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ui.R
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ui.R
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library(shiny)
library(tidyverse)
library(haven)
library(here)
library(kableExtra)
library(readxl)
library(shinyjs)
#library(plotly)
library(shinyBS)
library(shinythemes)
library(ggplot2)
# get links
setwd(here())
links <- read.csv("links.csv")
rownames(links) <- links$name
# not sure if this makes a difference
knitr::opts_knit$set(root.dir = here())
source(here("code","all_analysis.R"))
costs_temp_india <-
costs1_p2_f(
country_total_var = costs_data_in$total,
country_cost_var = costs_data_in$costs_by_country,
staff_time_var = staff_time_so,
select_var = list("india")
)
costs_temp_kenya <-
costs1_p2_f(
country_total_var = costs_data_in$total,
country_cost_var = costs_data_in$costs_by_country,
staff_time_var = staff_time_so,
select_var = list("kenya")
)
costs_temp_nigeria <-
costs1_p2_f(
country_total_var = costs_data_in$total,
country_cost_var = costs_data_in$costs_by_country,
staff_time_var = staff_time_so,
select_var = list("nigeria")
)
costs_temp_vietnam <-
costs1_p2_f(
country_total_var = costs_data_in$total,
country_cost_var = costs_data_in$costs_by_country,
staff_time_var = staff_time_so,
select_var = list("vietnam")
)
costs_temp_original <- 0.42
prevalence_india <- prevalence_r_so["india"]
prevalence_kenya <- prevalence_r_so["kenya"]
prevalence_nigeria <- prevalence_r_so["nigeria"]
prevalence_vietnam <- prevalence_r_so["vietnam"]
prevalence_original <- 0.92
length_temp_india <- 1
length_temp_kenya <- 1
length_temp_nigeria <- 1
length_temp_vietnam <- 1
length_temp_original <- 1
nsims <- 1e4
# Before each deployment: copy and paste 'data' and 'rawdata' folders into 'shiny_app\'
# here() creates conflicts with shiny deployment. Use source("all_analysis.R") instead
# source(here("code", "shiny_app", "all_analysis.R"))
#fluidPage is something must have
shinyUI(
fluidPage(
tags$head(
tags$style(HTML(
"
.main-container{
margin-right = 0px !important;
max-width: 98% !important;
}
"
))
),
theme = shinytheme("cerulean"),
navbarPage("Open Policy Analysis for Deworming Interventions: Open Output Component",
# Begin main policy estimate tab ----
tabPanel(
"Main Policy Estimate",
sidebarPanel(
style = "width: 100%; max-height: 700px; overflow-y: scroll;",
fluidRow(column(
12,
align = "center",
tags$a(
img(
src = "bitss_just_logo_transparent.png",
width = "10%",
height = "auto"
),
href = links['bitss_home','url']
),
tags$a(
img(
src = "cega_transparent.png",
width = "40%",
height = "auto"
),
href = links['cega_home','url']
),
tags$a(
img(
src = "OPA_layers.png",
width = "20%",
height = "auto"
),
href = links['dw_info','url']
)
)),
fluidRow(
style = "width: 100%; height: 100%;",
p(
"This visualization is one of three key components of an",
tags$a(href = links['dw_info','url'], "Open Policy Analysis (OPA)"),
"on the costs and benefits of
mass deworming interventions in various settings. These components are:",
tags$li(
tags$span(
"This app, which presents a single output that best represents the factual information required by policy makers to inform their position regarding a policy of mass deworming. Two additional tabs allow the reader to modify key assumptions and components and see how this output changes."
)
),
tags$li(
tags$a(href = links['dw_dd','url'], "A detailed report"),
"that describes how to obtain the policy estimate and describes each component of the analysis."
),
tags$li(
tags$a(href = links['dw_repo','url'], "A repository"),
"that contains all the materials needed to reproduce the analysis with minimal effort (report and interactive app)."
),
),
p(
"The app is the result of a collaboration between the",
tags$a(
href = links['bitss_home','url'],
"Berkeley Initiative
for Transparency in the Social Sciences"
),
"and",
tags$a(href = links['ev_action','url'],
"Evidence Action.")
),
p(
"See a full contributors list",
tags$a(href = links['readme','url'], "here."),
br(),
"See more OPA projects done by BITSS",
tags$a(href =links['bitss_projects','url'], "here.")
)
),
fluidRow(
id = "tPanel_main",
style = "max-width: 100%; max-height: 300px; position:relative;",
br(),
h4(strong("Description of Results")),
p(
"We simulate finding the lifetime income effects (net of intervention costs)
on treated children many times, then plot the values
to create this figure. The height of the curve represents
how often an outcome appeared, i.e. the highest point
means that particular value appeared the most frequently.
The blue line indicates that half of all values are
on either side of the line.",
br(),br(),
"Note: this is Version 1.0 of the OPA. Please kindly report any errors in the application",
tags$a(href =links['issues','url'], "here.")
)
)
),
mainPanel(
fluidRow(id = "output_id1_main", style = "width: 100%; height: 100%; position: relative",
plotOutput("plot1_main")
)
)
),
# end of main policy estimate tab ----
# Begin of key assumptions tab ----
tabPanel(
"Key Assumptions",
sidebarPanel(
div( id = "KA",
fluidRow(column(3),
column(
6,
align = "center",
tags$a(
img(
src = "OPA_layers_output.png",
width = "70%",
height = "auto"
)
)
)),
fluidRow(id = "tPanel1_ka",
style = "overflow-y: scroll; width: 100%; height: 100%; position:relative;",
numericInput(
"param_ka_costs2_ea",
label = h4("Yearly unit costs in new country (in 2018 USD)"),
value = round(cost1_in, 2),
min = 0, step = 0.1
),
numericInput(
"param_ka_prevl_r",
label = h4("Prevalence in new region (\\( \\eta_{new} \\))"),
min = 0 ,
max = 1,
value = round(prevalence_r_in, 2),
step = 0.1
),
numericInput(
"param_ka_years_of_treat_t",
label = h4("Length of treatment (years)"),
value = round(years_of_treat_t_so, 2),
min = 0,
max = 6,
step = 0.1,
),
helpText('For reference:'),
helpText(HTML(paste('
<html>
<head>
<style>
table, th, td {
border: 1px solid black;
border-collapse: collapse;
}
th, td {
padding: 5px;
}
th {
text-align: left;
}
</style>
</head>
<body>
<table style="width:100%">
<tr>
<th>Country</th>
<th>Unit Costs (USD)</th>
<th>Prevalence (%) </th>
<!-- <th>Length of Treatment</th> -->
</tr>
<tr>
<th>India (2018)</th>
<td><var>', round(costs_temp_india,2),'</var></td>
<td><var>',round(prevalence_india,2),'</var></td>
<!-- <td><var>', round(length_temp_india,2),'</var></td> -->
</tr>
<tr>
<th>Kenya (2018)</th>
<td><var>',round(costs_temp_kenya,2),'</var></td>
<td><var>',round(prevalence_kenya,2),'</var></td>
<!-- <td><var>',round(length_temp_kenya,2),'</var></td> -->
</tr>
<tr>
<th>Nigeria (2018)</th>
<td><var>',round(costs_temp_nigeria,2),'</var></td>
<td><var>',round(prevalence_nigeria,2),'</var></td>
<!-- <td><var>',round(length_temp_nigeria,2),'</var></td> -->
</tr>
<tr>
<th>Vietnam (2018)</th>
<td><var>',round(costs_temp_vietnam,2),'</var></td>
<td><var>',round(prevalence_vietnam,2),'</var></td>
<!-- <td><var>', round(length_temp_vietnam,2),'</var></td> -->
</tr>
<tr>
<th>Original Study (1999)</th>
<td><var>', round(costs_temp_original,2),'</var></td>
<td><var>', round(prevalence_original,2),'</var></td>
<!-- <td><var>', round(length_temp_original,2),'</var></td> -->
</tr>
</table>
</body>
</html>
'
))),
actionButton("updateKA", "Update Plot", class = "btn-primary"),
actionButton("resetKA", "Reset Inputs"),
downloadButton("downloadPlotKA", "Save Plot")
)
),
),
mainPanel(
fluidRow(id = "output_id1_ka", style = "width: 100%; height: 100%; position: relative",
plotOutput("plot1_ka")
)
)
),
# end of key assumptions tab ----
# Begin All assumptions tab ----
tabPanel(
"All Assumptions",
sidebarPanel(
div(id = "All",
fluidRow(column(3),
column(
6,
align = "center",
tags$a(
img(
src = "OPA_layers_assumptions.png",
width = "70%",
height = "auto"
)
)
)),
fluidRow(id = "tPanel",
style = "width: 100%; max-height: 100%; position: relative;",
# Begin policy estimate description ----
selectInput("policy_est",
h4("Choose Your Policy Estimate:"),
choices = policy_estimates_text,
selected = "A3. All income of A2. Main Policy Estimate"),
bsPopover(id="policy_est",
title= "",
content="In addition to the main policy estimate you can select all the other approaches described in the documentation",
placement="top"),
withMathJax(),
useShinyjs(),
conditionalPanel(
condition = "input.policy_est == 'A1. Tax revenue' ",
helpText(
HTML(paste("<p><a href = '",links['approach1','url'],"'>Approach 1.1.</a> Welfare measured as additional tax revenue.<br>
- Benefits: tax revenue over predicted effect on earnings.
Data from 10 year follow-up. No externalities. <br>
- Costs: costs of treatment in Kenya in 1998 plus additional
costs due to more schooling</p>"))
)
),
conditionalPanel(
condition = "input.policy_est == 'A1. With externalities. Tax' ",
helpText(
HTML(paste("<p><a href = '",links['approach1','url'],"'>Approach 1.1.</a> Welfare measured as additional tax revenue.<br>
- Benefits: tax revenue over predicted effect on earnings.
Data from 10 year follow-up. No externalities. <br>
- Costs: costs of treatment in Kenya in 1998 plus additional
costs due to more schooling</p>"))
)
),
conditionalPanel(
condition = "input.policy_est == 'A1. All income' ",
helpText(
HTML(paste("<p><a href = '",links['approach1','url'],"'>Approach 1.3.</a> Welfare measured as additional earnings.<br>
- Benefits: predicted additional earnings.
Data from 10 year follow-up. No externalities. <br>
- Costs: costs of treatment in Kenya in 1998 plus additional
costs due to more schooling</p>"))
)
),
conditionalPanel(
condition = "input.policy_est == 'A1. With ext. All income' ",
helpText(
HTML(paste("<p><a href = '",links['approach1','url'],"'>Approach 1.4.</a> Welfare measured as additional earnings.<br>
- Benefits: predicted additional earnings. Including externalities.
Data from 10 year follow-up. Including externalities. <br>
- Costs: costs of treatment in Kenya in 1998 plus additional
costs due to more schooling</p>"))
)
),
conditionalPanel(
condition = "input.policy_est == 'A2. Tax' ",
helpText(
HTML(paste("<p><a href = '",links['approach2','url'],"'>Approach 2.1.</a> Welfare measured as additional tax revenue.<br>
- Benefits: tax revenue over predicted effect on earnings.
Data from 10, 15 and 20 year follow-up. No externalities.<br>
- Costs: costs of treatment in Kenya in 1998 plus additional
costs due to more schooling</p>"))
)
),
conditionalPanel(
condition = "input.policy_est == 'A2. All income' ",
helpText(
HTML(paste("<p><a href = '",links['approach2','url'],"'>Approach 2.2.</a> Welfare measured as additional earnings.<br>
- Benefits: predicted additional earnings.
Data from 10, 15 and 20 year follow-up. No externalities.<br>
- Costs: costs of treatment in Kenya in 1998 plus additional
costs due to more schooling</p>"))
)
),
conditionalPanel(
condition = "input.policy_est == 'A3. All income of A1' ",
helpText(
HTML(paste("<p><a href = '",links['approach3','url'],"'>Approach 3.1.</a> Welfare measured as additional earnings.<br>
- Benefits: predicted additional earnings.
Data from 10 year follow-up. No externalities.
Adjusted for prevalence and length of treatment. <br>
- Costs: current implementation costs in several settings.</p>"))
)
),
conditionalPanel(
condition = "input.policy_est == 'A3. All income of A1, with ext.' ",
helpText(
HTML(paste("<p><a href = '",links['approach3','url'],"'>Approach 3.2.</a> Welfare measured as additional earnings.<br>
- Benefits: predicted additional earnings.
Data from 10 year follow-up. Including externalities.
Adjusted for prevalence and length of treatment.<br>
- Costs: current implementation costs in several settings.</p>"))
)
),
conditionalPanel(
condition = "input.policy_est == 'A3. All income of A2. Main Policy Estimate' ",
helpText(
HTML(paste("<p><a href = '",links['approach3','url'],"'>Approach 3.3.</a> Welfare measured as additional earnings.<br>
- Benefits: predicted additional earnings.
Data from 10, 15 and 20 year follow-up. No externalities.
Adjusted for prevalence and length of treatment.<br>
- Costs: current implementation costs in several settings.</p>"))
)
),
# end policy estimate description ----
checkboxInput("rescale",
label = "Click to rescale x-axis. Unclick to fix reference point",
value = FALSE),
numericInput("param_num_of_sim",
label = h4("Number of simulations"),
value = 1e4),
bsPopover(
id = "param_num_of_sim",
title = "",
content = "For faster computations, change the input to 100.",
placement = "top"
)
),
fluidRow(id = "tPanel1",
style = "overflow-y: scroll; width: 100%; max-height: 250px; position: relative",
tabsetPanel(
# Begin tabpanel research ----
tabPanel(
"Research",
br(),
a(id = "toggleResearchSDs", "Show/hide all standard deviations (SD)", href =
"#"),
bsPopover(
id = "toggleResearchSDs",
title = "",
content = "See section 2.4 in the documentation for how standard deviations are used",
placement = "top"
),
br(),
br(),
numericInput(
"param_lambda1_male",
label = ("\\( \\lambda_{1m} \\)"),
value = lambda1_so[1]
),
bsPopover(
id = "param_lambda1_male",
title = "",
content = "Increase in number of hours worked due to treatment (Male)",
placement = "top"
),
hidden(div(
id = "SD22",
numericInput(
"param_lambda1_male_sd",
label = "SD = ",
value = lambda1_sd_so[1])
)),
numericInput(
"param_lambda1_female",
label = ("\\( \\lambda_{1f} \\)"),
value = lambda1_so[2]
),
bsPopover(
id = "param_lambda1_female",
title = "",
content = "Increase in number of hours worked due to treatment (Female)",
placement = "top"
),
hidden(div(
id = "SD23",
numericInput(
"param_lambda1_female_sd",
label = "SD = ", value =
lambda1_sd_so[2])
)),
numericInput(
"param_lambda1_new",
label = ("\\(\\alpha^{pooled} \\)"),
value = round(lambda1_new_so,2)
),
bsPopover(
id = "param_lambda1_new",
title = "",
content = "Increase in yearly earnings (pooling 10, 15, 20 year follow-ups)",
placement = "top"
),
hidden(div(
id = "SD29",
numericInput("param_lambda1_new_sd", label = "SD = ", value = lambda1_new_sd_so)
)),
sliderInput(
"param_lambda2",
label = "\\( \\lambda_{2} \\)",
min = 0,
max = 2 * lambda2_so,
value = lambda2_so * 1
),
bsPopover(
id = "param_lambda2",
title = "",
content = "Increase in number of hours worked due to treatment (Externalities included)",
placement = "top"
),
hidden(div(
id = "SD24",
sliderInput(
"param_lambda2_sd",
label = "SD = ",
min = 0.1 * lambda2_sd_so,
max = 5 * lambda2_sd_so,
value = lambda2_sd_so,
step = 1e-2
)
)),
sliderInput(
"param_prevl_0",
label = "Prevalence in original study (\\( \\eta \\))",
min = 0,
max = 1,
value = prevalence_0_so
),
bsPopover(
id = "param_prevl_0",
title = "",
content = "Prevalence of parasitic worms in population (Miguel & Kremer 2004)",
placement = "top"
),
hidden(div(
id = "SD32",
sliderInput(
"param_prevl_0_sd",
label = "SD = ",
min = 0.0000001 ,
max = 1 ,
value = 0.1
)
)),
numericInput("param_wage_ag",
label = "Agri Wages (\\( w_{ag} \\))",
value = wage_ag_so),
bsPopover(
id = "param_wage_ag",
title = "",
content = "Average hourly wage of an agricultural worker (KSH)",
placement = "top"
),
hidden(div(
id = "SD5",
numericInput("param_wage_ag_sd",
label = "SD = ",
value = 0.1 * wage_ag_so)
)),
numericInput("param_wage_non_ag",
label = "Wages in Non-ag Jobs (\\( w_{ww} \\))",
value = round(wage_ww_so, 2)),
bsPopover(
id = "param_wage_non_ag",
title = "",
content = "Average hourly wage of a wage worker",
placement = "top"
),
hidden(div(
id = "SD6",
numericInput("param_wage_non_ag_sd",
label = "SD = ",
value = round(0.1 * wage_ww_so, 2))
)),
numericInput("param_profits_se",
label = "Profits se",
value = profits_se_so),
bsPopover(id = "param_profits_se",
title = "",
content = "Average monthly self-employed profits (self-reported)"),
hidden(div(
id = "SD7",
numericInput("param_profits_se_sd",
label = "SD = ",
value = 0.1 * profits_se_so)
)),
sliderInput(
"param_hours_se_cond",
label = "Hours se (>10)",
min = hours_se_cond_so / 2,
max = 2 * hours_se_cond_so,
value = hours_se_cond_so
),
bsPopover(
id = "param_hours_se_cond",
title = "",
content = "Average weekly hours worked (control group)",
placement = "top"
),
hidden(div(
id = "SD8",
sliderInput(
"param_hours_se_cond_sd",
label = "SD = ",
min = 0.000001 * hours_se_cond_so,
max = 1 * hours_se_cond_so,
value = 0.1 * hours_se_cond_so
)
)),
sliderInput(
"param_hours_ag",
label = "\\(\\ H_{ag} \\)",
min = hours_ag_so / 2,
max = 2 * hours_ag_so,
value = hours_ag_so
),
bsPopover(
id = "param_hours_ag",
title = "",
content = "Average weekly hours worked by agricultural workers (control group)",
placement = "top"
),
hidden(div(
id = "SD9",
sliderInput(
"param_hours_ag_sd",
label = "SD = ",
min = 0.000001 * hours_ag_so,
max = 1 * hours_ag_so,
value = 0.1 * hours_ag_so,
round = -4,
step = 0.001
)
)),
sliderInput(
"param_hours_ww",
label = "\\(\\ H_{ww} \\)",
min = hours_ww_so / 2,
max = 2 * hours_ww_so,
value = hours_ww_so
),
bsPopover(
id = "param_hours_ww",
title = "",
content = "Average weekly hours worked by wage earners (control group)",
placement = "top"
),
hidden(div(
id = "SD10",
sliderInput(
"param_hours_ww_sd",
label = "SD = ",
min = 0.000001 * hours_ww_so,
max = 1 * hours_ww_so,
value = 0.1 * hours_ww_so,
step = 0.001
)
)),
sliderInput(
"param_hours_se",
label = "\\(\\ H_{se} \\)",
min = hours_se_so / 2,
max = 2 * hours_se_so,
value = hours_se_so
),
bsPopover(
id = "param_hours_se",
title = "",
content = "Average weekly hours worked by self-employed workers (control group - non-agricultural)",
placement = "top"
),
hidden(div(
id = "SD11",
sliderInput(
"param_hours_se_sd",
label = "SD = ",
min = 0.000001 * hours_se_so,
max = 1 * hours_se_so,
value = 0.1 * hours_se_so,
step = 0.001
)
)),
numericInput(
"param_coef_exp1",
label = ("Coefficients of \\(X_{p} \\) (\\( \\beta_{1} \\))"),
value = coef_exp_so[1]
),
bsPopover(
id = "param_coef_exp1",
title = "",
content = "Teacher experience coefficient",
placement = "top"
),
hidden(div(
id = "SD40",
numericInput(
"param_coef_exp1_sd",
label = "SD = ",
value = 0.1 * coef_exp_so[1]
)
)),
numericInput(
"param_coef_exp2",
label = ("Coefficients of \\(X^{2}p \\) (\\( \\beta_{2} \\))"),
value = coef_exp_so[2]
),
bsPopover(
id = "param_coef_exp2",
title = "",
content = "Teacher experience coefficient squared",
placement = "top"
),
hidden(div(
id = "SD41",
numericInput(
"param_coef_exp2_sd",
label = "SD = ",
value = 0.1 * coef_exp_so[2]
)
)),
sliderInput(
"param_coverage",
label = "Coverage (\\( R \\))",
min = 0,
max = 1,
value = coverage_so,
step = 0.01
),
bsPopover(
id = "param_coverage",
title = "",
content = "Percent of treated primary school students",
placement = "top"
),
hidden(div(
id = "SD14",
sliderInput(
"param_coverage_sd",
label = "SD = ",
min = 0.000001 * coverage_so,
max = 1 * coverage_so,
value = 0.1 * coverage_so,
step = 0.000001
)
)),
sliderInput(
"param_q_full",
label = "Take-up (\\( Q(S_{2}) \\))",
min = 0,
max = 1,
value = q_full_so
),
bsPopover(
id = "param_q_full",
title = "",
content = "Take up rate with full subsidy on deworming treatment costs",
placement = "top"
),
hidden(div(
id = "SD25",
sliderInput(
"param_q_full_sd",
label = "SD = ",
min = 0.00000001 * q_full_so,
max = 1 * q_full_so,
value = 0.1 * q_full_so,
step = 1e-5
)
)),
sliderInput(
"param_q_zero",
label = "Take-up with no subsidy (\\( Q(S_{1}) \\))",
min = 0,
max = 1,
value = q_zero_so
),
bsPopover(
id = "param_q_zero",
title = "",
content = "Take up rate without subsidy on deworming treatment costs (S1 = 0) ",
placement = "top"
),
sliderInput(
"param_delta_ed_par",
label = "x * \\(\\Delta{E} \\)",
min = 0.0000001,
max = 4,
value = delta_ed_par_so
),
bsPopover(
id = "param_delta_ed_par",
title = "",
content = "Scale of effect on additional education (without externalities)",
placement = "top"
),
hidden(div(
id = "SD27",
sliderInput(
"param_delta_ed_par_sd",
label = "SD = ",
min = 0.0000001,
max = 4,
value = delta_ed_par_so * 0.1
)
)),
sliderInput(
"param_delta_ed__ext_par",
label = "x * \\(\\Delta{E} \\) (ext)",
min = 0.0000001,
max = 4,
value = delta_ed_ext_par_so
),
bsPopover(
id = "param_delta_ed__ext_par",
title = "",
content = "Scale of effect on additional education (with externalities)",
placement = "top"
),
hidden(div(
id = "SD28",
sliderInput(
"param_delta_ed__ext_par_sd",
label = "SD = ",
min = 0.0000001,
max = 4,
value = delta_ed_ext_par_so * 0.1
)
)),
numericInput("param_teach_sal", label = "Teacher salary", value = teach_sal_so),
bsPopover(
id = "param_teach_sal",
title = "",
content = "Average annual salary for Kenyan secondary school teacher",
placement = "top"
),
hidden(div(
id = "SD33",
numericInput("param_teach_sal_sd", label = "SD = ", value = 0.1 * teach_sal_so)
)),
numericInput("param_teach_ben", label = "Teacher benefits", value = teach_ben_so),
bsPopover(
id = "param_teach_ben",
title = "",
content = "Average annual benefits for Kenyan secondary school teacher (in KSH)",
placement = "top"
),
hidden(div(
id = "SD34",
numericInput("param_teach_ben_sd", label = "SD = ", value = 0.1 * teach_ben_so)
)),
numericInput("param_n_students", label = "Students per teacher", value = n_students_so),
bsPopover(
id = "param_n_students",
title = "",
content = "Average number for students per teacher",
placement = "top"
),
hidden(div(
id = "SD35",
numericInput(
"param_n_students_sd",
label = "SD = ",
value = 0.1 * n_students_so
)
)),
sliderInput(
"param_years_of_treat_0",
label = "Years of treatment in original study (\\(L_{0}\\))",
min = 0,
max = 6,
step = 0.01,
value = round(years_of_treat_0_so,2)
),
bsPopover(
id = "param_years_of_treat_0",
title = "",
content = "Difference in years of treatment between treatment and control group in original",
placement = "top"
),
hidden(div(
id = "SD18",
sliderInput(
"param_years_of_treat_0_sd",
label = "SD = ",
min = 0.000001 * years_of_treat_0_so,
max = 1 * years_of_treat_0_so,
value = 0.1 * years_of_treat_0_so,
step = 0.0001
)
)),
numericInput(
"param_unit_cost_local",
label = "Costs of T (local $)",
value = round(unit_cost_local_so,2)),
bsPopover(
id = "param_unit_cost_local",
title = "",
content = "Costs of deworming per capita (KSH) per children per year",
placement = "top"
),
hidden(div(
id = "SD16",
numericInput(
"param_unit_cost_local_sd",
label = "SD = ",
value = 0.1 * unit_cost_local_so
)
)),
numericInput("param_unit_cost_2017usdppp",
label = "Costs of T (USD)",
value = round(unit_cost_2017usdppp_so, 2)),
bsPopover(
id = "param_unit_cost_2017usdppp",
title = "",
content = "Costs of deworming per capita (USD) per children per year",
placement = "top"
),
hidden(div(
id = "SD17",
numericInput(
"param_unit_cost_2017usdppp_sd",
label = "SD = ",
value = 0.1 * unit_cost_2017usdppp_so
)
)),
sliderInput(#ASD
"param_costs_par",
label = "Costs adjustments = ",
min = costs_par_so / 2,
max = 20000 * costs_par_so,
value = costs_par_so
),
hidden(div(
id = "SD20",
sliderInput(
"param_costs_par_sd",
label = "SD = ",
min = 0.0000001 * costs_par_sd_so,
max = 10 * costs_par_sd_so,
value = costs_par_sd_so
)
)),
sliderInput(
"param_counts_par",
label = "Counts adjustment",
min = counts_par_so / 2,
max = 2 * counts_par_so,
value = counts_par_so
),
hidden(div(
id = "SD21",
sliderInput(
"param_counts_par_sd",
label = "SD = ",
min = 0.0000001 * counts_par_sd_so,
max = 10 * counts_par_sd_so,
value = counts_par_sd_so
)
))
),
# end tabpanel research ----
#
# Begin tabpanel data ----
tabPanel("Data",
br(),
a(id="toggleDataSDs", "Show/hide all standard deviations (SD)", href="#"),
br(),
br(),
numericInput(
"param_costs2_ea",
label = "Yearly unit costs in new country (in USD)",
value = round(cost1_in, 2),
min = 0
),
bsPopover(
id = "param_costs2_ea",
title = "",
content = "Yearly unit costs in new country (in 2018 USD)",
placement = "top"