{"Package":"rerddap","Title":"General Purpose Client for 'ERDDAP' Servers","Description":"General purpose R client for 'ERDDAP' servers. Includes\nfunctions to search for 'datasets', get summary information on\n'datasets', and fetch 'datasets', in either 'csv' or 'netCDF' format.\n'ERDDAP' information: http://upwell.pfeg.noaa.gov/erddap/information.html.","Version":"0.3.4","License":"MIT + file LICENSE","Authors@R":"c(person(\"Scott\", \"Chamberlain\", role = c(\"aut\",\"cre\"),\nemail = \"myrmecocystus@gmail.com\"))","URL":"https://github.com/ropensci/rerddap","BugReports":"http://www.github.com/ropensci/rerddap/issues","LazyData":"true","VignetteBuilder":"knitr","Imports":"methods, utils, stats, httr (>= 1.0.0), dplyr, data.table,\njsonlite (>= 0.9.17), xml2, digest, ncdf4","Suggests":"roxygen2, knitr, testthat","Enhances":"taxize","RoxygenNote":"5.0.1","NeedsCompilation":"no","Packaged":"2016-01-14 16:40:16 UTC; sacmac","Author":"Scott Chamberlain [aut, cre]","Maintainer":"Scott Chamberlain ","Repository":"CRAN","Date/Publication":"2016-01-14 22:50:52","readme":"rerddap\n=====\n\n\n\n[![Build Status](https://travis-ci.org/ropensci/rerddap.svg?branch=master)](https://travis-ci.org/ropensci/rerddap)\n[![Build status](https://ci.appveyor.com/api/projects/status/nw858vlk4wx05mxm?svg=true)](https://ci.appveyor.com/project/sckott/rerddap)\n[![codecov.io](https://codecov.io/github/ropensci/rerddap/coverage.svg?branch=master)](https://codecov.io/github/ropensci/rerddap?branch=master)\n[![rstudio mirror downloads](http://cranlogs.r-pkg.org/badges/grand-total/rerddap)](https://github.com/metacran/cranlogs.app)\n[![cran version](http://www.r-pkg.org/badges/version/rerddap)](http://cran.rstudio.com/web/packages/rerddap)\n\n`rerddap` is a general purpose R client for working with ERDDAP servers.\n\n## Installation\n\nFrom CRAN\n\n\n```r\ninstall.packages(\"rerddap\")\n```\n\nOr development version from GitHub\n\n\n```r\ndevtools::install_github(\"ropensci/rerddap\")\n```\n\n\n```r\nlibrary('rerddap')\n```\n\n## Background\n\nERDDAP is a server built on top of OPenDAP, which serves some NOAA data. You can get gridded data ([griddap](http://upwell.pfeg.noaa.gov/erddap/griddap/documentation.html)), which lets you query from gridded datasets, or table data ([tabledap](http://upwell.pfeg.noaa.gov/erddap/tabledap/documentation.html)) 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`.\n\n## NetCDF\n\n`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.\n\n## Caching\n\nData 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.\n\nWhen 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.\n\n## ERDDAP servers\n\nYou 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.\n\n## Search\n\nFirst, you likely want to search for data, specify either `griddadp` or `tabledap`\n\n\n```r\ned_search(query = 'size', which = \"table\")\n#> 11 results, showing first 20 \n#> title\n#> 1 CalCOFI Fish Sizes\n#> 2 CalCOFI Larvae Sizes\n#> 3 Channel Islands, Kelp Forest Monitoring, Size and Frequency, Natural Habitat\n#> 4 CalCOFI Larvae Counts Positive Tows\n#> 5 CalCOFI Tows\n#> 6 OBIS - ARGOS Satellite Tracking of Animals\n#> 7 GLOBEC NEP MOCNESS Plankton (MOC1) Data\n#> 8 GLOBEC NEP Vertical Plankton Tow (VPT) Data\n#> 9 NWFSC Observer Fixed Gear Data, off West Coast of US, 2002-2006\n#> 10 NWFSC Observer Trawl Data, off West Coast of US, 2002-2006\n#> 11 AN EXPERIMENTAL DATASET: Underway Sea Surface Temperature and Salinity Aboard the Oleander\n#> dataset_id\n#> 1 erdCalCOFIfshsiz\n#> 2 erdCalCOFIlrvsiz\n#> 3 erdCinpKfmSFNH\n#> 4 erdCalCOFIlrvcntpos\n#> 5 erdCalCOFItows\n#> 6 aadcArgos\n#> 7 erdGlobecMoc1\n#> 8 erdGlobecVpt\n#> 9 nwioosObsFixed2002\n#> 10 nwioosObsTrawl2002\n#> 11 nodcPJJU\n```\n\n\n```r\ned_search(query = 'size', which = \"grid\")\n#> 311 results, showing first 20 \n#> title\n#> 1 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0008) [time][eta_rho][xi_rho]\n#> 2 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0008) [time][eta_u][xi_u]\n#> 3 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0008) [time][eta_v][xi_v]\n#> 4 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0008) [time][s_rho][eta_rho][xi_rho]\n#> 5 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0008) [time][Nbed][eta_rho][xi_rho]\n#> 6 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0009) [time][eta_rho][xi_rho]\n#> 7 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0009) [time][eta_u][xi_u]\n#> 8 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0009) [time][eta_v][xi_v]\n#> 9 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0009) [time][s_rho][eta_rho][xi_rho]\n#> 10 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0009) [time][Nbed][eta_rho][xi_rho]\n#> 11 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0010) [time][eta_rho][xi_rho]\n#> 12 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0010) [time][eta_u][xi_u]\n#> 13 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0010) [time][eta_v][xi_v]\n#> 14 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0010) [time][s_rho][eta_rho][xi_rho]\n#> 15 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0010) [time][Nbed][eta_rho][xi_rho]\n#> 16 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0011) [time][eta_rho][xi_rho]\n#> 17 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0011) [time][eta_u][xi_u]\n#> 18 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0011) [time][eta_v][xi_v]\n#> 19 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0011) [time][s_rho][eta_rho][xi_rho]\n#> 20 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0011) [time][Nbed][eta_rho][xi_rho]\n#> dataset_id\n#> 1 whoi_cbae_ef31_0877\n#> 2 whoi_61f3_6dac_36c1\n#> 3 whoi_4eff_1b8e_513a\n#> 4 whoi_4d06_1f87_db0b\n#> 5 whoi_4849_5c78_58dc\n#> 6 whoi_28c3_4a74_191a\n#> 7 whoi_e627_874c_2b1b\n#> 8 whoi_9183_ea0f_9417\n#> 9 whoi_322d_c428_66b4\n#> 10 whoi_5f75_6229_f722\n#> 11 whoi_02c9_858d_bc77\n#> 12 whoi_689d_0109_9280\n#> 13 whoi_4f6e_439a_8e4b\n#> 14 whoi_5451_efbd_60ac\n#> 15 whoi_1ace_a9ee_f343\n#> 16 whoi_0524_c15f_28da\n#> 17 whoi_e5c5_72b2_af51\n#> 18 whoi_1fcf_de1b_c428\n#> 19 whoi_e305_a468_eabc\n#> 20 whoi_0657_2b81_14df\n```\n\n## Information\n\nThen you can get information on a single dataset\n\n\n```r\ninfo('whoi_62d0_9d64_c8ff')\n#> whoi_62d0_9d64_c8ff \n#> Dimensions (range): \n#> time: (2012-06-25T01:00:00Z, 2015-10-14T00:00:00Z) \n#> eta_v: (0, 334) \n#> xi_v: (0, 895) \n#> Variables: \n#> bedload_Vsand_01: \n#> Units: kilogram meter-1 s-1 \n#> bedload_Vsand_02: \n#> Units: kilogram meter-1 s-1 \n#> bedload_Vsand_03: \n#> Units: kilogram meter-1 s-1 \n#> bedload_Vsand_04: \n#> Units: kilogram meter-1 s-1 \n#> bedload_Vsand_05: \n#> Units: kilogram meter-1 s-1 \n#> bedload_Vsand_06: \n#> Units: kilogram meter-1 s-1 \n#> svstr: \n#> Units: newton meter-2 \n#> vbar: \n#> Units: meter second-1 \n#> wetdry_mask_v:\n```\n\n## griddap (gridded) data\n\n\n```r\n(out <- info('noaa_esrl_027d_0fb5_5d38'))\n#> noaa_esrl_027d_0fb5_5d38 \n#> Dimensions (range): \n#> time: (1850-01-01T00:00:00Z, 2014-05-01T00:00:00Z) \n#> latitude: (87.5, -87.5) \n#> longitude: (-177.5, 177.5) \n#> Variables: \n#> air: \n#> Range: -20.9, 19.5 \n#> Units: degC\n```\n\n\n```r\n(res <- griddap(out,\n time = c('2012-01-01', '2012-01-12'),\n latitude = c(21, 20),\n longitude = c(-80, -79)\n))\n#> noaa_esrl_027d_0fb5_5d38\n#> Path: [~/.rerddap/0c0d352c6ec861f6efadce493e270fd0.nc]\n#> Last updated: [2016-01-12 12:21:40]\n#> File size: [0 mb]\n#> Dimensions (dims/vars): [3 X 1]\n#> Dim names: time, latitude, longitude\n#> Variable names: CRUTEM3: Surface Air Temperature Monthly Anomaly\n#> data.frame (rows/columns): [1 X 4]\n#> time lat lon air\n#> 1 2012-01-01T00:00:00Z 22.5 -77.5 NA\n```\n\n## tabledap (tabular) data\n\n\n```r\n(out <- info('erdCalCOFIfshsiz'))\n#> erdCalCOFIfshsiz \n#> Variables: \n#> calcofi_species_code: \n#> Range: 19, 1550 \n#> common_name: \n#> cruise: \n#> fish_1000m3: \n#> Units: Fish per 1,000 cubic meters of water sampled \n#> fish_count: \n#> fish_size: \n#> Units: mm \n#> itis_tsn: \n#> latitude: \n#> Range: 32.515, 38.502 \n#> Units: degrees_north \n#> line: \n#> Range: 46.6, 93.3 \n#> longitude: \n#> Range: -128.5, -117.33 \n#> Units: degrees_east \n#> net_location: \n#> net_type: \n#> order_occupied: \n#> percent_sorted: \n#> Units: %/100 \n#> sample_quality: \n#> scientific_name: \n#> ship: \n#> ship_code: \n#> standard_haul_factor: \n#> station: \n#> Range: 28.0, 114.9 \n#> time: \n#> Range: 9.94464E8, 9.9510582E8 \n#> Units: seconds since 1970-01-01T00:00:00Z \n#> tow_number: \n#> Range: 2, 10 \n#> tow_type: \n#> volume_sampled: \n#> Units: cubic meters\n```\n\n\n```r\ntabledap(out, fields = c('longitude', 'latitude', 'fish_size', 'itis_tsn'),\n 'time>=2001-07-07', 'time<=2001-07-10')\n#> erdCalCOFIfshsiz\n#> Path: [~/.rerddap/52894d2daf4c71796c44775f06dc3f16.csv]\n#> Last updated: [2016-01-12 11:04:28]\n#> File size: [0.02 mb]\n#> Dimensions: [558 X 4]\n#> \n#> longitude latitude fish_size itis_tsn\n#> 2 -118.10667 32.738335 31.5 623625\n#> 3 -118.10667 32.738335 48.3 623625\n#> 4 -118.10667 32.738335 15.5 162221\n#> 5 -118.10667 32.738335 16.3 162221\n#> 6 -118.10667 32.738335 17.8 162221\n#> 7 -118.10667 32.738335 18.2 162221\n#> 8 -118.10667 32.738335 19.2 162221\n#> 9 -118.10667 32.738335 20.0 162221\n#> 10 -118.10667 32.738335 21.0 162221\n#> 11 -118.10667 32.738335 21.5 162221\n#> .. ... ... ... ...\n```\n\n## Meta\n\n* Please [report any issues or bugs](https://github.com/ropensci/rerddap/issues).\n* License: MIT\n* Get citation information for `rerddap` in R doing `citation(package = 'rerddap')`\n* Please note that this project is released with a [Contributor Code of Conduct](CONDUCT.md). By participating in this project you agree to abide by its terms.\n\n[![ropensci_footer](http://ropensci.org/public_images/github_footer.png)](http://ropensci.org)"}

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