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Recast values from one calendar to another

Source: R/recast.R
recast.Rd

The central conversion verb. Takes a data.frame keyed by slice in calendar from with one or more numeric value columns, and returns a data.frame keyed by slice in calendar to. Conversion goes through a shared instant grid built by expand_calendar() for the given year.

Usage

recast(
  x,
  from,
  to,
  year,
  key = "slice",
  values = NULL,
  rule = c("weighted_mean", "sum", "mean"),
  by = NULL,
  tz = "UTC"
)

Arguments

x

data.frame with a column named by key (default "slice") plus one or more numeric value columns.

from

Source Calendar.

to

Destination Calendar.

year

Integer scalar Gregorian year used to materialise both calendars on a shared grid.

key

Name of the slice key column in x. Default "slice".

values

Character vector of value columns to transform. Default: all numeric columns other than key.

rule

Aggregation rule for many-source-instants-per-target-slice (downsampling). One of:

  • "weighted_mean" — share-weighted mean (default; physical units like average load).

  • "sum" — sum (extensive quantities like total energy).

  • "mean" — unweighted mean.

by

Grid resolution for expand_calendar(). Defaults to the finest of the two calendars.

tz

Time zone for the shared grid. Default "UTC".

Value

A data.frame keyed by slice in to, with one row per slice in to and the same value columns as in x.

Details

Algorithm:

  1. Expand both calendars onto a shared instant grid for year.

  2. Join x onto the grid via from$slice, broadcasting each source slice's value to every grid instant it covers.

  3. Group by to$slice and aggregate per rule.

Instants present in one calendar but not the other (e.g. Feb 29) drop out silently for now (will be configurable in a later phase).

Examples

month_df <- data.frame(
  MONTH  = sprintf("m%02d", 1:12),
  share  = c(31,28,31,30,31,30,31,31,30,31,30,31) / 365,
  weight = c(31,28,31,30,31,30,31,31,30,31,30,31)
)
cal_m <- calendar_from_leaves(month_df, timeframes = "MONTH", name = "m12")

quarter_df <- data.frame(
  QUARTER = sprintf("Q%d", 1:4),
  share   = c(90, 91, 92, 92) / 365,
  weight  = c(90, 91, 92, 92)
)
cal_q <- calendar_from_leaves(quarter_df, timeframes = "QUARTER",
                              name = "q4")

x <- data.frame(
  slice = sprintf("m%02d", 1:12),
  load  = seq(100, 210, length.out = 12)
)
recast(x, from = cal_m, to = cal_q, year = 2021, rule = "weighted_mean")
#>   slice     load
#> 1    Q1 110.0000
#> 2    Q2 140.0000
#> 3    Q3 169.8913
#> 4    Q4 200.0000