![[Stable]](figures/lifecycle-stable.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_revision_type()– proportion of cases per revision outcome.prop_strata()– proportion of cases per stratum.prop_covariate_levels()– proportion of cases per level of each categorical covariate.date_ranges()– totals, date ranges andnow.triangle_occupancy()– how full the reporting triangle is, and how stale the object is.cumulative_growth()– ratio of one delay's running total to the previous one's.
Usage
cases_per_date(
x,
axis = c("event", "report", "revision"),
by_strata = NULL,
strata = NULL
)
delay_summary(
x,
delay = c("event_to_report", "event_to_revision", "report_to_revision"),
by_strata = NULL,
strata = NULL
)
zero_run_summary(
x,
axis = c("event", "report", "revision"),
by_strata = NULL,
strata = NULL
)
prop_censored(x, by_strata = NULL, strata = NULL)
prop_revision_type(x, by_strata = NULL, strata = NULL)
prop_strata(x, strata = NULL)
prop_covariate_levels(x, by_strata = NULL, strata = NULL)
date_ranges(x, by_strata = NULL, strata = NULL)
triangle_occupancy(x, 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"revision".- 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_revision"(the same span measured to the revision, so the two are comparable) or"report_to_revision"(the laboratory's turnaround, the.revision_delaycolumn).- 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)
# The last five years. The full twenty-year series gives the same shape
# of answer, it just takes longer to compute.
recent <- denguedat[denguedat$onset_week >= as.Date("2006-01-01"), ]
ndata <- tbl_now(recent,
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
#> n = dates on the grid; total = 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 260 14135 54.4 73.2 0 11 25 65 139 358
#> 2 per_event… Female 260 6998 26.9 36.4 0 6 12 31 71 189
#> 3 per_event… Male 260 7137 27.4 37.2 0 5 13 32 71 176
#> # 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
#> n = (event, report) cells; total = 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 event_to_… all 1673 14135 1.81 1.06 0 1 2 2 3 26
#> 2 event_to_… Female 842 6998 1.82 1.07 0 1 2 2 3 15
#> 3 event_to_… Male 831 7137 1.80 1.06 0 1 2 2 3 26
#>
#> ℹ Use `dplyr::filter()` or `tibble::as_tibble()` for the full schema.
# How sparse the series is.
zero_run_summary(ndata, axis = "event")
#> ── Summary of a <tbl_now> ──────────────────────────────────────────────────────
#> 3 rows in 1 component; strata: "Female" and "Male".
#>
#> zero_run
#> n = runs of consecutive zero dates; total = zero dates in those runs
#> 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_da… all 1 3 3 NA 3 3 3 3 3 3
#> 2 event_da… Female 5 8 1.6 0.894 1 1 1 2 3 3
#> 3 event_da… Male 5 7 1.4 0.894 1 1 1 1 3 3
#>
#> 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
#> n = (event, report) cells in the category; total = cases in the category
#> quantity n total prop
#> <chr> <int> <dbl> <dbl>
#> 1 strata = Female 842 6998 0.495
#> 2 strata = Male 831 7137 0.505
#>
#> 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
#> n = cells, or distinct dates on a date row; total = cases
#> quantity stratum n total date_min date_max
#> <chr> <chr> <int> <dbl> <date> <date>
#> 1 total_cases all 1673 14135 NA NA
#> 2 event_date all 257 14135 2006-01-02 2010-11-29
#> 3 report_date all 257 14135 2006-01-09 2010-12-20
#> 4 total_cases Female 842 6998 NA NA
#> 5 event_date Female 252 6998 2006-01-02 2010-11-29
#> 6 report_date Female 250 6998 2006-01-09 2010-12-20
#> 7 total_cases Male 831 7137 NA NA
#> 8 event_date Male 253 7137 2006-01-02 2010-11-29
#> 9 report_date Male 251 7137 2006-01-09 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
#> n = cells, or distinct dates on a date row
#> quantity stratum n value
#> <chr> <chr> <int> <dbl>
#> 1 max_delay all NA 26
#> 2 triangle_cells_observed all 1034 NA
#> 3 triangle_cells_possible all 6669 NA
#> 4 triangle_occupancy all NA 0.155
#> 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 842 NA
#> 9 triangle_cells_possible Female 6669 NA
#> 10 triangle_occupancy Female NA 0.126
#> ℹ 8 more rows.
#>
#> ℹ Use `dplyr::filter()` or `tibble::as_tibble()` for the full schema.
# How fast the running total is still growing. This is a distribution over
# event dates, so it fills `mean`/`q50` rather than the scalar `value`.
cumulative_growth(ndata, k = 3)
#> ── Summary of a <tbl_now> ──────────────────────────────────────────────────────
#> 9 rows in 1 component; strata: "Female" and "Male".
#>
#> growth
#> n = event dates; total = cases added
#> 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 107 2067 13.5 15.7 1 5.5 9 15 31.3 104
#> 2 delay 2 all 249 2908 2.45 2.24 1 1.4 1.71 2.5 4 18
#> 3 delay 3 all 255 859 1.21 0.616 1 1 1.10 1.2 1.38 8
#> 4 delay 1 Female 68 1530 14.2 14.6 1 5 9 19 30 71
#> 5 delay 2 Female 238 2812 2.51 2.90 1 1.33 1.67 2.46 4.5 31
#> 6 delay 3 Female 249 785 1.17 0.522 1 1 1.05 1.16 1.4 7.5
#> 7 delay 1 Male 77 1801 15.4 17.8 1 6 10 15 33 104
#> 8 delay 2 Male 236 2826 2.37 2.50 1 1.29 1.65 2.35 4 27
#> 9 delay 3 Male 251 825 1.20 0.621 1 1 1.06 1.2 1.4 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
#> n = cells, or distinct dates on a date row; total = cases
#> quantity stratum n total date_min date_max
#> <chr> <chr> <int> <dbl> <date> <date>
#> 1 total_cases all 1673 14135 NA NA
#> 2 event_date all 257 14135 2006-01-02 2010-11-29
#> 3 report_date all 257 14135 2006-01-09 2010-12-20
#> 4 total_cases Female 842 6998 NA NA
#> 5 event_date Female 252 6998 2006-01-02 2010-11-29
#> 6 report_date Female 250 6998 2006-01-09 2010-12-20
#> 7 total_cases Male 831 7137 NA NA
#> 8 event_date Male 253 7137 2006-01-02 2010-11-29
#> 9 report_date Male 251 7137 2006-01-09 2010-12-13
#> 10 now all NA NA 2010-12-20 2010-12-20
#> ℹ 1 more row.
#>
#> delay
#> n = (event, report) cells; total = 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 event_to_… all 1673 14135 1.81 1.06 0 1 2 2 3 26
#> 2 event_to_… Female 842 6998 1.82 1.07 0 1 2 2 3 15
#> 3 event_to_… Male 831 7137 1.80 1.06 0 1 2 2 3 26
#>
#> ℹ Use `dplyr::filter()` or `tibble::as_tibble()` for the full schema.