Print a tbl_now summary
print.tbl_now_summary_table.Rd![[Experimental]](figures/lifecycle-experimental.svg)
Prints the table summary() returned one component at a
time, dropping the columns that component does not populate. The full schema
is wide because it has to hold every block's statistics at once; no single
block fills more than a handful of them, and a table that is mostly NA is
hard to read for a reason that has nothing to do with the data.
The object is an ordinary tibble underneath, so
print(tibble::as_tibble(x)) gives the whole schema back and every dplyr
verb still works on it.
Usage
# S3 method for class 'tbl_now_summary_table'
print(x, ..., n = 10)Arguments
- x
A summary tibble, from summary() or one of the nowcast_summary_components.
- ...
Unused.
- n
Maximum number of rows to show per component.
Infshows all of them.
Examples
data(denguedat)
ndata <- tbl_now(denguedat,
event_date = "onset_week", report_date = "report_week",
strata = "gender", verbose = FALSE
)
summary(ndata)
#> ── Summary of a <tbl_now> ──────────────────────────────────────────────────────
#> 76 rows in 7 components; 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
#> 4 per_repor… all 1095 52987 48.4 54.3 0 14 29 64 111 420
#> 5 per_repor… Female 1095 26592 24.3 27.3 0 7 15 32 57 217
#> 6 per_repor… Male 1095 26395 24.1 27.5 0 7 15 32 54 203
#> # ℹ 1 more variable: prop_zero <dbl>
#>
#> 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
#> 4 report_da… all 3 3 1 0 1 1 1 1 1 1
#> 5 report_da… Female 15 17 1.13 0.352 1 1 1 1 2 2
#> 6 report_da… Male 19 22 1.16 0.501 1 1 1 1 2 3
#>
#> 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
#> 4 per_report_date lag 1 all 1094 0.885
#> 5 per_report_date lag 1 Female 1094 0.867
#> 6 per_report_date lag 1 Male 1094 0.878
#>
#> composition
#> quantity n total prop
#> <chr> <int> <dbl> <dbl>
#> 1 strata = Female 4133 26592 0.502
#> 2 strata = Male 4132 26395 0.498
#>
#> 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 19 more rows.
#>
#> completeness
#> quantity stratum n total mean sd min q25 q50 q75 q90
#> <chr> <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 delay <= 0 all 1090 2099 0.0381 0.0533 0 0 0.0220 0.0594 0.1
#> 2 delay <= 1 all 1090 26595 0.510 0.175 0 0.410 0.510 0.618 0.710
#> 3 delay <= 2 all 1090 44988 0.844 0.130 0 0.781 0.867 0.930 1
#> 4 delay <= 3 all 1090 49837 0.931 0.0850 0.104 0.9 0.953 1 1
#> 5 delay <= 4 all 1090 51451 0.963 0.0597 0.5 0.949 0.984 1 1
#> 6 delay <= 5 all 1090 52126 0.978 0.0449 0.5 0.972 1 1 1
#> 7 delay <= 6 all 1090 52505 0.988 0.0330 0.5 0.990 1 1 1
#> 8 delay <= 7 all 1090 52668 0.992 0.0275 0.5 1 1 1 1
#> 9 delay <= 0 Female 1081 1039 0.0367 0.0670 0 0 0 0.0556 0.111
#> 10 delay <= 1 Female 1081 13313 0.509 0.214 0 0.384 0.514 0.635 0.75
#> # ℹ 2 more variables: max <dbl>, prop <dbl>
#> ℹ 14 more rows.
#>
#> 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.
# One block on its own prints the same way.
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.
# Still a tibble.
print(tibble::as_tibble(summary(ndata)))
#> # A tibble: 76 × 18
#> component quantity stratum n total mean sd min q25 q50 q75
#> <chr> <chr> <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 cases per_event… all 1095 52987 48.4 53.3 0 14 30 64
#> 2 cases per_event… Female 1095 26592 24.3 26.7 0 7 15 32
#> 3 cases per_event… Male 1095 26395 24.1 27.0 0 7 15 31
#> 4 cases per_repor… all 1095 52987 48.4 54.3 0 14 29 64
#> 5 cases per_repor… Female 1095 26592 24.3 27.3 0 7 15 32
#> 6 cases per_repor… Male 1095 26395 24.1 27.5 0 7 15 32
#> 7 zero_run event_date all 2 4 2 1.41 1 1 1 3
#> 8 zero_run event_date Female 10 13 1.3 0.675 1 1 1 1
#> 9 zero_run event_date Male 8 13 1.62 0.916 1 1 1 2
#> 10 zero_run report_da… all 3 3 1 0 1 1 1 1
#> # ℹ 66 more rows
#> # ℹ 7 more variables: q90 <dbl>, max <dbl>, prop_zero <dbl>, prop <dbl>,
#> # value <dbl>, date_min <date>, date_max <date>