rerddap

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rerddap is a general purpose R client for working with ERDDAP servers.

Installation

From CRAN

install.packages("rerddap")

Or development version from GitHub

devtools::install_github("ropensci/rerddap")
library('rerddap')

Background

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 ncdf and ncdf4 packages make 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(). Most I think serve some kind of NOAA data, but there are a few that aren't NOAA data. If you know of more ERDDAP servers, send a pull request, or let us know.

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

ed_search(query = 'size', which = "table")
#> 11 results, showing first 20 
#>                                                                                         title
#> 1                                                                          CalCOFI Fish Sizes
#> 2                                                                        CalCOFI Larvae Sizes
#> 3                Channel Islands, Kelp Forest Monitoring, Size and Frequency, Natural Habitat
#> 4                                                         CalCOFI Larvae Counts Positive Tows
#> 5                                                                                CalCOFI Tows
#> 7                                                     GLOBEC NEP MOCNESS Plankton (MOC1) Data
#> 8                                                 GLOBEC NEP Vertical Plankton Tow (VPT) Data
#> 9                                                  OBIS - ARGOS Satellite Tracking of Animals
#> 10 AN EXPERIMENTAL DATASET: Underway Sea Surface Temperature and Salinity Aboard the Oleander
#> 11                            NWFSC Observer Fixed Gear Data, off West Coast of US, 2002-2006
#> 12                                 NWFSC Observer Trawl Data, off West Coast of US, 2002-2006
#>             dataset_id
#> 1     erdCalCOFIfshsiz
#> 2     erdCalCOFIlrvsiz
#> 3       erdCinpKfmSFNH
#> 4  erdCalCOFIlrvcntpos
#> 5       erdCalCOFItows
#> 7        erdGlobecMoc1
#> 8         erdGlobecVpt
#> 9            aadcArgos
#> 10            nodcPJJU
#> 11  nwioosObsFixed2002
#> 12  nwioosObsTrawl2002
ed_search(query = 'size', which = "grid")
#> 6 results, showing first 20 
#>                                                                                                   title
#> 6                                                       NOAA Global Coral Bleaching Monitoring Products
#> 13        USGS COAWST Forecast, US East Coast and Gulf of Mexico (Experimental) [time][eta_rho][xi_rho]
#> 14            USGS COAWST Forecast, US East Coast and Gulf of Mexico (Experimental) [time][eta_u][xi_u]
#> 15            USGS COAWST Forecast, US East Coast and Gulf of Mexico (Experimental) [time][eta_v][xi_v]
#> 16 USGS COAWST Forecast, US East Coast and Gulf of Mexico (Experimental) [time][s_rho][eta_rho][xi_rho]
#> 17  USGS COAWST Forecast, US East Coast and Gulf of Mexico (Experimental) [time][Nbed][eta_rho][xi_rho]
#>             dataset_id
#> 6             NOAA_DHW
#> 13 whoi_ed12_89ce_9592
#> 14 whoi_61c3_0b5d_cd61
#> 15 whoi_62d0_9d64_c8ff
#> 16 whoi_7dd7_db97_4bbe
#> 17 whoi_a4fb_2c9c_16a7

Information

Then you can get information on a single dataset

info('whoi_62d0_9d64_c8ff')
#> <ERDDAP info> whoi_62d0_9d64_c8ff 
#>  Dimensions (range):  
#>      time: (2012-06-25T01:00:00Z, 2015-07-01T00:00:00Z) 
#>      eta_v: (0, 334) 
#>      xi_v: (0, 895) 
#>  Variables:  
#>      bedload_Vsand_01: 
#>          Units: kilogram meter-1 s-1 
#>      bedload_Vsand_02: 
#>          Units: kilogram meter-1 s-1 
#>      bedload_Vsand_03: 
#>          Units: kilogram meter-1 s-1 
#>      bedload_Vsand_04: 
#>          Units: kilogram meter-1 s-1 
#>      bedload_Vsand_05: 
#>          Units: kilogram meter-1 s-1 
#>      bedload_Vsand_06: 
#>          Units: kilogram meter-1 s-1 
#>      svstr: 
#>          Units: newton meter-2 
#>      vbar: 
#>          Units: meter second-1 
#>      wetdry_mask_v:

griddap (gridded) data

(out <- info('noaa_esrl_027d_0fb5_5d38'))
#> <ERDDAP info> noaa_esrl_027d_0fb5_5d38 
#>  Dimensions (range):  
#>      time: (1850-01-01T00:00:00Z, 2014-05-01T00:00:00Z) 
#>      latitude: (87.5, -87.5) 
#>      longitude: (-177.5, 177.5) 
#>  Variables:  
#>      air: 
#>          Range: -20.9, 19.5 
#>          Units: degC
(res <- griddap(out,
  time = c('2012-01-01', '2012-01-12'),
  latitude = c(21, 20),
  longitude = c(-80, -79)
))
#> <ERDDAP griddap> noaa_esrl_027d_0fb5_5d38
#>    Path: [~/.rerddap/0c0d352c6ec861f6efadce493e270fd0.nc]
#>    Last updated: [2015-06-30 11:19:40]
#>    File size:    [0 mb]
#>    Dimensions (dims/vars):   [3 X 1]
#>    Dim names: time, latitude, longitude
#>    Variable names: CRUTEM3: Surface Air Temperature Monthly Anomaly
#>    data.frame (rows/columns):   [1 X 4]
#>                   time latitude longitude air
#> 1 2012-01-01T00:00:00Z     22.5     -77.5  NA

tabledap (tabular) data

(out <- info('erdCalCOFIfshsiz'))
#> <ERDDAP info> erdCalCOFIfshsiz 
#>  Variables:  
#>      calcofi_species_code: 
#>          Range: 19, 1550 
#>      common_name: 
#>      cruise: 
#>      fish_1000m3: 
#>          Units: Fish per 1,000 cubic meters of water sampled 
#>      fish_count: 
#>      fish_size: 
#>          Units: mm 
#>      itis_tsn: 
#>      latitude: 
#>          Range: 32.515, 38.502 
#>          Units: degrees_north 
#>      line: 
#>          Range: 46.6, 93.3 
#>      longitude: 
#>          Range: -128.5, -117.33 
#>          Units: degrees_east 
#>      net_location: 
#>      net_type: 
#>      order_occupied: 
#>      percent_sorted: 
#>          Units: %/100 
#>      sample_quality: 
#>      scientific_name: 
#>      ship: 
#>      ship_code: 
#>      standard_haul_factor: 
#>      station: 
#>          Range: 28.0, 114.9 
#>      time: 
#>          Range: 9.94464E8, 9.9510582E8 
#>          Units: seconds since 1970-01-01T00:00:00Z 
#>      tow_number: 
#>          Range: 2, 10 
#>      tow_type: 
#>      volume_sampled: 
#>          Units: cubic meters
tabledap(out, fields = c('longitude', 'latitude', 'fish_size', 'itis_tsn'),
         'time>=2001-07-07', 'time<=2001-07-10')
#> <ERDDAP tabledap> erdCalCOFIfshsiz
#>    Path: [~/.rerddap/52894d2daf4c71796c44775f06dc3f16.csv]
#>    Last updated: [2015-06-30 11:19:41]
#>    File size:    [0.02 mb]
#>    Dimensions:   [558 X 4]
#> 
#>     longitude  latitude fish_size itis_tsn
#> 2     -118.26    33.255      22.9   623745
#> 3     -118.26    33.255      22.9   623745
#> 4  -118.10667 32.738335      31.5   623625
#> 5  -118.10667 32.738335      48.3   623625
#> 6  -118.10667 32.738335      15.5   162221
#> 7  -118.10667 32.738335      16.3   162221
#> 8  -118.10667 32.738335      17.8   162221
#> 9  -118.10667 32.738335      18.2   162221
#> 10 -118.10667 32.738335      19.2   162221
#> 11 -118.10667 32.738335      20.0   162221
#> ..        ...       ...       ...      ...

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