## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  comment = "#>",
  message = FALSE,
  warning = FALSE,
  fig.width = 7,
  fig.height = 4,
  fig.align = "center",
  dpi = 300
)

## -----------------------------------------------------------------------------
library(tidyfault)
library(dplyr)
library(ggplot2)
library(tidyr)
library(tibble)

data("db_nodes")
data("db_edges")
data("db_outcomes_binary")
data("db_probs")

f <- curate(db_nodes, db_edges) |>
  equate() |>
  formulate()

db_outcomes_probs <- populate(db_outcomes_binary, db_probs)
knitr::kable(head(db_outcomes_probs, 6), digits = 3)

## -----------------------------------------------------------------------------
prob_top <- quantify(f, newdata = db_outcomes_probs, prob = TRUE)
prob_top
summary(prob_top)

## -----------------------------------------------------------------------------
data("it_security_nodes")
data("it_security_edges")

f_sec <- formulate(equate(curate(it_security_nodes, it_security_edges)))

# Illustrative constant failure rates (per hour), as in the paper source
it_security_rates <- tibble::tibble(
  DA = 0.25 / 24 / 365,
  EP = 0.20 / 24 / 365,
  IM = 0.09 / 24 / 365,
  LR = 0.13 / 24 / 365,
  MN = 0.29 / 24 / 365,
  PO = 0.16 / 24 / 365,
  PC = 0.22 / 24 / 365,
  PM = 0.15 / 24 / 365,
  VS = 0.11 / 24 / 365,
  WB = 0.17 / 24 / 365
)
rate_names <- names(it_security_rates)

t_hours <- seq(0, 50000, by = 1000)
it_security_projections <- crossing(t = t_hours, it_security_rates) |>
  mutate(across(all_of(rate_names), ~ pexp(q = t, rate = .x)))

it_security_projections$outcome <- quantify(
  f_sec,
  newdata = it_security_projections[, rate_names, drop = FALSE],
  prob = TRUE
)

it_security_projections |>
  select(t, outcome) |>
  head(6)

## -----------------------------------------------------------------------------
ggplot(it_security_projections, aes(x = t, y = outcome)) +
  geom_line() +
  labs(
    x = "Time (hours)",
    y = "Top-event probability"
  )