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brood() constructs a brood_df object for
vaccine coverage analysis. Unlike roost() which counts
events over time, vaccine coverage requires an explicitly documented
eligible-population denominator and a declared assessment window.
brood() makes both explicit in every object it
produces.
The output brood_df is designed for
bowerbird::brood_plot().
A brood is the full clutch under a parent bird’s care – every egg counted, every hatchling tracked, none overlooked. The gap between the clutch and the hatchlings is not an absence; it is information.
| Model | Use case | Input |
|---|---|---|
"pre_aggregated" |
Already-computed counts per stratum | Summary data frame |
"cohort" |
Record-level, one row per person | Wide or long dose data |
The "cohort" model covers single time-point snapshots,
birth cohort designs with person-time, and time-series sweeps for
interrupted time series analysis.
Supply a data frame with n_vaccinated and
n_eligible already computed. No per-person dose logic is
performed.
cov_static <- brood(
data.frame(
stratum = c("0-17", "18-49", "50-64", "65+"),
n_vaccinated = c(480L, 3550L, 2370L, 1760L),
n_eligible = c(1000L, 5000L, 3000L, 2000L)
),
denominator_notes = "ABS ERP 2024, Sunshine Coast LGA."
)
print(cov_static)
#> stratum date vaccine_type dose_number n_vaccinated n_eligible person_time
#> 1 0-17 <NA> <NA> <NA> 480 1000 NA
#> 2 18-49 <NA> <NA> <NA> 3550 5000 NA
#> 3 50-64 <NA> <NA> <NA> 2370 3000 NA
#> 4 65+ <NA> <NA> <NA> 1760 2000 NA
#> coverage reference_date intervention_period
#> 1 0.48 <NA> <NA>
#> 2 0.71 <NA> <NA>
#> 3 0.79 <NA> <NA>
#> 4 0.88 <NA> <NA>
#> window_description
#> 1 Pre-aggregated: denominators supplied externally.
#> 2 Pre-aggregated: denominators supplied externally.
#> 3 Pre-aggregated: denominators supplied externally.
#> 4 Pre-aggregated: denominators supplied externally.
#>
#> -- brood_meta --------------------------------------
#> population_model : pre_aggregated
#> validity_days : Inf (no expiry)
#> strata : 4
#> coverage range : 48% to 88%
#> window desc : Pre-aggregated: denominators supplied externally.
#> denominator : ABS ERP 2024, Sunshine Coast LGA.cov_multi <- brood(
data.frame(
stratum = rep(c("18-49", "50-64", "65+"), each = 2),
vaccine_type = rep(c("Moderna XBB.1.5", "Pfizer XBB.1.5"), 3),
n_vaccinated = c(1800L, 1750L, 1100L, 1270L, 800L, 960L),
n_eligible = c(5000L, 5000L, 3000L, 3000L, 2000L, 2000L)
),
vaccine_type_col = "vaccine_type",
denominator_notes = "AIR enrolled persons, SC HHS 2024."
)
print(cov_multi)
#> stratum date vaccine_type dose_number n_vaccinated n_eligible person_time
#> 1 18-49 <NA> Moderna XBB.1.5 <NA> 1800 5000 NA
#> 2 18-49 <NA> Pfizer XBB.1.5 <NA> 1750 5000 NA
#> 3 50-64 <NA> Moderna XBB.1.5 <NA> 1100 3000 NA
#> 4 50-64 <NA> Pfizer XBB.1.5 <NA> 1270 3000 NA
#> 5 65+ <NA> Moderna XBB.1.5 <NA> 800 2000 NA
#> 6 65+ <NA> Pfizer XBB.1.5 <NA> 960 2000 NA
#> coverage reference_date intervention_period
#> 1 0.3600000 <NA> <NA>
#> 2 0.3500000 <NA> <NA>
#> 3 0.3666667 <NA> <NA>
#> 4 0.4233333 <NA> <NA>
#> 5 0.4000000 <NA> <NA>
#> 6 0.4800000 <NA> <NA>
#> window_description
#> 1 Pre-aggregated: denominators supplied externally.
#> 2 Pre-aggregated: denominators supplied externally.
#> 3 Pre-aggregated: denominators supplied externally.
#> 4 Pre-aggregated: denominators supplied externally.
#> 5 Pre-aggregated: denominators supplied externally.
#> 6 Pre-aggregated: denominators supplied externally.
#>
#> -- brood_meta --------------------------------------
#> population_model : pre_aggregated
#> validity_days : Inf (no expiry)
#> strata : 3
#> coverage range : 35% to 48%
#> window desc : Pre-aggregated: denominators supplied externally.
#> denominator : AIR enrolled persons, SC HHS 2024.One row per person in data. Use
entry_date_col and one of the five assessment windows.
Inputs come from starling::murmuration() in wide
format.
set.seed(99)
n <- 120L
dialysis_cohort <- data.frame(
patient_id = paste0("D", seq_len(n)),
dialysis_start_date = seq(as.Date("2022-01-01"), by = "week", length.out = n),
dialysis_end_date = seq(as.Date("2022-01-01"), by = "week", length.out = n) +
sample(90:900, n, replace = TRUE),
age_group = sample(c("45-64","65-74","75+"), n, replace = TRUE,
prob = c(0.25, 0.45, 0.30)),
vax_date_1 = as.Date(ifelse(
sample(c(TRUE, FALSE), n, replace = TRUE, prob = c(0.65, 0.35)),
as.character(seq(as.Date("2022-01-01"), by = "week", length.out = n) +
sample(-400:400, n, replace = TRUE)),
NA_character_)),
vax_type_1 = sample(c("Influenza","COVID-19",NA), n, replace = TRUE,
prob = c(0.5, 0.3, 0.2)),
stringsAsFactors = FALSE
)cov_exit <- brood(
dialysis_cohort,
data_format = "wide",
population_model = "cohort",
vax_date_cols = "vax_date_1",
entry_date_col = "dialysis_start_date",
exit_date_col = "dialysis_end_date",
window = "at_exit",
stratum_col = "age_group",
denominator_notes = "Dialysis cohort SC HHS. At-exit = vaccinated by dialysis end date."
)
print(cov_exit)
#> stratum n_vaccinated n_eligible person_time coverage vaccine_type
#> 1 45-64 9 34 NA 0.2647059 <NA>
#> 2 65-74 8 45 NA 0.1777778 <NA>
#> 3 75+ 11 41 NA 0.2682927 <NA>
#> dose_number reference_date intervention_period
#> 1 <NA> <NA> <NA>
#> 2 <NA> <NA> <NA>
#> 3 <NA> <NA> <NA>
#> window_description
#> 1 Cohort: vaccinated at or before exit date.
#> 2 Cohort: vaccinated at or before exit date.
#> 3 Cohort: vaccinated at or before exit date.
#>
#> -- brood_meta --------------------------------------
#> population_model : cohort
#> window : at_exit
#> validity_days : Inf (no expiry)
#> strata : 3
#> coverage range : 17.8% to 26.8%
#> window desc : Cohort: vaccinated at or before exit date.
#> denominator : Dialysis cohort SC HHS. At-exit = vaccinated by dialysis end date.cov_pre <- brood(
dialysis_cohort,
data_format = "wide",
population_model = "cohort",
vax_date_cols = "vax_date_1",
vax_type_cols = "vax_type_1",
vax_target = "Influenza",
entry_date_col = "dialysis_start_date",
window = "pre_entry",
stratum_col = "age_group",
denominator_notes = "Dialysis cohort. Pre-entry = vaccinated before dialysis start."
)
print(cov_pre)
#> stratum n_vaccinated n_eligible person_time coverage vaccine_type
#> 1 45-64 3 34 NA 0.08823529 <NA>
#> 2 65-74 7 45 NA 0.15555556 <NA>
#> 3 75+ 13 41 NA 0.31707317 <NA>
#> dose_number reference_date intervention_period
#> 1 <NA> <NA> <NA>
#> 2 <NA> <NA> <NA>
#> 3 <NA> <NA> <NA>
#> window_description
#> 1 Cohort: valid dose before cohort entry (baseline).
#> 2 Cohort: valid dose before cohort entry (baseline).
#> 3 Cohort: valid dose before cohort entry (baseline).
#>
#> -- brood_meta --------------------------------------
#> population_model : cohort
#> window : pre_entry
#> validity_days : Inf (no expiry)
#> vax_target : Influenza
#> strata : 3
#> coverage range : 8.8% to 31.7%
#> window desc : Cohort: valid dose before cohort entry (baseline).
#> denominator : Dialysis cohort. Pre-entry = vaccinated before dialysis start.cov_post <- brood(
dialysis_cohort,
data_format = "wide",
population_model = "cohort",
vax_date_cols = "vax_date_1",
vax_type_cols = "vax_type_1",
vax_target = "Influenza",
entry_date_col = "dialysis_start_date",
window = "post_entry_days",
window_days = 365L,
stratum_col = "age_group",
denominator_notes = "Dialysis cohort. Post-entry window = 365 days from dialysis start."
)
print(cov_post)
#> stratum n_vaccinated n_eligible person_time coverage vaccine_type
#> 1 45-64 5 34 NA 0.1470588 <NA>
#> 2 65-74 10 45 NA 0.2222222 <NA>
#> 3 75+ 12 41 NA 0.2926829 <NA>
#> dose_number reference_date intervention_period
#> 1 <NA> <NA> <NA>
#> 2 <NA> <NA> <NA>
#> 3 <NA> <NA> <NA>
#> window_description
#> 1 Cohort: valid dose within 365 days of cohort entry.
#> 2 Cohort: valid dose within 365 days of cohort entry.
#> 3 Cohort: valid dose within 365 days of cohort entry.
#>
#> -- brood_meta --------------------------------------
#> population_model : cohort
#> window : post_entry_days
#> validity_days : Inf (no expiry)
#> vax_target : Influenza
#> strata : 3
#> coverage range : 14.7% to 29.3%
#> window desc : Cohort: valid dose within 365 days of cohort entry.
#> denominator : Dialysis cohort. Post-entry window = 365 days from dialysis start.cov_intervention <- brood(
dialysis_cohort,
data_format = "wide",
population_model = "cohort",
vax_date_cols = "vax_date_1",
vax_type_cols = "vax_type_1",
vax_target = "Influenza",
entry_date_col = "dialysis_start_date",
exit_date_col = "dialysis_end_date",
window = "post_intervention",
intervention_date = as.Date("2023-06-01"),
stratum_col = "age_group",
denominator_notes = paste0(
"Dialysis cohort. Post-intervention = vaccinated on or after 2023-06-01."
)
)
print(cov_intervention)
#> stratum n_vaccinated n_eligible person_time coverage vaccine_type
#> 1 45-64 4 34 NA 0.1176471 <NA>
#> 2 65-74 6 45 NA 0.1333333 <NA>
#> 3 75+ 7 41 NA 0.1707317 <NA>
#> dose_number reference_date intervention_period
#> 1 <NA> <NA> <NA>
#> 2 <NA> <NA> <NA>
#> 3 <NA> <NA> <NA>
#> window_description
#> 1 Cohort: valid dose on or after 2023-06-01.
#> 2 Cohort: valid dose on or after 2023-06-01.
#> 3 Cohort: valid dose on or after 2023-06-01.
#>
#> -- brood_meta --------------------------------------
#> population_model : cohort
#> window : post_intervention
#> validity_days : Inf (no expiry)
#> vax_target : Influenza
#> strata : 3
#> coverage range : 11.8% to 17.1%
#> window desc : Cohort: valid dose on or after 2023-06-01.
#> denominator : Dialysis cohort. Post-intervention = vaccinated on or after 2023-06-01.Birth cohort is a special case of the cohort model. Set
entry_date_col = "dob" and supply
eligibility_days to define the age-based eligibility
window. Person-time (days at risk from birth until vaccinated or window
expired) is computed automatically.
set.seed(42)
n <- 200L
birth_records <- data.frame(
baby_id = paste0("B", seq_len(n)),
dob = seq(as.Date("2024-01-01"), by = "day", length.out = n),
gestation = sample(c("Term","Preterm"), n, replace = TRUE, prob = c(0.85, 0.15)),
vax_date_1 = as.Date(ifelse(
sample(c(TRUE, FALSE), n, replace = TRUE, prob = c(0.55, 0.45)),
as.character(seq(as.Date("2024-01-01"), by = "day", length.out = n) +
sample(30:200, n, replace = TRUE)),
NA_character_)),
vax_type_1 = "nirsevimab",
stringsAsFactors = FALSE
)cov_birth <- brood(
birth_records,
data_format = "wide",
population_model = "cohort",
vax_date_cols = "vax_date_1",
vax_type_cols = "vax_type_1",
vax_target = "nirsevimab",
entry_date_col = "dob", # DOB is the cohort entry date
eligibility_days = 180L, # eligible from birth until 6 months
censor_date = as.Date("2024-12-31"),
stratum_col = "gestation",
denominator_notes = paste0(
"SCPHU birth cohort 2024-01-01 to 2024-12-31. ",
"Eligible = live births in SC LGA. ",
"Eligibility window = 180 days from birth. ",
"Administrative censor date = 2024-12-31."
)
)
print(cov_birth)
#> stratum n_vaccinated n_eligible person_time coverage vaccine_type
#> 1 Preterm 13 29 4350 0.4482759 <NA>
#> 2 Term 95 171 23980 0.5555556 <NA>
#> dose_number reference_date intervention_period
#> 1 <NA> <NA> <NA>
#> 2 <NA> <NA> <NA>
#> window_description
#> 1 Cohort: vaccinated at or before exit date.
#> 2 Cohort: vaccinated at or before exit date.
#>
#> -- brood_meta --------------------------------------
#> population_model : cohort
#> window : at_exit
#> validity_days : Inf (no expiry)
#> vax_target : nirsevimab
#> strata : 2
#> coverage range : 44.8% to 55.6%
#> window desc : Cohort: vaccinated at or before exit date.
#> denominator : SCPHU birth cohort 2024-01-01 to 2024-12-31. Eligible = live births in SC LGA. Eligibility window = 180 days from birth. Administrative censor date = 2024-12-31.The person_time column shows days at risk.
coverage = proportion vaccinated among those who had at
least one eligible day.
This is the backbone of brood(). Set
time_series = TRUE and supply ts_start,
ts_end, and ts_by to sweep
window = "current_at_date" across monthly (or other)
intervals automatically. Returns one row per stratum per time point –
the right shape for bowerbird::brood_plot().
When intervention_date is supplied, an
intervention_period column is automatically added,
labelling each row as "Pre-intervention" or
"Post-intervention".
set.seed(7)
n <- 120L
jak_cohort <- data.frame(
patient_id = paste0("P", seq_len(n)),
entry_date_nominal = as.Date("2000-01-01"), # placeholder entry date
age_group = sample(c("18-49","50-64","65+"), n, replace = TRUE),
vax_date_1 = as.Date(ifelse(
sample(c(TRUE, FALSE), n, replace = TRUE, prob = c(0.45, 0.55)),
as.character(as.Date("2024-01-01") + sample(0:730, n, replace = TRUE)),
NA_character_)),
vax_type_1 = "Shingrix",
vax_date_2 = as.Date(ifelse(
sample(c(TRUE, FALSE), n, replace = TRUE, prob = c(0.25, 0.75)),
as.character(as.Date("2024-06-01") + sample(0:365, n, replace = TRUE)),
NA_character_)),
vax_type_2 = "Shingrix",
stringsAsFactors = FALSE
)cov_ts <- brood(
jak_cohort,
data_format = "wide",
population_model = "cohort",
vax_date_cols = c("vax_date_1", "vax_date_2"),
vax_type_cols = c("vax_type_1", "vax_type_2"),
vax_target = "Shingrix",
validity_days = Inf,
entry_date_col = "entry_date_nominal",
time_series = TRUE,
ts_start = as.Date("2024-12-01"),
ts_end = as.Date("2026-06-01"),
ts_by = "month",
intervention_date = as.Date("2025-12-01"),
denominator_notes = "JAK-inhibitor cohort. Shingrix only, no expiry."
)
#> (*)> mudnester::brood() — time_series: 19 month time points from 2024-12-01 to 2026-06-01
print(cov_ts)
#> stratum n_vaccinated n_eligible person_time coverage reference_date
#> 1 Overall 33 120 NA 0.2750000 2024-12-01
#> 2 Overall 41 120 NA 0.3416667 2025-01-01
#> 3 Overall 47 120 NA 0.3916667 2025-02-01
#> 4 Overall 52 120 NA 0.4333333 2025-03-01
#> 5 Overall 55 120 NA 0.4583333 2025-04-01
#> 6 Overall 56 120 NA 0.4666667 2025-05-01
#> 7 Overall 59 120 NA 0.4916667 2025-06-01
#> 8 Overall 60 120 NA 0.5000000 2025-07-01
#> 9 Overall 61 120 NA 0.5083333 2025-08-01
#> 10 Overall 62 120 NA 0.5166667 2025-09-01
#> 11 Overall 67 120 NA 0.5583333 2025-10-01
#> 12 Overall 72 120 NA 0.6000000 2025-11-01
#> 13 Overall 73 120 NA 0.6083333 2025-12-01
#> 14 Overall 74 120 NA 0.6166667 2026-01-01
#> 15 Overall 74 120 NA 0.6166667 2026-02-01
#> 16 Overall 74 120 NA 0.6166667 2026-03-01
#> 17 Overall 74 120 NA 0.6166667 2026-04-01
#> 18 Overall 74 120 NA 0.6166667 2026-05-01
#> 19 Overall 74 120 NA 0.6166667 2026-06-01
#> intervention_period vaccine_type dose_number
#> 1 Pre-intervention <NA> <NA>
#> 2 Pre-intervention <NA> <NA>
#> 3 Pre-intervention <NA> <NA>
#> 4 Pre-intervention <NA> <NA>
#> 5 Pre-intervention <NA> <NA>
#> 6 Pre-intervention <NA> <NA>
#> 7 Pre-intervention <NA> <NA>
#> 8 Pre-intervention <NA> <NA>
#> 9 Pre-intervention <NA> <NA>
#> 10 Pre-intervention <NA> <NA>
#> 11 Pre-intervention <NA> <NA>
#> 12 Pre-intervention <NA> <NA>
#> 13 Post-intervention <NA> <NA>
#> 14 Post-intervention <NA> <NA>
#> 15 Post-intervention <NA> <NA>
#> 16 Post-intervention <NA> <NA>
#> 17 Post-intervention <NA> <NA>
#> 18 Post-intervention <NA> <NA>
#> 19 Post-intervention <NA> <NA>
#> window_description
#> 1 Time series: monthly 'current_at_date' coverage from 2024-12-01 to 2026-06-01, intervention line at 2025-12-01.
#> 2 Time series: monthly 'current_at_date' coverage from 2024-12-01 to 2026-06-01, intervention line at 2025-12-01.
#> 3 Time series: monthly 'current_at_date' coverage from 2024-12-01 to 2026-06-01, intervention line at 2025-12-01.
#> 4 Time series: monthly 'current_at_date' coverage from 2024-12-01 to 2026-06-01, intervention line at 2025-12-01.
#> 5 Time series: monthly 'current_at_date' coverage from 2024-12-01 to 2026-06-01, intervention line at 2025-12-01.
#> 6 Time series: monthly 'current_at_date' coverage from 2024-12-01 to 2026-06-01, intervention line at 2025-12-01.
#> 7 Time series: monthly 'current_at_date' coverage from 2024-12-01 to 2026-06-01, intervention line at 2025-12-01.
#> 8 Time series: monthly 'current_at_date' coverage from 2024-12-01 to 2026-06-01, intervention line at 2025-12-01.
#> 9 Time series: monthly 'current_at_date' coverage from 2024-12-01 to 2026-06-01, intervention line at 2025-12-01.
#> 10 Time series: monthly 'current_at_date' coverage from 2024-12-01 to 2026-06-01, intervention line at 2025-12-01.
#> 11 Time series: monthly 'current_at_date' coverage from 2024-12-01 to 2026-06-01, intervention line at 2025-12-01.
#> 12 Time series: monthly 'current_at_date' coverage from 2024-12-01 to 2026-06-01, intervention line at 2025-12-01.
#> 13 Time series: monthly 'current_at_date' coverage from 2024-12-01 to 2026-06-01, intervention line at 2025-12-01.
#> 14 Time series: monthly 'current_at_date' coverage from 2024-12-01 to 2026-06-01, intervention line at 2025-12-01.
#> 15 Time series: monthly 'current_at_date' coverage from 2024-12-01 to 2026-06-01, intervention line at 2025-12-01.
#> 16 Time series: monthly 'current_at_date' coverage from 2024-12-01 to 2026-06-01, intervention line at 2025-12-01.
#> 17 Time series: monthly 'current_at_date' coverage from 2024-12-01 to 2026-06-01, intervention line at 2025-12-01.
#> 18 Time series: monthly 'current_at_date' coverage from 2024-12-01 to 2026-06-01, intervention line at 2025-12-01.
#> 19 Time series: monthly 'current_at_date' coverage from 2024-12-01 to 2026-06-01, intervention line at 2025-12-01.
#>
#> -- brood_meta --------------------------------------
#> population_model : cohort
#> time_series : TRUE
#> time_series_by : month
#> n_time_points : 19
#> intervention : 2025-12-01
#> validity_days : Inf (no expiry)
#> vax_target : Shingrix
#> strata : 1
#> coverage range : 27.5% to 61.7%
#> window desc : Time series: monthly 'current_at_date' coverage from 2024-12-01 to 2026-06-01, intervention line at 2025-12-01.
#> denominator : JAK-inhibitor cohort. Shingrix only, no expiry.The result is 19 rows: one per month ( 19 time points) across 1
stratum (“Overall”). With stratum_col supplied, each
stratum gets its own row at every time point.
cov_ts_strat <- brood(
jak_cohort,
data_format = "wide",
population_model = "cohort",
vax_date_cols = c("vax_date_1", "vax_date_2"),
vax_type_cols = c("vax_type_1", "vax_type_2"),
vax_target = "Shingrix",
validity_days = Inf,
entry_date_col = "entry_date_nominal",
stratum_col = "age_group",
time_series = TRUE,
ts_start = as.Date("2024-12-01"),
ts_end = as.Date("2026-06-01"),
ts_by = "month",
intervention_date = as.Date("2025-12-01"),
denominator_notes = "JAK-inhibitor cohort by age group. Shingrix only."
)
#> (*)> mudnester::brood() — time_series: 19 month time points from 2024-12-01 to 2026-06-01
cat("Rows:", nrow(cov_ts_strat),
"= ", attr(cov_ts_strat, "n_time_points"), "months x",
attr(cov_ts_strat, "n_strata"), "age groups\n")
#> Rows: 57 = 19 months x 3 age groups
print(head(cov_ts_strat[, c("stratum","reference_date","intervention_period",
"n_vaccinated","n_eligible","coverage")], 9))
#> stratum reference_date intervention_period n_vaccinated n_eligible coverage
#> 1 18-49 2024-12-01 Pre-intervention 12 35 0.3428571
#> 2 50-64 2024-12-01 Pre-intervention 8 45 0.1777778
#> 3 65+ 2024-12-01 Pre-intervention 13 40 0.3250000
#> 4 18-49 2025-01-01 Pre-intervention 13 35 0.3714286
#> 5 50-64 2025-01-01 Pre-intervention 13 45 0.2888889
#> 6 65+ 2025-01-01 Pre-intervention 15 40 0.3750000
#> 7 18-49 2025-02-01 Pre-intervention 16 35 0.4571429
#> 8 50-64 2025-02-01 Pre-intervention 15 45 0.3333333
#> 9 65+ 2025-02-01 Pre-intervention 16 40 0.4000000
#>
#> -- brood_meta --------------------------------------
#> population_model :
#> strata :
#> coverage range : 17.8% to 45.7%All models return brood_df objects accepted by
bowerbird::brood_plot(). The time-series output
auto-detects as a line chart:
# Pre-aggregated: bar chart with target line
bowerbird::brood_plot(cov_static, target_line = 0.80,
title = "COVID-19 booster coverage by age group")
# Time series: line chart with vertical intervention line (auto-detected)
bowerbird::brood_plot(cov_ts,
title = "Monthly Shingrix coverage, JAK-inhibitor cohort")
# Stratified time series: one line per stratum
bowerbird::brood_plot(cov_ts_strat,
title = "Shingrix coverage by age group")Every brood() call should include
denominator_notes documenting:
brood() stores these notes as a metadata attribute.
print.brood_df() displays them and
bowerbird::brood_plot() can use them as a plot caption.
sessionInfo()
#> R version 4.5.2 (2025-10-31 ucrt)
#> Platform: x86_64-w64-mingw32/x64
#> Running under: Windows 11 x64 (build 26100)
#>
#> Matrix products: default
#> LAPACK version 3.12.1
#>
#> locale:
#> [1] LC_COLLATE=C LC_CTYPE=English_Australia.utf8
#> [3] LC_MONETARY=English_Australia.utf8 LC_NUMERIC=C
#> [5] LC_TIME=English_Australia.utf8
#>
#> time zone: Australia/Brisbane
#> tzcode source: internal
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] dplyr_1.2.0 mudnester_0.7.8
#>
#> loaded via a namespace (and not attached):
#> [1] vctrs_0.7.1 cli_3.6.5 knitr_1.51 rlang_1.2.0
#> [5] xfun_0.56 otel_0.2.0 generics_0.1.4 jsonlite_2.0.0
#> [9] glue_1.8.0 htmltools_0.5.9 sass_0.4.10 rmarkdown_2.31
#> [13] evaluate_1.0.5 jquerylib_0.1.4 tibble_3.3.1 fastmap_1.2.0
#> [17] yaml_2.3.12 lifecycle_1.0.5 compiler_4.5.2 pkgconfig_2.0.3
#> [21] rstudioapi_0.18.0 digest_0.6.39 R6_2.6.1 utf8_1.2.6
#> [25] tidyselect_1.2.1 pillar_1.11.1 magrittr_2.0.4 bslib_0.10.0
#> [29] tools_4.5.2 cachem_1.1.0These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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