rerddap
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 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()
. 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.
Search
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
#> 6 OBIS - ARGOS Satellite Tracking of Animals
#> 7 GLOBEC NEP MOCNESS Plankton (MOC1) Data
#> 8 GLOBEC NEP Vertical Plankton Tow (VPT) Data
#> 9 NWFSC Observer Fixed Gear Data, off West Coast of US, 2002-2006
#> 10 NWFSC Observer Trawl Data, off West Coast of US, 2002-2006
#> 11 AN EXPERIMENTAL DATASET: Underway Sea Surface Temperature and Salinity Aboard the Oleander
#> dataset_id
#> 1 erdCalCOFIfshsiz
#> 2 erdCalCOFIlrvsiz
#> 3 erdCinpKfmSFNH
#> 4 erdCalCOFIlrvcntpos
#> 5 erdCalCOFItows
#> 6 aadcArgos
#> 7 erdGlobecMoc1
#> 8 erdGlobecVpt
#> 9 nwioosObsFixed2002
#> 10 nwioosObsTrawl2002
#> 11 nodcPJJU
ed_search(query = 'size', which = "grid")
#> 311 results, showing first 20
#> title
#> 1 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0008) [time][eta_rho][xi_rho]
#> 2 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0008) [time][eta_u][xi_u]
#> 3 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0008) [time][eta_v][xi_v]
#> 4 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0008) [time][s_rho][eta_rho][xi_rho]
#> 5 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0008) [time][Nbed][eta_rho][xi_rho]
#> 6 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0009) [time][eta_rho][xi_rho]
#> 7 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0009) [time][eta_u][xi_u]
#> 8 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0009) [time][eta_v][xi_v]
#> 9 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0009) [time][s_rho][eta_rho][xi_rho]
#> 10 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0009) [time][Nbed][eta_rho][xi_rho]
#> 11 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0010) [time][eta_rho][xi_rho]
#> 12 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0010) [time][eta_u][xi_u]
#> 13 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0010) [time][eta_v][xi_v]
#> 14 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0010) [time][s_rho][eta_rho][xi_rho]
#> 15 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0010) [time][Nbed][eta_rho][xi_rho]
#> 16 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0011) [time][eta_rho][xi_rho]
#> 17 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0011) [time][eta_u][xi_u]
#> 18 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0011) [time][eta_v][xi_v]
#> 19 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0011) [time][s_rho][eta_rho][xi_rho]
#> 20 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0011) [time][Nbed][eta_rho][xi_rho]
#> dataset_id
#> 1 whoi_cbae_ef31_0877
#> 2 whoi_61f3_6dac_36c1
#> 3 whoi_4eff_1b8e_513a
#> 4 whoi_4d06_1f87_db0b
#> 5 whoi_4849_5c78_58dc
#> 6 whoi_28c3_4a74_191a
#> 7 whoi_e627_874c_2b1b
#> 8 whoi_9183_ea0f_9417
#> 9 whoi_322d_c428_66b4
#> 10 whoi_5f75_6229_f722
#> 11 whoi_02c9_858d_bc77
#> 12 whoi_689d_0109_9280
#> 13 whoi_4f6e_439a_8e4b
#> 14 whoi_5451_efbd_60ac
#> 15 whoi_1ace_a9ee_f343
#> 16 whoi_0524_c15f_28da
#> 17 whoi_e5c5_72b2_af51
#> 18 whoi_1fcf_de1b_c428
#> 19 whoi_e305_a468_eabc
#> 20 whoi_0657_2b81_14df
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, 2015-10-14T00:00:00Z)
#> 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: [2016-01-12 12:21:40]
#> 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 lat lon 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: [2016-01-12 11:04:28]
#> File size: [0.02 mb]
#> Dimensions: [558 X 4]
#>
#> longitude latitude fish_size itis_tsn
#> 2 -118.10667 32.738335 31.5 623625
#> 3 -118.10667 32.738335 48.3 623625
#> 4 -118.10667 32.738335 15.5 162221
#> 5 -118.10667 32.738335 16.3 162221
#> 6 -118.10667 32.738335 17.8 162221
#> 7 -118.10667 32.738335 18.2 162221
#> 8 -118.10667 32.738335 19.2 162221
#> 9 -118.10667 32.738335 20.0 162221
#> 10 -118.10667 32.738335 21.0 162221
#> 11 -118.10667 32.738335 21.5 162221
#> .. ... ... ... ...
Meta
- Please report any issues or bugs.
- License: MIT
- Get citation information for
rerddap
in 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.