
column(9, plotOutput('param.histo', width = "100%", height = "800px")
),
column(3,
h4("Map showing parameter values across Norway"),
leafletOutput('map.param_values', height = "800px")
)
)
), icon = icon("globe") # closing tab
), # Closing navbarMenu
navbarMenu("Table tools",
tabPanel("Table summary",
fluidRow(
h4("Table"),
DT::dataTableOutput('test.table')
)
# leafletOutput('map4table') # DOESNT WORK WITH THIS VERSION OF DT
), icon = icon("table") # closing tab
), # Closing navbarMenu
windowTitle = "Explore Big Mama!"
) # Closing navbarPage
# Closing UI
# to check out from Radiant https://github.com/vnijs/radiant/blob/master/inst/analytics/ui.R
# radiant@rady.ucsd.edu
NVE/FlomKart_ShinyApp documentation built on May 7, 2019, 6:03 p.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
# User interface file for Shiny App
ui <- navbarPage("Flood frequency analysis", # cut off: id = "nav",
tabPanel("About", icon = icon("info"),
fluidRow(
column(8, wellPanel(
HTML('
<p style="margin-left:1em; style="text-align:justify"> This app lets you explore various frequency analysis options for fitting flood data in Norway </p>
<p style="margin-left:1em; style="text-align:justify"> <b> Research questions </b> </p>
<p style="margin-left:1em; style="text-align:justify"> Which distribution gives the best fit to the data? </p>
<p style="margin-left:1em; style="text-align:justify"> Which estimation method provides the best fit to the data? </p>
<p style="margin-left:1em; style="text-align:justify"> Does the answer depend on local data availability? </p>
<p style="margin-left:1em; style="text-align:justify"> <big> <b> Probability distributions </b> </big> </p>
<div> <dl>
<p style="margin-left:1em; style="text-align:justify"> Gamma </p>
<p style="margin-left:1em; style="text-align:justify"> Gumbel </p>
<p style="margin-left:1em; style="text-align:justify"> GEV: Generalized extreme value </p>
<p style="margin-left:1em; style="text-align:justify"> GL: Generalized logistics </p>
<p style="margin-left:1em; style="text-align:justify"> Pearson III </p>
</div> </dl>
<p style="margin-left:1em; style="text-align:justify"> <b> Estimations methods </b> </p>
<p style="margin-left:1em; style="text-align:justify"> MLE : maximum likelihood estimation </p>
<p style="margin-left:1em; style="text-align:justify"> Lmom: Linear moments </p>
<p style="margin-left:1em; style="text-align:justify"> MOM: Ordinary moments </p>
<p style="margin-left:1em; style="text-align:justify"> Bayesian estimation </p>'
))
),
column(4, leafletOutput('map.groups_first.tab'))
),
fluidRow(
column(3, wellPanel(
selectInput(inputId='min_years', selected = '30', label='Select a minimum number of years',
choices = c(30,60,90,120))
)
),
column(3, wellPanel(
selectInput(inputId='max_years', selected = '150', label='Select a maximum number of years',
choices = c(60,90,120,150))
)
),
column(3, wellPanel(
selectInput(inputId='min_height', selected = '0', label='Select the catchment minimum elevation',
choices = seq(0,2500,500))
)
),
column(3, wellPanel(
selectInput(inputId='max_height', selected = '2500', label='Select the catchment maximum elevation',
choices = seq(0,2500,500))
)
)
),
fluidRow(
column(3,plotlyOutput('qdata_boxplot')),
column(3,plotlyOutput('qdata_barplot')),
column(3, wellPanel(
selectInput(inputId='station2rawplot', selected = station$number[37],
label = "Pick a station for the plots", choices = station$number)
)
)
# ,
# column(3, wellPanel(
# selectInput(inputId='station4region', selected = '1', label='Select a specific station',
# choices = c("0001", "0002"))
# )
# )
)
),
# tagList(
navbarMenu("Single station analysis", icon = icon("compress"),
tabPanel("Main plots",
fluidRow(
column(2, wellPanel(
selectInput(inputId='station', selected = station$number[37],
label = "Pick a station", choices = station$number)
)
),
bsTooltip("station", "A station can also be selected by clicking on its marker in the map below", "bottom", options = list(container="body")),
column(2, wellPanel(
selectInput(inputId='distr', selected = 'gamma', label='Probability distribution',
choices = distr.name)
)
),
bsTooltip("distr", "Gamma and gumbel are 2-parameter distributions, the others use 3", "top", options = list(container="body")),
column(2, wellPanel(
selectInput(inputId='method', label = "Estimation method", choices = method.name)
)
),
column(2, wellPanel(
selectInput(inputId='length', label = "Choose a record length", selected = 30,
choices = c('FULL RECORD', sampling_years)) # Sampling years should be recalculated by the app
)
),
column(2, wellPanel(
uiOutput("random.panel")
)
),
column(2, wellPanel(actionButton("help_loc_btn", "About this tab", class="btn-block"), br())),
bsModal("modal_loc", "About", "help_loc_btn", size="large",
HTML('
<p style="text-align:justify"> This app lets you explore various frequency analysis options for fitting flood data in Norway </p>
<p style="text-align:justify"> Our research questions are </p>
<p style="text-align:justify"> Which distribution gives the best fit to the data? </p>
<p style="text-align:justify"> Which estimation method provides the best fit to the data? </p>
<p style="text-align:justify"> Does the answer depend on local data availability? </p>'
))
),
fluidRow(
column(3, plotOutput('main.plot')),
column(3, plotOutput('rlevels.plot')), # plot4server_rlevel
column(3, plotOutput('cdf.plot')),
column(3, plotOutput('qq.plot')) # plot4server_rlevel
),
fluidRow(
column(5,
h4("Goodness of fit summary"),
formattableOutput("gof.table"),
# uiOutput("gof.table"), # Switch to this if plotting the interactive table (highlight)
h4("Summary for return levels, QS or BS"),
formattableOutput("gof.table2")
),
column(3, wellPanel(
selectInput(inputId='gof2table', selected = 'KS', label='Select a goodness of fit measure to summarize',
choices = c('CS', 'KS', 'AD'))),
wellPanel(),
wellPanel(
selectInput(inputId='gof2table2', selected = 'r.levels', label='Select a goodness of fit measure to summarize',
choices = c('r.levels', 'QS', 'BS')),
# selectInput(inputId='r.period4table', selected = 100, label='Choose a return period for the table',
# choices = return.periods)
uiOutput("r.period4table")
)
),
column(4, leafletOutput('map'))
) # closing last fluidrow
), # closing the first tab
tabPanel("Return levels",
fluidRow(
column(3, wellPanel(
selectInput(inputId='station4rlevels', selected = station$number[37],
label = "Pick a station", choices = station$number)
)),
column(3, wellPanel(
selectInput(inputId='r.period', selected = 100, label='Which return period to plot?',
choices = return.periods)
)
),
column(3, wellPanel(
selectInput(inputId='quantile', selected = 1, label='Mean, sd, min, max, q25, q50, q75',
choices = c(1, 2, 3, 4, 5, 6, 7))
)
)
),
fluidRow(plotOutput('plot.rlevels', width = "100%", height = "900px"))
), # closing tab
tabPanel("Quantile score",
fluidRow(
column(3, wellPanel(
selectInput(inputId='station4qs', selected = station$number[37],
label = "Pick a station", choices = station$number)
)),
column(3, wellPanel(
selectInput(inputId='r.period4qs', selected = 10, label='Which return period to plot?',
choices = rperiods.bs)
)
),
column(3, wellPanel(
selectInput(inputId='quantile4qs', selected = 1, label='Mean, sd, min, max, q25, q50, q75',
choices = c(1, 2, 3, 4, 5, 6, 7))
)
)
),
fluidRow(plotOutput('plot.qs', width = "100%", height = "900px"))
), # closing tab
tabPanel("Brier score",
fluidRow(
column(3, wellPanel(
selectInput(inputId='station4bs', selected = station$number[37],
label = "Pick a station", choices = station$number)
)),
column(3, wellPanel(
selectInput(inputId='r.period4bs', selected = 10, label='Which return period to plot?',
choices = rperiods.bs)
)
),
column(3, wellPanel(
selectInput(inputId='quantile4bs', selected = 1, label='Mean, sd, min, max, q25, q50, q75',
choices = c(1, 2, 3, 4, 5, 6, 7))
)
)
),
fluidRow(plotOutput('plot.bs', width = "100%", height = "900px"))
), # closing tab
tabPanel("NT",
fluidRow(
column(3, wellPanel(
selectInput(inputId='station4nt', selected = station$number[37],
label = "Pick a station", choices = station$number)
)),
column(3, wellPanel(
selectInput(inputId='r.period4nt', selected = 10, label='Which return period to plot?',
choices = rperiods.bs)
)
)
),
fluidRow(plotOutput('plot.nt', width = "100%", height = "900px"))
), # closing tab
tabPanel("Coefficients of variation",
fluidRow(
column(3, wellPanel(
selectInput(inputId='station4cv', selected = station$number[37],
label = "Pick a station", choices = station$number)
)),
column(3, wellPanel(
uiOutput("r.period4coefvar")
)
),
column(3, wellPanel(
selectInput(inputId='coeffvar2plot', selected = 'r.levels', label='Select a goodness of fit measure to summarize',
choices = c('r.levels', 'QS', 'BS'))
))
),
fluidRow(plotOutput('plot.rlevels_coeff', width = "100%", height = "900px"))
), # closing tab
tabPanel("Kolmogorov Smirnoff",
fluidRow(
column(3, wellPanel(
selectInput(inputId='station4ks', selected = station$number[37],
label = "Pick a station", choices = station$number)
)
)
),
fluidRow(
plotOutput('plot.ks', width = "100%", height = "900px")
)
), # closing tab
tabPanel("Anderson Darling",
fluidRow(
column(3, wellPanel(
selectInput(inputId='station4ad', selected = station$number[37],
label = "Pick a station", choices = station$number)
)
)
),
fluidRow(
plotOutput('plot.ad', width = "100%", height = "900px")
)
), # closing tab
tabPanel("Chi Square",
fluidRow(
column(3, wellPanel(
selectInput(inputId='station4cs', selected = station$number[37],
label = "Pick a station", choices = station$number)
)
)
),
fluidRow(
plotOutput('plot.cs', width = "100%", height = "900px")
)
) # closing tab
), # Closing navbarMenu
navbarMenu("Station averaged analysis", icon = icon("expand"),
tabPanel("Station-averaged CV",
fluidRow(
column(3, wellPanel(
uiOutput("r.period4coefvar_ave")
)
),
column(3, wellPanel(
selectInput(inputId='coeffvar2plot_ave', selected = 'r.levels', label='Select a goodness of fit measure to summarize',
choices = c('r.levels', 'QS', 'BS', 'NT'))
)),
column(3, wellPanel(
selectInput(inputId='min_years4coeff_ave', selected = '30', label='Select a minimum number of years',
choices = c(30,60,90,120))
)
),
column(3, wellPanel(
selectInput(inputId='max_years4coeff_ave', selected = '150', label='Select a maximum number of years',
choices = c(60,90,120,150))
)
)
),
fluidRow(
plotOutput('plot.rlevels_coeff_averaged', width = "100%", height = "800px")
)
), # closing tab
tabPanel("Station-averaged GOF",
fluidRow(
column(3, wellPanel(
selectInput(inputId='gof4ave', selected = 'KS', label='Select a goodness of fit measure',
choices = c('CS', 'KS', 'AD'))
)
),
column(4, wellPanel(
selectInput(inputId='min_years4ave', selected = '30', label='Select a minimum number of years',
choices = c(30,60,90,120))
)
),
column(4, wellPanel(
selectInput(inputId='max_years4ave', selected = '150', label='Select a maximum number of years',
choices = c(60,90,120,150))
)
)
),
fluidRow(
plotOutput('plot.gof_averaged', width = "100%", height = "900px")
)
) # closing tab
), # Closing navbarMenu
navbarMenu("Mapping tools",
tabPanel("Mapping GOF-based groups of stations",
fluidRow(
column(3, wellPanel(
selectInput(inputId='gof4st_groups', selected = 'KS', label='Select a goodness of fit measure',
choices = c('CS', 'KS', 'AD'))
)
),
column(3, wellPanel(
selectInput(inputId='minmax', label = "Want to get min GOF (best perf) or max GOF", choices = c("min", "max")) )
),
column(3, wellPanel(
selectInput(inputId='distr4st_groups', selected = 'gamma', label='Select a probability distribution',
choices = c(distr.name, "ALL"))
)
),
column(3, wellPanel(
selectInput(inputId='method4st_groups', label = "Select an estimation method", choices = c(method.name, "ALL")) )
)
),
fluidRow(
column(9, DT::dataTableOutput('group.table')
),
column(3,
h4("Maps of stations with common best method and distribution"),
leafletOutput('map.groups_from_gof', height = "800px")
)
)
),
tabPanel("Mapping parameter values",
fluidRow(
column(3, wellPanel(
selectInput(inputId='dist4param_maps', selected = 'GEV', label='Select a distribution',
choices = distr.name)
)
),
column(3, wellPanel(
selectInput(inputId='method4param_maps', selected = 'mle', label = "Select an estimation method", choices = method.name )
)
),
column(3, wellPanel(
selectInput(inputId='param4param_maps', selected = 1, label = "Which parameter to map", choices = c(1, 2, 3) )
)
)
),
fluidRow(
column(9, plotOutput('param.histo', width = "100%", height = "800px")
),
column(3,
h4("Map showing parameter values across Norway"),
leafletOutput('map.param_values', height = "800px")
)
)
), icon = icon("globe") # closing tab
), # Closing navbarMenu
navbarMenu("Table tools",
tabPanel("Table summary",
fluidRow(
h4("Table"),
DT::dataTableOutput('test.table')
)
# leafletOutput('map4table') # DOESNT WORK WITH THIS VERSION OF DT
), icon = icon("table") # closing tab
), # Closing navbarMenu
windowTitle = "Explore Big Mama!"
) # Closing navbarPage
# Closing UI
# to check out from Radiant https://github.com/vnijs/radiant/blob/master/inst/analytics/ui.R
# radiant@rady.ucsd.edu
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