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api.R
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suppressPackageStartupMessages({
library(DBI, quietly = TRUE)
library(dplyr, quietly = TRUE)
library(finbif, quietly = TRUE)
library(future, quietly = TRUE)
library(janitor, quietly = TRUE)
library(maps, quietly = TRUE)
library(promises, quietly = TRUE)
library(RPostgres, quietly = TRUE)
library(sf, quietly = TRUE)
library(tidyr, quietly = TRUE)
library(shiny.i18n, quietly = TRUE)
})
plan(multisession, workers = 2)
options(
finbif_use_cache = 24,
finbif_use_cache_metadata = 24,
finbif_timeout_offset = .1,
finbif_hide_progress = TRUE,
finbif_rate_limit = Inf,
finbif_max_page_size = 3000L,
finbif_use_async = FALSE
)
op <- options()
sanitise <- function(x) {
switch(x, `NULL` = NULL, x)
}
sanitise_lang <- function(lang) {
switch(lang, fi = "fi", "en")
}
get_children <- function(x, y = character(), cols) {
is_part_of <- cols$is_part_of
children <- cols[is_part_of %in% x & !is.na(is_part_of), "id"]
y <- c(y, children)
has_children <- cols[children, "has_children"]
if (!any(has_children, na.rm = TRUE)) {
y
} else {
get_children(children[has_children], y, cols)
}
}
collection_size <- function(x, cols) {
row <- cols[x, ]
size <- row[["collection_size"]]
if (is.na(size)) {
size
} else {
has_children <- row[["has_children"]]
if (!has_children) {
size
} else {
is_part_of <- cols[["is_part_of"]]
children <- get_children(x, cols = cols)
children_size <- cols[children, "collection_size"]
if (!all(is.na(children_size))) {
NA_integer_
} else {
size
}
}
}
}
has_specimens <- function(x, cols) {
row <- cols[x, ]
size <- row[["n_records"]]
if (!is.na(size)) {
TRUE
} else {
has_children <- isTRUE(row[["has_children"]])
if (!has_children) {
FALSE
} else {
is_part_of <- cols[["is_part_of"]]
children <- get_children(x, cols = cols)
children_size <- cols[children, "n_records"]
if (all(is.na(children_size))) {
FALSE
} else {
TRUE
}
}
}
}
child_is <- function(x, which, cols) {
children <- get_children(x, cols = cols)
lgl <- cols[children, which]
any(lgl)
}
isbotany <- c(
"fung", "phyt", "botan", "mycota", "lichen", "agaric", "phyll","mycetes",
"inales", "bacteria", "herbari", "algae", "virus", "vascular plant",
"kastikka"
)
iszoology <- c(
"ptera", "animal", "vertebrat", "nymph", "bird", "crustacea", "mammal",
"mollusc", "fish", "reptil", "zoolog", "oidea", "idae", "insect",
"arachnid", "squirrel", "chaoboridae", "skeleton", "butterfl", "zmut",
"aves"
)
isgeology <- c("the geological collections", "fossil ", "fossils ")
text <- c("long_name", "description", "methods", "taxonomic_coverage")
source("collections.R")
translator <- Translator$new(translation_json_path = "translation.json")
if (!dir.exists("logs")) dir.create("logs")
#* @get /healthz
#* @head /healthz
#* @serializer unboxedJSON
function() {
""
}
#----species-count----
#* @get /species-count
#* @serializer rds
function(restriction = "NULL", taxa = "NULL", source = "NULL") {
filter <- list()
filter[["restricted"]] <- sanitise(restriction)
filter[["informal_groups"]] <- sanitise(taxa)
filter[["collection"]] <- sanitise(source)
future_promise({
options(op)
db <- dbConnect(Postgres(), dbname = Sys.getenv("DB_NAME"))
options(finbif_cache_path = db)
ans <- fb_occurrence(
filter = filter,
select = "species_scientific_name",
aggregate = "records",
count_only = TRUE
)
dbDisconnect(db)
ans
}, seed = TRUE)
}
#----collection-count----
#* @get /collection-count
#* @serializer rds
function(restriction = "NULL", taxa = "NULL", source = "NULL") {
filter <- list()
filter[["restricted"]] <- sanitise(restriction)
filter[["informal_groups"]] <- sanitise(taxa)
filter[["collection"]] <- sanitise(source)
future_promise({
options(op)
db <- dbConnect(Postgres(), dbname = Sys.getenv("DB_NAME"))
options(finbif_cache_path = db)
ans <- fb_occurrence(
filter = filter,
select = "collection_id",
aggregate = "records",
count_only = TRUE
)
dbDisconnect(db)
ans
}, seed = TRUE)
}
#----quality-table----
#* @get /quality-table
#* @serializer rds
function(
collection_quality = "NULL",
restriction = "NULL",
taxa = "NULL",
source = "NULL",
lang = "en"
) {
filter <- list(exclude_missing_levels = FALSE)
filter[["restricted"]] <- sanitise(restriction)
filter[["informal_groups"]] <- sanitise(taxa)
filter[["collection"]] <- sanitise(source)
filter[["collection_quality"]] <- sanitise(collection_quality)
lang <- sanitise_lang(lang)
future_promise({
options(op)
db <- dbConnect(Postgres(), dbname = Sys.getenv("DB_NAME"))
options(finbif_cache_path = db)
translator$set_translation_language(lang)
levels = c(
"Expert verified",
"Community verified",
"Unassessed",
"Uncertain",
"Erroneous"
)
ans <-
fb_occurrence(
filter = filter,
select = "record_quality",
aggregate = "records",
n = "all"
) |>
mutate(record_quality = replace_na(record_quality, "Unassessed")) |>
group_by(record_quality) |>
summarise(n_records = sum(n_records), .groups = "drop") |>
mutate(
record_quality = factor(
record_quality,
levels = levels,
labels = translator$t(levels),
ordered = TRUE
)
) |>
arrange(record_quality) |>
adorn_totals(name = translator$t("Total"))
names(ans) <- translator$t(c("Verification Status", "Number of Records"))
dbDisconnect(db)
ans
}, seed = TRUE)
}
#----occurrence-plot----
#* @get /occurrence-plot
#* @serializer rds
function(restriction = "NULL", taxa = "NULL", source = "NULL", lang = "en") {
filter <- list(exclude_missing_levels = FALSE)
filter[["restricted"]] <- sanitise(restriction)
filter[["informal_groups"]] <- sanitise(taxa)
filter[["collection"]] <- sanitise(source)
lang <- sanitise_lang(lang)
future_promise({
options(op)
db <- dbConnect(Postgres(), dbname = Sys.getenv("DB_NAME"))
options(finbif_cache_path = db)
translator$set_translation_language(lang)
ans <-
fb_occurrence(
filter = filter,
select = c(Date = "first_load_date", Type = "superrecord_basis"),
aggregate = "records",
n = "all"
) |>
mutate(
Type = case_match(
Type,
"Specimen" ~ translator$t("Specimens"),
.default = translator$t("Observations")
),
Date = replace_na(Date, min(Date, na.rm = TRUE))
) |>
group_by(Type, Date) |>
summarise(Records = sum(n_records), .groups = "drop_last") |>
arrange(Date) |>
na.omit() |>
mutate(Records = cumsum(Records))
if (nrow(ans) < 1L) {
ans <- data.frame(
Type = translator$t(c("Observations", "Specimens")),
Date = as.character(Sys.Date()),
Records = 0
)
}
dbDisconnect(db)
names(ans) <- translator$t(names(ans))
ans
}, seed = TRUE)
}
#----species-plot----
#* @get /species-plot
#* @serializer rds
function(restriction = "NULL", taxa = "NULL", source = "NULL", lang = "en") {
filter <- list()
filter[["restricted"]] <- sanitise(restriction)
filter[["informal_groups"]] <- sanitise(taxa)
filter[["collection"]] <- sanitise(source)
lang <- sanitise_lang(lang)
future_promise({
options(op)
db <- dbConnect(Postgres(), dbname = Sys.getenv("DB_NAME"))
options(finbif_cache_path = db)
translator$set_translation_language(lang)
ans <-
fb_occurrence(
filter = filter, select = c(Species = "species_scientific_name"),
aggregate = "records", n = 10L
) |>
arrange(n_records) |>
mutate(Species = sprintf("<i>%s</i>", Species)) |>
mutate(Species = factor(Species, levels = Species, ordered = TRUE)) |>
rename(Records = n_records)
dbDisconnect(db)
names(ans) <- translator$t(names(ans))
ans
}, seed = TRUE)
}
#----annotations-plot----
#* @get /annotations-plot
#* @serializer rds
function(restriction = "NULL", taxa = "NULL", source = "NULL", lang = "en") {
filter <- list(annotated = TRUE)
filter[["restricted"]] <- sanitise(restriction)
filter[["informal_groups"]] <- sanitise(taxa)
filter[["collection"]] <- sanitise(source)
n <- 500L
lang <- sanitise_lang(lang)
future_promise({
options(op)
db <- dbConnect(Postgres(), dbname = Sys.getenv("DB_NAME"))
options(finbif_cache_path = db)
translator$set_translation_language(lang)
n_annotations <- fb_occurrence(
filter = filter,
select = "record_annotation_created",
sample = TRUE,
n = n
)
Date <- n_annotations[["record_annotation_created"]]
Date <- unlist(Date)
Date <- sort(Date)
if (is.null(Date)) {
Date <- Sys.Date()
Date <- as.character(Date)
Annotations <- 0
} else {
total_annotations <- attr(n_annotations, "nrec_avl")
Annotations <- length(Date)
mult <- total_annotations / n
Annotations <- seq_len(Annotations)
Annotations <- as.integer(Annotations * mult)
}
dbDisconnect(db)
ans <- data.frame(Date = Date, Annotations = Annotations)
names(ans) <- translator$t(names(ans))
ans
}, seed = TRUE)
}
#----datasets-plot----
#* @get /datasets-plot
#* @serializer rds
function(restriction = "NULL", taxa = "NULL", source = "NULL", lang = "en") {
filter <- list(subcollections = FALSE)
filter[["restricted"]] <- sanitise(restriction)
filter[["informal_groups"]] <- sanitise(taxa)
collection_source <- sanitise(source)
lang <- sanitise_lang(lang)
future_promise({
options(op)
db <- dbConnect(Postgres(), dbname = Sys.getenv("DB_NAME"))
options(finbif_cache_path = db)
translator$set_translation_language(lang)
cols <- fb_collections(
select = c(id, has_children, is_part_of),
supercollection = TRUE,
nmin = NA
)
filter[["collection"]] <- NULL
if (!is.null(collection_source)) {
filter[["collection"]] <- get_children(collection_source, cols = cols)
}
ans <-
fb_occurrence(
filter = filter,
select = c(
Type = "superrecord_basis",
Dataset = "collection",
id = "collection_id"
),
aggregate = "records",
n = "all",
locale = lang
) |>
mutate(
Type = case_match(
Type,
"Specimen" ~ translator$t("Specimens"),
"Näyte" ~ translator$t("Specimens"),
.default = translator$t("Observations")
),
Dataset = ifelse(
nchar(Dataset) > 23L,
paste0(trimws(substr(Dataset, 1L, 24L)), "\u2026"),
Dataset
),
Dataset = paste0(
Dataset, " (", sub("http://tun.fi/", "", id), ")"
),
id = NULL
) |>
group_by(Type, Dataset) |>
summarise(across(n_records, sum), .groups = "keep") |>
ungroup() |>
complete(Type, Dataset, fill = list(n_records= 0L)) |>
group_by(Dataset) |>
mutate(total_records = sum(n_records)) |>
arrange(-total_records, Dataset) |>
select(!total_records)
if (nrow(ans) > 50L) {
ans <-
ans |>
split(cummax(rep(0:1, each = 48L, length.out = nrow(ans))))
ans[[2L]] <-
ans[[2L]] |>
group_by(Type) |>
summarise(Dataset = translator$t("Other"), across(n_records, sum))
ans <- do.call(rbind, ans)
}
levels <-
filter(ans, Type == translator$t("Observations")) |>
pull(Dataset) |>
rev()
ans <-
mutate(ans, Dataset = factor(Dataset, levels = levels)) |>
rename(Records = n_records)
dbDisconnect(db)
names(ans) <- translator$t(names(ans))
ans
}, seed = TRUE)
}
#----municipality-map----
#* @get /municipality-map
#* @serializer rds
function(restriction = "NULL", taxa = "NULL", source = "NULL") {
filter <- list()
filter[["restricted"]] <- sanitise(restriction)
filter[["informal_groups"]] <- sanitise(taxa)
filter[["collection"]] <- sanitise(source)
future_promise({
options(op)
db <- dbConnect(Postgres(), dbname = Sys.getenv("DB_NAME"))
options(finbif_cache_path = db)
ans <-
fb_occurrence(
filter = filter,
select = c(municipality_name_fi = "finnish_municipality"),
aggregate = "records",
n = "all",
locale = "fi"
)
dbDisconnect(db)
ans
}, seed = TRUE)
}
#----bio-province-map----
#* @get /bio-province-map
#* @serializer rds
function(restriction = "NULL", taxa = "NULL", source = "NULL") {
filter <- list()
filter[["restricted"]] <- sanitise(restriction)
filter[["informal_groups"]] <- sanitise(taxa)
filter[["collection"]] <- sanitise(source)
future_promise({
options(op)
db <- dbConnect(Postgres(), dbname = Sys.getenv("DB_NAME"))
options(finbif_cache_path = db)
ans <-
fb_occurrence(
filter = filter,
select = c(MAAKUNTA_FI = "bio_province"),
aggregate = "records",
n = "all", locale = "fi"
)
dbDisconnect(db)
ans
}, seed = TRUE)
}
#----specimen-collections----
#* @get /specimen-collections
#* @serializer rds
function(
stat = "n_collections",
institution = "NULL",
discipline = "NULL",
lang = "en"
) {
institution <- sanitise(institution)
discipline <- sanitise(discipline)
lang <- sanitise_lang(lang)
future_promise({
options(op)
db <- dbConnect(Postgres(), dbname = Sys.getenv("DB_NAME"))
options(finbif_cache_path = db)
translator$set_translation_language(lang)
cols <- fb_collections(
select = c(
id,
long_name,
has_children,
description,
is_part_of,
methods,
notes,
taxonomic_coverage,
types_size,
owner,
collection_size,
digitized_size,
count
),
supercollection = TRUE,
nmin = NA,
local = lang
)
cols <- transform(cols, digitized_size = as.integer(digitized_size))
cols <- transform(
cols,
collection_size = ifelse(
is.na(collection_size),
round(count / digitized_size * 100),
as.integer(collection_size)
)
)
cols$n_specimens <- vapply(cols$id, collection_size, 0, cols = cols)
specimen_count <- fb_occurrence(
filter = list(superrecord_basis = "specimen", subcollections = FALSE),
select = c(id = "collection_id"),
aggregate = "records",
n = "all"
)
specimen_count[["id"]] <- sub("http://tun.fi/", "", specimen_count[["id"]])
cols <- merge(cols, specimen_count, all.x = TRUE)
rownames(cols) <- cols$id
cols <- subset(cols, vapply(cols$id, has_specimens, NA, cols = cols))
cols <- transform(
cols,
n_specimens_digitised = ifelse(is.na(n_records), 0L, n_records),
n_records = NULL
)
imaged_count <- fb_occurrence(
filter = list(
has_record_images = TRUE, superrecord_basis = "specimen",
subcollections = FALSE
),
select = c(id = "collection_id"),
aggregate = "records",
n = "all"
)
imaged_count[["id"]] <- sub("http://tun.fi/", "", imaged_count[["id"]])
cols <- merge(cols, imaged_count, all.x = TRUE)
cols <- transform(
cols,
n_specimens_imaged = ifelse(is.na(n_records), 0L, n_records),
n_records = NULL
)
rownames(cols) <- cols$id
cols <- transform(cols, prop_spec = n_specimens_digitised / count)
cols <- transform(cols, prop_spec = ifelse(is.nan(prop_spec), 1, prop_spec))
cols <- transform(
cols, n_specimens = ifelse(prop_spec < .5, NA, n_specimens)
)
cols <- transform(
cols,
n_specimens = ifelse(
is.na(n_specimens),
round(n_specimens_digitised / digitized_size * 100),
n_specimens
)
)
cols <- transform(
cols,
n_specimens = ifelse(
is.na(n_specimens) | is.nan(n_specimens),
n_specimens_digitised,
pmax(n_specimens, n_specimens_digitised)
)
)
text <- tolower(do.call(paste, cols[, text]))
cols$botany <- grepl(paste(isbotany, collapse = "|"), text)
cols$zoology <- grepl(paste(iszoology, collapse = "|"), text)
cols$geology <- grepl(paste(isgeology, collapse = "|"), text)
cols$botany <- cols$botany & !cols$zoology & !cols$geology
cols$zoology <- cols$zoology & !cols$botany & !cols$geology
cols <- transform(
cols, botany = botany | vapply(id, child_is, NA, "botany", cols = cols)
)
cols <- transform(
cols, zoology = zoology | vapply(id, child_is, NA, "zoology", cols = cols)
)
cols <- transform(
cols, geology = geology | vapply(id, child_is, NA, "geology", cols = cols)
)
cols <- transform(cols, NULL = TRUE)
if (!is.null(discipline)) {
cols <- filter(cols, .data[[discipline]])
}
if (!is.null(institution)) {
children <- get_children(institution, cols = cols)
cols <- filter(cols, id %in% c(institution, children))
}
cols <- filter(cols, n_specimens > 0L)
n_collections <- nrow(cols)
n_specimens <- pull(cols, n_specimens)
n_specimens <- sum(n_specimens)
n_specimens_digitised <- pull(cols, n_specimens_digitised)
n_specimens_digitised <- sum(n_specimens_digitised)
n_specimens_imaged <- pull(cols, n_specimens_imaged)
n_specimens_imaged <- sum(n_specimens_imaged)
tbl_data <- data.frame(
Collection = character(), Status = character(), n = integer()
)
if (nrow(cols) > 0L) {
tbl_data <-
cols |>
mutate(
Collection = ifelse(
nchar(long_name) > 37L,
paste0(trimws(substr(long_name, 1L, 38L)), "\u2026"),
long_name
),
Collection = paste0(Collection, " (", id, ")")
) |>
arrange(-n_specimens_digitised) |>
mutate(Undigitised = n_specimens - n_specimens_digitised) |>
mutate(`Digitised Only` = n_specimens_digitised - n_specimens_imaged) |>
mutate(Imaged = n_specimens_imaged) |>
select(Collection, Undigitised, `Digitised Only`, Imaged) |>
split(cummax(rep(0:1, each = 24L, length.out = nrow(cols))))
if (length(tbl_data) > 1L) {
tbl_data[[2L]] <- summarise(
tbl_data[[2L]],
Collection = translator$t("Other"),
across(!Collection, sum),
.groups = "drop_last"
)
}
statuses <- c("Imaged", "Digitised Only", "Undigitised")
tbl_data <-
do.call(rbind, tbl_data) |>
mutate(Collection = factor(Collection, levels = rev(Collection))) |>
pivot_longer(!Collection, names_to = "Status", values_to = "n") |>
mutate(
Status = factor(
Status, levels = statuses, labels = translator$t(statuses)
)
)
names(tbl_data) <- translator$t(c("Collection", "Status", "Specimens"))
}
dbDisconnect(db)
switch(
stat,
n_collections = n_collections,
n_specimens = n_specimens,
n_specimens_digitised = n_specimens_digitised,
n_specimens_imaged = n_specimens_imaged,
percent_digitised = round(n_specimens_digitised / n_specimens * 100),
percent_imaged = round(n_specimens_imaged / n_specimens * 100),
table = tbl_data
)
}, seed = TRUE)
}
#----progress-plot----
#* @get /progress-plot
#* @serializer rds
function(institution = "NULL", discipline = "NULL", lang = "en") {
institution <- sanitise(institution)
discipline <- sanitise(discipline)
lang <- sanitise_lang(lang)
future_promise({
options(op)
db <- dbConnect(Postgres(), dbname = Sys.getenv("DB_NAME"))
options(finbif_cache_path = db)
translator$set_translation_language(lang)
cols <- fb_collections(
select = c(
id,
long_name,
description,
methods,
taxonomic_coverage,
has_children,
is_part_of
),
supercollection = TRUE,
nmin = NA
)
specimen_count <- fb_occurrence(
filter = list(superrecord_basis = "specimen", subcollections = FALSE),
select = c(id = "collection_id"),
aggregate = "records",
n = "all"
)
specimen_count[["id"]] <- sub("http://tun.fi/", "", specimen_count[["id"]])
cols <- merge(cols, specimen_count, all.x = TRUE)