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.
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.
Stable version from CRAN
install.packages("rerddap")
Or, the development version from GitHub
devtools::install_github("ropensci/rerddap")
library("rerddap")
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
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
...
First, get information on a dataset to see time range, lat/long range, and variables.
(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
Then query for gridded data using the griddap() function
(res <- griddap(out,
time = c('2012-01-01', '2012-01-30'),
latitude = c(21, 10),
longitude = c(-80, -70)
))
#> <ERDDAP griddap> noaa_esrl_027d_0fb5_5d38
#> Path: [~/.rerddap/648ed11e8b911b65e39eb63c8df339df.nc]
#> Last updated: [2016-01-12 12:18:06]
#> 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): [18 X 4]
#> time lat lon air
#> 1 2012-01-01T00:00:00Z 22.5 -77.5 NA
#> 2 2012-01-01T00:00:00Z 22.5 -72.5 NA
#> 3 2012-01-01T00:00:00Z 22.5 -67.5 NA
#> 4 2012-01-01T00:00:00Z 17.5 -77.5 -0.1
#> 5 2012-01-01T00:00:00Z 17.5 -72.5 NA
#> 6 2012-01-01T00:00:00Z 17.5 -67.5 -0.2
#> 7 2012-01-01T00:00:00Z 12.5 -77.5 0.2
#> 8 2012-01-01T00:00:00Z 12.5 -72.5 NA
#> 9 2012-01-01T00:00:00Z 12.5 -67.5 0.3
#> 10 2012-02-01T00:00:00Z 22.5 -77.5 NA
#> .. ... ... ... ...
The output of griddap() is a list that you can explore further. Get the summary
res$summary
#> $filename
#> [1] "~/.rerddap/648ed11e8b911b65e39eb63c8df339df.nc"
#>
#> $writable
#> [1] FALSE
#>
#> $id
#> [1] 65536
#>
#> $safemode
#> [1] FALSE
#>
#> $format
#> [1] "NC_FORMAT_CLASSIC"
#>
...
Get the dimension variables
names(res$summary$dim)
#> [1] "time" "latitude" "longitude"
Get the data.frame (beware: you may want to just look at the head of the data.frame if large)
res$data
#> time lat lon air
#> 1 2012-01-01T00:00:00Z 22.5 -77.5 NA
#> 2 2012-01-01T00:00:00Z 22.5 -72.5 NA
#> 3 2012-01-01T00:00:00Z 22.5 -67.5 NA
#> 4 2012-01-01T00:00:00Z 17.5 -77.5 -0.10
#> 5 2012-01-01T00:00:00Z 17.5 -72.5 NA
#> 6 2012-01-01T00:00:00Z 17.5 -67.5 -0.20
#> 7 2012-01-01T00:00:00Z 12.5 -77.5 0.20
#> 8 2012-01-01T00:00:00Z 12.5 -72.5 NA
#> 9 2012-01-01T00:00:00Z 12.5 -67.5 0.30
#> 10 2012-02-01T00:00:00Z 22.5 -77.5 NA
#> 11 2012-02-01T00:00:00Z 22.5 -72.5 NA
#> 12 2012-02-01T00:00:00Z 22.5 -67.5 NA
#> 13 2012-02-01T00:00:00Z 17.5 -77.5 0.40
#> 14 2012-02-01T00:00:00Z 17.5 -72.5 NA
#> 15 2012-02-01T00:00:00Z 17.5 -67.5 0.20
#> 16 2012-02-01T00:00:00Z 12.5 -77.5 0.00
#> 17 2012-02-01T00:00:00Z 12.5 -72.5 NA
#> 18 2012-02-01T00:00:00Z 12.5 -67.5 0.32
(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:
...
(dat <- tabledap(out, 'time>=2001-07-07', 'time<=2001-07-10', fields = c('longitude', 'latitude', 'fish_size', 'itis_tsn', 'scientific_name')))
#> <ERDDAP tabledap> erdCalCOFIfshsiz
#> Path: [~/.rerddap/f013f9ee09bdb4184928d533e575e948.csv]
#> Last updated: [2016-01-12 12:18:07]
#> File size: [0.03 mb]
#> Dimensions: [558 X 5]
#>
#> longitude latitude fish_size itis_tsn scientific_name
#> 2 -118.10667 32.738335 31.5 623625 Lipolagus ochotensis
#> 3 -118.10667 32.738335 48.3 623625 Lipolagus ochotensis
#> 4 -118.10667 32.738335 15.5 162221 Argyropelecus sladeni
#> 5 -118.10667 32.738335 16.3 162221 Argyropelecus sladeni
#> 6 -118.10667 32.738335 17.8 162221 Argyropelecus sladeni
#> 7 -118.10667 32.738335 18.2 162221 Argyropelecus sladeni
#> 8 -118.10667 32.738335 19.2 162221 Argyropelecus sladeni
#> 9 -118.10667 32.738335 20.0 162221 Argyropelecus sladeni
#> 10 -118.10667 32.738335 21.0 162221 Argyropelecus sladeni
#> 11 -118.10667 32.738335 21.5 162221 Argyropelecus sladeni
#> .. ... ... ... ... ...
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$fish_size <- as.numeric(dat$fish_size)
tbl_df(dat) %>%
filter(fish_size > 30) %>%
group_by(scientific_name) %>%
summarise(mean_size = mean(fish_size)) %>%
arrange(desc(mean_size))
#> Source: local data frame [20 x 2]
#>
#> scientific_name mean_size
#> (chr) (dbl)
#> 1 Idiacanthus antrostomus 253.00000
#> 2 Stomias atriventer 189.25000
#> 3 Lestidiops ringens 98.70000
#> 4 Tarletonbeania crenularis 56.50000
#> 5 Ceratoscopelus townsendi 53.70000
#> 6 Stenobrachius leucopsarus 47.74538
#> 7 Sardinops sagax 47.00000
#> 8 Nannobrachium ritteri 43.30250
#> 9 Bathylagoides wesethi 43.09167
#> 10 Vinciguerria lucetia 42.00000
#> 11 Cyclothone acclinidens 40.80000
#> 12 Lipolagus ochotensis 39.72500
#> 13 Leuroglossus stilbius 38.35385
#> 14 Triphoturus mexicanus 38.21342
#> 15 Diaphus theta 37.88571
#> 16 Trachipterus altivelis 37.70000
#> 17 Symbolophorus californiensis 37.66000
#> 18 Nannobrachium regale 37.50000
#> 19 Merluccius productus 36.61333
#> 20 Argyropelecus sladeni 32.43333