Transform an object to count data — to

Transforms a tbl.now between count data types:

linelist: to count-incidence or count-cumulative
count-incidence: to count-cumulative
count-cumulative: to count-incidence

Usage

to_count(x, to = NULL, ...)

# S3 method for class 'tbl_now'
to_count(x, to = NULL, ...)

Arguments

x: Data to be transformed from linelist to count data
to: Either linelist, count-incidence or count-cumulative the resulting data-type to be created.
...: Additional arguments

Value

A tbl_now object of the requested to data type, with the counts aggregated into the case_count column.

Details

This is an S3 generic. This package provides methods for the following classes:

tbl_now: takes a tbl_now object and creates a new column with name n of counts of observations if data_type = "linelist".

Converting count-cumulative to count-incidence de-accumulates the series: within each event date (and grouping), ordered by report date, the incremental count is the cumulative total minus the previous one. Because cumulative totals can be revised downward, an increment can be negative; callers that need non-negative increments (for example tbl_now_to_baselinenowcast()) must handle or refuse that case.

Note

linelist data cannot be reconstructed from count-* data. Trying this will throw an error as you cannot un-count aggregated data.

Examples

data(denguedat)
ndata <- tbl_now(denguedat,
  event_date = "onset_week",
  report_date = "report_week",
  strata = "gender"
)
#> ℹ Identified data as <linelist-data> where each observation is a test.

to_count(ndata, to = "count-incidence")
#> # A tibble:  8,265 × 7
#> # Data type: "count-incidence"
#> # Frequency: Event: `weeks` | Report: `weeks`
#>    onset_week   report_week   .event_num .report_num gender         n .delay
#>    <date>       <date>             <dbl>       <dbl> <chr>      <int>  <dbl>
#>    [event_date] [report_date]      [...]       [...] [strata] [cases]  [...]
#>  1 1990-01-01   1990-01-01             0           0 Female         2      0
#>  2 1990-01-01   1990-01-08             0           1 Female        13      1
#>  3 1990-01-01   1990-01-15             0           2 Female        16      2
#>  4 1990-01-01   1990-01-22             0           3 Female         7      3
#>  5 1990-01-01   1990-03-05             0           9 Female         1      9
#>  6 1990-01-01   1990-01-01             0           0 Male           1      0
#>  7 1990-01-01   1990-01-08             0           1 Male          11      1
#>  8 1990-01-01   1990-01-15             0           2 Male           7      2
#>  9 1990-01-01   1990-01-22             0           3 Male           1      3
#> 10 1990-01-01   1990-01-29             0           4 Male           1      4
#> # ────────────────────────────────────────────────────────────────────────────────
#> # Now: 2010-12-20 | Event date: "onset_week" | Report date: "report_week"
#> # Strata: "gender"
#> # ────────────────────────────────────────────────────────────────────────────────
#> # ℹ 8,255 more rows

data("covidat")
suppressWarnings({
  ndata <- tbl_now(covidat,
    event_date = "date_of_symptom_onset",
    report_date = "date_of_registry",
    strata = "sex"
  )
  to_count(ndata)
})
#> ℹ Identified data as <linelist-data> where each observation is a test.
#> # A tibble:  40,822 × 7
#> # Data type: "linelist"
#> # Frequency: Event: `days` | Report: `days`
#>    date_of_registry date_of_symptom_onset sex          n .event_num .report_num
#>    <date>           <date>                <chr>    <int>      <dbl>       <dbl>
#>    [report_date]    [event_date]          [strata] [...]      [...]       [...]
#>  1 2020-04-05       2020-03-06            FEMALE       1          0          30
#>  2 2020-04-05       2020-03-06            MALE         1          0          30
#>  3 2020-04-05       2020-03-07            FEMALE       1          1          30
#>  4 2020-04-07       2020-03-11            MALE         1          5          32
#>  5 2020-04-05       2020-03-18            FEMALE       1         12          30
#>  6 2020-04-08       2020-03-23            FEMALE       1         17          33
#>  7 2020-04-05       2020-03-24            MALE         1         18          30
#>  8 2020-04-06       2020-03-26            FEMALE       1         20          31
#>  9 2020-04-05       2020-03-27            FEMALE       1         21          30
#> 10 2020-04-05       2020-03-31            FEMALE       1         25          30
#> # ────────────────────────────────────────────────────────────────────────────────
#> # Now: 2023-01-01 | Event date: "date_of_symptom_onset" | Report date:
#> # "date_of_registry"
#> # Strata: "sex"
#> # ────────────────────────────────────────────────────────────────────────────────
#> # ℹ 40,812 more rows
#> # ℹ 1 more variable: .delay <dbl>