Convert reviser vintages to reporting_triangle format

This S3 method converts a reviser vintages object (wide format, class tbl_pubdate) to a reporting_triangle object, enabling use of baselinenowcast's nowcasting methods.

Usage

# S3 method for class 'tbl_pubdate'
as_reporting_triangle(data, delays_unit = NULL, ...)

Arguments

data

A reviser vintages object in wide format (class tbl_pubdate), with a time column and one column per publication date.

delays_unit

Character string specifying the temporal granularity of the delays, one of "days", "weeks", "months", or "years". If NULL (default), the unit is inferred from the smallest non-zero pub_date - time gap in data (1 day -> "days", 7 days -> "weeks"). Pass it explicitly to override or for monthly/yearly triangles.

...

Additional arguments passed to as_reporting_triangle.data.frame().

Value

A reporting_triangle object. See reporting_triangle for details on the structure.

Details

Reviser vintages store cumulative reported values at each publication date. The conversion takes differences between successive publication dates per time point to recover incremental counts, and uses the delay implied by pub_date - time (in delays_unit) to build the reporting triangle.

A tbl_pubdate carries no record of which delay unit was used when the vintages were created. By default this function infers delays_unit from the realised delays (the gaps between each pub_date and its time), which means weekly reference dates with daily delays are correctly inferred as "days" and vice versa. Pass delays_unit explicitly to override the inference or for monthly/yearly triangles.

The reviser package must be installed to use this function.

See also

• as_reviser_vintages() for converting to reviser vintages

• as_reporting_triangle.data.frame() for the underlying method

Reporting triangle construction and validation [.reporting_triangle(), [<-.reporting_triangle(), as.data.frame.reporting_triangle(), as.matrix.reporting_triangle(), as_ChainLadder_triangle(), as_reporting_triangle(), as_reporting_triangle.data.frame(), as_reporting_triangle.matrix(), as_reporting_triangle.triangle(), as_reviser_vintages(), assert_reporting_triangle(), get_delays_from_dates(), get_delays_unit(), get_max_delay(), get_mean_delay(), get_quantile_delay(), get_reference_dates(), get_report_dates(), get_reporting_structure(), head.reporting_triangle(), is_reporting_triangle(), new_reporting_triangle(), print.reporting_triangle(), reporting_triangle-class, summary.reporting_triangle(), tail.reporting_triangle(), truncate_to_delay(), truncate_to_quantile(), validate_reporting_triangle()

Examples

# Create a reporting triangle
data_as_of_df <- syn_nssp_df[syn_nssp_df$report_date <= "2026-04-01", ]
rep_tri <- as_reporting_triangle(data = data_as_of_df)
#> ℹ Using max_delay = 154 from data

# Convert to reviser vintages
vintages <- as_reviser_vintages(rep_tri)

# Convert back to reporting_triangle; `delays_unit` is inferred from the
# spacing of the `time` column.
rep_tri_2 <- as_reporting_triangle(data = vintages)
#> ℹ Using max_delay = 154 from data
print(rep_tri_2)
#> Reporting Triangle
#> Delays unit: days
#> Reference dates: 2025-10-25 to 2026-04-01
#> Max delay: 154
#> Structure: 1
#> 
#> Showing last 10 of 159 rows
#> Showing first 10 of 155 columns
#> 
#>              0   1  2  3  4  5  6  7  8  9
#> 2026-03-23 210 131 34 50 35 12  1 25 20  6
#> 2026-03-24 221  96 22 10 13  6  0  5  9 NA
#> 2026-03-25 291 129 17 26 42 29 23 25 NA NA
#> 2026-03-26 179  96 22 50  9  8 18 NA NA NA
#> 2026-03-27 284  40 41 54 28 12 NA NA NA NA
#> 2026-03-28 217  78 46 14 39 NA NA NA NA NA
#> 2026-03-29 336 161 62 13 NA NA NA NA NA NA
#> 2026-03-30 296  53 55 NA NA NA NA NA NA NA
#> 2026-03-31 210 108 NA NA NA NA NA NA NA NA
#> 2026-04-01 236  NA NA NA NA NA NA NA NA NA
#> 
#> Use print(x, n_rows = NULL, n_cols = NULL) to see all data