Convert linelist data to aggregate format

This method takes an epidist_linelist_data object (see as_epidist_linelist_data()) and aggregates it by counting unique combinations of the required time variables (primary/secondary event times and observation time) and any additional variables specified in by. The result is a more compact representation of the same data where each row represents multiple identical observations with the count stored in the n column.

Usage

# S3 method for class 'epidist_linelist_data'
as_epidist_aggregate_data(data, by = NULL, ...)

Arguments

data

The data to convert

by

Character vector of additional variables to stratify by, beyond the required time variables.

...

Additional arguments passed to methods

Value

An object of class epidist_aggregate_data.

See also

Other aggregate_data: as_epidist_aggregate_data(), as_epidist_aggregate_data.data.frame(), as_epidist_aggregate_data.default(), assert_epidist.epidist_aggregate_data(), is_epidist_aggregate_data(), new_epidist_aggregate_data()

Examples

# Default stratification by required time variables only
sierra_leone_ebola_data |>
  as_epidist_linelist_data(
    pdate_lwr = "date_of_symptom_onset",
    sdate_lwr = "date_of_sample_tested"
  ) |>
  as_epidist_aggregate_data()
#> ℹ No primary event upper bound provided, using the primary event lower bound + 1 day as the assumed upper bound.
#> ℹ No secondary event upper bound provided, using the secondary event lower bound + 1 day as the assumed upper bound.
#> ℹ No observation time column provided, using 2015-09-14 as the observation date (the maximum of the secondary event upper bound).
#> # A tibble: 2,453 × 6
#>    ptime_lwr ptime_upr stime_lwr stime_upr obs_time     n
#>        <dbl>     <dbl>     <dbl>     <dbl>    <dbl> <int>
#>  1         0         1         5         6      484     1
#>  2         2         3         7         8      484     2
#>  3         3         4         8         9      484     4
#>  4         4         5         9        10      484     6
#>  5         8         9        13        14      484     1
#>  6         9        10        14        15      484     3
#>  7        11        12        16        17      484     7
#>  8        12        13        17        18      484     7
#>  9        13        14        18        19      484     1
#> 10        13        14        20        21      484     1
#> # ℹ 2,443 more rows

# Additional stratification by other variables
sierra_leone_ebola_data |>
  as_epidist_linelist_data(
    pdate_lwr = "date_of_symptom_onset",
    sdate_lwr = "date_of_sample_tested"
  ) |>
  as_epidist_aggregate_data(by = "age")
#> ℹ No primary event upper bound provided, using the primary event lower bound + 1 day as the assumed upper bound.
#> ℹ No secondary event upper bound provided, using the secondary event lower bound + 1 day as the assumed upper bound.
#> ℹ No observation time column provided, using 2015-09-14 as the observation date (the maximum of the secondary event upper bound).
#> # A tibble: 7,667 × 7
#>    ptime_lwr ptime_upr stime_lwr stime_upr obs_time     n   age
#>        <dbl>     <dbl>     <dbl>     <dbl>    <dbl> <int> <dbl>
#>  1         0         1         5         6      484     1    20
#>  2         2         3         7         8      484     1    42
#>  3         2         3         7         8      484     1    45
#>  4         3         4         8         9      484     1    15
#>  5         3         4         8         9      484     1    19
#>  6         3         4         8         9      484     1    50
#>  7         3         4         8         9      484     1    55
#>  8         4         5         9        10      484     1     8
#>  9         4         5         9        10      484     1    27
#> 10         4         5         9        10      484     1    38
#> # ℹ 7,657 more rows