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library(shiny)
source("../../analysis/R/decode.R")
source("../../analysis/R/simulation.R")
source("../../analysis/R/encode.R")
Plot <- function(x, color = "grey") {
n <- nrow(x)
if (n < 16) {
par(mfrow = c(n, 1), mai = c(0, .5, .5, 0))
} else if (n < 64) {
par(mfrow = c(n / 2, 2), mai = c(0, .5, .5, 0))
} else {
par(mfrow = c(n / 4, 4), mai = c(0, .5, .5, 0))
}
for (i in 1:nrow(x)) {
barplot(x[i, ], main = paste0("Cohort ", i), col = color, border = color)
}
}
shinyServer(function(input, output) {
# Example state global variable.
es <- list()
# Example buttons states.
ebs <- rep(0, 3)
Params <- reactive({
list(k = as.numeric(input$size),
h = as.numeric(input$hashes),
m = as.numeric(input$instances),
p = as.numeric(input$p),
q = as.numeric(input$q),
f = as.numeric(input$f))
})
PopParams <- reactive({
list(as.numeric(input$nstrs),
as.numeric(input$nonzero),
input$decay,
as.numeric(input$expo),
as.numeric(input$background)
)
})
DecodingParams <- reactive({
list(as.numeric(input$alpha),
input$correction)
})
Sample <- reactive({
input$sample
N <- input$N
params <- Params()
pop_params <- PopParams()
decoding_params <- DecodingParams()
prop_missing <- input$missing
fit <- GenerateSamples(N, params, pop_params,
alpha = decoding_params[[1]],
correction = decoding_params[[2]],
prop_missing = prop_missing)
fit
})
# Results summary.
output$pr <- renderTable({
Sample()$summary
},
include.rownames = FALSE, include.colnames = FALSE)
# Results table.
output$tab <- renderDataTable({
Sample()$fit
},
options = list(iDisplayLength = 100))
# Epsilon.
output$epsilon <- renderTable({
Sample()$privacy
},
include.rownames = FALSE, include.colnames = FALSE, digits = 4)
# True distribution.
output$probs <- renderPlot({
samp <- Sample()
probs <- samp$probs
detected <- match(samp$fit[, 1], samp$strs)
detection_frequency <- samp$privacy[7, 2]
PlotPopulation(probs, detected, detection_frequency)
})
# True bits patterns.
output$truth <- renderPlot({
truth <- Sample()$truth
Plot(truth[, -1, drop = FALSE], color = "darkblue")
})
# Lasso plot.
output$lasso <- renderPlot({
fit <- Sample()$lasso
if (!is.null(fit)) {
plot(fit)
}
})
output$resid <- renderPlot({
resid <- Sample()$residual
params <- Params()
plot(resid, xlab = "Bloom filter bits", ylab = "Residuals")
abline(h = c(-1.96, 1.96), lty = 2, col = 2)
sq <- qnorm(.025 / length(resid))
abline(h = c(sq, -sq), lty = 2, col = 3, lwd = 2)
abline(h = c(-3, 3), lty = 2, col = 4, lwd = 2)
abline(v = params$k * (0:params$m), lty = 2, col = "blue")
legend("topright", legend = paste0("SD = ", round(sd(resid), 2)), bty = "n")
})
# Estimated bits patterns.
output$ests <- renderPlot({
ests <- Sample()$ests
Plot(ests, color = "darkred")
})
# Estimated vs truth.
output$ests_truth <- renderPlot({
plot(unlist(Sample()$ests), unlist(Sample()$truth[, -1]),
xlab = "Estimates", ylab = "Truth", pch = 19)
abline(0, 1, lwd = 4, col = "darkred")
})
output$example <- renderPlot({
params <- Params()
strs <- Sample()$strs
map <- Sample()$map
samp <- Sample()
# First run on app start.
value <- sample(strs, 1)
res <- Encode(value, map, strs, params, N = input$N)
if (input$new_user > ebs[1]) {
res <- Encode(es$value, map, strs, params, N = input$N)
ebs[1] <<- input$new_user
} else if (input$new_value > ebs[2]) {
res <- Encode(value, map, strs, params, cohort = es$cohort, id = es$id,
N = input$N)
ebs[2] <<- input$new_value
} else if (input$new_report > ebs[3]) {
res <- Encode(es$value, map, strs, params, B = es$B,
BP = es$BP, cohort = es$cohort, id = es$id, N = input$N)
ebs[3] <<- input$new_report
}
es <<- res
ExamplePlot(res, params$k, c(ebs, input$new_user, input$new_value, input$new_report))
})
})
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