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Crosswalk between two calendars over the base grid

Source: R/map.R
calendar_map.Rd

Materialises the A -> base -> B route as a table: for the requested model year(s), one row per pair of overlapping timeslices with

Usage

calendar_map(from, to, year, by = NULL, tz = "UTC")

Arguments

from, to

Calendar objects (both must be named).

year

Integer vector of model year(s); multi-year maps carry all years in the year column.

by

Grid resolution (seq.POSIXt step). Default: the finest timeframe of the two calendars.

tz

Time zone of the grid. Default "UTC".

Value

A data.frame with columns year, <from name>, <to name> (NA = uncovered by to), n_from, n_overlap, w.

Details

  • n_from – grid points in the from timeslice (its full set, before any target coverage is considered),

  • n_overlap – grid points the pair shares,

  • w – the share weight of the overlap (leaves$share[from] * n_overlap / n_from), the quantity "weighted_mean" aggregation uses.

The two label columns are named by the calendars' names (so the map joins directly onto datasets labelled by join_calendar()); rows with an NA target label are grid points to does not cover. A crosswalk registered with register_calendar_map() is returned as-is instead of being derived from the grid.

Examples

m12 <- calendar_build("m12")
q4  <- calendar_build("q4")
calendar_map(m12, q4, year = 2021)
#>    year m12 q4 n_from n_overlap          w
#> 1  2021 m01 Q1     31        31 0.08493151
#> 2  2021 m02 Q1     28        28 0.07671233
#> 3  2021 m03 Q1     31        31 0.08493151
#> 4  2021 m04 Q2     30        30 0.08219178
#> 5  2021 m05 Q2     31        31 0.08493151
#> 6  2021 m06 Q2     30        30 0.08219178
#> 7  2021 m07 Q3     31        31 0.08493151
#> 8  2021 m08 Q3     31        31 0.08493151
#> 9  2021 m09 Q3     30        30 0.08219178
#> 10 2021 m10 Q4     31        31 0.08493151
#> 11 2021 m11 Q4     30        30 0.08219178
#> 12 2021 m12 Q4     31        31 0.08493151