Reporting-completion trend: the delay curve by year and recent months
Source:R/reporting_completion.R
reporting_completion_trend_v1.RdConvenience over [reporting_completion_v1]: the completion curve sliced by calendar `year` (all years) and by `month` (the most recent `n_months`, per series), stacked with a `scope` column. One table that shows whether reporting is speeding up or slowing down over time.
Value
A data.table: the [reporting_completion_v1] columns plus a `scope` column ("year"/"month"), the year rows followed by the last-`n_months` month rows. Empty when no series has enough settled data.
See also
Neither package vignette covers this function;
vignette("pipeline", package = "csalert") runs
reporting_completion_v1, which this one wraps.
Other reporting completion functions:
reporting_completion_v1()
Examples
w <- cstime::dates_by_isoyearweek$isoyearweek; i <- match("2023-01", w)
d <- data.table::data.table(
isoyearweek_reference = w[i + rep(0:39, each = 3)],
isoyearweek_reporting = w[i + rep(0:39, each = 3) + rep(0:2, 40)],
numerator = 10, indicator = "x", location = "n", age = "total", sex = "total")
tri <- csfmt_reporting_triangle_v3(d, id_cols = c("indicator", "location", "age", "sex"))
reporting_completion_trend_v1(tri, max_delay = 3, n_months = 6)
#> indicator location age sex period n_settled mean_delay complete_by_md
#> <char> <char> <char> <char> <char> <int> <num> <num>
#> 1: x n total total 2023 40 1 1
#> 2: x n total total 2023-04 4 1 1
#> 3: x n total total 2023-05 4 1 1
#> 4: x n total total 2023-06 5 1 1
#> 5: x n total total 2023-07 4 1 1
#> 6: x n total total 2023-08 5 1 1
#> 7: x n total total 2023-09 4 1 1
#> pct_delay0 pct_delay1 pct_delay2 scope
#> <num> <num> <num> <char>
#> 1: 33.3 66.7 100 year
#> 2: 33.3 66.7 100 month
#> 3: 33.3 66.7 100 month
#> 4: 33.3 66.7 100 month
#> 5: 33.3 66.7 100 month
#> 6: 33.3 66.7 100 month
#> 7: 33.3 66.7 100 month