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Change in a rate between periods

Source: R/rate.R
rate_change.Rd

The change in a rate from one period to the period lag places earlier, with the exact conditional interval for the rate ratio.

Usage

rate_change(x, ...)

# S3 method for class 'data.frame'
rate_change(
  x,
  count,
  population,
  period,
  by = NULL,
  lag = 1L,
  per = 1000,
  conf_level = 0.95,
  min_count = 0,
  ...
)

Arguments

x

A data frame.

...

Passed to methods.

count

Column of counts: non-negative whole numbers.

population

Column of exposure: positive.

period

Column of periods. Sorted, and compared on the period value rather than on row position, so a missing year gives no comparison instead of a silent comparison against the wrong year.

by

Character vector of grouping columns.

lag

How many periods back to compare against. Default 1.

per

The rate denominator: a positive number, or "1k", "10k", "100k", "1m".

conf_level

Confidence level for the interval.

min_count

Comparisons where the earlier count is at or below this are flagged and their percent change withheld, the way yoy() withholds a percent change off a tiny base.

Value

A data frame of class rmbl_rate_change with the grouping columns, period, count, population, rate, previous_rate, rate_ratio, pct_change, pct_lower, pct_upper and flag.

Details

This is not the percent change of two rates treated as measured numbers. Both the counts and the denominators move between periods, and an interval that ignores the denominators understates the uncertainty of the change. The construction here conditions on the total of the two counts and corrects for the ratio of the two exposures, which is yoy()'s exact conditional-binomial interval generalised to unequal denominators: with equal populations it reduces to exactly that.

See also

rate(), yoy() for change in a count or a measured quantity.

Examples

d <- data.frame(
  year = rep(2021:2023, each = 2),
  division = rep(c("North", "South"), 3),
  stops = c(400, 70, 430, 66, 455, 61),
  residents = c(120000, 41000, 122000, 41500, 125000, 42000))

# North's count rose while its population rose too: the rate change
# is smaller than the count change, which is the reason to use it
rate_change(d, stops, residents, year, by = "division", per = "100k")
#> ── Change in rate per 100,000, lag 1, 95% exact conditional interval  
#>  division year count population     rate previous_rate rate_ratio pct_change
#>     North 2021   400     120000 333.3333            NA         NA         NA
#>     North 2022   430     122000 352.4590      333.3333  1.0573770   5.737705
#>     North 2023   455     125000 364.0000      352.4590  1.0327442   3.274419
#>     South 2021    70      41000 170.7317            NA         NA         NA
#>     South 2022    66      41500 159.0361      170.7317  0.9314974  -6.850258
#>     South 2023    61      42000 145.2381      159.0361  0.9132395  -8.676046
#>   pct_lower pct_upper                 flag
#>          NA        NA no comparison period
#>   -7.937165  21.46533                 <NA>
#>   -9.679876  18.10199                 <NA>
#>          NA        NA no comparison period
#>  -34.473784  32.29053                 <NA>
#>  -36.596300  31.35600                 <NA>
#> ──────────────────────────────────────────────────────────────────