## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  message = FALSE,
  warning = FALSE,
  out.width = "100%",
  fig.height = 7,
  dpi = 300
)

## ----setup--------------------------------------------------------------------
library(outbreaker2)
library(o2ools)

## ----inputs-------------------------------------------------------------------
# The input data 
head(linelist)

# outbreaker2 result
out

## -----------------------------------------------------------------------------
out_id <- identify(out, ids = linelist$name)
head(out_id)

## -----------------------------------------------------------------------------
trees <- get_trees(out_id, kappa = TRUE, t_inf = TRUE, group = linelist$group)
#100th sample
head(trees[[100]])

## -----------------------------------------------------------------------------
true_tree <- data.frame(
  from = as.character(outbreaker2::fake_outbreak$ances),
  to   = linelist$id,
  stringsAsFactors = FALSE
)

accuracy <- get_accuracy(out, true_tree)
hist(accuracy)

## -----------------------------------------------------------------------------
entropy <- get_entropy(out_id)
barplot(
  entropy,
  horiz = TRUE,
  las = 1
  )


## -----------------------------------------------------------------------------
# augment linelist with summaries of t_inf and kappa
o2linelist <- augment_linelist(
  out, linelist,
  params  = c("t_inf","kappa"),
  summary_fns = list(
    mean = function(x) mean(x, na.rm = TRUE),
    q25  = function(x) quantile(x, .25, na.rm = TRUE),
    q75  = function(x) quantile(x, .75, na.rm = TRUE)
  )
)
head(o2linelist)

## -----------------------------------------------------------------------------
consensus_tree <- get_consensus(out)
head(consensus_tree)

## -----------------------------------------------------------------------------
library(epicontacts)
epi <- make_epicontacts(
  linelist = o2linelist,
  contacts = subset(consensus_tree, !is.na(from)),
  #remove NA edges (i.e. imports)
  directed = TRUE
)
# plot(epi) basic plot

# colour setting
epi$linelist$group <- factor(epi$linelist$group, levels = c("hcw", "patient"))
my_pal <- function(n) {
  c("orange", "purple")[1:n]
}

vis_epicontacts(
  epi,
  thin = FALSE, #show imports
  label = "name",
  node_shape = "group",
  shapes = c("hcw" = "user-md", "patient" = "bed"), # https://fontawesome.com/v4/icons/
  node_color = "group",
  edge_arrow = "to",
  col_pal       = my_pal,
  edge_col_pal  = my_pal,
)



## -----------------------------------------------------------------------------
library(igraph)
library(tidygraph)
library(ggraph)

g <- epicontacts:::as.igraph.epicontacts(epi) |>
  as_tbl_graph()

layout_data <- create_layout(g, layout = 'kk')
layout_data$x <- layout_data$onset

p <- ggraph(layout_data)+
  geom_edge_link(
    aes(
      edge_width = frequency,
      color = .N()$group[from] # .N() to accesses node data
    ),
    arrow = arrow(length = unit(2.5, 'mm')),
    end_cap = circle(3, 'mm')
  ) +
  geom_node_point(
    aes(fill = group), 
    shape = 21,
    colour = "black",
    size = 5
  ) +
  geom_node_text(
    aes(label = epicontacts_name),
    repel = TRUE, size = 3, nudge_y = 0.1, bg.color = "white", bg.r = 0.1
  ) +
  scale_edge_width("Posterior support",
                   range = c(0.1, 1),
                   breaks = c(0.8, 0.9, 1)) +
  scale_fill_manual(
    "",
    values = c("hcw" = "orange", "patient" = "purple"),
    breaks = c("hcw", "patient")
  ) +
  scale_edge_colour_manual(
    "",
    values = c("hcw" = "orange", "patient" = "purple"),
    breaks = c("hcw", "patient")
  )+
  theme_bw() +
  labs(x = "Onset date",
       y = "")+
  theme(
    axis.line.y = element_blank(),
    axis.text.y = element_blank(),
    axis.ticks.y = element_blank(),
    panel.grid.major.y = element_blank(),
    panel.grid.minor.y = element_blank(),
    legend.position = "bottom"
  )

print(p)

## -----------------------------------------------------------------------------
max_post <- trees[[which.max(out$post)]]

#Same as before, create and plot an epicontacts object
epi <- make_epicontacts(
  linelist = o2linelist,
  contacts = subset(max_post, !is.na(from)), #remove NA edges (i.e. imports)
  directed = TRUE
)

## -----------------------------------------------------------------------------
out_direct <- filter_chain(out, param = "kappa", thresh = 1, comparator = "==", target = "alpha")