Given a forecast data frame, group it by all its grp_* columns.
Arguments
- df
A forecast data frame
- ...
Additional arguments to be passed to
dplyr::group_by()(e.x..add,.drop)
Examples
# `{casteval}` exports an example data frame for grouping
casteval:::group_all(groups1)
#> # A tibble: 16 × 7
#> # Groups: grp_variable, grp_province, grp_scenario [8]
#> time grp_variable grp_province grp_scenario val_q5 val_q95 val_mean
#> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 1 hosp ON 1 10 20 15
#> 2 1 case ON 1 1000 2000 1500
#> 3 1 hosp QC 1 11 21 16
#> 4 1 case QC 1 1100 2100 1600
#> 5 1 hosp ON 2 15 25 20
#> 6 1 case ON 2 1500 2500 2000
#> 7 1 hosp QC 2 16 26 21
#> 8 1 case QC 2 1600 2600 2100
#> 9 2 hosp ON 1 50 60 55
#> 10 2 case ON 1 5000 6000 5500
#> 11 2 hosp QC 1 51 61 56
#> 12 2 case QC 1 5100 6100 5600
#> 13 2 hosp ON 2 55 65 60
#> 14 2 case ON 2 5500 6500 6000
#> 15 2 hosp QC 2 56 66 61
#> 16 2 case QC 2 5600 6600 6100
dplyr::group_by(groups1, time) |> casteval:::group_all(.add=TRUE)
#> # A tibble: 16 × 7
#> # Groups: time, grp_variable, grp_province, grp_scenario [16]
#> time grp_variable grp_province grp_scenario val_q5 val_q95 val_mean
#> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 1 hosp ON 1 10 20 15
#> 2 1 case ON 1 1000 2000 1500
#> 3 1 hosp QC 1 11 21 16
#> 4 1 case QC 1 1100 2100 1600
#> 5 1 hosp ON 2 15 25 20
#> 6 1 case ON 2 1500 2500 2000
#> 7 1 hosp QC 2 16 26 21
#> 8 1 case QC 2 1600 2600 2100
#> 9 2 hosp ON 1 50 60 55
#> 10 2 case ON 1 5000 6000 5500
#> 11 2 hosp QC 1 51 61 56
#> 12 2 case QC 1 5100 6100 5600
#> 13 2 hosp ON 2 55 65 60
#> 14 2 case ON 2 5500 6500 6000
#> 15 2 hosp QC 2 56 66 61
#> 16 2 case QC 2 5600 6600 6100