Aggregate counts across ages

aggregate_age_counts() provides aggregation of counts across ages (in years). It is similar to a cut() and tapply() pattern but optimised for speed over flexibility. It takes a specified set of breaks representing the left hand limits of a closed open interval, i.e [x, y), and returns the corresponding interval and upper bounds. The resulting intervals span from the minimum break through to the maximum age. Missing values are grouped as NA.

Usage

aggregate_age_counts(counts, ages = seq_along(counts) - 1L, breaks)

Arguments

counts

[numeric].

Vector of counts to be aggregated.

ages

[numeric].

Vector of age in years.

Double values are coerced to integer prior to categorisation / aggregation.

For aggregate_age_counts(), these must corresponding to the counts entry and will defaults to 0:(N-1) where N is the number of counts present.

No (non-missing) age can be less than the minimum break.

breaks

[numeric].

1 or more cut points in increasing (strictly) order.

These correspond to the left hand side of the desired intervals (e.g. the closed side of [x, y).

Double values are coerced to integer prior to categorisation.

Value

A data frame with 4 entries; interval, lower_bound, upper_bound and an associated count.

Examples

# default ages generated if only counts provided (here ages will be 0:64)
aggregate_age_counts(counts = 1:65, breaks = c(0L, 1L, 5L, 15L, 25L, 45L, 65L))
#>    interval lower_bound upper_bound count
#> 1    [0, 1)           0           1     1
#> 2    [1, 5)           1           5    14
#> 3   [5, 15)           5          15   105
#> 4  [15, 25)          15          25   205
#> 5  [25, 45)          25          45   710
#> 6  [45, 65)          45          65  1110
#> 7 [65, Inf)          65         Inf     0

# NA ages are handled with their own grouping
ages <- 1:65
ages[1:44] <- NA
aggregate_age_counts(
    counts = 1:65,
    ages = ages,
    breaks = c(0L, 1L, 5L, 15L, 25L, 45L, 65L)
)
#>    interval lower_bound upper_bound count
#> 1    [0, 1)           0           1     0
#> 2    [1, 5)           1           5     0
#> 3   [5, 15)           5          15     0
#> 4  [15, 25)          15          25     0
#> 5  [25, 45)          25          45     0
#> 6  [45, 65)          45          65  1090
#> 7 [65, Inf)          65         Inf    65
#> 8      <NA>          NA          NA   990