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Add scale by baseline option #121

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Merged
merged 3 commits into from
Jun 15, 2021
Merged

Add scale by baseline option #121

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07892f7
add scale by baseline
kateharwood Jun 11, 2021
460d002
updating comment:
kateharwood Jun 11, 2021
176e0c3
show/hide recolor button
kateharwood Jun 11, 2021
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48 changes: 37 additions & 11 deletions dashboard/app.R
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Original file line number Diff line number Diff line change
Expand Up @@ -73,11 +73,16 @@ ui <- fluidPage(padding=0,
"Absolute Error" = "ae",
"Coverage" = "coverage")),
conditionalPanel(condition = "input.scoreType != 'coverage'",
tags$p(id="scale-score", "Y-Axis Scale"),
tags$p(id="scale-score", "Y-Axis Score Scale"),
checkboxInput(
"logScale",
"Log Scale",
value = FALSE,
),
checkboxInput(
"scaleByBaseline",
"Scale by Baseline Forecaster",
value = FALSE,
)
),
selectInput(
Expand Down Expand Up @@ -248,7 +253,7 @@ server <- function(input, output, session) {
# CREATE MAIN PLOT
##################
summaryPlot = function(scoreDf, targetVariable, scoreType, forecasters,
horizon, loc, coverageInterval = NULL, colorSeed, logScale) {
horizon, loc, coverageInterval = NULL, colorSeed, logScale, scaleByBaseline) {
allLocations = FALSE
if (loc == TOTAL_LOCATIONS) {
allLocations = TRUE
Expand Down Expand Up @@ -288,6 +293,8 @@ server <- function(input, output, session) {
filteredScoreDf <- filteredScoreDf %>% rename(Score = !!coverageInterval)
title = "Coverage"
}

# Totaling over all locations
locationsIntersect = list()
if (allLocations || scoreType == "coverage") {
filteredScoreDf = filteredScoreDf %>% filter(!is.na(Score))
Expand Down Expand Up @@ -315,12 +322,12 @@ server <- function(input, output, session) {
summarize(Score = sum(Score), actual = sum(actual))
output$renderAggregateText = renderText(paste(aggregateText, " Locations included: "))
}

if (length(locationsIntersect) == 0) {
output$renderWarningText <- renderText("The selected forecasters do not have data for any locations in common.")
output$renderLocations <- renderText("")
output$renderAggregateText = renderText("")
hideElement("truthPlot")
hideElement("refresh-colors")
return()
}
else {
Expand All @@ -341,6 +348,7 @@ server <- function(input, output, session) {

# Render truth plot with observed values
showElement("truthPlot")
showElement("refresh-colors")
truthDf = filteredScoreDf
output$truthPlot <- renderPlotly({
truthPlot(truthDf, targetVariable, locationsIntersect, allLocations || scoreType == "coverage")
Expand All @@ -349,8 +357,19 @@ server <- function(input, output, session) {
# Format and transform data
filteredScoreDf = filteredScoreDf[c("Forecaster", "Week_End_Date", "Score", "ahead")]
filteredScoreDf = filteredScoreDf %>% mutate(across(where(is.numeric), ~ round(., 2)))
if (logScale && scoreType != 'coverage') {
filteredScoreDf$Score = log10(filteredScoreDf$Score)
if (scoreType != 'coverage') {
if (scaleByBaseline) {
baselineDf = filteredScoreDf %>% filter(Forecaster %in% 'COVIDhub-baseline')
filteredScoreDfMerged = merge(filteredScoreDf, baselineDf, by=c("Week_End_Date","ahead"))
# Scaling score by baseline forecaster
filteredScoreDfMerged$Score.x = filteredScoreDfMerged$Score.x / filteredScoreDfMerged$Score.y
filteredScoreDf = filteredScoreDfMerged %>%
rename(Forecaster = Forecaster.x, Score = Score.x) %>%
select(Forecaster, Week_End_Date, ahead, Score)
}
if (logScale) {
filteredScoreDf$Score = log10(filteredScoreDf$Score)
}
}

titleText = paste0('<b>',title,'</b>','<br>', '<sup>',
Expand Down Expand Up @@ -386,7 +405,9 @@ server <- function(input, output, session) {
if (scoreType == "coverage") {
p = p + geom_hline(yintercept = .01 * as.integer(coverageInterval))
}
if (!logScale) {
if (logScale) {
p = p + scale_y_continuous(label = function(x) paste0("10^", x))
} else {
p = p + scale_y_continuous(limits = c(0,NA), labels = scales::comma)
}
plotHeight = 550 + (length(horizon)-1)*100
Expand All @@ -407,7 +428,6 @@ server <- function(input, output, session) {
return(finalPlot)
}


###################
# CREATE TRUTH PLOT
###################
Expand Down Expand Up @@ -435,7 +455,7 @@ server <- function(input, output, session) {
#############
output$summaryPlot <- renderPlotly({
summaryPlot(df, input$targetVariable, input$scoreType, input$forecasters,
input$aheads, input$location, input$coverageInterval, colorSeed, input$logScale)
input$aheads, input$location, input$coverageInterval, colorSeed, input$logScale, input$scaleByBaseline)
})

###################
Expand All @@ -446,7 +466,7 @@ server <- function(input, output, session) {
colorSeed = floor(runif(1, 1, 1000))
output$summaryPlot <- renderPlotly({
summaryPlot(df, input$targetVariable, input$scoreType, input$forecasters,
input$aheads, input$location, input$coverageInterval, colorSeed, input$logScale)
input$aheads, input$location, input$coverageInterval, colorSeed, input$logScale, input$scaleByBaseline)
})
})

Expand Down Expand Up @@ -520,15 +540,21 @@ server <- function(input, output, session) {
updateCoverageChoices(session, df, input$targetVariable, input$forecasters, input$coverageInterval, output)
})

# Ensure there is always one ahead value selected and one forecaster selected
# Ensure the minimum necessary input selections
observe({
# Ensure there is always one ahead selected
if(length(input$aheads) < 1) {
updateCheckboxGroupInput(session, "aheads",
selected = 1)
}
# Ensure there is always one forecaster selected
if(length(input$forecasters) < 1) {
updateSelectInput(session, "forecasters",
selected = c("COVIDhub-ensemble"))
selected = c("COVIDhub-baseline"))
}
# Ensure COVIDhub-baseline is selected when scaling by baseline
if(input$scaleByBaseline && !("COVIDhub-baseline" %in% input$forecasters)) {
updateSelectInput(session, "forecasters", selected = c(input$forecasters, "COVIDhub-baseline"))
}
})
}
Expand Down