Source: ZooDB — the SIO Ocean Informatics zooplankton database (Mark Ohman Lab, SIO Pelagic Invertebrate Collection). Per-taxon CSVs are scripted from the portal’s save.php endpoint (public access) by libs/download_zoodb.R and consolidated to zoodb_holoplankton.csv (see the Acquire step below + by_taxon/_PROVENANCE.md).
cce-lter (curated by CCE-LTER / Ohman Lab; sibling of cce-lter_euphausiids, which the ZooDB methods note as a separate database)measurement_value = 0 means analyzed-but-absent. Coverage varies by taxon/cruise — a missing (sample, taxon) is not analyzed, so we never impute zeros.unpooled net tows (station-resolved) + 155 pooled per-cruise regional composites (Lavaniegos & Ohman 2007; no per-station position; a cruise/region may hold >1 composite, e.g. shallow vs deep strata).taxon_worms.csv).zooplankton_abundance (count/1000 m³), zooplankton_abundance_areal (count/m²), zooplankton_biomass_carbon (mg C/m²). Raw (uncorrected for net type).This ingest proceeds with documented provisional decisions (cruise/ship resolution, timezone, raw vs corrected, pooled positioning); each is tracked in the Questions for Data Providers section.
graph LR
A[zoodb_holoplankton.csv<br/>10,316 sample x taxon rows] --> B[zoodb_sample<br/>503 samples + keys]
A --> C[zoodb_measurement<br/>long: abundance x2 + biomass]
A --> D[zoodb_taxon<br/>33 higher taxa + WoRMS]
C -.sample_id.-> B
C -.taxon_id.-> D
B -.ship/cruise/grid.-> E[(shared refs)]
devtools::load_all(here::here("../calcofi4db"))
devtools::load_all(here::here("../calcofi4r"))
librarian::shelf(
CalCOFI/calcofi4db, CalCOFI/calcofi4r,
DBI, dplyr, DT, fs, glue, here, janitor, jsonlite, knitr, lubridate, purrr,
readr, sf, stringr, tibble, tidyr, units, quiet = T)
options(readr.show_col_types = F)
options(DT.options = list(scrollX = TRUE))
source(here("libs/ingest.R")) # overwrite, dir_data
cc <- read_calcofi_meta(here("ingest_cce-lter_zoodb.qmd"))
provider <- cc$provider
dataset <- cc$dataset
tables_owned <- cc$tables_owned
dir_label <- glue("{provider}_{dataset}")
dir_parquet <- here(glue("data/parquet/{dir_label}"))
db_path <- here(glue("data/wrangling/{dir_label}.duckdb"))
if (overwrite) {
if (file_exists(db_path)) file_delete(db_path)
if (file_exists(paste0(db_path, ".wal"))) file_delete(paste0(db_path, ".wal"))
if (dir_exists(paste0(db_path, ".tmp"))) dir_delete(paste0(db_path, ".tmp"))
}
dir_create(dirname(db_path))
con <- get_duckdb_con(db_path)
load_duckdb_extension(con, "spatial")
meas_type_csv <- here("metadata/measurement_type.csv")
d_meas_type <- read_csv(meas_type_csv)ZooDB is an interactive portal, not a file download. libs/download_zoodb.R scripts its public login + save.php CSV export — the 33 higher taxa × 2 abundance units (pooled=combined, raw/uncorrected, all years/months/times/ regions) — and merges the two unit files per taxon (on the source RowNumber) into zoodb_holoplankton.csv. It runs once then caches; set overwrite_all = TRUE in libs/ingest.R to re-scrape. Exact query parameters are recorded in by_taxon/_PROVENANCE.md.
source(here("libs/download_zoodb.R"))
zoo_dir <- path_expand(glue("{dir_data}/cce-lter/ZooDB"))
zoo_csv <- download_zoodb(zoo_dir, overwrite = overwrite_all)ZooDB: using cached zoodb_holoplankton.csv The consolidated long table (one row per taxon × sample) carries N/A for the per-station fields on pooled rows, read as NA.
stopifnot("zoodb_holoplankton.csv not found" = file_exists(zoo_csv))
# archive source (consolidated + per-taxon exports + portal method PDFs) to GCS
sync_to_gcs(
local_dir = zoo_dir,
gcs_prefix = glue("archive/{provider}/{dataset}"),
bucket = "calcofi-files-public",
exclude = c(".DS_Store", "*.tmp", "*.gdoc"))# A tibble: 81 × 4
file action size reason
<chr> <chr> <dbl> <chr>
1 by_taxon/_all_taxa__1000m3.csv skipped 44108 checksum match
2 by_taxon/_all_taxa__m2.csv skipped 43502 checksum match
3 by_taxon/_manifest.csv skipped 5867 checksum match
4 by_taxon/_PROVENANCE.md skipped 2393 checksum match
5 by_taxon/amphipoda_gammaridea__1000m3.csv skipped 41943 checksum match
6 by_taxon/amphipoda_gammaridea__m2.csv skipped 41572 checksum match
7 by_taxon/amphipoda_hyperiidea__1000m3.csv skipped 42494 checksum match
8 by_taxon/amphipoda_hyperiidea__m2.csv skipped 41898 checksum match
9 by_taxon/appendicularia__1000m3.csv skipped 42757 checksum match
10 by_taxon/appendicularia__m2.csv skipped 42170 checksum match
# ℹ 71 more rowsd_raw <- read_csv(zoo_csv, na = c("", "NA", "N/A"))
cat(glue("Read {format(nrow(d_raw), big.mark=',')} rows, {ncol(d_raw)} columns ",
"from {basename(zoo_csv)}"), "\n")Read 10,316 rows, 19 columns from zoodb_holoplankton.csv Canonical field names follow metadata/field_dictionary.csv. A sample_key uniquely identifies each sample: U|cruise|towbegin|line|station (unpooled) or P|cruise|date|region (pooled).
d <- d_raw |>
transmute(
taxon_slug = taxon,
source = str_to_lower(source),
cruise_orig = cruise,
ship_name = ship,
date = as.Date(date),
line = suppressWarnings(as.numeric(line)),
station = suppressWarnings(as.numeric(station)),
region = region,
datetime_start_utc = as_datetime(tow_begin), # Q01: tz assumed local/ship
datetime_end_utc = as_datetime(tow_end),
latitude = suppressWarnings(as.numeric(latitude)),
longitude = suppressWarnings(as.numeric(longitude)),
max_depth_m = suppressWarnings(as.numeric(max_depth_m)),
min_depth_m = suppressWarnings(as.numeric(min_depth_m)),
net_type = net_type,
n_tows_pooled = suppressWarnings(as.integer(n_tows_pooled)),
abundance_per_1000m3 = suppressWarnings(as.numeric(abundance_per_1000m3)),
abundance_per_m2 = suppressWarnings(as.numeric(abundance_per_m2)),
biomass_mgC_per_m2 = suppressWarnings(as.numeric(biomass_mgC_per_m2))) |>
mutate(
site_key = if_else(
is.na(line) | is.na(station), NA_character_,
sprintf("%05.1f %05.1f", line, station)),
# pooled composites can co-occur per cruise/region (e.g. shallow vs deep
# strata) — n_tows_pooled disambiguates the otherwise-identical key
sample_key = if_else(
source == "unpooled",
paste("U", cruise_orig, datetime_start_utc, line, station, sep = "|"),
paste("P", cruise_orig, date, region, n_tows_pooled, sep = "|")))
cat(glue(
"Cleaned {format(nrow(d), big.mark=',')} (sample x taxon) rows; ",
"{n_distinct(d$sample_key)} samples ",
"({n_distinct(d$sample_key[d$source=='unpooled'])} unpooled, ",
"{n_distinct(d$sample_key[d$source=='pooled'])} pooled); ",
"{n_distinct(d$taxon_slug)} taxa"), "\n")Cleaned 10,316 (sample x taxon) rows; 506 samples (351 unpooled, 155 pooled); 33 taxa zoodb_taxon is the 33 higher taxa with WoRMS AphiaIDs + classification, resolved offline and cached in metadata/cce-lter/zoodb/taxon_worms.csv (33/33 matched; 2 WoRMS-unaccepted concepts flagged, Q07).
d_worms <- read_csv(here("metadata/cce-lter/zoodb/taxon_worms.csv"))
zoodb_taxon <- d_worms |>
arrange(taxon_zoodb) |>
transmute(
taxon_id = row_number(),
taxon_zoodb, taxon_slug,
aphia_id = as.integer(aphia_id),
scientific_name = scientific_name_accepted,
rank, taxon_status = status,
kingdom, phylum, class, order_taxon = order, family) # 'order' is a SQL reserved word
stopifnot("taxon_slug mismatch source vs WoRMS map" =
all(unique(d$taxon_slug) %in% zoodb_taxon$taxon_slug))
dbWriteTable(con, "zoodb_taxon", zoodb_taxon, overwrite = TRUE)
cat(glue("zoodb_taxon: {nrow(zoodb_taxon)} taxa ",
"({sum(!is.na(zoodb_taxon$aphia_id))} WoRMS-matched, ",
"{sum(zoodb_taxon$taxon_status=='unaccepted')} unaccepted [Q07])"), "\n")zoodb_taxon: 33 taxa (33 WoRMS-matched, 2 unaccepted [Q07]) One row per distinct sample. Resolve ship_key/ship_nodc against the shared ship registry, derive cruise_key as YYYY-MM-{ship_nodc} (validated against cruise), and fall back to a unique year-month match for pooled samples (which have no ship). Q01 (timezone) and Q02 (cruise mapping) are tracked.
# distinct sample-level attributes (constant across taxa within a sample)
d_sample0 <- d |>
distinct(
sample_key, source, cruise_orig, ship_name, date,
datetime_start_utc, datetime_end_utc, line, station, site_key, region,
latitude, longitude, max_depth_m, min_depth_m, net_type, n_tows_pooled)
stopifnot("sample_key not unique over sample attributes" =
n_distinct(d_sample0$sample_key) == nrow(d_sample0))
# shared reference tables from the ichthyo ingest
load_prior_tables(
con, parquet_dir = here("data/parquet/swfsc_ichthyo"),
tables = c("ship", "cruise", "grid"), geom_tables = c("grid"), as_view = TRUE)# A tibble: 3 × 3
table rows has_geom
<chr> <dbl> <lgl>
1 cruise 691 FALSE
2 grid 218 TRUE
3 ship 48 FALSE d_ship <- dbGetQuery(con, "SELECT ship_key, ship_name, ship_nodc FROM ship") |>
mutate(ship_name_norm = str_squish(str_to_upper(ship_name))) |>
distinct(ship_name_norm, .keep_all = TRUE)
# year-month -> unique cruise_key (pooled samples + fallback)
cr_ym <- dbGetQuery(con, "SELECT cruise_key, date_ym FROM cruise") |>
mutate(ym = format(as.Date(date_ym), "%Y%m")) |>
group_by(ym) |>
summarize(cruise_key_ym = if (n() == 1) cruise_key[1] else NA_character_,
.groups = "drop")
valid_ck <- dbGetQuery(con, "SELECT DISTINCT cruise_key FROM cruise")$cruise_key
d_sample <- d_sample0 |>
mutate(ship_name_norm = ship_name |>
str_replace("^R/?V\\.?\\s+", "") |> str_to_upper() |> str_squish()) |>
left_join(d_ship |> select(ship_name_norm, ship_key, ship_nodc),
by = "ship_name_norm") |>
mutate(
ym = format(date, "%Y%m"),
cruise_key_ship = if_else(
is.na(ship_nodc), NA_character_,
as.character(glue("{format(date, '%Y-%m')}-{ship_nodc}"))),
cruise_key_ship = if_else(cruise_key_ship %in% valid_ck,
cruise_key_ship, NA_character_)) |>
left_join(cr_ym, by = "ym") |>
mutate(cruise_key = coalesce(cruise_key_ship, cruise_key_ym)) |>
arrange(source, date, cruise_orig, region, line, station, sample_key) |>
mutate(sample_id = row_number(), .before = 1)
# id <-> key map for the measurement join, then drop the key from the table
sample_map <- d_sample |> select(sample_id, sample_key)
zoodb_sample <- d_sample |>
select(sample_id, source, cruise_orig, cruise_key, ship_name, ship_key,
date, datetime_start_utc, datetime_end_utc, line, station, site_key,
region, latitude, longitude, max_depth_m, min_depth_m, net_type,
n_tows_pooled)
dbWriteTable(con, "zoodb_sample", zoodb_sample, overwrite = TRUE)
n_ship <- sum(!is.na(d_sample$ship_key))
n_cruise <- sum(!is.na(d_sample$cruise_key))
cat(glue(
"zoodb_sample: {nrow(zoodb_sample)} samples; ",
"ship_key {n_ship}/{nrow(d_sample)} ({round(100*n_ship/nrow(d_sample),1)}%), ",
"cruise_key {n_cruise}/{nrow(d_sample)} ({round(100*n_cruise/nrow(d_sample),1)}%)"), "\n")zoodb_sample: 506 samples; ship_key 351/506 (69.4%), cruise_key 384/506 (75.9%) Unpooled tows get a point geometry and a CalCOFI grid_key; pooled samples have no position (NULL geometry / grid, Q05).
add_point_geom(con, "zoodb_sample", lon_col = "longitude", lat_col = "latitude")
grid_stats <- assign_grid_key(con, "zoodb_sample")
grid_stats |> datatable(caption = "Grid assignment (unpooled tows)")The three measured quantities pivot into long form, joined to sample_id (via sample_key) and taxon_id (via taxon_slug). Explicit zeros (analyzed- but-absent) are retained; only NA / non-finite values are dropped.
meas_recode <- c(
abundance_per_1000m3 = "zooplankton_abundance",
abundance_per_m2 = "zooplankton_abundance_areal",
biomass_mgC_per_m2 = "zooplankton_biomass_carbon")
zoodb_measurement <- d |>
left_join(sample_map, by = "sample_key") |>
left_join(zoodb_taxon |> select(taxon_id, taxon_slug), by = "taxon_slug") |>
select(sample_id, taxon_id,
abundance_per_1000m3, abundance_per_m2, biomass_mgC_per_m2) |>
pivot_longer(
cols = c(abundance_per_1000m3, abundance_per_m2, biomass_mgC_per_m2),
names_to = "src_col", values_to = "measurement_value") |>
filter(!is.na(measurement_value), is.finite(measurement_value)) |>
mutate(measurement_type = unname(meas_recode[src_col])) |>
arrange(sample_id, taxon_id, measurement_type) |>
transmute(measurement_id = row_number(),
sample_id, taxon_id, measurement_type, measurement_value)
dbWriteTable(con, "zoodb_measurement", zoodb_measurement, overwrite = TRUE)
cat(glue(
"zoodb_measurement: {format(nrow(zoodb_measurement), big.mark=',')} rows ",
"({format(sum(zoodb_measurement$measurement_value==0), big.mark=',')} explicit zeros = ",
"analyzed-but-absent, Q04)"), "\n")zoodb_measurement: 30,948 rows (13,152 explicit zeros = analyzed-but-absent, Q04) zoo_types <- tibble(
measurement_type = c("zooplankton_abundance", "zooplankton_abundance_areal",
"zooplankton_biomass_carbon"),
description = c(
"Zooplankton numerical abundance (volumetric density) per net tow, by higher taxon. Raw (uncorrected for net type).",
"Zooplankton areal abundance (depth-integrated density) per net tow, by higher taxon. Raw (uncorrected).",
"Zooplankton carbon biomass per net tow, by higher taxon. Raw (uncorrected for net type)."),
units = c("count/1000m3", "count/m2", "mgC/m2"),
is_canonical = c(TRUE, NA, TRUE),
`_source_column` = c("abundance_per_1000m3", "abundance_per_m2", "biomass_mgC_per_m2"),
`_source_table` = "zoodb_measurement",
`_source_datasets` = "cce-lter_zoodb",
`_qual_column` = NA_character_, `_prec_column` = NA_character_)
new_types <- zoo_types |> filter(!measurement_type %in% d_meas_type$measurement_type)
if (nrow(new_types) > 0) {
d_meas_type <- bind_rows(d_meas_type, new_types)
write_csv(d_meas_type, meas_type_csv)
cat(glue("Added measurement type(s): {paste(new_types$measurement_type, collapse=', ')}"), "\n")
} else cat("zooplankton measurement types already registered\n")zooplankton measurement types already registereddbWriteTable(con, "measurement_type", d_meas_type, overwrite = TRUE)d_dataset <- ingest_yaml_to_dataset_df(read_ingest_yaml(here()))
dbWriteTable(con, "dataset", d_dataset, overwrite = TRUE)
cat(glue("dataset: {nrow(d_dataset)} dataset(s) registered"), "\n")dataset: 12 dataset(s) registered zoodb_rels <- list(
primary_keys = list(
zoodb_sample = "sample_id",
zoodb_measurement = "measurement_id",
zoodb_taxon = "taxon_id",
measurement_type = "measurement_type"),
foreign_keys = list(
list(table = "zoodb_measurement", column = "sample_id",
ref_table = "zoodb_sample", ref_column = "sample_id"),
list(table = "zoodb_measurement", column = "taxon_id",
ref_table = "zoodb_taxon", ref_column = "taxon_id"),
list(table = "zoodb_measurement", column = "measurement_type",
ref_table = "measurement_type", ref_column = "measurement_type")))
cc_erd(
con,
tables = c("zoodb_sample", "zoodb_measurement", "zoodb_taxon",
"measurement_type", "dataset"),
rels = zoodb_rels,
colors = list(
lightblue = c("zoodb_sample", "zoodb_measurement"),
lightgreen = "zoodb_taxon",
lightyellow = "measurement_type",
white = "dataset"))
build_relationships_json(
rels = zoodb_rels, output_dir = dir_parquet,
provider = provider, dataset = dataset)[1] "/Users/bbest/Github/CalCOFI/workflows/data/parquet/cce-lter_zoodb/relationships.json"results <- validate_for_release(con, checks = "all", strict = FALSE)
cat("Validation:", ifelse(results$passed, "PASSED", "FAILED"), "\n")Validation: FAILED if (length(results$errors) > 0)
cat("Errors:\n", paste("-", results$errors, collapse = "\n"), "\n")Errors:
- Table 'zoodb_sample' has 122 NULL values in required column 'cruise_key'
- Table 'zoodb_sample' has 155 NULL values in required column 'ship_key'
- Table 'zoodb_sample' has 155 NULL values in required column 'site_key'
- Table 'zoodb_sample' has 156 NULL values in required column 'grid_key' if (length(results$warnings) > 0)
cat("Warnings:\n", paste("-", results$warnings, collapse = "\n"), "\n")Warnings:
- Missing expected tables: site, tow, net, larva, species # NULLs in cross-dataset keys (cruise_key, ship_key, site_key, grid_key) are the
# EXPECTED unmatched / not-applicable remainder (pooled samples have no station;
# cruise/ship mapping is partial — Q02). Not a hard failure.
cov <- dbGetQuery(con,
"SELECT
AVG(CASE WHEN ship_key IS NOT NULL THEN 1 ELSE 0 END) ship,
AVG(CASE WHEN cruise_key IS NOT NULL THEN 1 ELSE 0 END) cruise,
AVG(CASE WHEN grid_key IS NOT NULL THEN 1 ELSE 0 END) grid
FROM zoodb_sample")
cat(glue("Match coverage: ship_key {round(100*cov$ship,1)}%, ",
"cruise_key {round(100*cov$cruise,1)}%, grid_key {round(100*cov$grid,1)}%"), "\n")Match coverage: ship_key 69.4%, cruise_key 75.9%, grid_key 69.2% # FK integrity: every measurement resolves to a sample and a taxon
orphan <- dbGetQuery(con,
"SELECT
SUM(CASE WHEN s.sample_id IS NULL THEN 1 ELSE 0 END) AS no_sample,
SUM(CASE WHEN t.taxon_id IS NULL THEN 1 ELSE 0 END) AS no_taxon
FROM zoodb_measurement m
LEFT JOIN zoodb_sample s USING (sample_id)
LEFT JOIN zoodb_taxon t USING (taxon_id)")
cat(glue("Orphan measurements: {orphan$no_sample} w/o sample, {orphan$no_taxon} w/o taxon"), "\n")Orphan measurements: 0 w/o sample, 0 w/o taxon samp_cols <- dbGetQuery(con,
"SELECT column_name FROM information_schema.columns
WHERE table_name='zoodb_sample' AND data_type NOT LIKE 'GEOMETRY%'")$column_name
dbGetQuery(con, glue("SELECT {paste(samp_cols, collapse=', ')} FROM zoodb_sample LIMIT 100")) |>
datatable(caption = "zoodb_sample — first 100 rows", rownames = FALSE, filter = "top")dbGetQuery(con,
"SELECT m.measurement_id, m.sample_id, x.taxon_zoodb, m.measurement_type, m.measurement_value
FROM zoodb_measurement m JOIN zoodb_taxon x USING (taxon_id)
ORDER BY m.measurement_id LIMIT 100") |>
datatable(caption = "zoodb_measurement — first 100 rows", rownames = FALSE)dbGetQuery(con,
"SELECT t.taxon_id, t.taxon_zoodb, t.scientific_name, t.aphia_id, t.rank,
t.phylum, t.class, COUNT(DISTINCT m.sample_id) AS n_samples
FROM zoodb_taxon t LEFT JOIN zoodb_measurement m USING (taxon_id)
GROUP BY ALL ORDER BY t.taxon_id") |>
datatable(caption = "zoodb_taxon — 33 higher taxa (samples analyzed)", rownames = FALSE)dir_create(dir_parquet)
mismatches <- list(
measurement_types = collect_measurement_type_mismatches(
con, here("metadata/measurement_type.csv")),
cruise_keys = collect_cruise_key_mismatches(con, "zoodb_sample"))
parquet_stats <- write_parquet_outputs(
con = con,
output_dir = dir_parquet,
tables = c("zoodb_sample", "zoodb_measurement", "zoodb_taxon",
"measurement_type", "dataset"),
sort_by = list(zoodb_measurement = "sample_id"),
strip_provenance = FALSE,
mismatches = mismatches)
parquet_stats |> mutate(file = basename(path)) |> select(-path) |>
datatable(caption = "Parquet export statistics")d_tbls_rd <- read_csv(here("metadata/cce-lter/zoodb/tbls_redefine.csv"))
d_flds_rd <- read_csv(here("metadata/cce-lter/zoodb/flds_redefine.csv"))
metadata_path <- build_metadata_json(
con = con,
d_tbls_rd = d_tbls_rd,
d_flds_rd = d_flds_rd,
metadata_derived_csv = here("metadata/cce-lter/zoodb/metadata_derived.csv"),
output_dir = dir_parquet,
tables = c("zoodb_sample", "zoodb_measurement", "zoodb_taxon"),
set_comments = TRUE,
provider = provider,
dataset = dataset,
workflow_url = cc$workflow_url,
tables_owned = tables_owned)sync_to_gcs(
local_dir = dir_parquet,
gcs_prefix = glue("ingest/{dir_label}"),
bucket = "calcofi-db")# A tibble: 8 × 4
file action size reason
<chr> <chr> <dbl> <chr>
1 dataset.parquet uploaded 8904 crc32c changed
2 manifest.json uploaded 2703 crc32c changed
3 measurement_type.parquet uploaded 5793 crc32c changed
4 metadata.json uploaded 8831 crc32c changed
5 relationships.json skipped 727 checksum match
6 zoodb_measurement.parquet skipped 114501 checksum match
7 zoodb_sample.parquet skipped 18704 checksum match
8 zoodb_taxon.parquet skipped 3766 checksum matchFollow-up questions for CCE-LTER (Mark Ohman, Bertha Lavaniegos, Linsey Sala), ranked by importance. This ingest proceeded with the documented provisional decisions noted above; high answers may change cruise/ship matching or the timezone assumption on the next run. Tracked in metadata/cce-lter/zoodb/questions.csv.
read_csv(here(glue("metadata/{provider}/{dataset}/questions.csv"))) |>
arrange(factor(priority, c("blocker", "high", "normal", "low")), id) |>
select(priority, question, context, related_field, status) |>
datatable(
caption = "Questions for data providers (ranked by importance)",
options = list(dom = "t", pageLength = 20), rownames = FALSE)close_duckdb(con)
cat(glue("Parquet outputs written to: {dir_parquet}"), "\n")Parquet outputs written to: /Users/bbest/Github/CalCOFI/workflows/data/parquet/cce-lter_zoodb devtools::session_info()─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.5.2 (2025-10-31)
os macOS Sequoia 15.7.1
system aarch64, darwin20
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz Europe/Rome
date 2026-06-26
pandoc 3.8.3 @ /opt/homebrew/bin/ (via rmarkdown)
quarto 1.8.25 @ /usr/local/bin/quarto
─ Packages ───────────────────────────────────────────────────────────────────
! package * version date (UTC) lib source
abind 1.4-8 2024-09-12 [1] CRAN (R 4.5.0)
arrow 24.0.0 2026-04-29 [1] CRAN (R 4.5.2)
askpass 1.2.1 2024-10-04 [1] CRAN (R 4.5.0)
assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.5.0)
backports 1.5.1 2026-04-03 [1] CRAN (R 4.5.2)
base64enc 0.1-6 2026-02-02 [1] CRAN (R 4.5.2)
bit 4.6.0 2025-03-06 [1] CRAN (R 4.5.0)
bit64 4.8.2 2026-05-19 [1] CRAN (R 4.5.2)
blob 1.3.0 2026-01-14 [1] CRAN (R 4.5.2)
broom 1.0.13 2026-05-14 [1] CRAN (R 4.5.2)
bslib 0.11.0 2026-05-16 [1] CRAN (R 4.5.2)
cachem 1.1.0 2024-05-16 [1] CRAN (R 4.5.0)
P calcofi4db * 2.8.2 2026-06-07 [?] load_all()
P calcofi4r * 1.3.0 2026-06-05 [?] load_all()
class 7.3-23 2025-01-01 [1] CRAN (R 4.5.2)
classInt 0.4-11 2025-01-08 [1] CRAN (R 4.5.0)
cli 3.6.6 2026-04-09 [1] CRAN (R 4.5.2)
codetools 0.2-20 2024-03-31 [1] CRAN (R 4.5.2)
crayon 1.5.3 2024-06-20 [1] CRAN (R 4.5.0)
crosstalk 1.2.2 2025-08-26 [1] CRAN (R 4.5.0)
curl 7.1.0 2026-04-22 [1] CRAN (R 4.5.2)
data.table 1.18.4 2026-05-06 [1] CRAN (R 4.5.2)
DBI * 1.3.0 2026-02-25 [1] CRAN (R 4.5.2)
dbplyr 2.5.2 2026-02-13 [1] CRAN (R 4.5.2)
desc 1.4.3 2023-12-10 [1] CRAN (R 4.5.0)
devtools 2.5.0 2026-03-14 [1] CRAN (R 4.5.2)
DiagrammeR 1.0.12 2026-04-27 [1] CRAN (R 4.5.2)
DiagrammeRsvg 0.1 2016-02-04 [1] CRAN (R 4.5.0)
digest 0.6.39 2025-11-19 [1] CRAN (R 4.5.2)
dm 1.1.2 2026-05-17 [1] CRAN (R 4.5.2)
dplyr * 1.2.1 2026-04-03 [1] CRAN (R 4.5.2)
DT * 0.34.0 2025-09-02 [1] CRAN (R 4.5.0)
duckdb 1.5.2 2026-04-13 [1] CRAN (R 4.5.2)
dygraphs 1.1.1.6 2018-07-11 [1] CRAN (R 4.5.0)
e1071 1.7-17 2025-12-18 [1] CRAN (R 4.5.2)
ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.5.0)
evaluate 1.0.5 2025-08-27 [1] CRAN (R 4.5.0)
farver 2.1.2 2024-05-13 [1] CRAN (R 4.5.0)
fastmap 1.2.0 2024-05-15 [1] CRAN (R 4.5.0)
fs * 2.1.0 2026-04-18 [1] CRAN (R 4.5.2)
fuzzyjoin 0.1.8 2026-02-20 [1] CRAN (R 4.5.2)
gargle 1.6.1 2026-01-29 [1] CRAN (R 4.5.2)
generics 0.1.4 2025-05-09 [1] CRAN (R 4.5.0)
geojsonsf 2.0.5 2025-11-26 [1] CRAN (R 4.5.2)
ggplot2 4.0.3 2026-04-22 [1] CRAN (R 4.5.2)
glue * 1.8.1 2026-04-17 [1] CRAN (R 4.5.2)
googleAuthR 2.0.2.1 2026-01-09 [1] CRAN (R 4.5.2)
googleCloudStorageR 0.7.0 2021-12-16 [1] CRAN (R 4.5.0)
googledrive 2.1.2 2025-09-10 [1] CRAN (R 4.5.0)
gtable 0.3.6 2024-10-25 [1] CRAN (R 4.5.0)
here * 1.0.2 2025-09-15 [1] CRAN (R 4.5.0)
highcharter 0.9.5 2026-04-22 [1] CRAN (R 4.5.2)
hms 1.1.4 2025-10-17 [1] CRAN (R 4.5.0)
htmltools 0.5.9 2025-12-04 [1] CRAN (R 4.5.2)
htmlwidgets 1.6.4 2023-12-06 [1] CRAN (R 4.5.0)
httpuv 1.6.17 2026-03-18 [1] CRAN (R 4.5.2)
httr 1.4.8 2026-02-13 [1] CRAN (R 4.5.2)
httr2 1.2.2 2025-12-08 [1] CRAN (R 4.5.2)
igraph 2.3.2 2026-05-29 [1] CRAN (R 4.5.2)
isoband 0.3.0 2025-12-07 [1] CRAN (R 4.5.2)
janitor * 2.2.1 2024-12-22 [1] CRAN (R 4.5.0)
jquerylib 0.1.4 2021-04-26 [1] CRAN (R 4.5.0)
jsonlite * 2.0.0 2025-03-27 [1] CRAN (R 4.5.0)
KernSmooth 2.23-26 2025-01-01 [1] CRAN (R 4.5.2)
knitr * 1.51 2025-12-20 [1] CRAN (R 4.5.2)
later 1.4.8 2026-03-05 [1] CRAN (R 4.5.2)
lattice 0.22-9 2026-02-09 [1] CRAN (R 4.5.2)
lazyeval 0.2.3 2026-04-04 [1] CRAN (R 4.5.2)
leafem 0.2.5 2025-08-28 [1] CRAN (R 4.5.0)
leaflet 2.2.3 2025-09-04 [1] CRAN (R 4.5.0)
librarian 1.8.1 2021-07-12 [1] CRAN (R 4.5.0)
lifecycle 1.0.5 2026-01-08 [1] CRAN (R 4.5.2)
lubridate * 1.9.5 2026-02-04 [1] CRAN (R 4.5.2)
magrittr 2.0.5 2026-04-04 [1] CRAN (R 4.5.2)
mapgl 0.5.0 2026-06-24 [1] Github (bbest/mapgl@1e52f60)
mapview 2.11.4 2025-09-08 [1] CRAN (R 4.5.0)
markdown 2.0 2025-03-23 [1] CRAN (R 4.5.0)
Matrix 1.7-5 2026-03-21 [1] CRAN (R 4.5.2)
memoise 2.0.1 2021-11-26 [1] CRAN (R 4.5.0)
mgcv 1.9-4 2025-11-07 [1] CRAN (R 4.5.0)
mime 0.13 2025-03-17 [1] CRAN (R 4.5.0)
nlme 3.1-169 2026-03-27 [1] CRAN (R 4.5.2)
openssl 2.4.1 2026-05-14 [1] CRAN (R 4.5.2)
otel 0.2.0 2025-08-29 [1] CRAN (R 4.5.0)
pillar 1.11.1 2025-09-17 [1] CRAN (R 4.5.0)
pkgbuild 1.4.8 2025-05-26 [1] CRAN (R 4.5.0)
pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.5.0)
pkgload 1.5.1 2026-04-01 [1] CRAN (R 4.5.2)
plotly 4.12.0 2026-01-24 [1] CRAN (R 4.5.2)
png 0.1-9 2026-03-15 [1] CRAN (R 4.5.2)
promises 1.5.0 2025-11-01 [1] CRAN (R 4.5.0)
proxy 0.4-29 2025-12-29 [1] CRAN (R 4.5.2)
purrr * 1.2.2 2026-04-10 [1] CRAN (R 4.5.2)
quantmod 0.4.28 2025-06-19 [1] CRAN (R 4.5.0)
R6 2.6.1 2025-02-15 [1] CRAN (R 4.5.0)
rappdirs 0.3.4 2026-01-17 [1] CRAN (R 4.5.2)
raster 3.6-32 2025-03-28 [1] CRAN (R 4.5.0)
RColorBrewer 1.1-3 2022-04-03 [1] CRAN (R 4.5.0)
Rcpp 1.1.1-1.1 2026-04-24 [1] CRAN (R 4.5.2)
readr * 2.2.0 2026-02-19 [1] CRAN (R 4.5.2)
rlang 1.2.0 2026-04-06 [1] CRAN (R 4.5.2)
rlist 0.4.6.2 2021-09-03 [1] CRAN (R 4.5.0)
rmarkdown 2.31 2026-03-26 [1] CRAN (R 4.5.2)
rnaturalearth 1.2.0 2026-01-19 [1] CRAN (R 4.5.2)
rnaturalearthhires 1.0.0.9000 2025-10-02 [1] Github (ropensci/rnaturalearthhires@e4736f6)
RPostgres 1.4.10 2026-02-16 [1] CRAN (R 4.5.2)
rprojroot 2.1.1 2025-08-26 [1] CRAN (R 4.5.0)
rstudioapi 0.18.0 2026-01-16 [1] CRAN (R 4.5.2)
S7 0.2.2 2026-04-22 [1] CRAN (R 4.5.2)
sass 0.4.10 2025-04-11 [1] CRAN (R 4.5.0)
satellite 1.0.6 2025-08-21 [1] CRAN (R 4.5.0)
scales 1.4.0 2025-04-24 [1] CRAN (R 4.5.0)
sessioninfo 1.2.3 2025-02-05 [1] CRAN (R 4.5.0)
sf * 1.1-1 2026-05-06 [1] CRAN (R 4.5.2)
shiny 1.14.0 2026-06-21 [1] CRAN (R 4.5.2)
shinyWidgets 0.9.1 2026-03-09 [1] CRAN (R 4.5.2)
snakecase 0.11.1 2023-08-27 [1] CRAN (R 4.5.0)
sp 2.2-1 2026-02-13 [1] CRAN (R 4.5.2)
stars 0.7-2 2026-04-03 [1] CRAN (R 4.5.2)
stringi 1.8.7 2025-03-27 [1] CRAN (R 4.5.0)
stringr * 1.6.0 2025-11-04 [1] CRAN (R 4.5.0)
terra 1.9-34 2026-06-19 [1] CRAN (R 4.5.2)
tibble * 3.3.1 2026-01-11 [1] CRAN (R 4.5.2)
tidyr * 1.3.2 2025-12-19 [1] CRAN (R 4.5.2)
tidyselect 1.2.1 2024-03-11 [1] CRAN (R 4.5.0)
timechange 0.4.0 2026-01-29 [1] CRAN (R 4.5.2)
TTR 0.24.4 2023-11-28 [1] CRAN (R 4.5.0)
tzdb 0.5.0 2025-03-15 [1] CRAN (R 4.5.0)
units * 1.0-1 2026-03-11 [1] CRAN (R 4.5.2)
usethis 3.2.1 2025-09-06 [1] CRAN (R 4.5.0)
utf8 1.2.6 2025-06-08 [1] CRAN (R 4.5.0)
uuid 1.2-2 2026-01-23 [1] CRAN (R 4.5.2)
V8 8.2.0 2026-04-21 [1] CRAN (R 4.5.2)
vctrs 0.7.3 2026-04-11 [1] CRAN (R 4.5.2)
viridisLite 0.4.3 2026-02-04 [1] CRAN (R 4.5.2)
visNetwork 2.1.4 2025-09-04 [1] CRAN (R 4.5.0)
vroom 1.7.1 2026-03-31 [1] CRAN (R 4.5.2)
withr 3.0.3 2026-06-19 [1] CRAN (R 4.5.2)
xfun 0.59 2026-06-19 [1] CRAN (R 4.5.2)
xtable 1.8-8 2026-02-22 [1] CRAN (R 4.5.2)
xts 0.14.2 2026-02-28 [1] CRAN (R 4.5.2)
yaml 2.3.12 2025-12-10 [1] CRAN (R 4.5.2)
zip 2.3.3 2025-05-13 [1] CRAN (R 4.5.0)
zoo 1.8-15 2025-12-15 [1] CRAN (R 4.5.2)
[1] /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/library
* ── Packages attached to the search path.
P ── Loaded and on-disk path mismatch.
──────────────────────────────────────────────────────────────────────────────