Joins two runs on time_series_id and isoyearweek, and returns one row per
series, week and value column. csfmt_interpret() finds the value columns of
current, and the parts of each name.
Arguments
- current, previous
The output of
ens_collapse()for two runs.
Value
A long data.table with time_series_id, isoyearweek, the columns
among indicator_tag, location_code, age and sex that exist, and:
column: the name of the value column,cur,prv: its value incurrentand inprevious,role,q,level: the parts of its name.
Details
time_series_id is a hash of the identity columns, so a series has the same id
in both runs. A week in only one run gets NA for the other run.
Identity columns MUST use the csfmt schema names
csfmt_interpret() reads every column outside the csfmt schema as a value
column. location_code and indicator_tag are schema names, but location
and indicator are not. So with location or indicator, cur and prv
become character.
This function still returns a table. qc_week_over_week_v1() then stops with
non-numeric argument to binary operator.
See also
vignette("pipeline", package = "csalert"), section 9.
Other quality control functions:
qc_surveillance_data_v1(),
qc_week_over_week_v1()
Examples
# 40 reference weeks, each reported 3, 10 and 17 days after its Monday
mondays <- as.Date("2023-01-02") + 7 * (0:39)
set.seed(1)
d <- data.table::data.table(
isoyearweek_reference = format(rep(mondays, each = 3), "%G-%V"),
reporting_date = rep(mondays, each = 3) + rep(c(3, 10, 17), 40),
numerator = rpois(120, c(30, 15, 5)),
indicator_tag = "x", location_code = "nation", age = "total", sex = "total"
)
d <- d[reporting_date <= mondays[40] + 6]
id <- c("indicator_tag", "location_code", "age", "sex")
# Set the seed inside `run()`. Then the two runs differ only by the data
# revision, and not by their Monte-Carlo draws.
run <- function(x) {
set.seed(2)
ens_collapse(nowcast_delay_ecdf_v1(
csfmt_reporting_triangle_v3(x, id_cols = id),
max_delay_days = 21, n_sim = 200
))
}
# last week's run saw one reference week before a correction raised it
cur <- run(d)
d_prv <- data.table::copy(d)
d_prv[
isoyearweek_reference == format(mondays[11], "%G-%V"),
numerator := numerator - 5
]
#> Index: <isoyearweek_reference>
#> isoyearweek_reference reporting_date numerator indicator_tag location_code
#> <char> <Date> <int> <char> <char>
#> 1: 2023-01 2023-01-05 26 x nation
#> 2: 2023-01 2023-01-12 20 x nation
#> 3: 2023-01 2023-01-19 8 x nation
#> 4: 2023-02 2023-01-12 38 x nation
#> 5: 2023-02 2023-01-19 16 x nation
#> ---
#> 113: 2023-38 2023-09-28 21 x nation
#> 114: 2023-38 2023-10-05 3 x nation
#> 115: 2023-39 2023-09-28 24 x nation
#> 116: 2023-39 2023-10-05 20 x nation
#> 117: 2023-40 2023-10-05 20 x nation
#> age sex
#> <char> <char>
#> 1: total total
#> 2: total total
#> 3: total total
#> 4: total total
#> 5: total total
#> ---
#> 113: total total
#> 114: total total
#> 115: total total
#> 116: total total
#> 117: total total
prv <- run(d_prv)
# with the seed held fixed, only the corrected week moves
compare_results(cur, prv)[q == 0.5 & abs(cur - prv) > 0]
#> time_series_id isoyearweek indicator_tag location_code age sex
#> <char> <char> <char> <char> <char> <char>
#> 1: d8da72e3fbb5fd29 2023-11 x nation total total
#> column cur prv role q level
#> <char> <num> <num> <char> <num> <char>
#> 1: numerator_nowcasted_q50x0 46 31 nowcasted 0.5 <NA>