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app.R
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#
# This is a Shiny web application. You can run the application by clicking
# the 'Run App' button above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
source("global.r")
# Define UI for application that draws a histogram
ui <- fluidPage(
tags$html(class = "no-js", lang="en"),
tags$head(
tags$title('NARS Pop Est Tool| US EPA'),
tags$link(rel = "stylesheet", type = "text/css", href = "style.css"),
includeHTML("www/header.html")),
shinyjs::useShinyjs(),
# Application title
# Instructions ------------------------------------------------------------
navbarPage(title=span("NARS Population Estimate Calculation Tool (v. 2.3.1)",
style = "font-weight: bold; font-size: 24px"),
selected='instructions',position='static-top',
# Panel with instructions for using this tool
tabPanel(title='Instructions for Use',value='instructions',
h2("Overview"),
p('This Shiny app allows for calculation of population estimates as performed for
the National Aquatic Resource Surveys (NARS) and the plotting of results.
Estimates based on categorical and continuous variables are possible.
This app does not include all possible options but does allow typical settings
used by NARS for creating population estimates.'),
h3("Instructions for Use"),
bsCollapsePanel(title = h4(strong("Prepare Data for Analysis")),
tags$ol(
tags$li("Select data file and upload. If the data are to be loaded from a URL, check the
box to do so and paste or enter the URL for the file."),
tags$li("The variables in that file will populate dropdown lists on that tab."),
tags$li("Select variables to serve as site IDs, weights, response variables, and subpopulations
(if desired). If only overall or 'national' estimates are desired, check the box for overall analysis."),
tags$li("If data are to be used for change analysis, select the variable that distinguishes between design cycles (we usually assume this variable represents year)."),
tags$li(p("Select the type of variance you want to use. ",
strong("Local neighborhood variance"),
" uses a site's nearest neighbors to estimate variance, tending to
result in smaller variance values than variance based on a simple random sample. This approach is ",
em("recommended"),"and is the approach used in
NARS estimates. It requires site coordinates to be provided."),
tags$ul(
tags$li("For local neighborhood variance, select coordinate variables
(Albers projection, or some other projection)."),
tags$li("For simple random sample (SRS) variance, selecting a stratum variable
to better estimate variance is advised but not required. Coordinates are
not used with this type of variance."))),
br(),
tags$li("You may subset the data for analysis by up to one categorical variable. To do
this, select the check box to subset, then select the variable to subset by.
Finally, select one or more categories by which to subset data."),
tags$li("Click on the left hand button to view the full dataset if necessary."),
tags$li("Click on the right hand button above the data to subset the data before
proceeding to the analysis tabs.")
)),
bsCollapsePanel(title = h4(strong("Minimum Requirements for Analysis")),
tags$ul(
tags$li("The R package", strong("spsurvey, v.5.5 or later"),
"is required.
Be sure to update this package if an older version is
already installed." ),
tags$li("All variables must be contained in one file and include site IDs,
weights, response variables, subpopulations (if any), and optionally,
coordinates and/or design stratum (depending on type of variance desired)." ),
tags$li("All sites included in the dataset should have weight > 0. Any
records with a missing weight or a weight of 0 will be dropped
before analysis."),
tags$li("Input data should include",
strong("only one row per site and year/survey cycle"),
"(based on the variables for site ID and year/survey cycle selected). No within year revisits should be included, and all variables used in analysis should be separate columns in the dataset (i.e., wide format)."),
tags$li("Only delimited files, such as comma- and tab-delimited, are accepted for upload."),
tags$li("If local neighborhood variance is desired, coordinates must be
provided in some type of projection, such as Albers."),
tags$li("If variance based on a simple random sample is desired (or if
coordinates are not available), the design stratum should be provided
to better estimate variance."),
tags$li("If change or trend analysis is intended, all desired years of data must be
contained in one file, with a single variable that identifies
the individual years or survey cycles included.")
)),
bsCollapsePanel(title = h4(strong("Run Population Estimates")),
tags$ol(
tags$li("Select the type of analysis (categorical or continuous)."),
tags$li("Select the confidence level for estimating confidence intervals (90% or 95%).
The default is 95%."),
tags$li("If year or design cycle variable was selected on the Prepare Data for
Analysis tab, select year or cycle of interest."),
tags$li("For continuous analysis, select either CDFs (cumulative distribution
functions), percentiles, means, or totals."),
tags$li("Note that if data are missing for continuous variables,
those sites are ignored in analysis."),
tags$li("Click on the Run/Refresh Estimates button. Depending on the number of
responses, subpopulations, and type of analysis, it may take a few seconds
to several minutes."),
tags$li("If desired, download results, data used in analysis, and code to a zip file by clicking
the button labeled", strong("Save estimates, data, and code to a .zip file."))
),
tags$ol(strong("Outputs for categorical analysis:")),
tags$li("Type = Subpopulation group"),
tags$li("Subpopulation = Subpopulation name"),
tags$li("Indicator = Name of indicator"),
tags$li("Category = Category of indicator or Total"),
tags$li("nResp = Number of responses in category"),
tags$li("Estimate.P = Estimated percent of resource in category"),
tags$li("StdError.P = Estimated standard error of percent estimate"),
tags$li("MarginofError.P = Margin of error of percent estimate, representing difference between estimate and confidence bounds"),
tags$li("LCBXXPct.P = Lower confidence bound for percent, where XX represents the confidence level"),
tags$li("UCBXXPct.P = Upper confidence bound for percent, where XX represents the confidence level"),
tags$li("Estimate.U = Estimated amount of resource in category in same units as weights used"),
tags$li("StdError.U = Estimated standard error of amount of resource estimate"),
tags$li("MarginofError.U = Margin of error of amount of resource estimate, representing
difference between estimate and confidence bounds"),
tags$li("LCBXXPct.U = Lower confidence bound for amount of resource, where XX represents the
confidence level"),
tags$li("UCBXXPct.U = Upper confidence bound for amount of resource, where XX represents the
confidence level"),
br(),
tags$ol(strong("Outputs for continuous analysis:")),
tags$li("Type = Subpopulation group"),
tags$li("Subpopulation = Subpopulation name"),
tags$li("Indicator = Name of indicator"),
tags$li("Value = Value of indicator (CDF only)"),
tags$li("Statistic = Value of indicator (Percentiles only)"),
tags$li("nResp = Number of responses in category"),
tags$li("Estimate.P = Estimated percent of resource at or below value (CDF only)"),
tags$li("Estimate = Estimated value for given percentile (Percentiles only)"),
tags$li("StdError.P = Estimated standard error of percent estimate (CDF only)"),
tags$li("StdError = Estimated standard error of mean, variance, or standard deviation
estimate (Percentiles only)"),
tags$li("LCBXXPct.P = Lower confidence bound for percent, where XX represents the confidence level (CDF only)"),
tags$li("UCBXXPct.P = Upper confidence bound for percent, where XX represents the confidence level (CDF only"),
tags$li("LCBXXPct = Lower confidence bound for percentile estimate, where XX represents the
confidence level (Percentiles only)"),
tags$li("UCBXXPct = Upper confidence bound for percentile estimate, where XX represents the
confidence level (Percentiles only)"),
tags$li("Estimate.U = Estimated amount of resource at or below value in same units as weights
used (CDF only)"),
tags$li("StdError.U = Estimated standard error of amount of resource at or below estimate (CDF only)"),
tags$li("LCBXXPct.U = Lower confidence bound for amount of resource, where XX represents the
confidence level (CDF only)"),
tags$li("UCBXXPct.U = Upper confidence bound for amount of resource, where XX represents the
confidence level (CDF only)")
),
bsCollapsePanel(title = h4(strong("Run Change Analysis")),
tags$ol(
tags$li("First select the two years (or sets of years) to compare."),
tags$li("Select type of data to analyze (categorical or continuous)."),
tags$li("Select the confidence level for estimating confidence intervals (90% or 95%).
The default is 95%."),
tags$li("If continuous data are selected, select parameter on which to test
for differences (mean or median)."),
tags$li("Click on the Run/Refresh Estimates button. Depending on the number of
responses, subpopulations, and type of analysis, it may take a few
seconds to several minutes."),
tags$li("If any data are changed in the Prepare Data for Analysis tab, years
must be re-selected before running analysis."),
tags$li("To save outputs, including estimates, data used in analysis, and code, click the button labeled", strong("Save change estimates, data, and code as .zip file."))
),
tags$ul(strong("Outputs for Categorical Analysis:"),
tags$li("Survey_1 = Year or design cycle of first survey"),
tags$li("Survey_2 = Year or design cycle of second survey"),
tags$li("Type = Subpopulation group"),
tags$li("Subpopulation = Subpopulation name"),
tags$li("Indicator = Name of indicator"),
tags$li("Category = Category of indicator or Total"),
tags$li("DiffEst.P = Estimate of difference in percentage (Survey_2 - Survey_1)"),
tags$li("StdError.P = Estimated standard error of change percent estimate"),
tags$li("MarginofError.P = Margin of error of change percent estimate, representing
difference between estimate and confidence bounds"),
tags$li("LCBXXPct.P = Lower confidence bound for change percent, where XX represents
the confidence level"),
tags$li("UCBXXPct.P = Upper confidence bound for change percent, where XX represents
the confidence level"),
tags$li("DiffEst.U = Estimated amount of change in resource in category in same units
as weights used"),
tags$li("StdError.U = Estimated standard error of amount of change in resource estimate"),
tags$li("MarginofError.U = Margin of error of amount of change in resource estimate, representing
difference between estimate and confidence bounds"),
tags$li("LCBXXPct.U = Lower confidence bound for amount of change resource, where XX represents the
confidence level"),
tags$li("UCBXXPct.U = Upper confidence bound for amount of change resource, where XX represents the
confidence level"),
tags$li("nResp_1, nResp_2 = Number of responses in category in survey 1 and survey 2, respectively"),
tags$li("Estimate.P_1, Estimate.P_2 = Estimated percent of resource in category in survey 1
and survey 2, respectively"),
tags$li("StdError.P_1, StdError.P_2 = Estimated standard error of percent estimate in survey 1
and survey 2, respectively"),
tags$li("MarginofError.P_1, MarginofError.P_2 = Margin of error of percent estimate,
representing difference between estimate and confidence bounds in survey 1
and survey 2, respectively"),
tags$li("LCBXXPct.P_1, LCBXXPct.P_2 = Lower confidence bound for percent, where XX represents
the confidence level in survey 1 and survey 2, respectively"),
tags$li("UCBXXPct.P_1, UCBXXPct.P_2 = Upper confidence bound for percent, where XX represents
the confidence level in survey 1 and survey 2, respectively"),
tags$li("Estimate.U_1, Estimate.U_2 = Estimated amount of resource in category in same units
as weights used, in survey 1 and survey 2, respectively"),
tags$li("StdError.U_1, StdError.U_2 = Estimated standard error of amount of resource estimate
in survey 1 and survey 2, respectively"),
tags$li("MarginofError.U_1, MarginofError.U_2 = Margin of error of amount of resource estimate,
representing difference between estimate and confidence bounds in survey 1 and survey 2,
respectively"),
tags$li("LCBXXPct.U_1, LCBXXPct.U_2 = Lower confidence bound for amount of resource, where
XX represents the confidence level in survey 1 and survey 2, respectively"),
tags$li("UCBXXPct.U_1, UCBXXPct.U_2 = Upper confidence bound for amount of resource,
where XX represents the confidence level in survey 1 and survey 2, respectively")
),
tags$ul(strong("Outputs for Continuous Analysis (Means):"),
tags$li("Survey_1 = Year or design cycle of first survey"),
tags$li("Survey_2 = Year or design cycle of second survey"),
tags$li("Type = Subpopulation group"),
tags$li("Subpopulation = Subpopulation name"),
tags$li("Indicator = Name of indicator"),
tags$li("DiffEst = Estimate of difference in mean for indicator (Survey_2 - Survey_1)"),
tags$li("StdError = Estimated standard error of change estimate"),
tags$li("MarginofError = Margin of error of estimated mean, representing
difference between estimate and confidence bounds"),
tags$li("LCBXXPct = Lower confidence bound for estimated mean, where XX represents
the confidence level"),
tags$li("UCBXXPct = Upper confidence bound for estimated mean, where XX represents
the confidence level"),
tags$li("nResp_1, nResp_2 = Number of responses for indicator in survey 1 and survey 2, respectively"),
tags$li("Estimate_1, Estimate_2 = Estimated mean of resource for indicator in survey 1
and survey 2, respectively"),
tags$li("StdError_1, StdError_2 = Estimated standard error of estimated mean in survey 1
and survey 2, respectively"),
tags$li("MarginofError_1, MarginofError_2 = Margin of error of estimated mean,
representing difference between estimate and confidence bounds in survey 1
and survey 2, respectively"),
tags$li("LCBXXPct_1, LCBXXPct_2 = Lower confidence bound for indicator mean, where XX represents
the confidence level in survey 1 and survey 2, respectively"),
tags$li("UCBXXPct_1, UCBXXPct_2 = Upper confidence bound for indicator mean, where XX represents
the confidence level in survey 1 and survey 2, respectively"),
),
tags$ul(strong("Outputs for Continuous Analysis (Medians):"),
tags$li("All of the output variable names match those for Categorical Change Analysis (see above)"),
tags$li("The two categories for this output are Greater_Than_Median and Less_Than_Median,
but the interpretations are the same")
)),
bsCollapsePanel(title = h4(strong("Run Trend Analysis")),
tags$ol(
tags$li("Trend analysis runs on only categorical response variables, using options selected on data preparation tab."),
tags$li("Trend will be run using all years in the dataset."),
tags$li("If a response is missing for all observations in a particular year, that year will be excluded from the analysis of that response and a warning will be displayed."),
tags$li("The confidence level for trend analysis is set at 95%."),
tags$li("Click on the Run/Refresh Estimates button. Depending on the number of
responses and subpopulations, it may take a few seconds to several minutes to run."),
tags$li("To save outputs, including estimates, data used in analysis, code, and sample sizes by year and response variable, click the button labeled", strong("Save trend results, data, code, and sample sizes as .zip file."))
),
tags$ul(strong("Outputs for Categorical Trend Analysis:"),
tags$li("Survey_1 = Year or design cycle of first survey"),
tags$li("Survey_2 = Year or design cycle of second survey"),
tags$li("Type = Subpopulation group"),
tags$li("Subpopulation = Subpopulation name"),
tags$li("Indicator = Name of indicator or response variable"),
tags$li("Category = Category of indicator"),
tags$li("Trend_Estimate = estimate of trend slope"),
tags$li("Trend_Std_Error = standard error of trend slope"),
tags$li("Trend_LCB95Pct = lower 95% confidence bound of trend slope"),
tags$li("Trend_UCB95Pct = upper 95% confidence bound of trend slope"),
tags$li("Trend_p_Value = p-value for trend slope"),
tags$li("Intercept_Estimate = estimate of trend intercept"),
tags$li("Intercept_Std_Error = standard error of trend intercept"),
tags$li("Intercept_LCB95Pct = lower 95% confidence bound of trend intercept"),
tags$li("Intercept_UCB95Pct = upper 95% confidence bound of trend intercept"),
tags$li("Intercept_p_Value = p-value for trend intercept"),
tags$li("R_Squared = R-squared for trend"),
tags$li("Adj_R_Squared = Adjusted R-squared for trend"))
),
bsCollapsePanel(title = h4(strong("Plot Categorical Estimates")),
tags$ol(
tags$li("Either run population estimates on categorical data either within the
app or import results into the app."),
tags$li("Variables in dataset must match those expected as output from
spsurvey::cat_analysis function:", strong("Type, Subpopulation, Indicator,
Category, Estimate.P, StdError.P, LCBXXPct.P, UCBXXPct.P, Estimate.U,
StdError.U, LCBXXPct.U, UCBXXPct.U, where XX represents the confidence
level.")),
tags$li("Select either proportion or unit estimates to plot from Estimate Type."),
tags$li("Select Category values that represent Good, Fair, Poor, Not Assessed,
and Other condition classes. More than one value per condition class
in the dataset may be selected. For example, if one response uses
Good/Fair/Poor and another used At or Below Benchmark/Above Benchmark,
both Good and At or Below Benchmark can be selected."),
tags$li(strong("Optional:"), "add plot title and define resource type/unit
(i.e., stream length, number of lakes, coastal or wetland area)"),
tags$li("Click the Plot/Refresh Button to create plots."),
tags$li("From the menus on the right-hand side of the page, select the
Indicator of interest, and then the Subpopulation Group. Then
select the Subpopulation of interest. The upper plot shows the
individual subpopulation and the lower plot show a particular
condition class across all subpopulations."),
tags$li("To show confidence bound values, click the box above the main
and/or subpopulation plots."),
tags$li("The default order of the subpopulations in the lower plot is
alphabetical, but to sort by the estimate of the", em("Good"),
"class,
click the box for", em("Sort Subpopulations by Good Condition"),"."),
tags$li("Select", em("Download Estimate Plot"), "or",
em("Download Subpopulation Plot"), "to
save a .png file of the output. ")
)),
bsCollapsePanel(title = h4(strong("Plot Continuous Estimates")),
tags$ol(
tags$li("Either run population estimates on continuous data to obtain
CDF estimates within the app, or import results into the app."),
tags$li("Variables in dataset must match those expected as output from
spsurvey::cont_analysis function:", strong("Type, Subpopulation, Indicator,
Value, Estimate.P, StdError.P, LCBXXPct.P, UCBXXPct.P, Estimate.U,
StdError.U, LCBXXPct.U, UCBXXPct.U, where XX represents the confidence level.")),
tags$li("Select either proportion or unit estimates to plot
from Estimate Type."),
tags$li(strong("Optional:"), "Add plot title, indicator units,
and define resource type/unit."),
tags$li("Click the Plot Continuous Estimates button."),
tags$li("Select indicator from dropdown, then select a subpopulation group. Add
or remove subpopulations to the plot from the",
em("Add/Remove Subpopulations"),
"dropdown."),
tags$li(strong("Optional:"), "Add an Indicator Threshold, add confidence bands, and/or
change the x-axis to log10 scale. Be aware that if you have values
that are below or equal to zero, points will be excluded from the
plot if the", em("Log Scale X-Axis"), "option is selected.")
)),
br(),
p('Contact Karen Blocksom at [email protected] with questions or feedback.'),
h3('Disclaimer'),
p('The United States Environmental Protection Agency (EPA) GitHub project code is provided
on an "as is" basis and the user assumes responsibility for its use. EPA has relinquished
control of the information and no longer has responsibility to protect the integrity,
confidentiality, or availability of the information. Any reference to specific commercial
products, processes, or services by service mark, trademark, manufacturer, or otherwise,
does not constitute or imply their endorsement, recommendation or favoring by EPA. The EPA
seal and logo shall not be used in any manner to imply endorsement of any commercial product
or activity by EPA or the United States Government.')),
# Prepare Data ------------------------------------------------------------
# Panel to import and select data to analyze
tabPanel(title='Prepare Data for Analysis',value="prepdata",
fluidRow(
# Input: Select a file ---
column(3,
add_busy_spinner(spin='fading-circle', position='full-page'),
checkboxInput("websource", 'Input file from URL instead of local directory', FALSE),
# Read in file from local computer
conditionalPanel(condition="input.websource == false",
fileInput(inputId='file1', buttonLabel='Browse...',
label='Select a delimited file for analysis',
multiple=FALSE, accept=c('text/csv','text/comma-separated-values,text/plain','.csv')
)),
# Read in file from website URL - need to add a button to signal it should start uploading
conditionalPanel(condition="input.websource == true",
textInput("urlfile", "Paste or enter full URL here."),
actionButton("urlbtn", "Load file from URL")),
# Horizontal line ----
tags$hr(),
# Input: checkbox if file has header, default to TRUE ----
checkboxInput('header','Header',TRUE),
# Input: Select delimiter ----
radioButtons("sep","Separator",
choices = c(Comma = ",",
Semicolon = ";",
Tab = "\t"),
selected = ","),
# Horizontal line
tags$hr(),
# Input: Select number of rows to display
radioButtons("disp","Display",
choices = c(Head = 'head',
All = 'all'),
selected='head'),
checkboxInput("subcheck","Subset data using a single categorical variable", FALSE),
conditionalPanel(condition="input.subcheck == true",
selectizeInput('subvar', "Select variable to use for subsetting", choices=NULL, multiple=FALSE),
selectizeInput("subcat","Select one or more categories by which to subset data", choices=NULL,
multiple=TRUE))),
# Provide dropdown menus to allow user to select site, weight, and response variables from those in the imported dataset
column(3,
shiny::selectizeInput("siteVar", label="Select site variable",
choices=NULL, multiple=FALSE) %>%
helper(type = "inline", icon = 'exclamation', colour='#B72E16',
title = "Site variable selection",
content = paste("Select a site variable. If multiple years and resampled
sites are included in the dataset, be sure the site
variable has the same value across years."),
size = "s", easyClose = TRUE, fade = TRUE),
selectizeInput("weightVar","Select weight variable", choices=NULL, multiple=FALSE),
selectizeInput("respVar","Select up to 10 response variables - must all be either categorical or numeric",
choices=NULL, multiple=TRUE),
checkboxInput('chboxYear', 'Check box if performing change analysis or need to subset data by year
or cycle for population estimates'),
conditionalPanel(condition="input.chboxYear == true",
selectizeInput("yearVar","Select year variable",
choices=NULL, multiple=FALSE)),
checkboxInput('natpop','Calculate overall (all sites) estimates?', TRUE),
checkboxInput('subpop', 'Calculate estimates for subpopulations?', FALSE),
# If national estimates box is NOT checked, show subpopulation dropdown list
conditionalPanel(condition = 'input.subpop == true',
selectizeInput("subpopVar","Select up to 10 subpopulation variables",
choices=NULL, multiple=TRUE)),
h5(p(strong("If ANY changes have been made to your choices, you MUST click the button to prepare data for analysis again!"), style="color:#B72E16"))
),
# Set up type of variance to use in estimates: local or SRS
column(3,
radioButtons('locvar',"Type of variance estimate to use (select one)",
choices = c('Local neighborhood variance (recommended, used for NARS,
requires site coordinates)' = 'local',
'Simple Random Sample (requires stratum but not site
coordinates)' = 'SRS'),
select = 'local'),
# If local, user must select x and y coordinates and convert to Albers if in lat/long
conditionalPanel(condition = "input.locvar == 'local'",
selectizeInput("coordxVar","Select the X coordinate variable
(required only for local neighborhood variance)",
choices=NULL, multiple=FALSE, selected=NULL),
selectizeInput("coordyVar","Select the Y coordinate variable
(required only for local neighborhood variance)",
choices=NULL, multiple=FALSE, selected=NULL)
),
# Select stratum if Simple Random Sample
selectizeInput("stratumVar","Select a categorical stratum variable if desired. May be used for either variance type.",
choices=NULL, multiple=FALSE)
)
),
# Press button to subset data for analysis - must click here first
column(4, actionButton("resetBtn", "Click to revert back to full dataset or change data display")),
column(4, actionButton("subsetBtn", strong("Click HERE to prepare data for analysis"), style="color:#B72E16"), offset=2),
hr(),
br(),
# Show a table of the data
h4("Data for Analysis"),
DT::dataTableOutput("contents")
),
# Run Population Estimates ------------------------------------------------
# Tab to run population estimates
tabPanel(title="Run Population Estimates",value="runest",
fluidRow(
column(3,
# add_busy_bar(color="red", centered=TRUE),
# User must select categorical or continuous variable analysis, depending on response variables selected
radioButtons("atype","Type of Analysis (pick one)",
choices = c('Categorical (for character variables)' = 'categ',
'Continuous (for numeric variables)' = 'contin'),
selected='categ'),
# If continuous analysis selected, select whether CDFs or percentiles are desired.
conditionalPanel(condition = "input.atype == 'contin'",
radioButtons("cdf_pct", "Show CDF, percentile, mean, or total results",
choices = c(CDF = 'cdf', Percentiles = 'pct', Means = 'mean', Totals = 'total'),
selected = 'pct')),
conditionalPanel(condition="input.chboxYear==true",
selectizeInput('selYear', 'Select the year for analysis',
choices=NULL, multiple=FALSE)),
radioButtons('ciSize', 'Confidence level',
choices = c('90%' = '90', '95%' = '95'),
selected = '95'),
p("If the", strong("Run/Refresh Estimates"), "button is grayed out, return to the",
strong("Prepare Data for Analysis"), "tab and click the button that says",
strong("Click HERE to prepare data for analysis")),
p("Note that if all values are very small, the results may appear as zeroes. Save
and view output file to see the results with full digits."),
# Once data are prepared, user needs to click to run estimates, or rerun estimates on refreshed data
shinyjs::disabled(actionButton('runBtn', "Run/Refresh estimates")),
hr(),
# Click to download results into a comma-delimited file
shinyjs::disabled(downloadButton("dwnldcsv","Save estimates, data, and code to a .zip file"))),
# Show results here
column(8,
h4("If output is not as expected, be sure you chose the correct ",
strong("Type of Analysis"), "(Categorical or Continuous) for your data",
style="color:#225D9D"),
h4("Warnings"),
DT::dataTableOutput("warnest"),
br(),
br(),
h4("Analysis Output"),
DT::dataTableOutput("popest")
)
)
),
# Run Change Analysis -----------------------------------------------------
tabPanel(title="Run Change Analysis", value='change',
fluidRow(
h4(" ", "If a different set of response variables from those
used in the population estimates is desired,
return to the", strong("Prepare Data for Analysis"),
"tab to re-select variables. Then click the button to
prepare data for analysis again.",
style="color:#225D9D"),
column(3,
# User must select years to compare
selectizeInput('chgYear1',"Select two years of data to compare in desired order",
choices=NULL, multiple=TRUE),
radioButtons("chgCatCont", "Type of variables to analyze",
choices = c(Categorical = 'chgCat', Continuous = 'chgCont'),
select = 'chgCat'),
conditionalPanel(condition = "input.chgCatCont == 'chgCont'",
radioButtons("testType", "Base test on mean or median",
choices = c(Mean='mean', Median='median'),
selected = 'mean')),
radioButtons('ciSize_chg', 'Confidence level',
choices = c('90%' = '90', '95%' = '95'),
selected = '95'),
# Once data are prepared, user needs to click to run estimates, or rerun estimates on refreshed data
hr(),
p("If the", strong("Run/Refresh Estimates"), "button is grayed out, return to the",
strong("Prepare Data for Analysis"), "tab and click the button that says",
strong("Click HERE to prepare data for analysis")),
shinyjs::disabled(actionButton('chgBtn', "Run/Refresh estimates")),
hr(),
# Click to download results into a comma-delimited file
shinyjs::disabled(downloadButton("chgcsv","Save change estimates, data, and code as .zip file"))),
column(8,
h4("Warnings"),
DT::dataTableOutput("warnchg"),
br(),
br(),
h4("Change Analysis Output"),
DT::dataTableOutput("changes")
)
)
),
# Trend Analysis ------------------------------
tabPanel(title="Run Trend Analysis", value='trend',
fluidRow(
h3("Use for categorical analysis only - Please note:"),
tags$ul(
tags$li("This is only designed for analysis of categorical
variables and may convert any numerical variable to categorical.
Any subsetting of data to align different years or cycles
must be done either on the", strong("Prepare Data for Analysis"),
"tab or before importing the dataset."),
br(),
tags$li("If a different set of
response variables from those
used in the population or change estimates is desired,
return to the", strong("Prepare Data for Analysis"),
"tab to re-select variables. Then click the button to
prepare data for analysis again."),
br(),
tags$li("If the", strong("Run/Refresh Estimates"), "button is grayed out, return to the",
strong("Prepare Data for Analysis"), "tab and click the button that says",
strong("Click HERE to prepare data for analysis"))
),
column(3,
# Once data are prepared, user needs to click to run estimates, or rerun estimates on refreshed data
shinyjs::disabled(actionButton('trendBtn', "Run/Refresh estimates")),
hr(),
# Click to download results into a comma-delimited file
shinyjs::disabled(downloadButton("trendcsv","Save trend results, data, code, and sample sizes as .zip file"))),
column(8,
h4("Warnings"),
DT::dataTableOutput("warntrend"),
h4("Trend Analysis Output"),
DT::dataTableOutput("trends")
)
)
),
# Plot Data ---------------------------------------------------------------
####Categorical Plot UI####
tabPanel(title="Plot Categorical Estimates",
fluidRow(
sidebarPanel(
radioButtons(inputId="catinput",
label=strong("Choose Categorical Estimate Dataset to Use:"),
choices=c("Upload Estimate Data File", "Current Estimate Data"),
selected = "Upload Estimate Data File",
inline=FALSE),
uiOutput("catui"),
selectInput(inputId = "Estimate",
label = HTML("<b>Select Estimate Type</b>"),
choices = c("Proportion Estimates" = "P Estimates", "Unit Estimates" = "U Estimates"),
multiple = FALSE,
width = "300px") %>%
#Estimate Type helper
helper(type = "inline",
icon = 'circle-question',
title = "Estimate Type",
content = c("<b>Proportion Estimates:</b> Proportion of observations that
belong to each level of the categorical variable.",
"<b>Unit Estimates:</b> Total units (i.e. extent) that belong
to each level of the categorical variable (total number
(point resources), total line length (linear network), or
total area (areal network))."),
size = "s", easyClose = TRUE, fade = TRUE),
tags$style(HTML(".shiny-output-error-validation {color: #ff0000; font-weight: bold;}")),
selectInput(inputId = "Good",
label = HTML("<b>Select <span style='color: #5796d1'>'Good'</span> Condition Classes</b>"),
choices = "",
selected = NULL,
multiple = TRUE,
width = "300px") %>%
#Condition Category helper
helper(type = "inline", icon = 'circle-question',
title = "Condition Category Color",
content = paste("The following inputs ask you to define the condition classes
which will be used in the plots. You may use the same
category for multiple conditions.", strong(em("For example, if
your dataset
contains some responses for which Good is the best condition
and some for which Low is the best, you can select both for
plotting. Only those applicable to a given response will be
shown in the plot."))),
size = "s", easyClose = TRUE, fade = TRUE),
selectInput(inputId = "Fair",
label = HTML("<b>Select <span style='color: #EE9A00'>'Fair'</span> Condition Classes</b>"),
choices = "",
selected = NULL,
multiple = TRUE,
width = "300px"),
selectInput(inputId = "Poor",
label = HTML("<b>Select <span style='color: #f55b5b'>'Poor'</span> Condition Classes</b>"),
choices = "",
selected = NULL,
multiple = TRUE,
width = "300px"),
selectInput(inputId = "Not_Assessed",
label = HTML("<b>Select <span style='color: #c77505'>'Not Assessed'</span> Condition Classes</b>"),
choices = "",
selected = NULL,
multiple = TRUE,
width = "300px"),
selectInput(inputId = "Other",
label = HTML("<b>Select <span style='color: #d15fee'>'Other'</span> Condition Classes</b>"),
choices = "",
selected = NULL,
multiple = TRUE,
width = "300px"),
textInput("title", "Add a Plot Title", value = "", width = "300px", placeholder = "Optional"),
textInput("resource", "Define Resource Type/Unit", value = "",
width = "300px", placeholder = "Resource") %>%
#Resource Type helper
helper(type = "inline", icon = 'circle-question',
title = "Resource Type",
content = c("This input defines the plot axis label. Resource Type is
the resource evaluated in your design (e.g., Stream Miles,
Wetland Area, Coastal Area, Lakes)."),
size = "s", easyClose = TRUE, fade = TRUE),
actionButton("plotbtn", strong("Plot/Refresh Estimates"), icon=icon("chart-bar")),
br(),br(),
width = 3),
mainPanel(
conditionalPanel(condition="input.plotbtn",
column(3,
tags$head(tags$style(HTML("#Ind_Plot ~ .selectize-control.single .selectize-input {background-color: #FFD133;}"))),
selectInput(inputId = "Ind_Plot",
label = HTML("<b>Select Indicator</b>"),
choices = "",
multiple = FALSE,
width = "200px") %>%
#Indicator input helper
helper(type = "inline",
icon = 'circle-question',
title = "Indicator",
content = c("Select the indicator to display
categorical estimates by population."),
size = "s", easyClose = TRUE, fade = TRUE),
#column(3,
tags$head(tags$style(HTML("#Type_Plot ~ .selectize-control.single .selectize-input {background-color: #FFD133;}"))),
selectInput(inputId = "Type_Plot",
label = HTML("<b>Select Subpopulation Group</b>"),
choices = NULL,
multiple = FALSE,
width = "200px") %>%
#Subpop Group input helper
helper(type = "inline",
icon = 'circle-question',
title = "Subpopulation Group",
content = c("Select the Subpopulation group
to generate individual subpopulation
estimates and to use in the subpopulation
comparison plot."),
size = "s", easyClose = TRUE, fade = TRUE))),
column(8, offset = 2,
h3(strong(HTML("<center>Categorical Estimates by Population<center/>") %>%
#Condition estimate helper
helper(type = "inline",
icon = 'circle-question',
title = "Categorical Estimates",
content = c("Cycle through populations to display
categorical estimates and download the plot,
if desired."),
size = "s", easyClose = TRUE, fade = TRUE)))),
fluidRow(
column(3, offset=1,
conditionalPanel(condition="input.plotbtn",
selectInput(inputId = "Tot_Pop",
label = HTML("<b>Select Subpopulation</b>"),
choices = NULL,
selected = NULL,
multiple = FALSE))),
column(3, offset=1,
conditionalPanel(condition="input.plotbtn",
checkboxInput(inputId = "indconlim",
label= "Add Confidence Limit Values",
value = FALSE,
width = NULL)))),
fluidRow(
column(3, offset = 1,
conditionalPanel(condition="input.plotbtn",
downloadButton("downloadPlot1", "Download Estimate Plot")))),
plotOutput("plot", width = "75%", height = "300px"),
br(), br(), br(),
column(8, offset = 2,
h3(strong(HTML("<center>Subpopulation Comparison<center/>") %>%
#Subpopulation helper
helper(type = "inline",
icon = 'circle-question',
title = "Subpopulation Comparison",
content = c("Cycle through population groups to compare categorical estimates by condition and download plot, if desired. If there are no subpopulations, no subpopulation plots will appear."),
size = "s", easyClose = TRUE, fade = TRUE)))),
fluidRow(
column(3, offset=1,
conditionalPanel(condition="input.plotbtn",
# tags$head(tags$style(HTML("#Con_Plot ~ .selectize-control.single .selectize-input {border: 1px solid #33ACFF;}"))),
selectInput(inputId = "Con_Plot",
label = HTML("<b>Select Condition</b>"),
choices = "",
multiple = FALSE))),
column(3, offset=1,
conditionalPanel(condition="input.plotbtn",
checkboxInput(inputId = "goodsort",
label= HTML("Sort Subpopulations by <span style='color: #5796d1'>'Good'</span> Condition"),
value = FALSE,
width = NULL),
checkboxInput(inputId = "subconlim",
label= "Add Confidence Limit Values",
value = FALSE,
width = NULL)))),
fluidRow(
column(3, offset=1,
conditionalPanel(condition="input.plotbtn",
downloadButton('downloadPlot2', "Download Subpopulation Plot")))),
plotOutput("plot2", width = "75%"))
)),
####Continuous Plot UI####
tabPanel(title="Plot Continuous Estimates",
sidebarPanel(
radioButtons(inputId="coninput",
label=strong("Choose Cumulative Distribution Function (CDF) Estimate Dataset to Use:"),
choices=c("Upload Estimate Data File", "Current Estimate Data"),
selected = "Upload Estimate Data File",
inline=FALSE),
uiOutput("conui"),
selectInput(inputId = "Estimate_CDF",
label = HTML("<b>Select Estimate Type</b>"),
choices = c("Proportion Estimates" = "P Estimates_CDF", "Unit Estimates" = "U Estimates_CDF"),
multiple = FALSE,
width = "300px") %>%
#Estimate Type helper
helper(type = "inline",
icon = 'circle-question',
title = "Estimate Type",
content = c("<b>Proportion Estimates:</b> Proportion of
observations that belong to each level of the
categorical variable.",
"<b>Unit Estimates:</b> Total units (i.e. extent)
that belong to each level of the categorical
variable (total number (point resources), total
line length (linear network), or total area
(areal network))."),
size = "s", easyClose = TRUE, fade = TRUE),
textInput("title2", "Add a Plot Title", value = "", width = "300px", placeholder = "Optional"),
textInput("units", "Add Indicator Units", value = "", width = "300px", placeholder = "Optional"),
textInput("resource2", "Define Resource Type/Unit", value = "", width = "300px", placeholder = "Resource") %>%
#Resource Type helper
helper(type = "inline",
icon = 'circle-question',
title = "Resource Type",
content = c("This input defines the plot axis label. Resource
Type is the resource evaluated in your design
(e.g., Stream Miles, Wetland Area, Coastline)."),
size = "s", easyClose = TRUE, fade = TRUE),
actionButton("plotbtncon", strong("Plot Continuous Estimates"), icon=icon("chart-bar"))
,width=4),#sidebarPanel
mainPanel(
column(3, offset=1,
conditionalPanel(condition="input.plotbtncon",
selectInput(inputId = "Ind_Con",
label = HTML("<b>Select Indicator</b>"),
choices = "",
multiple = FALSE)),
conditionalPanel(condition="input.plotbtncon",
selectInput(inputId = "Pop_Con",
label = HTML("<b>Select Population</b>"),
choices = "",
multiple = FALSE)),
conditionalPanel(condition="input.plotbtncon",
selectInput(inputId = "SubPop_Con",
label = HTML("<b>Add/Remove Subpopulations</b>"),
choices = "",
multiple = TRUE))),
fluidRow(
column(3, offset=1,
conditionalPanel(condition="input.plotbtncon",
numericInput("Thresh", "Indicator Threshold (optional)",
value = NULL),
checkboxInput(inputId = "conflim",
label="Add Confidence Limits",
value = FALSE,
width = NULL),
checkboxInput(inputId = "log",
label="Log Scale X-Axis",
value = FALSE,
width = NULL)))),
column(8, offset = 2,
h3(strong(HTML("<center>CDF Estimates<center/>") %>%
#CDF helper
helper(type = "inline",
icon = 'circle-question',
title = "Cumulative Distribution Function (CDF)",
content = c("A Cumulative Distribution Function calculates
the cumulative probability for a given value and
can be used to determine the probability that a
random observation that is taken from the
population will be less than or equal to a certain
value."),
size = "s", easyClose = TRUE, fade = TRUE))),
hr(),
h4("NOTE: Plotting and downloading may take a while if there are multiple
subpopulations. PLEASE BE PATIENT.")),
fluidRow(
column(3,
conditionalPanel(condition="input.plotbtncon",
downloadButton("download1", "Download CDF Plot")))),
plotOutput("CDFsubplot", width = "75%"),
br(), br(), br(),
column(8, offset = 2,
h3(strong(HTML("<center>Distribution of Estimates by Population<center/>") %>%
#CDF helper
helper(type = "inline",
icon = 'circle-question',
title = "Ridgeline Distribution Plot",
content = c("A <b>Ridgeline Plot</b>, also known as a Joy Plot,
is used to visualize distributions of several groups.
Each group produces a density curve which overlaps
with each other to help visualize differences.",
"",
"Small vertical lines represent values.",
"Large vertical lines represent quartiles."),
size = "s", easyClose = TRUE, fade = TRUE)))),
fluidRow(
column(3,
conditionalPanel(condition="input.plotbtncon",
downloadButton("download2", "Download Distribution Plot")))),
br(),
plotOutput("Distplot", width = "75%")
))
),
# Site Footer ----
includeHTML("www/footer.html")) # END fluidPage
# Begin Server ----
server <- function(input, output, session) {
observe_helpers()
# Read in Data for Preparation --------------------------------------------
# Read in data file as selected
dataIn <- reactive({
if(input$websource==FALSE){
file1 <- input$file1
req(file1)
df <- read.table(input$file1$datapath,
header = input$header,
sep = input$sep,
stringsAsFactors=F)
}else{
if(input$urlbtn>0){
df <- read.table(url(input$urlfile),
header = input$header,
sep = input$sep,
stringsAsFactors=F)
}
}
df
})
# Need code to show initial version of dataIn() before subsetting or pressing the button to reset
observeEvent(input$subsetBtn, {
shinyjs::disable('dwnldcsv')
shinyjs::enable('chgBtn')
shinyjs::enable('runBtn')
shinyjs::disable('chgcsv')
shinyjs::enable('trendBtn')
shinyjs::disable('trendcsv')
if(input$disp == 'head'){
output$contents <- DT::renderDataTable({head(dataOut())}, options = list(digits=5, rownames=F,
scrollX=TRUE, scrollY=TRUE))
}else{
output$contents <- DT::renderDataTable({dataOut()}, options = list(digits=5, rownames=F,
scrollX=TRUE, scrollY=TRUE))
}
})
observeEvent(input$resetBtn, {
if(input$disp == 'head'){
output$contents <- DT::renderDataTable({head(dataIn())},
options = list(digits=5, rownames=F,
scrollX=TRUE, scrollY=TRUE))
}else{
output$contents <- DT::renderDataTable({dataIn()}, options = list(digits=5, rownames=F,
scrollX=TRUE, scrollY=TRUE))
}
})
# Use current dataset to refresh dropdown list of variables.
observe({
vars <- colnames(dataIn())
updateSelectizeInput(session, "weightVar", "Select weight variable", choices=vars)
updateSelectizeInput(session, 'respVar', 'Select up to 10 response variables - All must be either categorical or numeric',
choices=vars, selected = NULL,
options = list(maxItems=10))
updateSelectizeInput(session, 'coordxVar', "Select the X coordinate variable \n(required only
for local neighborhood variance)",
choices=vars, selected = NULL)
updateSelectizeInput(session, 'coordyVar', "Select the Y coordinate variable\n(required only
for local neighborhood variance)",
choices=vars, selected = NULL)
updateSelectizeInput(session, 'siteVar', 'Select site variable', choices=vars)
updateSelectizeInput(session, 'subpopVar', 'Select up to 10 subpopulation variables \n(required if not
national estimates only)', choices=vars, selected=NULL,
options = list(maxItems=10))
updateSelectizeInput(session, "stratumVar", "Select a categorical stratum variable if desired. May be used for either variance type.",
choices=c('None', vars), selected='None')
updateSelectizeInput(session, "yearVar","Select year variable",
choices=c('', vars))
updateSelectizeInput(session, "subvar", choices=vars)
updateSelectizeInput(session, 'szwtVar', choices=vars)
})
observeEvent(input$subvar,{
catchoices <- as.character(sort(unique(dataIn()[[input$subvar]])))
updateSelectizeInput(session, 'subcat', choices = catchoices, selected=NULL, server=TRUE)
})
# After Prepare Data for Analysis Button Clicked --------------------------
# Once subset button is clicked, validate selections to make sure any variable only occurs in set of selections
dataOut <- eventReactive(input$subsetBtn,{
if(input$subsetBtn > 0){
if(input$chboxYear==TRUE){