Find duplicate forecasts

Identify duplicate forecasts, i.e. instances where there is more than one forecast for the same prediction target.

Uses get_forecast_type_ids() to determine the type-specific columns (beyond the forecast unit) that identify a unique row. For forecast objects the type is detected automatically. For plain data.frames you should pass type (e.g. "quantile", "sample") so that the correct columns are used. Calling on a plain data.frame without type is deprecated; it falls back to column-name detection but this behaviour will be removed in a future version.

Usage

get_duplicate_forecasts(
  data,
  forecast_unit = NULL,
  type = NULL,
  counts = FALSE
)

Arguments

data

A data.frame (or similar) with predicted and observed values. See the "Target format" section in Details for additional information on the required input format.

forecast_unit

(optional) Name of the columns in data (after any renaming of columns) that denote the unit of a single forecast. See get_forecast_unit() for details. If NULL (the default), all columns that are not required columns are assumed to form the unit of a single forecast. If specified, all columns that are not part of the forecast unit (or required columns) will be removed.

type

Character string naming the forecast type, corresponding to the class suffix after forecast_ (e.g. "quantile" for class forecast_quantile, "sample" for forecast_sample). Used to determine type-specific ID columns when data is not already a forecast object. Ignored when data already inherits from forecast.

counts

Should the output show the number of duplicates per forecast unit instead of the individual duplicated rows? Default is FALSE.

Value

A data.frame with all rows for which a duplicate forecast was found

Examples

example <- rbind(example_quantile, example_quantile[1000:1010])
get_duplicate_forecasts(example, type = "quantile")
#>     location target_end_date target_type observed location_name forecast_date
#>       <char>          <Date>      <char>    <num>        <char>        <Date>
#>  1:       DE      2021-05-22      Deaths     1285       Germany    2021-05-17
#>  2:       DE      2021-05-22      Deaths     1285       Germany    2021-05-17
#>  3:       DE      2021-05-22      Deaths     1285       Germany    2021-05-17
#>  4:       DE      2021-05-22      Deaths     1285       Germany    2021-05-17
#>  5:       DE      2021-05-22      Deaths     1285       Germany    2021-05-17
#>  6:       DE      2021-05-22      Deaths     1285       Germany    2021-05-17
#>  7:       DE      2021-05-29       Cases    31653       Germany    2021-05-10
#>  8:       DE      2021-05-29       Cases    31653       Germany    2021-05-10
#>  9:       DE      2021-05-29       Cases    31653       Germany    2021-05-10
#> 10:       DE      2021-05-29       Cases    31653       Germany    2021-05-10
#> 11:       DE      2021-05-29       Cases    31653       Germany    2021-05-10
#> 12:       DE      2021-05-29       Cases    31653       Germany    2021-05-10
#> 13:       DE      2021-05-29       Cases    31653       Germany    2021-05-10
#> 14:       DE      2021-05-29       Cases    31653       Germany    2021-05-10
#> 15:       DE      2021-05-29       Cases    31653       Germany    2021-05-10
#> 16:       DE      2021-05-29       Cases    31653       Germany    2021-05-10
#> 17:       DE      2021-05-29       Cases    31653       Germany    2021-05-10
#> 18:       DE      2021-05-29       Cases    31653       Germany    2021-05-10
#> 19:       DE      2021-05-29       Cases    31653       Germany    2021-05-10
#> 20:       DE      2021-05-29       Cases    31653       Germany    2021-05-10
#> 21:       DE      2021-05-29       Cases    31653       Germany    2021-05-10
#> 22:       DE      2021-05-29       Cases    31653       Germany    2021-05-10
#>     location target_end_date target_type observed location_name forecast_date
#>       <char>          <Date>      <char>    <num>        <char>        <Date>
#>     quantile_level predicted                 model horizon
#>              <num>     <int>                <char>   <num>
#>  1:          0.950      1464  epiforecasts-EpiNow2       1
#>  2:          0.950      1464  epiforecasts-EpiNow2       1
#>  3:          0.975      1642  epiforecasts-EpiNow2       1
#>  4:          0.975      1642  epiforecasts-EpiNow2       1
#>  5:          0.990      1951  epiforecasts-EpiNow2       1
#>  6:          0.990      1951  epiforecasts-EpiNow2       1
#>  7:          0.010     28999 EuroCOVIDhub-ensemble       3
#>  8:          0.010     28999 EuroCOVIDhub-ensemble       3
#>  9:          0.025     32612 EuroCOVIDhub-ensemble       3
#> 10:          0.025     32612 EuroCOVIDhub-ensemble       3
#> 11:          0.050     36068 EuroCOVIDhub-ensemble       3
#> 12:          0.050     36068 EuroCOVIDhub-ensemble       3
#> 13:          0.100     41484 EuroCOVIDhub-ensemble       3
#> 14:          0.100     41484 EuroCOVIDhub-ensemble       3
#> 15:          0.150     47110 EuroCOVIDhub-ensemble       3
#> 16:          0.150     47110 EuroCOVIDhub-ensemble       3
#> 17:          0.200     50929 EuroCOVIDhub-ensemble       3
#> 18:          0.200     50929 EuroCOVIDhub-ensemble       3
#> 19:          0.250     54561 EuroCOVIDhub-ensemble       3
#> 20:          0.250     54561 EuroCOVIDhub-ensemble       3
#> 21:          0.300     57739 EuroCOVIDhub-ensemble       3
#> 22:          0.300     57739 EuroCOVIDhub-ensemble       3
#>     quantile_level predicted                 model horizon
#>              <num>     <int>                <char>   <num>