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

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

``` r
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

``` r
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
#>
#> 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
```

``` r
Ā
#> Error in eval(expr, envir, enclos): object 'Ā' not found
```

^{Created on 2024-09-29 with [reprex v2.1.0](https://reprex.tidyverse.org)}