In appliedepi/epirhandbook: Data Used in The Epidemiology R Handbook

pacman::p_load(
     rio,
     here,
     tidyverse,
     flexdashboard,
     flextable,
     incidence2,
     epicontacts)

# import the linelist into R
linelist <- rio::import(here::here("data", "linelist_cleaned.rds"))

Column {data-width=650}

Transmission chain (select cases)

# load package
pacman::p_load(epicontacts)

## generate contacts
contacts <- linelist %>%
  transmute(
    infector = infector,
    case_id = case_id,
    location = sample(c("Community", "Nosocomial"), n(), TRUE),
    duration = sample.int(10, n(), TRUE)
  ) %>%
  drop_na(infector)

## generate epicontacts object
epic <- make_epicontacts(
  linelist = linelist,
  contacts = contacts,
  id = "case_id",
  from = "infector",
  to = "case_id",
  directed = TRUE
)

## subset epicontacts object
sub <- epic %>%
  subset(
    node_attribute = list(date_onset = c(as.Date(c("2014-06-30", "2014-06-01"))))
  ) %>%
 thin("contacts")

# temporal plot
plot(
  sub,
  x_axis = "date_onset",
  node_color = "outcome",
  col_pal = c(Death = "firebrick", Recover = "green"),
  arrow_size = 0.5,
  node_size = 13,
  label = FALSE,
  height = 700,
  width = 700
)

Column {data-width=350}

Epidemic curve by age

age_outbreak <- incidence(
  linelist,
  date_index = date_onset,   # date of onset for x-axis
  interval = "week",         # weekly aggregation of cases
  groups = age_cat)

# plot with different color palette
plot(age_outbreak, fill = age_cat, col_pal = muted, title = "'muted' incidence2 palette")

Table of

table <- linelist %>% 
  # filter
  ########
  filter(!is.na(outcome) & hospital != "Missing") %>%  # Remove cases with missing outcome or hospital

  # Get summary values per hospital-outcome group
  ###############################################
  group_by(hospital, outcome) %>%                      # Group data
  summarise(                                           # Create new summary columns of indicators of interest
    N = n(),                                            # Number of rows per hospital-outcome group     
    ct_value = median(ct_blood, na.rm=T)) %>%           # median CT value per group

  # add totals
  ############
  bind_rows(                                           # Bind the previous table with this mini-table of totals
    linelist %>% 
      filter(!is.na(outcome) & hospital != "Missing") %>%
      group_by(outcome) %>%                            # Grouped only by outcome, not by hospital    
      summarise(
        N = n(),                                       # Number of rows for whole dataset     
        ct_value = median(ct_blood, na.rm=T))) %>%     # Median CT for whole dataset

  # Pivot wider and format
  ########################
  mutate(hospital = replace_na(hospital, "Total")) %>% 
  pivot_wider(                                         # Pivot from long to wide
    values_from = c(ct_value, N),                       # new values are from ct and count columns
    names_from = outcome) %>%                           # new column names are from outcomes
  mutate(                                              # Add new columns
    N_Known = N_Death + N_Recover,                      # number with known outcome
    Pct_Death = N_Death / N_Known * 100,               # percent cases who died
    Pct_Recover = N_Recover/N_Known * 100) %>%         # percent who recovered
  select(                                              # Re-order columns
    hospital, N_Known,                                   # Intro columns
    N_Recover, Pct_Recover, ct_value_Recover,            # Recovered columns
    N_Death, Pct_Death, ct_value_Death)  %>%             # Death columns
  arrange(N_Known)                                    # Arrange rows from lowest to highest (Total row at bottom)

table %>% 
     flextable::qflextable()