rerddap is a general purpose R client for working with ERDDAP servers.
From CRAN
Or development version from GitHub
Some users may experience an installation error, stating to install 1 or more packages, e.g., you may need DBI, in which case do, for example, install.packages("DBI") before installing 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 directory on your machine determined by the hoardr package. 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 the cache directory 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: 9 x 2
#> title dataset_id
#> <chr> <chr>
#> 1 Channel Islands, Kelp Forest Monitoring, Size and Frequ… erdCinpKfmSFNH
#> 2 CalCOFI Larvae Sizes erdCalCOFIlrvsiz
#> 3 GLOBEC NEP MOCNESS Plankton (MOC1) Data, 2000-2002 erdGlobecMoc1
#> 4 GLOBEC NEP Vertical Plankton Tow (VPT) Data, 1997-2001 erdGlobecVpt
#> 5 NWFSC Observer Fixed Gear Data, off West Coast of US, 2… nwioosObsFixed2…
#> 6 NWFSC Observer Trawl Data, off West Coast of US, 2002-2… nwioosObsTrawl2…
#> 7 CalCOFI Larvae Counts Positive Tows erdCalCOFIlrvcn…
#> 8 CalCOFI Tows erdCalCOFItows
#> 9 AN EXPERIMENTAL DATASET: Underway Sea Surface Temperatu… nodcPJJUed_search(query = "size", which = "grid")
#> # A tibble: 11 x 2
#> title dataset_id
#> <chr> <chr>
#> 1 Extended AVHRR Polar Pathfinder Fundamental Clima… noaa_ngdc_da08_dcdf_…
#> 2 Extended AVHRR Polar Pathfinder Fundamental Clima… noaa_ngdc_0fe5_a4b9_…
#> 3 Extended AVHRR Polar Pathfinder Fundamental Clima… noaa_ngdc_5253_bf9e_…
#> 4 Extended AVHRR Polar Pathfinder Fundamental Clima… noaa_ngdc_0f24_2f8c_…
#> 5 Archived Suite of NOAA Coral Reef Watch Operation… noaa_nodc_9f8b_ab7e_…
#> 6 Archived Suite of NOAA Coral Reef Watch Operation… noaa_nodc_da4e_3fc9_…
#> 7 USGS COAWST Forecast, US East Coast and Gulf of M… whoi_geoport_ed12_89…
#> 8 USGS COAWST Forecast, US East Coast and Gulf of M… whoi_geoport_61c3_0b…
#> 9 USGS COAWST Forecast, US East Coast and Gulf of M… whoi_geoport_62d0_9d…
#> 10 USGS COAWST Forecast, US East Coast and Gulf of M… whoi_geoport_7dd7_db…
#> 11 USGS COAWST Forecast, US East Coast and Gulf of M… whoi_geoport_a4fb_2c…Then you can get information on a single dataset
info("erdMBchla1day")
#> <ERDDAP info> erdMBchla1day
#> Base URL: https://upwell.pfeg.noaa.gov/erddap/
#> Dimensions (range):
#> time: (2006-01-01T12:00:00Z, 2019-06-30T12:00:00Z)
#> altitude: (0.0, 0.0)
#> latitude: (-45.0, 65.0)
#> longitude: (120.0, 320.0)
#> Variables:
#> chlorophyll:
#> Units: mg m-3(out <- info("erdMBchla1day"))
#> <ERDDAP info> erdMBchla1day
#> Base URL: https://upwell.pfeg.noaa.gov/erddap/
#> Dimensions (range):
#> time: (2006-01-01T12:00:00Z, 2019-06-30T12:00:00Z)
#> altitude: (0.0, 0.0)
#> latitude: (-45.0, 65.0)
#> longitude: (120.0, 320.0)
#> Variables:
#> chlorophyll:
#> Units: mg m-3(res <- griddap(out,
time = c("2015-01-01", "2015-01-03"),
latitude = c(14, 15),
longitude = c(125, 126)
))
#> <ERDDAP griddap> erdMBchla1day
#> Path: [/Users/sckott/Library/Caches/R/rerddap/4d844aa48552049c3717ac94ced5f9b8.nc]
#> Last updated: [2019-07-01 12:06:12]
#> 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 x 5
#> time lat lon altitude chlorophyll
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 2015-01-01T12:00:00Z 14 125 0 NA
#> 2 2015-01-01T12:00:00Z 14 125. 0 NA
#> 3 2015-01-01T12:00:00Z 14 125. 0 NA
#> 4 2015-01-01T12:00:00Z 14 125. 0 NA
#> 5 2015-01-01T12:00:00Z 14 125. 0 NA
#> 6 2015-01-01T12:00:00Z 14 125. 0 NA
#> 7 2015-01-01T12:00:00Z 14 125. 0 NA
#> 8 2015-01-01T12:00:00Z 14 125. 0 NA
#> 9 2015-01-01T12:00:00Z 14 125. 0 NA
#> 10 2015-01-01T12:00:00Z 14 125. 0 NA
#> # … with 5,033 more rows(out <- info("erdCinpKfmBT"))
#> <ERDDAP info> erdCinpKfmBT
#> Base URL: https://upwell.pfeg.noaa.gov/erddap/
#> 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
...tabledap("erdCinpKfmBT", "time>=2007-06-24", "time<=2007-07-01")
#> <ERDDAP tabledap> erdCinpKfmBT
#> Path: [/Users/sckott/Library/Caches/R/rerddap/268b2474e9e613336b900d3289304bb0.csv]
#> Last updated: [2019-07-01 12:06:14]
#> File size: [0.01 mb]
#> # A tibble: 37 x 53
#> station longitude latitude depth time Aplysia_califor… Aplysia_califor…
#> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 Anacap… -119.416… 34.0 16.0 2007… 0.009722223 0.01
#> 2 Anacap… -119.383… 34.0 17.0 2007… 0.0 0
#> 3 Anacap… -119.366… 34.0 6.0 2007… 0.0 0
#> 4 Anacap… -119.383… 34.0 11.0 2007… 0.16 0.17
#> 5 Anacap… -119.416… 34.0 11.0 2007… 0.03 0.01
#> 6 Anacap… -119.35 34.0166… 5.0 2007… 0.0 0
#> 7 Anacap… -119.35 34.0 8.0 2007… 0.008333334 0.01
#> 8 SanCle… -118.533… 33.0 11.0 2007… NaN NaN
#> 9 SanCle… -118.533… 32.95 10.0 2007… NaN NaN
#> 10 SanCle… -118.4 32.8 13.0 2007… 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>,
...rerddap in R doing citation(package = 'rerddap')