Source: ZooScan — the SIO Ocean Informatics ZooScan database (Mark Ohman Lab; interface by Marina Frants). The portal’s bulk download is disabled, so libs/download_zooscan.R scripts its public login + PRPOOS plot CGI (/cgi-bin/tssubplot_new.py), whose returned Plotly page embeds the underlying per-station values as a data:text/csv URI. Each bioclass is fetched in both plot modes and consolidated to zooscan_prpoos.csv (see the Acquire step + by_taxon/_PROVENANCE.md).
cce-lter (Ohman Lab / CCE-LTER; sibling of cce-lter_zoodb and cce-lter_euphausiids).measurement_value = 0 means imaged-but-absent.zooscan_abundance (No./m²), zooscan_biomass_carbon (mg C/m²), zooscan_feret_diameter (mm), zooscan_carbon_individual (µg C).graph LR
A[zooscan_prpoos.csv<br/>taxon x sample rows] --> B[zooscan_sample<br/>station tows + keys]
A --> C[zooscan_measurement<br/>long: abundance, biomass, feret, indiv-C]
A --> D[zooscan_taxon<br/>23 bioclasses + 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, overwrite_all, dir_data
cc <- read_calcofi_meta(here("ingest_cce-lter_zooscan.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)libs/download_zooscan.R scripts the ZooScan portal (public login + PRPOOS plot CGI) for the 23 bioclasses × 2 plot modes and consolidates the embedded per-station CSVs into zooscan_prpoos.csv. It runs once then caches; set overwrite_all = TRUE in libs/ingest.R to re-scrape.
source(here("libs/download_zooscan.R"))
zs_dir <- path_expand(glue("{dir_data}/cce-lter/ZooScan"))
zs_csv <- download_zooscan(zs_dir, overwrite = overwrite_all)ZooScan: using cached zooscan_prpoos.csv The cruise code is YYYYMM + a 2-letter ship code (e.g. 200507NH = Jul 2005, R/V New Horizon). We parse year/month/ship_key from it, build site_key from line + station, and a sample_key per station tow.
zs_dir <- path_expand(glue("{dir_data}/cce-lter/ZooScan"))
# archive source (consolidated + per-taxon extracts) to GCS for provenance
sync_to_gcs(
local_dir = zs_dir,
gcs_prefix = glue("archive/{provider}/{dataset}"),
bucket = "calcofi-files-public",
exclude = c(".DS_Store", "*.tmp", "*.gdoc"))# A tibble: 49 × 4
file action size reason
<chr> <chr> <dbl> <chr>
1 by_taxon/_manifest.csv uploaded 1632 new file
2 by_taxon/_PROVENANCE.md uploaded 1909 new file
3 by_taxon/appendicularia__basic.csv uploaded 135968 new file
4 by_taxon/appendicularia__extra.csv uploaded 130227 new file
5 by_taxon/bryozoan_larvae__basic.csv uploaded 131162 new file
6 by_taxon/bryozoan_larvae__extra.csv uploaded 128057 new file
7 by_taxon/chaetognatha__basic.csv uploaded 136459 new file
8 by_taxon/chaetognatha__extra.csv uploaded 131599 new file
9 by_taxon/cnidaria_ctenophores__basic.csv uploaded 135265 new file
10 by_taxon/cnidaria_ctenophores__extra.csv uploaded 131408 new file
# ℹ 39 more rowsd_raw <- read_csv(zs_csv)
num <- function(x) suppressWarnings(as.numeric(trimws(as.character(x))))
d <- d_raw |>
transmute(
taxon_slug = taxon,
cruise_orig = trimws(cruise),
line = num(line),
station = num(station),
latitude = num(latitude),
longitude = num(longitude),
max_depth_m = num(max_depth_m),
min_depth_m = num(min_depth_m),
cruise_mid_date = as.Date(cruise_mid_date),
station_date = as.Date(station_date),
local_time_pst = trimws(local_time_pst),
day_night = trimws(day_night),
abundance_per_m2 = num(abundance_per_m2),
biomass_mgC_per_m2 = num(biomass_mgC_per_m2),
feret_diameter_mm = num(feret_diameter_mm),
carbon_content_indiv = num(carbon_content_indiv)) |>
mutate(
# ZooScan reports longitude as positive degrees West — flip to negative (°E)
longitude = -abs(longitude),
year = as.integer(str_sub(cruise_orig, 1, 4)),
month = as.integer(str_sub(cruise_orig, 5, 6)),
ship_key = str_sub(cruise_orig, 7),
site_key = if_else(
is.na(line) | is.na(station), NA_character_,
sprintf("%05.1f %05.1f", line, station)),
datetime_local_pst = suppressWarnings(
ymd_hm(paste(station_date, local_time_pst), quiet = TRUE)),
sample_key = paste(cruise_orig, line, station, station_date, sep = "|"))
cat(glue(
"Read {format(nrow(d), big.mark=',')} (sample x taxon) rows; ",
"{n_distinct(d$sample_key)} samples; {n_distinct(d$taxon_slug)} taxa; ",
"lines {paste(sort(unique(d$line)), collapse='/')}; ",
"{min(d$year)}-{max(d$year)}"), "\n")Read 34,109 (sample x taxon) rows; 1483 samples; 23 taxa; lines 80/87/90; 2005-2026 zooscan_taxon is the 23 bioclasses with WoRMS AphiaIDs + classification, resolved offline and cached in metadata/cce-lter/zooscan/taxon_worms.csv (19/23 matched; 4 non-taxonomic operational classes, Q03).
d_worms <- read_csv(here("metadata/cce-lter/zooscan/taxon_worms.csv"))
zooscan_taxon <- d_worms |>
arrange(taxon_slug) |>
transmute(
taxon_id = row_number(),
taxon_zooscan, taxon_slug,
aphia_id = as.integer(aphia_id),
scientific_name = scientific_name_accepted,
rank, taxon_status = status, taxon_note,
kingdom, phylum, class, order_taxon = order, family)
stopifnot("taxon_slug mismatch source vs WoRMS map" =
all(unique(d$taxon_slug) %in% zooscan_taxon$taxon_slug))
dbWriteTable(con, "zooscan_taxon", zooscan_taxon, overwrite = TRUE)
cat(glue("zooscan_taxon: {nrow(zooscan_taxon)} bioclasses ",
"({sum(!is.na(zooscan_taxon$aphia_id))} WoRMS-matched, ",
"{sum(zooscan_taxon$taxon_status=='non-taxonomic')} non-taxonomic [Q03])"), "\n")zooscan_taxon: 23 bioclasses (19 WoRMS-matched, 4 non-taxonomic [Q03]) One row per distinct station tow. ship_key (the cruise-code suffix) joins the shared ship registry for ship_nodc/ship_name; cruise_key is YYYY-MM-{ship_nodc} validated against the cruise registry.
# position/depth are sample properties, but the per-class plot CGI leaves them
# NA on some taxa's rows (e.g. gelatinous classes) — recover the (identical)
# non-NA value per sample; identity columns are constant within sample_key
d_sample0 <- d |>
group_by(sample_key) |>
summarize(
cruise_orig = first(cruise_orig), year = first(year), month = first(month),
ship_key = first(ship_key), line = first(line), station = first(station),
site_key = first(site_key), cruise_mid_date = first(cruise_mid_date),
station_date = first(station_date), local_time_pst = first(local_time_pst),
day_night = first(day_night), datetime_local_pst = first(datetime_local_pst),
latitude = mean(latitude, na.rm = TRUE),
longitude = mean(longitude, na.rm = TRUE),
max_depth_m = mean(max_depth_m, na.rm = TRUE),
min_depth_m = mean(min_depth_m, na.rm = TRUE),
.groups = "drop")
stopifnot("sample_key not unique over sample attributes" =
n_distinct(d_sample0$sample_key) == nrow(d_sample0))
load_prior_tables(
con, parquet_dir = here("data/parquet/swfsc_ichthyo"),
tables = c("ship", "cruise", "grid"), geom_tables = c("grid"), as_view = TRUE)# A tibble: 3 × 3
table rows has_geom
<chr> <dbl> <lgl>
1 cruise 691 FALSE
2 grid 218 TRUE
3 ship 48 FALSE d_ship <- dbGetQuery(con, "SELECT ship_key, ship_nodc, ship_name FROM ship")
valid_ck <- dbGetQuery(con, "SELECT DISTINCT cruise_key FROM cruise")$cruise_key
d_sample <- d_sample0 |>
left_join(d_ship, by = "ship_key") |>
mutate(
cruise_key = if_else(
is.na(ship_nodc), NA_character_,
sprintf("%04d-%02d-%s", year, month, ship_nodc)),
cruise_key = if_else(cruise_key %in% valid_ck, cruise_key, NA_character_)) |>
arrange(cruise_orig, line, station, station_date) |>
mutate(sample_id = row_number(), .before = 1)
sample_map <- d_sample |> select(sample_id, sample_key)
zooscan_sample <- d_sample |>
select(sample_id, cruise_orig, cruise_key, ship_key, ship_name,
line, station, site_key, latitude, longitude,
max_depth_m, min_depth_m, cruise_mid_date, station_date,
local_time_pst, datetime_local_pst, day_night)
dbWriteTable(con, "zooscan_sample", zooscan_sample, overwrite = TRUE)
n_ship <- sum(!is.na(d_sample$ship_name))
n_cruise <- sum(!is.na(d_sample$cruise_key))
cat(glue(
"zooscan_sample: {nrow(zooscan_sample)} samples; ",
"ship {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")zooscan_sample: 1483 samples; ship 1483/1483 (100%), cruise_key 1043/1483 (70.3%) add_point_geom(con, "zooscan_sample", lon_col = "longitude", lat_col = "latitude")
grid_stats <- assign_grid_key(con, "zooscan_sample")
grid_stats |> datatable(caption = "Grid assignment")The four ZooScan metrics pivot into long form, joined to sample_id (via sample_key) and taxon_id (via taxon_slug). Explicit zeros (imaged-but- absent) are retained; only NA / non-finite values are dropped.
meas_recode <- c(
abundance_per_m2 = "zooscan_abundance",
biomass_mgC_per_m2 = "zooscan_biomass_carbon",
feret_diameter_mm = "zooscan_feret_diameter",
carbon_content_indiv = "zooscan_carbon_individual")
zooscan_measurement <- d |>
left_join(sample_map, by = "sample_key") |>
left_join(zooscan_taxon |> select(taxon_id, taxon_slug), by = "taxon_slug") |>
select(sample_id, taxon_id, all_of(names(meas_recode))) |>
pivot_longer(cols = all_of(names(meas_recode)),
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, "zooscan_measurement", zooscan_measurement, overwrite = TRUE)
cat(glue(
"zooscan_measurement: {format(nrow(zooscan_measurement), big.mark=',')} rows ",
"({format(sum(zooscan_measurement$measurement_value==0), big.mark=',')} explicit zeros)"), "\n")zooscan_measurement: 126,692 rows (9,769 explicit zeros) zs_types <- tibble(
measurement_type = c("zooscan_abundance", "zooscan_biomass_carbon",
"zooscan_feret_diameter", "zooscan_carbon_individual"),
description = c(
"Zooplankton areal abundance from ZooScan optical imaging, by image-classified bioclass.",
"Estimated zooplankton carbon biomass from ZooScan optical imaging, by bioclass.",
"Mean Feret diameter (organism size) from ZooScan optical imaging, by bioclass.",
"Mean individual carbon content from ZooScan optical imaging, by bioclass."),
units = c("count/m2", "mgC/m2", "mm", "ugC"),
is_canonical = c(TRUE, TRUE, NA, NA),
`_source_column` = c("abundance_per_m2", "biomass_mgC_per_m2",
"feret_diameter_mm", "carbon_content_indiv"),
`_source_table` = "zooscan_measurement",
`_source_datasets` = "cce-lter_zooscan",
`_qual_column` = NA_character_, `_prec_column` = NA_character_)
new_types <- zs_types |> filter(!measurement_type %in% d_meas_type$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$measurement_type, collapse=', ')}"), "\n")
} else cat("zooscan measurement types already registered\n")zooscan measurement types already registereddbWriteTable(con, "measurement_type", d_meas_type, overwrite = TRUE)d_dataset <- ingest_yaml_to_dataset_df(read_ingest_yaml(here()))
dbWriteTable(con, "dataset", d_dataset, overwrite = TRUE)
cat(glue("dataset: {nrow(d_dataset)} dataset(s) registered"), "\n")dataset: 12 dataset(s) registered zooscan_rels <- list(
primary_keys = list(
zooscan_sample = "sample_id",
zooscan_measurement = "measurement_id",
zooscan_taxon = "taxon_id",
measurement_type = "measurement_type"),
foreign_keys = list(
list(table = "zooscan_measurement", column = "sample_id",
ref_table = "zooscan_sample", ref_column = "sample_id"),
list(table = "zooscan_measurement", column = "taxon_id",
ref_table = "zooscan_taxon", ref_column = "taxon_id"),
list(table = "zooscan_measurement", column = "measurement_type",
ref_table = "measurement_type", ref_column = "measurement_type")))
cc_erd(
con,
tables = c("zooscan_sample", "zooscan_measurement", "zooscan_taxon",
"measurement_type", "dataset"),
rels = zooscan_rels,
colors = list(
lightblue = c("zooscan_sample", "zooscan_measurement"),
lightgreen = "zooscan_taxon",
lightyellow = "measurement_type",
white = "dataset"))
build_relationships_json(
rels = zooscan_rels, output_dir = dir_parquet,
provider = provider, dataset = dataset)[1] "/Users/bbest/Github/CalCOFI/workflows/data/parquet/cce-lter_zooscan/relationships.json"results <- validate_for_release(con, checks = "all", strict = FALSE)
cat("Validation:", ifelse(results$passed, "PASSED", "FAILED"), "\n")Validation: FAILED if (length(results$errors) > 0)
cat("Errors:\n", paste("-", results$errors, collapse = "\n"), "\n")Errors:
- Table 'zooscan_sample' has 440 NULL values in required column 'cruise_key'
- Table 'zooscan_taxon' has 4 NULL values in required column 'aphia_id' # NULLs in cross-dataset keys (cruise_key for cruises absent from the registry)
# are the EXPECTED unmatched remainder (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 zooscan_sample")
cat(glue("Match coverage: ship_key {round(100*cov$ship,1)}%, ",
"cruise_key {round(100*cov$cruise,1)}%, grid_key {round(100*cov$grid,1)}%"), "\n")Match coverage: ship_key 100%, cruise_key 70.3%, grid_key 100% orphan <- dbGetQuery(con,
"SELECT SUM(CASE WHEN s.sample_id IS NULL THEN 1 ELSE 0 END) AS no_sample,
SUM(CASE WHEN t.taxon_id IS NULL THEN 1 ELSE 0 END) AS no_taxon
FROM zooscan_measurement m
LEFT JOIN zooscan_sample s USING (sample_id)
LEFT JOIN zooscan_taxon t USING (taxon_id)")
cat(glue("Orphan measurements: {orphan$no_sample} w/o sample, {orphan$no_taxon} w/o taxon"), "\n")Orphan measurements: 0 w/o sample, 0 w/o taxon samp_cols <- dbGetQuery(con,
"SELECT column_name FROM information_schema.columns
WHERE table_name='zooscan_sample' AND data_type NOT LIKE 'GEOMETRY%'")$column_name
dbGetQuery(con, glue("SELECT {paste(samp_cols, collapse=', ')} FROM zooscan_sample LIMIT 100")) |>
datatable(caption = "zooscan_sample — first 100 rows", rownames = FALSE, filter = "top")dbGetQuery(con,
"SELECT m.measurement_id, m.sample_id, x.taxon_zooscan, m.measurement_type, m.measurement_value
FROM zooscan_measurement m JOIN zooscan_taxon x USING (taxon_id)
ORDER BY m.measurement_id LIMIT 100") |>
datatable(caption = "zooscan_measurement — first 100 rows", rownames = FALSE)dbGetQuery(con,
"SELECT t.taxon_id, t.taxon_zooscan, t.scientific_name, t.aphia_id, t.rank,
t.taxon_note, COUNT(DISTINCT m.sample_id) AS n_samples
FROM zooscan_taxon t LEFT JOIN zooscan_measurement m USING (taxon_id)
GROUP BY ALL ORDER BY t.taxon_id") |>
datatable(caption = "zooscan_taxon — 23 bioclasses", 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, "zooscan_sample"))
parquet_stats <- write_parquet_outputs(
con = con,
output_dir = dir_parquet,
tables = c("zooscan_sample", "zooscan_measurement", "zooscan_taxon",
"measurement_type", "dataset"),
sort_by = list(zooscan_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/zooscan/tbls_redefine.csv"))
d_flds_rd <- read_csv(here("metadata/cce-lter/zooscan/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/zooscan/metadata_derived.csv"),
output_dir = dir_parquet,
tables = c("zooscan_sample", "zooscan_measurement", "zooscan_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 new file
2 manifest.json uploaded 2721 new file
3 measurement_type.parquet uploaded 5793 new file
4 metadata.json uploaded 7922 new file
5 relationships.json uploaded 745 new file
6 zooscan_measurement.parquet uploaded 667710 new file
7 zooscan_sample.parquet uploaded 15234 new file
8 zooscan_taxon.parquet uploaded 3820 new fileFollow-up questions for CCE-LTER (Mark Ohman, Marina Frants), tracked in metadata/cce-lter/zooscan/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_zooscan 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)
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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)
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googledrive 2.1.2 2025-09-10 [1] CRAN (R 4.5.0)
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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)
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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)
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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)
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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)
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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)
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purrr * 1.2.2 2026-04-10 [1] CRAN (R 4.5.2)
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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)
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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)
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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)
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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.
──────────────────────────────────────────────────────────────────────────────