# Retrieve Data From SHARK

## SHARK

SHARK is the Swedish Ocean Archive’s (Svenskt HavsARKivs) platform for data downloads. It stores biological, physical, and chemical marine environmental monitoring data. On behalf of the Swedish Agency for Marine and Water Management, SMHI serves as the national data host for oceanography and marine biology. SMHI is also recognized by UNESCO as a National Oceanographic Data Center (NODC) within the Oceanographic Data and Information (IODE) programme. The data can be accessed via a web interface or directly through the API, as demonstrated in this tutorial using `SHARK4R`.

## Getting Started

#### Installation

You can install the latest version of `SHARK4R` from CRAN using:

    install.packages("SHARK4R")

Load the `SHARK4R` library:

    library(SHARK4R)

## Retrieve Data Table

Data can be retrieved with the same filtering options available in SHARK. To see the available filtering options, please refer to `get_shark_options()` and the information below.

    # Retrieve chlorophyll data for April to June from 2019 to 2020
    shark_data <- get_shark_data(fromYear = 2019,
                                 toYear = 2020,
                                 months = c(4, 5, 6),
                                 dataTypes = "Chlorophyll",
                                 verbose = FALSE)

    # Print data
    print(shark_data)

    ## # A tibble: 179 × 72
    ##    delivery_datatype check_status_sv data_checked_by_sv visit_year visit_month
    ##    <chr>             <chr>           <chr>                   <dbl>       <dbl>
    ##  1 Chlorophyll       Klar            Leverantör               2020           6
    ##  2 Chlorophyll       Klar            Leverantör               2020           6
    ##  3 Chlorophyll       Klar            Leverantör               2020           6
    ##  4 Chlorophyll       Klar            Leverantör               2020           6
    ##  5 Chlorophyll       Klar            Leverantör               2020           6
    ##  6 Chlorophyll       Klar            Leverantör               2020           6
    ##  7 Chlorophyll       Klar            Leverantör               2020           6
    ##  8 Chlorophyll       Klar            Leverantör               2020           6
    ##  9 Chlorophyll       Klar            Leverantör               2020           6
    ## 10 Chlorophyll       Klar            Leverantör               2020           6
    ## # ℹ 169 more rows
    ## # ℹ 67 more variables: station_name <chr>, reported_station_name <chr>,
    ## #   sample_location_id <dbl>, station_id <dbl>, sample_project_name_sv <lgl>,
    ## #   sample_orderer_name_sv <lgl>, visit_id <dbl>, visit_date <lgl>,
    ## #   shark_sample_id_md5 <chr>, sample_date <date>, sample_time <time>,
    ## #   sample_enddate <lgl>, sample_endtime <lgl>, sample_latitude_dm <chr>,
    ## #   sample_longitude_dm <chr>, sample_latitude_dd <dbl>, …

## Get SHARK API Options

Filtering options, including data types, dataset names, stations, taxa, and more, can be retrieved using the `get_shark_options()` function.

    # Retrieve available search options
    shark_options <- get_shark_options()

    # List the names of the available options
    names(shark_options)

    # View available datatypes
    dataTypes <- shark_options$dataTypes
    print(dataTypes)

    # View available dataset names
    datasetNames <- shark_options$datasets
    head(datasetNames) # Print first few dataset names

    ##  [1] "datasets"              "dataTypes"             "parameters"
    ##  [4] "minYear"               "maxYear"               "months"
    ##  [7] "checkStatuses"         "qualityFlags"          "deliverers"
    ## [10] "orderers"              "projects"              "redListedCategories"
    ## [13] "taxa"                  "stations"              "seaAreas"
    ## [16] "vattenDistrikt"        "typOmraden"            "seaBasins"
    ## [19] "counties"              "municipalities"        "waterCategories"
    ## [22] "helcomOspar"           "parametersForDatatype" "headerlangers"
    ## [25] "minYearPerDatatype"

    ##  [1] "Bacterioplankton"      "Chlorophyll"           "Epibenthos"
    ##  [4] "Grey seal"             "Harbour Porpoise"      "Harbour seal"
    ##  [7] "Physical and Chemical" "Phytoplankton"         "Picoplankton"
    ## [10] "Plankton Barcoding"    "Plankton Imaging"      "Primary production"
    ## [13] "Profile"               "Ringed seal"           "Seal pathology"
    ## [16] "Sedimentation"         "Zoobenthos"            "Zooplankton"

    ## [1] "SHARK_Bacterioplankton_ABUND_2023_UMSC_version_2024-06-04.zip"
    ## [2] "SHARK_Bacterioplankton_ABU_2006_UMSC_Bactabund_version_2024-09-27.zip"
    ## [3] "SHARK_Bacterioplankton_ABU_2007_UMSC_Bactabund_version_2024-09-27.zip"
    ## [4] "SHARK_Bacterioplankton_ABU_2008_UMSC_Bactabund_version_2024-09-27.zip"
    ## [5] "SHARK_Bacterioplankton_ABU_2009_UMSC_Bactabund_version_2024-09-27.zip"
    ## [6] "SHARK_Bacterioplankton_ABU_2010_UMSC_Bactabund_version_2024-09-27.zip"

## Retrieve Datasets (Zip-archives)

In addition to accessing data in tabular form, you can also download complete datasets packaged as zip archives. This is useful if you want to store complete datasets locally for further analysis.

To explore all available dataset names, use the `get_shark_options()`. Once you know which datasets you need, you can pass their names (or partial names) to the `get_shark_datasets()` function.

    # Select a dataset name (e.g., the first two in the list)
    dataset_name <- datasetNames[1:2]

    # Download the dataset as a zip-archive to a temporary directory
    shark_data_zip <- get_shark_datasets(dataset_name,
                                         save_dir = tempdir(),
                                         verbose = FALSE) # Quiet output

    # Print the paths to the downloaded files
    print(shark_data_zip)

    ## $`SHARK_Bacterioplankton_ABUND_2023_UMSC_version_2024-06-04.zip`
    ## [1] "/tmp/RtmpAbga6P/SHARK_Bacterioplankton_ABUND_2023_UMSC_version_2024-06-04.zip"
    ##
    ## $`SHARK_Bacterioplankton_ABU_2006_UMSC_Bactabund_version_2024-09-27.zip`
    ## [1] "/tmp/RtmpAbga6P/SHARK_Bacterioplankton_ABU_2006_UMSC_Bactabund_version_2024-09-27.zip"

Please note that `SHARK4R` also includes useful functions for reading local SHARK data files, such as `read_shark()` and `read_shark_deliv()`.

## Citation

    ## To cite package 'SHARK4R' in publications use:
    ##
    ##   Lindh, M. and Torstensson, A. (2026). SHARK4R: Accessing and
    ##   Validating Marine Environmental Data from 'SHARK' and Related
    ##   Databases. R package version 1.2.0.
    ##   https://CRAN.R-project.org/package=SHARK4R
    ##
    ## A BibTeX entry for LaTeX users is
    ##
    ##   @Manual{,
    ##     title = {SHARK4R: Accessing and Validating Marine Environmental Data from 'SHARK' and Related Databases},
    ##     author = {Markus Lindh and Anders Torstensson},
    ##     year = {2026},
    ##     note = {R package version 1.2.0},
    ##     url = {https://CRAN.R-project.org/package=SHARK4R},
    ##   }