Check fine data against an independent parent total

Source: R/reconcile.R

Aggregates data from from to to and compares the result with totals, the parent figure from another source. Reports the gap by default; can also close it.

reconcile_scale(
  data,
  x,
  from,
  to,
  totals,
  key = NULL,
  values = NULL,
  rule = "sum",
  weight = NULL,
  balance = c("none", "residual", "proportional"),
  tolerance = 1e-09
)

Arguments

data

The fine data, keyed at from.

x

A NestedScale.

from, to

Frame names: where the data is, and the coarser frame whose totals it is checked against.

totals

The independent parent figures: a table keyed at to carrying the same value columns as data, plus any identifier columns (which are matched, so a per-year total works).

key

Column of data holding the codes at from; defaults as in recast_scale().

values

Value columns to check. Default: the numeric columns common to data and totals.

rule

How the value aggregates. "sum" (default) is the question "does it add up"; an intensive quantity reconciles as "weighted_mean" instead.

weight

Weight column for rule = "weighted_mean".

balance

What to do about the gap:

  • "none" (default) – report it and change nothing.

  • "residual" – return the data with each group's gap written onto its declared residual unit at from. Needs exactly one residual per affected group, and rule = "sum".

  • "proportional" – return the data scaled by target / aggregated within each group, so every group hits its total.

tolerance

Relative size below which a gap counts as rounding; sets the ok column, and gaps within it are left alone when balancing.

Value

With balance = "none", a long gap table: the to column, any identifiers, value (which column), aggregated, target, gap (target - aggregated), rel_gap and ok. With a balancing method, the adjusted data keyed as the input was, carrying that same table as its "reconciliation" attribute.

Examples

lf <- data.frame(
  country = c("DE", "DE", "DE"),
  unit = c("DE1", "DE2", "DE_XR"),
  pop = c(40, 30, 0), stringsAsFactors = FALSE
)
s <- scale_from_leaftable(lf,
  frames = c("country", "unit"), key = "unit",
  weights = "pop", name = "geo",
  residuals = list(unit = "DE_XR")
)
fine <- data.frame(unit = c("DE1", "DE2", "DE_XR"), gdp = c(400, 300, 0))
published <- data.frame(country = "DE", gdp = 750)

reconcile_scale(fine, s, from = "unit", to = "country", published)
#>   country value aggregated target gap    rel_gap    ok
#> 1      DE   gdp        700    750  50 0.06666667 FALSE

reconcile_scale(fine, s,
  from = "unit", to = "country", published,
  balance = "residual"
)
#>    unit gdp
#> 1   DE1 400
#> 2   DE2 300
#> 3 DE_XR  50