inst/app/manager/ui.R
In jentjr/manager: Tools for plotting and analyzing environmental data
shinyUI(
navbarPage("MANAGER",
tabPanel("Data",
fluidPage(
fluidRow(
column(2,
selectDataUI("select_data"),
actionButton("run_query", "Submit Query")
),
column(10,
tabsetPanel(
tabPanel("Data",
dataTableOutput("data_table"),
downloadButton(outputId = "data_table_download",
label = "Download Data")
),
tabPanel("Summary",
dataTableOutput("summary_table"),
downloadButton(outputId = "summary_table_download",
label = "Download Data")
),
tabPanel("Wide Table",
dataTableOutput("wide_table")
)
)
)
)
)
),
tabPanel("Map",
fluidPage(
leafletOutput("mapplot", height = 725)
)
),
navbarMenu("Plots",
tabPanel("Distribution Plots",
fluidPage(
fluidRow(
column(2,
uiOutput("select_distribution_wells"),
uiOutput("select_distribution_params"),
radioButtons(
inputId = "dist_plot_type",
label = "Type of Distribution Plot",
choices = c("Regular", "Censored"),
selected = "Regular"
),
conditionalPanel(
condition = "input.dist_plot_type == 'Censored'",
selectInput(
inputId = "cen_dist_side",
label = "Censoring Side",
choices = c("left", "right")
),
selectInput(
inputId = "cen_dist_test",
label = "Select test",
choices = c("Shapiro-Francia" = "sf",
"Shapiro-Wilk" = "sw",
"Prob-Plot-Corr-Coeff" = "ppcc")
),
selectInput(
inputId = "cen_dist_dist",
label = "Distribution",
choices = c("Normal" = "norm", "Lognormal" = "lnorm")
),
selectInput(
inputId = "cen_dist_method",
label = "Select method to compute plotting position",
choices = c("michael-schucany",
"modified kaplan-meier",
"nelson",
"hirsch-stedinger")
),
numericInput(
inputId = "cen_dist_plot.pos.con",
label = "Scalar for plotting position constant",
value = 0.375,
min = 0,
max = 1
)
),
conditionalPanel(
condition = "input.dist_plot_type == 'Regular'",
selectInput(
inputId = "dist_type",
label = "Type of Distribution",
choices = row.names(Distribution.df),
selected = "norm"
)
)
),
column(10,
plotOutput("gof_plot")
)
)
)
),
tabPanel("Correlation",
fluidPage(
fluidRow(
column(2,
uiOutput("select_corr_wells"),
uiOutput("select_corr_params")
),
column(10,
plotOutput("corr_plot", height = 700, width = "100%")
)
)
)
),
tabPanel("Time Series",
fluidRow(
column(2,
uiOutput("select_ts_wells"),
uiOutput("select_ts_params"),
downloadButton(outputId = "ts_download",
label = "Download Plots")
),
column(10,
uiOutput("ts_out"))
)
),
tabPanel("Boxplots",
fluidRow(
column(2,
uiOutput("select_boxplot_wells"),
uiOutput("select_boxplot_params"),
selectInput(inputId = "boxplot_fill", label = "Fill Variable",
choices = c("location_id", "location_class", "water_class"),
selected = "location_class"),
selectInput(inputId = "box_y_transform", label = "Transform y scale",
c("identity", "log", "log10", "sqrt", "boxcox")),
numericInput(inputId = "box_iqr_mult", label = "IQR Multiplier",
value = 3, min = 0, max = 3),
numericInput(inputId = "box_pnt_size", label = "Point size",
value = 2, min = 1),
checkboxInput(inputId = "box_points", label = "Show points", FALSE),
downloadButton("box_download", "Download Plots")
),
column(10,
uiOutput("boxplot_out")
)
)
)
),
# Geochemical Plots Navigation Bar ---------------------------------------------
navbarMenu("Geochemical Plots",
tabPanel("Piper Diagram",
fluidPage(
fluidRow(
column(2,
uiOutput("select_piper_wells"),
uiOutput("select_piper_x_cation"),
textInput(inputId = "x_cation_label", label = "X Cation Label",
value = "Ca"),
uiOutput("select_piper_y_cation"),
textInput(inputId = "y_cation_label", label = "Y Cation Label",
value = "Mg"),
uiOutput("select_piper_z_cation"),
textInput(inputId = "z_cation_label", label = "Z Cation Label",
value = "Na + K"),
uiOutput("select_piper_x_anion"),
textInput(inputId = "x_anion_label", label = "X Anion Label",
value = "Cl + F"),
uiOutput("select_piper_y_anion"),
textInput(inputId = "y_anion_label", label = "Y Anion Label",
value = "HCO3 + CO2"),
uiOutput("select_piper_z_anion"),
textInput(inputId = "z_anion_label", label = "Z Anion Label",
value = "SO4"),
textInput(inputId = "x_z_anion_label",label = "X-Z Anion Label",
value = "SO4 + Cl + F"),
textInput(inputId = "x_y_cation_label", label = "X Y Cation Label",
value = "Ca + Mg"),
checkboxInput(inputId = "TDS_plot",
label = "Scale by Total Dissolved Solids"),
uiOutput("select_piper_tds"),
textInput(inputId = "piper_title", label = "Enter Plot Title",
value = "Piper Diagram"),
downloadButton("piper_download", "Download Plot")
),
column(10,
plotOutput("piper_plot", height = 700, width = "100%")
)
)
)
),
tabPanel("Stiff Diagram",
fluidPage(
fluidRow(
column(2,
uiOutput("select_stiff_wells"),
uiOutput("select_stiff_dates"),
uiOutput("select_stiff_calcium"),
uiOutput("select_stiff_magnesium"),
uiOutput("select_stiff_potassium"),
uiOutput("select_stiff_sodium"),
uiOutput("select_stiff_chloride"),
uiOutput("select_stiff_sulfate"),
uiOutput("select_stiff_alkalinity"),
checkboxInput(inputId = "tds_stiff",
label = "Scale by Total Dissolved Solids"),
uiOutput("select_stiff_tds"),
checkboxInput(inputId = "stiff_lines", label = "Display lines"),
selectInput(inputId = "stiff_group", label = "Grouping Variable",
choices = c("location_id", "sample_date"),
selected = "location_id"),
downloadButton("stiff_download", "Download Plots")
),
column(10,
uiOutput("stiff_diagram")
)
)
)
),
tabPanel("Schoeller Diagram",
fluidPage(
fluidRow(
column(2,
uiOutput("select_schoeller_wells"),
uiOutput("select_schoeller_dates"),
uiOutput("select_schoeller_magnesium"),
uiOutput("select_schoeller_calcium"),
uiOutput("select_schoeller_potassium"),
uiOutput("select_schoeller_sodium"),
uiOutput("select_schoeller_chloride"),
uiOutput("select_schoeller_sulfate"),
uiOutput("select_schoeller_alkalinity"),
selectInput(inputId = "facet_schoeller", label = "Facet plot by:",
c("location_id", "sample_date")),
textInput(inputId = "schoeller_title", label = "Enter Plot Title",
value = "Schoeller Diagram"),
downloadButton("schoeller_download", "Download Plots")
),
column(10,
plotOutput("schoeller_diagram_out")
)
)
)
)
),
tabPanel("Outliers",
sidebarLayout(
sidebarPanel(
uiOutput("outlier_wells"),
uiOutput("outlier_analytes"),
uiOutput("outlier_date_ranges"),
selectInput(
inputId = "outlier_test_name",
label = "Outlier Test",
choices = c("Rosner", "Grubb", "Dixon")
),
conditionalPanel(
condition = "input.outlier_test_name == 'Rosner'",
numericInput(
inputId = "rosnerN",
label = "Number of suspected outliers",
value = 2, min = 0
),
numericInput(
inputId = "rosnerAlpha",
label = "alpha",
value = 0.01, min = 0, max = 1
)
),
conditionalPanel(
condition = "input.outlier_test_name == 'Grubb'",
selectInput(
inputId = "grubbType",
label = "Type of Test",
choices = c("10" = 10, "11" = 11, "20" = 20)
),
selectInput(
inputId = "grubbOpposite",
label = "Choose Opposite",
choices = c("FALSE" = 0, "TRUE" = 1)
),
selectInput(
inputId = "grubbSide",
label = "Treat as two-sided",
choices = c("FALSE" = 0, "TRUE" = 1)
)
),
conditionalPanel(
condition = "input.outlier_test_name == 'Dixon'",
selectInput(
inputId = "dixonType",
label = "Type of Test",
choices = c("0" = 0, "10" = 10, "11" = 11,
"12" = 12, "20" = 20, "21" = 21)
),
selectInput(
inputId = "dixonOpposite",
label = "Choose Opposite",
choices = c("FALSE" = 0, "TRUE" = 1)
),
selectInput(
inputId = "dixonSide",
label = "Treat as two-sided",
choices = c("TRUE" = 1, "FALSE" = 0)
)
)
),
mainPanel(
verbatimTextOutput("outlier_test")
# br(),
# dataTableOutput("outlier_table")
)
)
),
tabPanel("Trends",
sidebarLayout(
sidebarPanel(
uiOutput("trend_wells"),
uiOutput("trend_analytes"),
uiOutput("trend_date_ranges")
),
mainPanel(
verbatimTextOutput("trend_test")
)
)
),
navbarMenu("Statistical Intervals",
tabPanel("Confidence Intervals",
fluidPage(
fluidRow(
column(2,
uiOutput("select_conf_int_wells"),
uiOutput("select_conf_int_analytes"),
uiOutput("select_conf_int_date_range"),
radioButtons("conf_int_type",
label = "Select tail type",
choices = c("lower", "upper", "two-sided"),
selected = "two-sided"),
numericInput("conf_int_conf",
label = "Confidence Level",
value = 0.99,
min = 0,
max = 1,
step = 0.01)
),
column(10,
dataTableOutput("conf_int_out")
)
)
)
),
tabPanel("Tolerance Intervals",
fluidPage(
fluidRow(
column(2,
uiOutput("select_tol_int_wells"),
uiOutput("select_tol_int_analytes"),
uiOutput("select_tol_int_date_range")
),
column(10,
dataTableOutput("tol_int_out")
)
)
)
),
tabPanel("Prediction Intervals",
tabsetPanel(
tabPanel("Intrawell",
fluidPage(
fluidRow(
column(2,
uiOutput("wells_intra"),
uiOutput("analytes_intra"),
uiOutput("date_ranges_intra"),
numericInput(inputId = "intra_n.mean", label = "Specify a
positive integer for the sample size associated
with the future averages. The default value is
n.mean=1 (i.e., individual observations).
Note that all future averages must be based on the
same sample size", value = 1, min = 0),
numericInput(inputId = "intra_k", label = "Specify a positive
integer for the k-of-m rule (rule='k.of.m'), a
positive integer specifying the minimum number of
observations (or averages) out of m observations
(or averages) (all obtained on one future sampling
“occassion”) the prediction interval should contain
with confidence level conf.level. The default value
is k=1. This argument is ignored when the argument
rule is not equal to 'k.of.m'.", value = 1, min = 0),
numericInput(inputId = "intra_m", label = "positive integer
specifying the maximum number of future observations
(or averages) on one future sampling “occasion”. The
default value is m=2, except when rule='Modified.CA',
in which case this argument is ignored and m is
automatically set equal to 4.",
value = 2, min = 0),
numericInput(inputId = "intra_r", label = "positive integer
specifying the number of future sampling “occasions”.
The default value is r=1.", value = 1, min = 0),
selectInput(inputId = "intra_rule", label = "character string
specifying which rule to use. The possible values are
'k.of.m' (k-of-m rule; the default), 'CA' (California
rule), and 'Modified.CA' (modified California rule)",
choices = c("k.of.m", "CA", "Modified.CA"),
selected = "k.of.m"),
selectInput(inputId = "intra_pi.type", label = "Specify what kind
of prediction interval to compute. The possible values
are 'upper' (the default), and 'lower'",
choices = c("two-sided", "upper", "lower"),
selected = "upper"),
numericInput(inputId = "intra_conf", label = "Enter a value
between 0 and 1 indicating the confidence level of the
prediction interval",
value = 0.95, min = 0, max = 1)
),
column(10,
dataTableOutput("intra_limit_out"),
br(),
uiOutput("ts_intra_out")
)
)
)
),
tabPanel("Interwell",
fluidPage(
fluidRow(
column(2,
uiOutput("select_wells_inter"),
uiOutput("select_analyte_inter"),
uiOutput("select_date_ranges_inter"),
numericInput(inputId = "inter_n.mean", label = "Specify a positive
integer for the sample size associated with the future
averages. The default value is n.mean=1 (i.e.,
individual observations). Note that all future averages
must be based on the same sample size",
value = 1, min = 0),
numericInput(inputId = "inter_k", label = "Specify a positive integer
for the k-of-m rule (rule='k.of.m'), a positive integer
specifying the minimum number of observations (or averages)
out of m observations (or averages)
(all obtained on one future sampling “occassion”)
the prediction interval should contain with confidence
level conf.level. The default value is k=1.
This argument is ignored when the argument rule is not
equal to 'k.of.m'.", value = 1, min = 0),
numericInput(inputId = "inter_m", label = "positive integer specifying
the maximum number of future observations (or averages) on
one future sampling “occasion”. The default value is m=2,
except when rule='Modified.CA', in which case this argument
is ignored and m is automatically set equal to 4.",
value = 2, min = 0),
numericInput(inputId = "inter_r", label = "positive integer specifying
the number of future sampling “occasions”. The default
value is r=1.", value = 1, min = 0),
selectInput(inputId = "inter_rule", label = "character string specifying
which rule to use. The possible values are 'k.of.m'
(k-of-m rule; the default), 'CA' (California rule), and
'Modified.CA' (modified California rule)",
choices = c("k.of.m", "CA", "Modified.CA"),
selected = "k.of.m"),
selectInput(inputId = "inter_pi.type", label = "Specify what kind
of prediction interval to compute. The possible values
are 'upper' (the default), and 'lower'",
choices = c("two-sided", "upper", "lower"),
selected = "upper"),
numericInput(inputId = "inter_conf", label = "Enter a value
between 0 and 1 indicating the confidence level of the
prediction interval",
value = 0.95, min = 0, max = 1)
),
column(10,
dataTableOutput("inter_limit_out")
)
)
)
),
tabPanel("Test Power",
fluidPage(
fluidRow(
column(2,
numericInput(inputId = "power_n",
label = "Number of background samples",
value = 8, min = 1),
numericInput(inputId = "power_n_mean", label = "Sample size for
future averages",
value = 1, min = 1),
numericInput(inputId = "power_k", label = "k in k.of.m",
value = 1, min = 1),
numericInput(inputId = "power_m", label = "m",
value = 2, min = 1),
numericInput(inputId = "power_r", label = "r",
value = 1, min = 1),
numericInput(inputId = "conf_power", label = "conf.level",
value = 0.95)
),
column(10,
plotOutput("power_plot"),
br(),
helpText("The Unified Guidance recommends that when background is
approximately normal, any statistical test should be able to
detect a 3 standard deviation increase at least 55-60% of the
time and a 4 standard deviation increase with at least 80-85%
probability.")
)
)
)
) # End Test Power tabPanel
) # End tabsetPanel for Inter and Intra Intervals
) # End Prediction Intervals
), # End of Statisitcal Intervals
navbarMenu("Clustering",
tabPanel("Hierarchical",
fluidPage(
fluidRow(
column(2,
uiOutput("select_wells_hca"),
uiOutput("select_analyte_hca"),
uiOutput("select_date_ranges_hca"),
selectInput("clust_dist_method",
label = "Select Distance Method",
choices = c("euclidean",
"maximum",
"manhattan",
"canberra",
"binary",
"minkowski"),
selected = "euclidean"),
selectInput("clust_agg_method",
label = "Cluster Agglomeration Method",
choices = c("ward.D",
"ward.D2",
"single",
"complete",
"average",
"mcquitty",
"median",
"centroid"),
selected = "average"),
numericInput("hca_colors",
label = "Number of groups",
val = 1,
min = 1),
checkboxInput("hca_horiz",
label = "Plot horizontally")
),
column(10,
plotOutput("hca_out")
)
)
)
),
tabPanel("k-means",
fluidPage(
fluidRow(
column(2,
uiOutput("select_wells_kmeans"),
uiOutput("select_analyte_kmeans"),
uiOutput("select_date_ranges_kmeans"),
selectInput("kmeans_dist_method",
label = "Select Distance Method",
choices = c("euclidean",
"maximum",
"manhattan",
"canberra",
"binary",
"minkowski"),
selected = "euclidean"),
numericInput("kmeans_centers",
label = "Number of clusters",
value = 2,
min = 2),
selectInput("kmeans_algorithm",
label = "K-Means Algorithm",
choices = c("Hartigan-Wong",
"Lloyd",
"Forgy",
"MacQueen"),
selected = "Hartigan-Wong")
),
column(10,
plotOutput("kmeans_out")
)
)
)
)
)
))
jentjr/manager documentation built on Jan. 12, 2024, 9:45 p.m.
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shinyUI(
navbarPage("MANAGER",
tabPanel("Data",
fluidPage(
fluidRow(
column(2,
selectDataUI("select_data"),
actionButton("run_query", "Submit Query")
),
column(10,
tabsetPanel(
tabPanel("Data",
dataTableOutput("data_table"),
downloadButton(outputId = "data_table_download",
label = "Download Data")
),
tabPanel("Summary",
dataTableOutput("summary_table"),
downloadButton(outputId = "summary_table_download",
label = "Download Data")
),
tabPanel("Wide Table",
dataTableOutput("wide_table")
)
)
)
)
)
),
tabPanel("Map",
fluidPage(
leafletOutput("mapplot", height = 725)
)
),
navbarMenu("Plots",
tabPanel("Distribution Plots",
fluidPage(
fluidRow(
column(2,
uiOutput("select_distribution_wells"),
uiOutput("select_distribution_params"),
radioButtons(
inputId = "dist_plot_type",
label = "Type of Distribution Plot",
choices = c("Regular", "Censored"),
selected = "Regular"
),
conditionalPanel(
condition = "input.dist_plot_type == 'Censored'",
selectInput(
inputId = "cen_dist_side",
label = "Censoring Side",
choices = c("left", "right")
),
selectInput(
inputId = "cen_dist_test",
label = "Select test",
choices = c("Shapiro-Francia" = "sf",
"Shapiro-Wilk" = "sw",
"Prob-Plot-Corr-Coeff" = "ppcc")
),
selectInput(
inputId = "cen_dist_dist",
label = "Distribution",
choices = c("Normal" = "norm", "Lognormal" = "lnorm")
),
selectInput(
inputId = "cen_dist_method",
label = "Select method to compute plotting position",
choices = c("michael-schucany",
"modified kaplan-meier",
"nelson",
"hirsch-stedinger")
),
numericInput(
inputId = "cen_dist_plot.pos.con",
label = "Scalar for plotting position constant",
value = 0.375,
min = 0,
max = 1
)
),
conditionalPanel(
condition = "input.dist_plot_type == 'Regular'",
selectInput(
inputId = "dist_type",
label = "Type of Distribution",
choices = row.names(Distribution.df),
selected = "norm"
)
)
),
column(10,
plotOutput("gof_plot")
)
)
)
),
tabPanel("Correlation",
fluidPage(
fluidRow(
column(2,
uiOutput("select_corr_wells"),
uiOutput("select_corr_params")
),
column(10,
plotOutput("corr_plot", height = 700, width = "100%")
)
)
)
),
tabPanel("Time Series",
fluidRow(
column(2,
uiOutput("select_ts_wells"),
uiOutput("select_ts_params"),
downloadButton(outputId = "ts_download",
label = "Download Plots")
),
column(10,
uiOutput("ts_out"))
)
),
tabPanel("Boxplots",
fluidRow(
column(2,
uiOutput("select_boxplot_wells"),
uiOutput("select_boxplot_params"),
selectInput(inputId = "boxplot_fill", label = "Fill Variable",
choices = c("location_id", "location_class", "water_class"),
selected = "location_class"),
selectInput(inputId = "box_y_transform", label = "Transform y scale",
c("identity", "log", "log10", "sqrt", "boxcox")),
numericInput(inputId = "box_iqr_mult", label = "IQR Multiplier",
value = 3, min = 0, max = 3),
numericInput(inputId = "box_pnt_size", label = "Point size",
value = 2, min = 1),
checkboxInput(inputId = "box_points", label = "Show points", FALSE),
downloadButton("box_download", "Download Plots")
),
column(10,
uiOutput("boxplot_out")
)
)
)
),
# Geochemical Plots Navigation Bar ---------------------------------------------
navbarMenu("Geochemical Plots",
tabPanel("Piper Diagram",
fluidPage(
fluidRow(
column(2,
uiOutput("select_piper_wells"),
uiOutput("select_piper_x_cation"),
textInput(inputId = "x_cation_label", label = "X Cation Label",
value = "Ca"),
uiOutput("select_piper_y_cation"),
textInput(inputId = "y_cation_label", label = "Y Cation Label",
value = "Mg"),
uiOutput("select_piper_z_cation"),
textInput(inputId = "z_cation_label", label = "Z Cation Label",
value = "Na + K"),
uiOutput("select_piper_x_anion"),
textInput(inputId = "x_anion_label", label = "X Anion Label",
value = "Cl + F"),
uiOutput("select_piper_y_anion"),
textInput(inputId = "y_anion_label", label = "Y Anion Label",
value = "HCO3 + CO2"),
uiOutput("select_piper_z_anion"),
textInput(inputId = "z_anion_label", label = "Z Anion Label",
value = "SO4"),
textInput(inputId = "x_z_anion_label",label = "X-Z Anion Label",
value = "SO4 + Cl + F"),
textInput(inputId = "x_y_cation_label", label = "X Y Cation Label",
value = "Ca + Mg"),
checkboxInput(inputId = "TDS_plot",
label = "Scale by Total Dissolved Solids"),
uiOutput("select_piper_tds"),
textInput(inputId = "piper_title", label = "Enter Plot Title",
value = "Piper Diagram"),
downloadButton("piper_download", "Download Plot")
),
column(10,
plotOutput("piper_plot", height = 700, width = "100%")
)
)
)
),
tabPanel("Stiff Diagram",
fluidPage(
fluidRow(
column(2,
uiOutput("select_stiff_wells"),
uiOutput("select_stiff_dates"),
uiOutput("select_stiff_calcium"),
uiOutput("select_stiff_magnesium"),
uiOutput("select_stiff_potassium"),
uiOutput("select_stiff_sodium"),
uiOutput("select_stiff_chloride"),
uiOutput("select_stiff_sulfate"),
uiOutput("select_stiff_alkalinity"),
checkboxInput(inputId = "tds_stiff",
label = "Scale by Total Dissolved Solids"),
uiOutput("select_stiff_tds"),
checkboxInput(inputId = "stiff_lines", label = "Display lines"),
selectInput(inputId = "stiff_group", label = "Grouping Variable",
choices = c("location_id", "sample_date"),
selected = "location_id"),
downloadButton("stiff_download", "Download Plots")
),
column(10,
uiOutput("stiff_diagram")
)
)
)
),
tabPanel("Schoeller Diagram",
fluidPage(
fluidRow(
column(2,
uiOutput("select_schoeller_wells"),
uiOutput("select_schoeller_dates"),
uiOutput("select_schoeller_magnesium"),
uiOutput("select_schoeller_calcium"),
uiOutput("select_schoeller_potassium"),
uiOutput("select_schoeller_sodium"),
uiOutput("select_schoeller_chloride"),
uiOutput("select_schoeller_sulfate"),
uiOutput("select_schoeller_alkalinity"),
selectInput(inputId = "facet_schoeller", label = "Facet plot by:",
c("location_id", "sample_date")),
textInput(inputId = "schoeller_title", label = "Enter Plot Title",
value = "Schoeller Diagram"),
downloadButton("schoeller_download", "Download Plots")
),
column(10,
plotOutput("schoeller_diagram_out")
)
)
)
)
),
tabPanel("Outliers",
sidebarLayout(
sidebarPanel(
uiOutput("outlier_wells"),
uiOutput("outlier_analytes"),
uiOutput("outlier_date_ranges"),
selectInput(
inputId = "outlier_test_name",
label = "Outlier Test",
choices = c("Rosner", "Grubb", "Dixon")
),
conditionalPanel(
condition = "input.outlier_test_name == 'Rosner'",
numericInput(
inputId = "rosnerN",
label = "Number of suspected outliers",
value = 2, min = 0
),
numericInput(
inputId = "rosnerAlpha",
label = "alpha",
value = 0.01, min = 0, max = 1
)
),
conditionalPanel(
condition = "input.outlier_test_name == 'Grubb'",
selectInput(
inputId = "grubbType",
label = "Type of Test",
choices = c("10" = 10, "11" = 11, "20" = 20)
),
selectInput(
inputId = "grubbOpposite",
label = "Choose Opposite",
choices = c("FALSE" = 0, "TRUE" = 1)
),
selectInput(
inputId = "grubbSide",
label = "Treat as two-sided",
choices = c("FALSE" = 0, "TRUE" = 1)
)
),
conditionalPanel(
condition = "input.outlier_test_name == 'Dixon'",
selectInput(
inputId = "dixonType",
label = "Type of Test",
choices = c("0" = 0, "10" = 10, "11" = 11,
"12" = 12, "20" = 20, "21" = 21)
),
selectInput(
inputId = "dixonOpposite",
label = "Choose Opposite",
choices = c("FALSE" = 0, "TRUE" = 1)
),
selectInput(
inputId = "dixonSide",
label = "Treat as two-sided",
choices = c("TRUE" = 1, "FALSE" = 0)
)
)
),
mainPanel(
verbatimTextOutput("outlier_test")
# br(),
# dataTableOutput("outlier_table")
)
)
),
tabPanel("Trends",
sidebarLayout(
sidebarPanel(
uiOutput("trend_wells"),
uiOutput("trend_analytes"),
uiOutput("trend_date_ranges")
),
mainPanel(
verbatimTextOutput("trend_test")
)
)
),
navbarMenu("Statistical Intervals",
tabPanel("Confidence Intervals",
fluidPage(
fluidRow(
column(2,
uiOutput("select_conf_int_wells"),
uiOutput("select_conf_int_analytes"),
uiOutput("select_conf_int_date_range"),
radioButtons("conf_int_type",
label = "Select tail type",
choices = c("lower", "upper", "two-sided"),
selected = "two-sided"),
numericInput("conf_int_conf",
label = "Confidence Level",
value = 0.99,
min = 0,
max = 1,
step = 0.01)
),
column(10,
dataTableOutput("conf_int_out")
)
)
)
),
tabPanel("Tolerance Intervals",
fluidPage(
fluidRow(
column(2,
uiOutput("select_tol_int_wells"),
uiOutput("select_tol_int_analytes"),
uiOutput("select_tol_int_date_range")
),
column(10,
dataTableOutput("tol_int_out")
)
)
)
),
tabPanel("Prediction Intervals",
tabsetPanel(
tabPanel("Intrawell",
fluidPage(
fluidRow(
column(2,
uiOutput("wells_intra"),
uiOutput("analytes_intra"),
uiOutput("date_ranges_intra"),
numericInput(inputId = "intra_n.mean", label = "Specify a
positive integer for the sample size associated
with the future averages. The default value is
n.mean=1 (i.e., individual observations).
Note that all future averages must be based on the
same sample size", value = 1, min = 0),
numericInput(inputId = "intra_k", label = "Specify a positive
integer for the k-of-m rule (rule='k.of.m'), a
positive integer specifying the minimum number of
observations (or averages) out of m observations
(or averages) (all obtained on one future sampling
“occassion”) the prediction interval should contain
with confidence level conf.level. The default value
is k=1. This argument is ignored when the argument
rule is not equal to 'k.of.m'.", value = 1, min = 0),
numericInput(inputId = "intra_m", label = "positive integer
specifying the maximum number of future observations
(or averages) on one future sampling “occasion”. The
default value is m=2, except when rule='Modified.CA',
in which case this argument is ignored and m is
automatically set equal to 4.",
value = 2, min = 0),
numericInput(inputId = "intra_r", label = "positive integer
specifying the number of future sampling “occasions”.
The default value is r=1.", value = 1, min = 0),
selectInput(inputId = "intra_rule", label = "character string
specifying which rule to use. The possible values are
'k.of.m' (k-of-m rule; the default), 'CA' (California
rule), and 'Modified.CA' (modified California rule)",
choices = c("k.of.m", "CA", "Modified.CA"),
selected = "k.of.m"),
selectInput(inputId = "intra_pi.type", label = "Specify what kind
of prediction interval to compute. The possible values
are 'upper' (the default), and 'lower'",
choices = c("two-sided", "upper", "lower"),
selected = "upper"),
numericInput(inputId = "intra_conf", label = "Enter a value
between 0 and 1 indicating the confidence level of the
prediction interval",
value = 0.95, min = 0, max = 1)
),
column(10,
dataTableOutput("intra_limit_out"),
br(),
uiOutput("ts_intra_out")
)
)
)
),
tabPanel("Interwell",
fluidPage(
fluidRow(
column(2,
uiOutput("select_wells_inter"),
uiOutput("select_analyte_inter"),
uiOutput("select_date_ranges_inter"),
numericInput(inputId = "inter_n.mean", label = "Specify a positive
integer for the sample size associated with the future
averages. The default value is n.mean=1 (i.e.,
individual observations). Note that all future averages
must be based on the same sample size",
value = 1, min = 0),
numericInput(inputId = "inter_k", label = "Specify a positive integer
for the k-of-m rule (rule='k.of.m'), a positive integer
specifying the minimum number of observations (or averages)
out of m observations (or averages)
(all obtained on one future sampling “occassion”)
the prediction interval should contain with confidence
level conf.level. The default value is k=1.
This argument is ignored when the argument rule is not
equal to 'k.of.m'.", value = 1, min = 0),
numericInput(inputId = "inter_m", label = "positive integer specifying
the maximum number of future observations (or averages) on
one future sampling “occasion”. The default value is m=2,
except when rule='Modified.CA', in which case this argument
is ignored and m is automatically set equal to 4.",
value = 2, min = 0),
numericInput(inputId = "inter_r", label = "positive integer specifying
the number of future sampling “occasions”. The default
value is r=1.", value = 1, min = 0),
selectInput(inputId = "inter_rule", label = "character string specifying
which rule to use. The possible values are 'k.of.m'
(k-of-m rule; the default), 'CA' (California rule), and
'Modified.CA' (modified California rule)",
choices = c("k.of.m", "CA", "Modified.CA"),
selected = "k.of.m"),
selectInput(inputId = "inter_pi.type", label = "Specify what kind
of prediction interval to compute. The possible values
are 'upper' (the default), and 'lower'",
choices = c("two-sided", "upper", "lower"),
selected = "upper"),
numericInput(inputId = "inter_conf", label = "Enter a value
between 0 and 1 indicating the confidence level of the
prediction interval",
value = 0.95, min = 0, max = 1)
),
column(10,
dataTableOutput("inter_limit_out")
)
)
)
),
tabPanel("Test Power",
fluidPage(
fluidRow(
column(2,
numericInput(inputId = "power_n",
label = "Number of background samples",
value = 8, min = 1),
numericInput(inputId = "power_n_mean", label = "Sample size for
future averages",
value = 1, min = 1),
numericInput(inputId = "power_k", label = "k in k.of.m",
value = 1, min = 1),
numericInput(inputId = "power_m", label = "m",
value = 2, min = 1),
numericInput(inputId = "power_r", label = "r",
value = 1, min = 1),
numericInput(inputId = "conf_power", label = "conf.level",
value = 0.95)
),
column(10,
plotOutput("power_plot"),
br(),
helpText("The Unified Guidance recommends that when background is
approximately normal, any statistical test should be able to
detect a 3 standard deviation increase at least 55-60% of the
time and a 4 standard deviation increase with at least 80-85%
probability.")
)
)
)
) # End Test Power tabPanel
) # End tabsetPanel for Inter and Intra Intervals
) # End Prediction Intervals
), # End of Statisitcal Intervals
navbarMenu("Clustering",
tabPanel("Hierarchical",
fluidPage(
fluidRow(
column(2,
uiOutput("select_wells_hca"),
uiOutput("select_analyte_hca"),
uiOutput("select_date_ranges_hca"),
selectInput("clust_dist_method",
label = "Select Distance Method",
choices = c("euclidean",
"maximum",
"manhattan",
"canberra",
"binary",
"minkowski"),
selected = "euclidean"),
selectInput("clust_agg_method",
label = "Cluster Agglomeration Method",
choices = c("ward.D",
"ward.D2",
"single",
"complete",
"average",
"mcquitty",
"median",
"centroid"),
selected = "average"),
numericInput("hca_colors",
label = "Number of groups",
val = 1,
min = 1),
checkboxInput("hca_horiz",
label = "Plot horizontally")
),
column(10,
plotOutput("hca_out")
)
)
)
),
tabPanel("k-means",
fluidPage(
fluidRow(
column(2,
uiOutput("select_wells_kmeans"),
uiOutput("select_analyte_kmeans"),
uiOutput("select_date_ranges_kmeans"),
selectInput("kmeans_dist_method",
label = "Select Distance Method",
choices = c("euclidean",
"maximum",
"manhattan",
"canberra",
"binary",
"minkowski"),
selected = "euclidean"),
numericInput("kmeans_centers",
label = "Number of clusters",
value = 2,
min = 2),
selectInput("kmeans_algorithm",
label = "K-Means Algorithm",
choices = c("Hartigan-Wong",
"Lloyd",
"Forgy",
"MacQueen"),
selected = "Hartigan-Wong")
),
column(10,
plotOutput("kmeans_out")
)
)
)
)
)
))
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