Merging DESCRIPTION

Author: ctesta01

README.md

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+# tabby2 
+
+A friendly user tool for visualizing state level TB predictions and interventions. 
+
+Online at https://ppmltools.org/tabby2
+
+# Installation and Setup
+
+Clone the project 
+
+    git clone git@github.com:ppml/tabby2.git
+
+Note that tabby2 makes use of Cario, a library in R which 
+depends on having a few packages available.
+
+On Debian based Linuxs this means having the following packages 
+installed:
+
+    sudo apt install libgtk2.0-dev libcairo2-dev xvfb xauth xfonts-base libxt-dev
+
+Now we can go ahead and install the R package dependencies: 
+
+    # move into our project on terminal:
+    cd tabby2/
+    git checkout beta/
+    R
+
+    # in R:
+    devtools::install_deps("utilities/")
+    devtools::install_deps("tabby1utilities/")
+
+We will also need MITUS, the model of tuberculosis that Tabby2 renders the 
+outcomes of. 
+
+    # in terminal 
+    cd .. # clone MITUS next to tabby2, or any other directory works fine too
+    git clone git@github.com:PPML/MITUS.git
+    cd MITUS/
+    R
+
+    # in R: 
+    devtools::install("./", dependencies=TRUE)
+
+We also need the package MITUSCalibPlots, which handles the generation of 
+calibration plots from stored data in the MITUS package. 
+
+    # in terminal 
+    cd .. # clone MITUS_Calib_Plots next to tabby2, or any other directory works fine too
+    git clone git@github.com:PPML/MITUS_Calib_Plots.git
+    cd MITUS_Calib_Plots/
+    R
+
+    # in R: 
+    devtools::install("./", dependencies=TRUE)
+
+We also need the `tabus` package, the package which does fast reshaping of outcomes from MITUS 
+for plotting with ggplot2. 
+
+    # in terminal 
+    cd .. # clone reshape_mitus_results_for_tabby2 next to tabby2, or any other directory works fine too
+    git clone git@github.com:ppml/reshape_mitus_results_for_tabby.git
+    cd reshape_mitus_results_for_tabby/
+    R
+
+    # in R: 
+    devtools::install("./", dependencies=TRUE)
+
+Now we can run Tabby2
+
+    # in terminal 
+    cd ../
+    cd tabby2/
+    R
+
+    # in R: 
+    shiny::runApp()
+

VERSION.md

@@ -0,0 +1,42 @@
+# Tabby2 Versioning System
+
+This document provides the specification for how the Tabby2 versioning system
+should be used.
+
+The purpose of the versioning system is to alert users of the Tabby2 web
+application to changes to Tabby2 which they may want to take note of.
+
+### Location of the Version Number
+
+The authority on the Tabby2 version number is specified in the
+`utilities/DESCRIPTION` file "Version:" line item. 
+
+This is done so that Tabby2 can render its version number in many places from
+one single source of authority.
+
+### Changelog
+
+The changelog providing brief descriptions of each version increment is 
+stored in `utilities/inst/Rmd/changelog.Rmd`.
+
+### Version Format
+
+The format of the Tabby2 version is given by three numbers separated by two
+periods, e.g. "Tabby2 Version 2.0.1"
+
+These version numbers are ordered in decreasing significance. 
+
+Changes to the first of these version numbers should indicate a major change in
+the scope of the project, such as changes in the funding source or
+methodological approach.
+
+Changes to the middle of these version numbers should indicate a significant
+release, indicating the successful completion of another round of Scientific
+Web Tool Clearance at the Centers for Disease Control and Prevention has been
+performed for Tabby2. 
+
+Changes to the last of these version numbers reflect smaller changes to the
+contents of Tabby2 to which the user should be alerted, such as the expansion
+and improvement of the underlying model, improvements to the user interface, or
+changes which affect the numbers depicted in downloadable outcomes from the
+application. 

globals.R

@@ -1,28 +1,37 @@
+
+geographies <- setNames(nm = state.abb, state.name)
+geographies[['US']] <- 'United States'
+geographies[['DC']] <- 'District of Columbia'
+
+available_geographies <- geographies[scan_for_available_geographies(names(geographies))]
+
+invert_geographies <- setNames(nm = unname(geographies), object = names(geographies))
+
 tabnames <- c(
   about = "Introduction",
   scenarios = "Scenarios",
   predefined = "Predefined Scenarios",
-  customscenarios = "Build Scenarios",
+  customscenarios = "Build Custom Scenarios",
   estimates = "Estimates",
   timetrends = "Time Trends",
   agegroups = "Age Groups",
   calibration = "Comparison to Recent Data",
-  # downloads = "Downloads",
   readmore = "Further Description",
+  changelog = "Changelog",
 	feedback = "Feedback"
 )
 
 tabcontents <- list(
-  about = uiOutput('aboutUI'),
+  about = aboutUI(available_geographies),# uiOutput('aboutUI'),
   scenarios = NULL,
   predefined = standardInterventionsUI(),
   customscenarios = scenariosUI(),
   estimates = tabby1Estimates('tabby1'),
   timetrends = tabby1TimeTrends('tabby1'),
   agegroups = tabby1AgeGroups('tabby1'),
   calibration = comparison_to_recent_data(),
-  # downloads = downloadsAndSettingsUI(),
   readmore = readmoreUI(),
+  changelog = changelogUI(),
 	feedback = feedbackForm()
 )
 

inst/md/about.md

@@ -1,23 +1,33 @@
-Tabby2 is a web application providing predictions of future TB
-epidemiology in the United States under a range of different assumptions
-and policy scenarios. Users can select a health outcome and
-subpopulation of interest, then select one or multiple scenarios to
-compare. Results are displayed graphically, and following inputs from
-the user the graphs update automatically to reflect the new selections.
-Visualizations can be downloaded in various formats, and the estimates
-underlying the graphs can also be downloaded in tabular format.
+Tabby2 is a web application that provides projections of future tuberculosis
+(TB) epidemiology in the United States under a range of TB and LTBI testing and
+treatment scenarios that can be chosen by the user. The application includes
+several predefined scenarios, which explore changes in the care cascades for TB
+disease and latent TB infection (LTBI). The application also allows users to
+define new scenarios, which can project the impact of greater targeted testing
+and treatment of high-risk populations, or changes to the performance of
+current TB control programs. After specifying scenarios, users can select a
+health outcome and subpopulation of interest, and then view results of one or
+more of the scenarios they have specified. Results are displayed graphically,
+and these graphs can be adjusted based on user input to include additional
+results or focus on particular population subgroups. Visualizations can be
+downloaded in various formats, and the estimates underlying the graphs can also
+be downloaded in tabular format.
 
-The estimates shown by Tabby2 are based on a transmission-dynamic model
-of TB epidemiology in the US, incorporating TB transmission and natural
-history; TB drug resistance patterns; prior and future TB prevention and
-control; the impact of HIV on TB; heterogeneity in TB risks among
-US-born and foreign-born populations; and age-based differences in
-disease mechanisms and risk factor prevalence. Detailed methods
-(analytic methods, scenario and outcome definitions) and main results
-for this analysis are described in detail in "Prospects for tuberculosis
-elimination in the United States: results of a transmission dynamic
-model".
+The estimates shown by Tabby2 are based on a mathematical model of TB
+epidemiology in the United States, which incorporates TB transmission and
+natural history; prior and future TB prevention and control; heterogeneity in
+TB risks among U.S.-born and non-U.S.-born populations; and age-based
+differences in disease mechanisms and risk factor prevalence. This model has
+been fit to local data from each of the following high TB incidence states:
+California, Florida, Georgia, Illinois, Massachusetts, New Jersey, New York,
+Pennsylvania, Texas, Virginia, and Washington. The model has also been fit to
+aggregate data for the United States to allow national-level analyses. These
+analyses expand on an earlier study on future TB epidemiology in the United
+States (Menzies et al 2018. “Prospects for tuberculosis elimination in the
+United States: results of a transmission dynamic model” Am J Epid
+187(9):2011-2020) (<https://academic.oup.com/aje/article/187/9/2011/4995883>)).  
+
+This research was funded by the CDC, National Center for HIV, Viral Hepatitis,
+STD, and TB Prevention Epidemiologic and Economic Modeling Agreement (NEEMA, \#
+5U38PS004644-01).
 
-This web tool was funded by the CDC, National Center for
-HIV, Viral Hepatitis, STD, and TB Prevention Epidemiologic and Economic
-Modeling Agreement (NEEMA, \# 5U38PS004644-01).

inst/md/changelog.md

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+
+# Changelog
+
+
+
+<strong> This is Tabby2 version 2.0.0.</strong>
+
+Version 2.0.0 represents the first version of Tabby2 which has 
+passed the CDC Scientific Web Tool Clearance process.