This function downloads the latest available Nordic Marine Phytoplankton Group (NOMP) biovolume zip archive from SMHI, unzips it, and reads the first Excel file by default. You can also specify which file in the archive to read.
Usage
get_nomp_list(
year = as.numeric(format(Sys.Date(), "%Y")),
file = NULL,
sheet = NULL,
force = FALSE,
base_url = NULL,
clean_cache_days = 30,
verbose = TRUE
)Arguments
- year
Numeric year to download. Default is current year; if not available, previous years are automatically tried.
- file
Character string specifying which file in the zip archive to read. Defaults to the first Excel file in the archive.
- sheet
Character or numeric; the name or index of the sheet to read from the Excel file. If neither argument specifies the sheet, defaults to the first sheet.
- force
Logical; if
TRUE, forces re-download of the zip file even if cached copy exists.- base_url
Base URL (without "/nomp_taxa_biovolumes_and_carbon_YYYY.zip") for the NOMP biovolume files. Defaults to the SMHI directory.
- clean_cache_days
Numeric; if not
NULL, cached NOMP zip files older than this number of days will be automatically deleted and replaced by a new download. Defaults to 30. Set toNULLto disable automatic cleanup.- verbose
A logical indicating whether to print progress messages. Default is TRUE.
See also
clean_shark4r_cache() to manually clear cached files.
Examples
# \donttest{
# Get the latest available list
nomp_list <- get_nomp_list()
#> File for year 2026 not available. Trying previous year...
#> File for year 2025 downloaded and cached.
head(nomp_list)
#> # A tibble: 6 × 39
#> List `HELCOM area` `OSPAR area` Division Class Order Genus Species SFLAG
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 PEG_BVOL2… x NA CYANOBA… Cyan… CHRO… Apha… Aphano… NA
#> 2 PEG_BVOL2… x NA CYANOBA… Cyan… CHRO… Apha… Aphano… NA
#> 3 PEG_BVOL2… x NA CYANOBA… Cyan… CHRO… Apha… Aphano… NA
#> 4 PEG_BVOL2… x NA CYANOBA… Cyan… CHRO… Apha… Aphano… NA
#> 5 PEG_BVOL2… x NA CYANOBA… Cyan… CHRO… Apha… Aphano… NA
#> 6 PEG_BVOL2… x NA CYANOBA… Cyan… CHRO… Apha… Aphano… NA
#> # ℹ 30 more variables: STAGE <chr>, Author <chr>, AphiaID <dbl>,
#> # AphiaID_link <chr>, Trophy <chr>, Geometric_shape <chr>, FORMULA <chr>,
#> # SizeClassNo <dbl>, Nonvalid_SIZCL <lgl>, Not_accepted_name <chr>,
#> # Unit <chr>, SizeRange <chr>, `Length(l1)µm` <dbl>, `Length(l2)µm` <dbl>,
#> # `Width(w)µm` <dbl>, `Height(h)µm` <dbl>, `Diameter(d1)µm` <dbl>,
#> # `Diameter(d2)µm` <dbl>, `No_of_cells/counting_unit` <dbl>,
#> # `Calculated_volume_µm3 (with formula) - NOT IMPORTED, NOT handled by ICES` <dbl>, …
# Get the 2023 list and clean old cache files older than 60 days
nomp_list_2023 <- get_nomp_list(2023, clean_cache_days = 60)
#> File for year 2023 downloaded and cached.
head(nomp_list_2023)
#> # A tibble: 6 × 37
#> List `HELCOM area` `OSPAR area` Division Class Order Genus Species SFLAG
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 PEG_BVOL2… x NA CYANOPH… Cyan… CHRO… Apha… Aphano… NA
#> 2 PEG_BVOL2… x NA CYANOPH… Cyan… CHRO… Apha… Aphano… NA
#> 3 PEG_BVOL2… x NA CYANOPH… Cyan… CHRO… Apha… Aphano… NA
#> 4 PEG_BVOL2… x NA CYANOPH… Cyan… CHRO… Apha… Aphano… NA
#> 5 PEG_BVOL2… x NA CYANOPH… Cyan… CHRO… Apha… Aphano… NA
#> 6 PEG_BVOL2… x NA CYANOPH… Cyan… CHRO… Apha… Aphano… NA
#> # ℹ 28 more variables: STAGE <chr>, Author <chr>, AphiaID <dbl>,
#> # AphiaID_link <chr>, Trophy <chr>, Geometric_shape <chr>, FORMULA <chr>,
#> # SizeClassNo <dbl>, Nonvalid_SIZCL <lgl>, Not_accepted_name <chr>,
#> # Unit <chr>, SizeRange <chr>, `Length(l1)µm` <dbl>, `Length(l2)µm` <dbl>,
#> # `Width(w)µm` <dbl>, `Height(h)µm` <dbl>, `Diameter(d1)µm` <dbl>,
#> # `Diameter(d2)µm` <dbl>, `No_of_cells/counting_unit` <chr>,
#> # Calculated_volume_µm3 <dbl>, Comment <chr>, …
# }