# rerddap introduction

#### Scott Chamberlain

#### 2023-06-29

`rerddap` is a general purpose R client for working with ERDDAP™ servers. ERDDAP™ is a server built on top of OPenDAP, which serves some NOAA data. You can get gridded data (griddap), which lets you query from gridded datasets, or table data (tabledap) which lets you query from tabular datasets. In terms of how we interface with them, there are similarties, but some differences too. We try to make a similar interface to both data types in `rerddap`.

## NetCDF

`rerddap` supports NetCDF format, and is the default when using the `griddap()` function. NetCDF is a binary file format, and will have a much smaller footprint on your disk than csv. The binary file format means it’s harder to inspect, but the `ncdf4` package makes it easy to pull data out and write data back into a NetCDF file. Note the the file extension for NetCDF files is `.nc`. Whether you choose NetCDF or csv for small files won’t make much of a difference, but will with large files.

## Caching

Data files downloaded are cached in a single hidden directory `~/.rerddap` on your machine. It’s hidden so that you don’t accidentally delete the data, but you can still easily delete the data if you like.

When you use `griddap()` or `tabledap()` functions, we construct a MD5 hash from the base URL, and any query parameters - this way each query is separately cached. Once we have the hash, we look in `~/.rerddap` for a matching hash. If there’s a match we use that file on disk - if no match, we make a http request for the data to the ERDDAP™ server you specify.

## ERDDAP™ servers

You can get a data.frame of ERDDAP™ servers using the function `servers()`. The list of ERDDAP™ servers is drawn from the Awesome ERDDAP™ page maintained by the Irish Marine Institute . If you know of more ERDDAP™ servers, follow the instructions on that page to add the server.

## Install

Stable version from CRAN

    install.packages("rerddap")

Or, the development version from GitHub

    remotes::install_github("ropensci/rerddap")

    library("rerddap")

## Search

First, you likely want to search for data, specify either `griddadp` or `tabledap`

    ed_search(query = 'size', which = "table")
    #> # A tibble: 36 × 2
    #>    title                                                              dataset_id
    #>    <chr>                                                              <chr>
    #>  1 CCE Prey Size and Hard Part Size Regressions                       mmtdPreyS…
    #>  2 CCE Teleost Prey Size and Hard Part Size Measurements              mmtdTeleo…
    #>  3 CalCOFI Larvae Sizes                                               erdCalCOF…
    #>  4 CCE Non-Teleost Prey Size and Hard Part Size Measurements          mmtdNonTe…
    #>  5 Channel Islands, Kelp Forest Monitoring, Size and Frequency, Natu… erdCinpKf…
    #>  6 File Names from the AWS S3 noaa-goes16 Bucket                      awsS3Noaa…
    #>  7 File Names from the AWS S3 noaa-goes17 Bucket                      awsS3Noaa…
    #>  8 PacIOOS Beach Camera 001: Waikiki, Oahu, Hawaii                    BEACHCAM-…
    #>  9 PacIOOS Beach Camera 003: Waimea Bay, Oahu, Hawaii                 BEACHCAM-…
    #> 10 PacIOOS Beach Camera 004: Waimea Bay (Offshore), Oahu, Hawaii      BEACHCAM-…
    #> # ℹ 26 more rows

    ed_search(query = 'size', which = "grid")
    #> # A tibble: 103 × 2
    #>    title                                                              dataset_id
    #>    <chr>                                                              <chr>
    #>  1 Audio data from a local source.                                    testGridW…
    #>  2 Main Hawaiian Islands Multibeam Bathymetry Synthesis: 50-m Bathym… hmrg_bath…
    #>  3 Main Hawaiian Islands Multibeam Bathymetry Synthesis: 50-m Bathym… hmrg_bath…
    #>  4 Coastal Upwelling Transport Index (CUTI), Daily                    erdCUTIda…
    #>  5 SST smoothed frontal gradients                                     FRD_SSTgr…
    #>  6 Coastal Upwelling Transport Index (CUTI), Monthly                  erdCUTImo…
    #>  7 SST smoothed frontal gradients, Lon0360                            FRD_SSTgr…
    #>  8 Biologically Effective Upwelling Transport Index (BEUTI), Daily    erdBEUTId…
    #>  9 Biologically Effective Upwelling Transport Index (BEUTI), Monthly  erdBEUTIm…
    #> 10 Daily averaged and put on grid 4x daily NCEP reanalysis (psi.2012) noaa_psl_…
    #> # ℹ 93 more rows

There is now a convenience function to search over a list of ERDDAP™ servers, designed to work with the function `servers()`

    server_list <- c(
      emodnet_physics = 'https://erddap.emodnet-physics.eu/erddap/',
      irish_marine_institute = 'https://erddap.marine.ie/erddap/'
    )
    global_search(query = 'size', server_list, 'griddap')
    #>                                                                                                                                                              title
    #> 1                                          EMODnet Physics - Total Suspended Matter - GridSeriesObservation - Concentration of total suspended matter - BALTIC SEA
    #> 2                                   EMODnet Physics - Total Suspended Matter - GridSeriesObservation - Concentration of total suspended matter - MEDITERRANEAN SEA
    #> 3                  EMODnet Physics - Total Suspended Matter - GridSeriesObservation - Concentration of total suspended matter - MEDITERRANEAN SEA - LOW RESOLUTION
    #> 4                                                                                                           EMODnet Physics - TEMPERATURE YEARLY RECORDING DENSITY
    #> 5                                                                  EMODPACE - Monthly sea level derived from CMEMS-DUACS (DT-2018) satellite altimetry (1993-2019)
    #> 6                                           EMODnet Physics - Total Suspended Matter - GridSeriesObservation - Concentration of total suspended matter - NORTH SEA
    #> 7                                                       EMODPACE - Absolute sea level trend (1993 – 2019) - derived from CMEMS-DUACS (DT-2018) satellite altimetry
    #> 8 EMODPACE - Sea Level monthly mean, EurAsia. This product is based, uses and reprocess the CMEMS product id. SEALEVEL_GLO_PHY_CLIMATE_L4_REP_OBSERVATIONS_008_057
    #> 9                                                                                                                                 COMPASS-NEATL Hindcast 2016-2020
    #>                                  dataset_id
    #> 1                             TSM_BALTICSEA
    #> 2                                 TSM_MBSEA
    #> 3                   TSM_MBSEA_LOWRESOLUTION
    #> 4                           ERD_EP_TEMP_DNS
    #> 5 EMODPACE_SLEV_MONTHLY_MEAN_DESEASONALIZED
    #> 6                              TSM_NORTHSEA
    #> 7                       EMODPACE_SLEV_TREND
    #> 8                EMODPACE_SLEV_MONTHLY_MEAN
    #> 9               compass_neatl_hindcast_grid
    #>                                         url
    #> 1 https://erddap.emodnet-physics.eu/erddap/
    #> 2 https://erddap.emodnet-physics.eu/erddap/
    #> 3 https://erddap.emodnet-physics.eu/erddap/
    #> 4 https://erddap.emodnet-physics.eu/erddap/
    #> 5 https://erddap.emodnet-physics.eu/erddap/
    #> 6 https://erddap.emodnet-physics.eu/erddap/
    #> 7 https://erddap.emodnet-physics.eu/erddap/
    #> 8 https://erddap.emodnet-physics.eu/erddap/
    #> 9          https://erddap.marine.ie/erddap/

## Information

Then you can get information on a single dataset

    info('erdCalCOFIlrvsiz')
    #> <ERDDAP™ info> erdCalCOFIlrvsiz
    #>  Base URL: https://upwell.pfeg.noaa.gov/erddap
    #>  Dataset Type: tabledap
    #>  Variables:
    #>      calcofi_species_code:
    #>          Range: 19, 946
    #>      common_name:
    #>      cruise:
    #>      itis_tsn:
    #>      larvae_10m2:
    ...

## griddap (gridded) data

First, get information on a dataset to see time range, lat/long range, and variables.

    (out <- info('erdMBchla1day'))
    #> <ERDDAP info> erdMBchla1day
    #>  Base URL: https://upwell.pfeg.noaa.gov/erddap
    #>  Dataset Type: griddap
    #>  Dimensions (range):
    #>      time: (2006-01-01T12:00:00Z, 2023-06-27T12:00:00Z)
    #>      altitude: (0.0, 0.0)
    #>      latitude: (-45.0, 65.0)
    #>      longitude: (120.0, 320.0)
    #>  Variables:
    #>      chlorophyll:
    #>          Units: mg m-3

Then query for gridded data using the `griddap()` function

    (res <- griddap(out,
      time = c('2015-01-01','2015-01-03'),
      latitude = c(14, 15),
      longitude = c(125, 126)
    ))
    #> <ERDDAP™ griddap> erdMBchla1day
    #>    Path: [/var/folders/xw/mcmsdzzx4mgbttplylgs7ysh0000gp/T//RtmpoME6FV/R/rerddap/4d844aa48552049c3717ac94ced5f9b8.nc]
    #>    Last updated: [2023-06-29 13:18:53.945082]
    #>    File size:    [0.03 mb]
    #>    Dimensions (dims/vars):   [4 X 1]
    #>    Dim names: time, altitude, latitude, longitude
    #>    Variable names: Chlorophyll Concentration in Sea Water
    #>    data.frame (rows/columns):   [5043 X 5]
    #> # A tibble: 5,043 × 5
    #>    longitude  latitude  altitude time                 chlorophyll
    #>    <dbl[1d]> <dbl[1d]> <dbl[1d]> <chr>                      <dbl>
    #>  1      125         14         0 2015-01-01T12:00:00Z          NA
    #>  2      125.        14         0 2015-01-01T12:00:00Z          NA
    #>  3      125.        14         0 2015-01-01T12:00:00Z          NA
    #>  4      125.        14         0 2015-01-01T12:00:00Z          NA
    #>  5      125.        14         0 2015-01-01T12:00:00Z          NA
    #>  6      125.        14         0 2015-01-01T12:00:00Z          NA
    #>  7      125.        14         0 2015-01-01T12:00:00Z          NA
    #>  8      125.        14         0 2015-01-01T12:00:00Z          NA
    #>  9      125.        14         0 2015-01-01T12:00:00Z          NA
    #> 10      125.        14         0 2015-01-01T12:00:00Z          NA
    #> # ℹ 5,033 more rows

The output of `griddap()` is a list that you can explore further. Get the summary

    res$summary
    #> $filename
    #> [1] "/var/folders/xw/mcmsdzzx4mgbttplylgs7ysh0000gp/T//RtmpoME6FV/R/rerddap/4d844aa48552049c3717ac94ced5f9b8.nc"
    #>
    #> $writable
    #> [1] FALSE
    #>
    #> $id
    #> [1] 65536
    #>
    #> $error
    #> [1] FALSE
    #>
    #> $safemode
    #> [1] FALSE
    #>
    ...

Get the dimension variables

    names(res$summary$dim)
    #> [1] "time"      "altitude"  "latitude"  "longitude"

Get the data.frame (beware: you may want to just look at the `head` of the data.frame if large)

    head(res$data)
    #>   longitude latitude altitude                 time chlorophyll
    #> 1   125.000       14        0 2015-01-01T12:00:00Z          NA
    #> 2   125.025       14        0 2015-01-01T12:00:00Z          NA
    #> 3   125.050       14        0 2015-01-01T12:00:00Z          NA
    #> 4   125.075       14        0 2015-01-01T12:00:00Z          NA
    #> 5   125.100       14        0 2015-01-01T12:00:00Z          NA
    #> 6   125.125       14        0 2015-01-01T12:00:00Z          NA

## tabledap (tabular) data

    (out <- info('erdCalCOFIlrvsiz'))
    #> <ERDDAP™ info> erdCalCOFIlrvsiz
    #>  Base URL: https://upwell.pfeg.noaa.gov/erddap
    #>  Dataset Type: tabledap
    #>  Variables:
    #>      calcofi_species_code:
    #>          Range: 19, 946
    #>      common_name:
    #>      cruise:
    #>      itis_tsn:
    #>      larvae_10m2:
    ...

    (dat <- tabledap('erdCalCOFIlrvsiz', fields=c('latitude','longitude','larvae_size',
      'scientific_name'), 'time>=2011-01-01', 'time<=2011-12-31'))
    #> <ERDDAP™ tabledap> erdCalCOFIlrvsiz
    #>    Path: [/var/folders/xw/mcmsdzzx4mgbttplylgs7ysh0000gp/T//RtmpoME6FV/R/rerddap/db7389db5b5b0ed9c426d5c13bc43d18.csv]
    #>    Last updated: [2023-06-29 13:18:57.579066]
    #>    File size:    [0.05 mb]
    #> # A tibble: 1,304 × 4
    #>    latitude  longitude  larvae_size scientific_name
    #>    <chr>     <chr>      <chr>       <chr>
    #>  1 32.956665 -117.305   4.5         Engraulis mordax
    #>  2 32.91     -117.4     5.0         Merluccius productus
    #>  3 32.511665 -118.21167 2.0         Merluccius productus
    #>  4 32.511665 -118.21167 3.0         Merluccius productus
    #>  5 32.511665 -118.21167 5.5         Merluccius productus
    #>  6 32.511665 -118.21167 6.0         Merluccius productus
    #>  7 32.511665 -118.21167 2.8         Merluccius productus
    #>  8 32.511665 -118.21167 3.0         Sardinops sagax
    #>  9 32.511665 -118.21167 5.0         Sardinops sagax
    #> 10 32.511665 -118.21167 2.5         Engraulis mordax
    #> # ℹ 1,294 more rows

Since both `griddap()` and `tabledap()` give back data.frame’s, it’s easy to do downstream manipulation. For example, we can use `dplyr` to filter, summarize, group, and sort:

    library("dplyr")
    dat$larvae_size <- as.numeric(dat$larvae_size)
    dat %>%
      group_by(scientific_name) %>%
      summarise(mean_size = mean(larvae_size)) %>%
      arrange(desc(mean_size))
    #> # A tibble: 7 × 2
    #>   scientific_name       mean_size
    #>   <chr>                     <dbl>
    #> 1 Anoplopoma fimbria        23.3
    #> 2 Engraulis mordax           9.26
    #> 3 Sardinops sagax            7.28
    #> 4 Merluccius productus       5.48
    #> 5 Tactostoma macropus        5
    #> 6 Scomber japonicus          3.4
    #> 7 Trachurus symmetricus      3.29