Individual blocks of a tbl_now summary
nowcast_summary_components.Rd![[Experimental]](figures/lifecycle-experimental.svg)
summary() answers a dozen questions about a tbl_now at
once. When you only want one of them – for a report, a dashboard, or a check
inside a script – call that block directly instead of computing the rest and
filtering it away.
Every one of these returns the same schema as summary() itself, so they can
be stacked with dplyr::bind_rows(), compared across datasets, or used alone:
cases_per_date()– case counts per date on one axis.delay_summary()– the case-weighted delay distribution.zero_run_summary()– lengths of the runs of consecutive zero dates.prop_censored()– proportion of cases flagged censored.prop_validation_type()– proportion of cases per validation outcome.prop_strata()– proportion of cases per stratum.prop_covariate_levels()– proportion of cases per level of each categorical covariate.case_autocorrelation()– lagged autocorrelation of the case series.date_ranges()– totals, date ranges andnow.triangle_occupancy()– how full the reporting triangle is, and how stale the object is.reporting_completeness()– share of each event date's eventual total that had arrived by delayd, as a distribution over event dates (mean,sd, the quantiles) plus the pooled share inprop.cumulative_growth()– ratio of one delay's running total to the previous one's.
Usage
cases_per_date(
x,
axis = c("event", "report", "validation"),
by_strata = NULL,
strata = NULL
)
delay_summary(
x,
delay = c("event_to_report", "event_to_validation", "report_to_validation"),
by_strata = NULL,
strata = NULL
)
zero_run_summary(
x,
axis = c("event", "report", "validation"),
by_strata = NULL,
strata = NULL
)
prop_censored(x, by_strata = NULL, strata = NULL)
prop_validation_type(x, by_strata = NULL, strata = NULL)
prop_strata(x, strata = NULL)
prop_covariate_levels(x, by_strata = NULL, strata = NULL)
case_autocorrelation(
x,
lags = 1,
axis = c("event", "report", "validation"),
by_strata = NULL,
strata = NULL
)
date_ranges(x, by_strata = NULL, strata = NULL)
triangle_occupancy(x, by_strata = NULL, strata = NULL)
reporting_completeness(
x,
delays = NULL,
mature_only = TRUE,
by_strata = NULL,
strata = NULL
)
cumulative_growth(x, k = 7, by_strata = NULL, strata = NULL)Arguments
- x
A
tbl_nowobject.- axis
Which time axis to describe:
"event","report"or"validation".- by_strata
Logical. Add one set of rows per stratum on top of the pooled (
"all") rows. Defaults toTRUEwhen the object has strata.- strata
Character vector of columns to stratify by. Defaults to
get_strata(x).- delay
Which delay to describe:
"event_to_report"(the reporting delay),"event_to_validation"(the same span measured to the validation, so the two are comparable) or"report_to_validation"(the laboratory's turnaround, the.validation_delaycolumn).- lags
Integer vector of lags.
- delays
Integer vector of delays to report completeness at. Defaults to every observed delay.
- mature_only
Logical. Drop event dates too recent to have been fully reported. The cutoff is
nowminus the 95th percentile of the delay distribution – the same ruleautoplot.tbl_now()uses.- k
Number of delays for the growth ratios.
Value
A tibble in the schema documented in tbl_now_summary: one row per
quantity and stratum, with component, quantity and stratum identifying
the row and the remaining columns holding whichever statistics apply.
See also
summary(), which stacks all of these into one table and
documents the schema; diagnose() for what is wrong with the data rather
than what is in it; autoplot() for the same information as
pictures. The
Diagnosing a tbl_now article
walks through them in order.
Examples
data(denguedat)
ndata <- tbl_now(denguedat,
event_date = "onset_week",
report_date = "report_week",
strata = "gender",
verbose = FALSE
)
# How many cases per week of onset, and how long they took to be reported.
cases_per_date(ndata, axis = "event")
#> ── Summary of a <tbl_now> ──────────────────────────────────────────────────────
#> 3 rows in 1 component; strata: "Female" and "Male".
#>
#> cases
#> quantity stratum n total mean sd min q25 q50 q75 q90 max
#> <chr> <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 per_event… all 1095 52987 48.4 53.3 0 14 30 64 104 358
#> 2 per_event… Female 1095 26592 24.3 26.7 0 7 15 32 52 189
#> 3 per_event… Male 1095 26395 24.1 27.0 0 7 15 31 53 176
#> # ℹ 1 more variable: prop_zero <dbl>
#>
#> ℹ Use `dplyr::filter()` or `tibble::as_tibble()` for the full schema.
delay_summary(ndata)
#> ── Summary of a <tbl_now> ──────────────────────────────────────────────────────
#> 3 rows in 1 component; strata: "Female" and "Male".
#>
#> delay
#> quantity stratum n total mean sd min q25 q50 q75 q90 max
#> <chr> <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 event_to_… all 8265 52987 1.74 1.21 0 1 1 2 3 26
#> 2 event_to_… Female 4133 26592 1.74 1.20 0 1 1 2 3 15
#> 3 event_to_… Male 4132 26395 1.74 1.22 0 1 1 2 3 26
#>
#> ℹ Use `dplyr::filter()` or `tibble::as_tibble()` for the full schema.
# How sparse the series is, and how strongly one week predicts the next.
zero_run_summary(ndata, axis = "event")
#> ── Summary of a <tbl_now> ──────────────────────────────────────────────────────
#> 3 rows in 1 component; strata: "Female" and "Male".
#>
#> zero_run
#> quantity stratum n total mean sd min q25 q50 q75 q90 max
#> <chr> <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 event_date all 2 4 2 1.41 1 1 1 3 3 3
#> 2 event_date Female 10 13 1.3 0.675 1 1 1 1 2 3
#> 3 event_date Male 8 13 1.62 0.916 1 1 1 2 3 3
#>
#> ℹ Use `dplyr::filter()` or `tibble::as_tibble()` for the full schema.
case_autocorrelation(ndata, lags = 1)
#> ── Summary of a <tbl_now> ──────────────────────────────────────────────────────
#> 3 rows in 1 component; strata: "Female" and "Male".
#>
#> autocorrelation
#> quantity stratum n value
#> <chr> <chr> <int> <dbl>
#> 1 per_event_date lag 1 all 1094 0.958
#> 2 per_event_date lag 1 Female 1094 0.944
#> 3 per_event_date lag 1 Male 1094 0.941
#>
#> ℹ Use `dplyr::filter()` or `tibble::as_tibble()` for the full schema.
# What the data is made of, and how far it reaches.
prop_strata(ndata)
#> ── Summary of a <tbl_now> ──────────────────────────────────────────────────────
#> 2 rows in 1 component.
#>
#> composition
#> quantity n total prop
#> <chr> <int> <dbl> <dbl>
#> 1 strata = Female 4133 26592 0.502
#> 2 strata = Male 4132 26395 0.498
#>
#> ℹ Use `dplyr::filter()` or `tibble::as_tibble()` for the full schema.
prop_censored(ndata)
#> ── Summary of a <tbl_now> ──────────────────────────────────────────────────────
#> Nothing to summarise.
date_ranges(ndata)
#> ── Summary of a <tbl_now> ──────────────────────────────────────────────────────
#> 11 rows in 1 component; strata: "Female" and "Male".
#>
#> coverage
#> quantity stratum n total date_min date_max
#> <chr> <chr> <int> <dbl> <date> <date>
#> 1 total_cases all 8265 52987 NA NA
#> 2 event_date all 1091 52987 1990-01-01 2010-11-29
#> 3 report_date all 1092 52987 1990-01-01 2010-12-20
#> 4 total_cases Female 4133 26592 NA NA
#> 5 event_date Female 1082 26592 1990-01-01 2010-11-29
#> 6 report_date Female 1078 26592 1990-01-01 2010-12-20
#> 7 total_cases Male 4132 26395 NA NA
#> 8 event_date Male 1082 26395 1990-01-01 2010-11-29
#> 9 report_date Male 1073 26395 1990-01-01 2010-12-13
#> 10 now all NA NA 2010-12-20 2010-12-20
#> ℹ 1 more row.
#>
#> ℹ Use `dplyr::filter()` or `tibble::as_tibble()` for the full schema.
triangle_occupancy(ndata)
#> ── Summary of a <tbl_now> ──────────────────────────────────────────────────────
#> 18 rows in 1 component; strata: "Female" and "Male".
#>
#> coverage
#> quantity stratum n value
#> <chr> <chr> <int> <dbl>
#> 1 max_delay all NA 26
#> 2 triangle_cells_observed all 5154 NA
#> 3 triangle_cells_possible all 29214 NA
#> 4 triangle_occupancy all NA 0.176
#> 5 now_gap_event all NA 3
#> 6 now_gap_report all NA 0
#> 7 max_delay Female NA 15
#> 8 triangle_cells_observed Female 4133 NA
#> 9 triangle_cells_possible Female 29214 NA
#> 10 triangle_occupancy Female NA 0.141
#> ℹ 8 more rows.
#>
#> ℹ Use `dplyr::filter()` or `tibble::as_tibble()` for the full schema.
# The two that matter most for nowcasting: what share of a week's eventual
# total had arrived by delay d, and how fast the total is still growing.
# Both are distributions over event dates, so they fill `mean`/`q50` -- and
# completeness also `prop`, the pooled share -- rather than the scalar
# `value` column.
reporting_completeness(ndata, delays = 0:3) |>
dplyr::select(quantity, stratum, n, mean, q50, prop)
#> # A tibble: 12 × 6
#> quantity stratum n mean q50 prop
#> <chr> <chr> <int> <dbl> <dbl> <dbl>
#> 1 delay <= 0 all 1090 0.0381 0.0220 0.0396
#> 2 delay <= 1 all 1090 0.510 0.510 0.502
#> 3 delay <= 2 all 1090 0.844 0.867 0.850
#> 4 delay <= 3 all 1090 0.931 0.953 0.941
#> 5 delay <= 0 Female 1081 0.0367 0 0.0391
#> 6 delay <= 1 Female 1081 0.509 0.514 0.501
#> 7 delay <= 2 Female 1081 0.849 0.879 0.850
#> 8 delay <= 3 Female 1081 0.933 0.971 0.942
#> 9 delay <= 0 Male 1081 0.0384 0 0.0402
#> 10 delay <= 1 Male 1081 0.516 0.509 0.504
#> 11 delay <= 2 Male 1081 0.839 0.867 0.849
#> 12 delay <= 3 Male 1081 0.929 0.967 0.941
cumulative_growth(ndata, k = 3)
#> ── Summary of a <tbl_now> ──────────────────────────────────────────────────────
#> 9 rows in 1 component; strata: "Female" and "Male".
#>
#> growth
#> quantity stratum n total mean sd min q25 q50 q75 q90 max
#> <chr> <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 delay 1 all 631 9752 9.93 9.16 1 5 7.6 12 18 104
#> 2 delay 2 all 1078 9282 1.93 1.30 1 1.36 1.6 2 2.83 18
#> 3 delay 3 all 1089 2607 1.16 0.336 1 1.02 1.09 1.19 1.33 8
#> 4 delay 1 Female 471 8292 10.7 8.92 1 5 8 13 20 71
#> 5 delay 2 Female 1045 9219 1.95 1.57 1 1.33 1.61 2 3 31
#> 6 delay 3 Female 1076 2406 1.13 0.295 1 1 1.06 1.15 1.31 7.5
#> 7 delay 1 Male 469 8251 10.5 9.95 1 5 8 12 19 104
#> 8 delay 2 Male 1040 9035 1.88 1.39 1 1.33 1.57 2 2.56 27
#> 9 delay 3 Male 1075 2379 1.14 0.357 1 1 1.06 1.17 1.33 8
#>
#> ℹ Use `dplyr::filter()` or `tibble::as_tibble()` for the full schema.
# Every block shares one schema, so they stack.
dplyr::bind_rows(
date_ranges(ndata),
delay_summary(ndata)
)
#> ── Summary of a <tbl_now> ──────────────────────────────────────────────────────
#> 14 rows in 2 components; strata: "Female" and "Male".
#>
#> coverage
#> quantity stratum n total date_min date_max
#> <chr> <chr> <int> <dbl> <date> <date>
#> 1 total_cases all 8265 52987 NA NA
#> 2 event_date all 1091 52987 1990-01-01 2010-11-29
#> 3 report_date all 1092 52987 1990-01-01 2010-12-20
#> 4 total_cases Female 4133 26592 NA NA
#> 5 event_date Female 1082 26592 1990-01-01 2010-11-29
#> 6 report_date Female 1078 26592 1990-01-01 2010-12-20
#> 7 total_cases Male 4132 26395 NA NA
#> 8 event_date Male 1082 26395 1990-01-01 2010-11-29
#> 9 report_date Male 1073 26395 1990-01-01 2010-12-13
#> 10 now all NA NA 2010-12-20 2010-12-20
#> ℹ 1 more row.
#>
#> delay
#> quantity stratum n total mean sd min q25 q50 q75 q90 max
#> <chr> <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 event_to_… all 8265 52987 1.74 1.21 0 1 1 2 3 26
#> 2 event_to_… Female 4133 26592 1.74 1.20 0 1 1 2 3 15
#> 3 event_to_… Male 4132 26395 1.74 1.22 0 1 1 2 3 26
#>
#> ℹ Use `dplyr::filter()` or `tibble::as_tibble()` for the full schema.