add data wrangling challenge to get required input columns for {cfr}

[avallecam]

we need to isolate deaths from the outcomes variable that includes deaths and recoveries

library(simulist)
library(linelist)
library(incidence2)
library(cfr)
library(tidyverse)

# run this together --------------------
set.seed(111)

dat <- sim_linelist() %>% 
  as_tibble()
# run this together --------------------

challenge (or assessment)
from a tidy {linelist} to {incidence2} and {cfr}
learning outcome: recognize the “tidy data” framework in a linelist

• known outcome (recovered, death) in one column only
• temporal states (quarantine, UCI, hospitalization) w/admision and discharge

dat %>% 
  dplyr::select(id, date_onset, outcome,date_outcome) %>% 
  # [key] data wrangling step ---------------------------------------------------
  # isolate deaths from outcomes
  dplyr::mutate(
    date_death = dplyr::case_when(
      outcome == "died" ~ date_outcome,
      TRUE ~ NA_Date_
    )
  ) %>% 
  # [end] of key data step ------------------------------------------------------
  linelist::make_linelist(
    id = "id",
    date_onset = "date_onset",
    date_death = "date_death"
  ) %>% 
  linelist::validate_linelist() %>% 
  linelist::tags_df() %>% 
  incidence2::incidence(date_index = c("date_onset","date_death")) %>% 
  cfr::prepare_data(
    cases_variable = "date_onset",
    deaths_variable = "date_death"
  ) %>% 
  dplyr::as_tibble()
#> NAs in cases and deaths are being replaced with 0s: Set `fill_NA = FALSE` to prevent this.
#> # A tibble: 294 × 3
#>    date       deaths cases
#>    <date>      <int> <int>
#>  1 2023-01-01      0     1
#>  2 2023-01-02      0     0
#>  3 2023-01-03      0     0
#>  4 2023-01-04      0     0
#>  5 2023-01-05      0     0
#>  6 2023-01-06      0     0
#>  7 2023-01-07      0     0
#>  8 2023-01-08      0     0
#>  9 2023-01-09      0     0
#> 10 2023-01-10      0     0
#> # ℹ 284 more rows

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^{Created on 2024-09-29 with reprex v2.1.0}