rerddap is a general purpose R client for working with ERDDAP servers.
From CRAN
install.packages("rerddap")Or development version from GitHub
devtools::install_github("ropensci/rerddap")library('rerddap')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.
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.
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.
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")
#> # A tibble: 10 × 2
#> title
#> <chr>
#> 1 CalCOFI Larvae Sizes
#> 2 Channel Islands, Kelp Forest Monitoring, Size and Frequency, Natural Habita
#> 3 NWFSC Observer Fixed Gear Data, off West Coast of US, 2002-2006
#> 4 NWFSC Observer Trawl Data, off West Coast of US, 2002-2006
#> 5 CalCOFI Larvae Counts Positive Tows
#> 6 CalCOFI Tows
#> 7 OBIS - ARGOS Satellite Tracking of Animals
#> 8 GLOBEC NEP MOCNESS Plankton (MOC1) Data
#> 9 GLOBEC NEP Vertical Plankton Tow (VPT) Data
#> 10 AN EXPERIMENTAL DATASET: Underway Sea Surface Temperature and Salinity Aboa
#> # ... with 1 more variables: dataset_id <chr>ed_search(query = 'size', which = "grid")
#> # A tibble: 349 × 2
#> title
#> <chr>
#> 1 COAWST Hindcast:MVCO/CBlast 2007:ripples with SWAN-40m res (00 dir roms) [t
#> 2 COAWST Hindcast:MVCO/CBlast 2007:ripples with SWAN-40m res (00 dir roms) [t
#> 3 COAWST Hindcast:MVCO/CBlast 2007:ripples with SWAN-40m res (00 dir roms) [t
#> 4 COAWST Hindcast:MVCO/CBlast 2007:ripples with SWAN-40m res (00 dir roms) [t
#> 5 COAWST Hindcast:MVCO/CBlast 2007:ripples with SWAN-40m res (00 dir roms) [t
#> 6 Yakutat, Alaska Coastal Digital Elevation Model (Regional, yakutat ak 8s)
#> 7 Yakutat, Alaska Coastal Digital Elevation Model (Regional, yakutat ak 8 3s)
#> 8 Yakutat, Alaska Coastal Digital Elevation Model (Regional, yakutat 8 15s)
#> 9 Whittier, Alaska Coastal Digital Elevation Model (whittier ak 8 15s)
#> 10 Unalaska, Alaska Coastal Digital Elevation Model (Regional, unalaska ak 815
#> # ... with 339 more rows, and 1 more variables: dataset_id <chr>Then you can get information on a single dataset
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(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', '2012-01-31'),
latitude = c(25, 20),
longitude = c(-80, -79)
))
#> <ERDDAP griddap> noaa_esrl_027d_0fb5_5d38
#> Path: [~/.rerddap/0b06f35e31a352f7b9d6f53f349eb4e5.nc]
#> Last updated: [2017-01-17 09:04:50]
#> 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): [4 X 4]
#> # A tibble: 4 × 4
#> time lat lon air
#> <chr> <dbl> <dbl> <dbl>
#> 1 2012-01-01T00:00:00Z 27.5 -77.5 NA
#> 2 2012-01-01T00:00:00Z 22.5 -77.5 NA
#> 3 2012-02-01T00:00:00Z 27.5 -77.5 2
#> 4 2012-02-01T00:00:00Z 22.5 -77.5 NA(out <- info('erdCinpKfmBT'))
#> <ERDDAP info> erdCinpKfmBT
#> Variables:
#> Aplysia_californica_Mean_Density:
#> Range: 0.0, 0.95
#> Units: m-2
#> Aplysia_californica_StdDev:
#> Range: 0.0, 0.35
#> Aplysia_californica_StdErr:
#> Range: 0.0, 0.1
#> Crassedoma_giganteum_Mean_Density:
#> Range: 0.0, 0.92
#> Units: m-2
#> Crassedoma_giganteum_StdDev:
#> Range: 0.0, 0.71
#> Crassedoma_giganteum_StdErr:
...tabledap('erdCinpKfmBT', 'time>=2007-06-24', 'time<=2007-07-01')
#> <ERDDAP tabledap> erdCinpKfmBT
#> Path: [~/.rerddap/bf9c854c009fb9c6d0f2643436bc8ee6.csv]
#> Last updated: [2017-01-17 08:55:16]
#> File size: [0.01 mb]
#> # A tibble: 37 × 53
#> station longitude latitude depth time Aplysia_californica_Mean_Density Aplysia_californica_StdDev
#> * <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 Anacapa_AdmiralsReef -119.416666666667 34.0 16.0 2007-07-01T00:00:00Z 0.009722223 0.01
#> 2 Anacapa_BlackSeaBassReef -119.383333333333 34.0 17.0 2007-07-01T00:00:00Z 0.0 0.00
#> 3 Anacapa_CathedralCove -119.366666666667 34.0 6.0 2007-07-01T00:00:00Z 0.0 0.00
#> 4 Anacapa_EastFishCamp -119.383333333333 34.0 11.0 2007-07-01T00:00:00Z 0.16 0.17
#> 5 Anacapa_Keyhole -119.416666666667 34.0 11.0 2007-07-01T00:00:00Z 0.03 0.01
#> 6 Anacapa_LandingCove -119.35 34.0166666666667 5.0 2007-07-01T00:00:00Z 0.0 0.00
#> 7 Anacapa_Lighthouse -119.35 34.0 8.0 2007-07-01T00:00:00Z 0.008333334 0.01
#> 8 SanClemente_BoyScoutCamp -118.533333333333 33.0 11.0 2007-07-01T00:00:00Z NaN NaN
#> 9 SanClemente_EelPoint -118.533333333333 32.95 10.0 2007-07-01T00:00:00Z NaN NaN
#> 10 SanClemente_HorseBeachCove -118.4 32.8 13.0 2007-07-01T00:00:00Z NaN NaN
#> # ... with 27 more rows, and 46 more variables: Aplysia_californica_StdErr <dbl>, Crassedoma_giganteum_Mean_Density <chr>, Crassedoma_giganteum_StdDev <dbl>,
#> # Crassedoma_giganteum_StdErr <dbl>, Haliotis_corrugata_Mean_Density <chr>, Haliotis_corrugata_StdDev <dbl>, Haliotis_corrugata_StdErr <dbl>,
#> # Haliotis_fulgens_Mean_Density <chr>, Haliotis_fulgens_StdDev <dbl>, Haliotis_fulgens_StdErr <dbl>, Haliotis_rufescens_Mean_Density <chr>,
#> # Haliotis_rufescens_StdDev <dbl>, Haliotis_rufescens_StdErr <dbl>, Kelletia_kelletii_Mean_Density <chr>, Kelletia_kelletii_StdDev <dbl>,
#> # Kelletia_kelletii_StdErr <dbl>, Lophogorgia_chilensis_Mean_Density <chr>, Lophogorgia_chilensis_StdDev <dbl>, Lophogorgia_chilensis_StdErr <dbl>,
#> # Lytechinus_anamesus_Mean_Density <chr>, Lytechinus_anamesus_StdDev <dbl>, Lytechinus_anamesus_StdErr <dbl>, Megathura_crenulata_Mean_Density <chr>,
#> # Megathura_crenulata_StdDev <dbl>, Megathura_crenulata_StdErr <dbl>, Muricea_californica_Mean_Density <chr>, Muricea_californica_StdDev <dbl>,
#> # Muricea_californica_StdErr <dbl>, Muricea_fruticosa_Mean_Density <chr>, Muricea_fruticosa_StdDev <dbl>, Muricea_fruticosa_StdErr <dbl>,
...- Please report any issues or bugs.
- License: MIT
- Get citation information for
rerddapin R doingcitation(package = 'rerddap') - Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
