General Purpose Client for 'ERDDAP' Servers

General purpose R client for 'ERDDAP' servers. Includes functions to search for 'datasets', get summary information on 'datasets', and fetch 'datasets', in either 'csv' or 'netCDF' format. 'ERDDAP' information: < https://upwell.pfeg.noaa.gov/erddap/information.html>.


Build Status Build status codecov.io rstudio mirror downloads cran version

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')

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")
#> # 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>

Information

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

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', '2012-01-31'),
  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

tabledap (tabular) data

(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>,
...

Meta

  • Please report any issues or bugs.
  • License: MIT
  • Get citation information for rerddap in R doing citation(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.

ropensci_footer

News

rerddap 0.4.2

NEW FEATURES

  • Now using hoardr to manage caching paths and such (#60). Also now asking users where they want to cache files, either in a rappdirs user cache dir or a temp directory. Now on tests and examples we use temp dirs.
  • Related to above, new functions cache_info() to get cache path and number of cached files, and cache_setup() to set cache path.
  • Related to above, cache_details(), cache_list(), and cache_delete() lose their cache_path parameter - now cache path is set package wide and we use the same cache path, so no need to set in the fxn call.

MINOR IMPROVEMENTS

  • Fixes to a number of griddap() and tabledap() examples to use datasets that still exist (previous examples used datasets that are no gone)

rerddap 0.4.0

NEW FEATURES

  • New vignette added that goes in to much more depth than the original vignette (#51) thx to @rmendels
  • info() function gains new attribute url with the base url for the ERDDAP server used (#42)
  • Replaced usage of internal compact data.frame code to use tibble package (#45)

MINOR IMPROVEMENTS

  • Added another ERDDAP server to servers() function (#49)
  • Changed base URLs for default ERDDAP server from http to https (#50)
  • Added note to docs for griddap() and tabledap() for how to best deal with 500 server errors (#48)
  • Replaced all dplyr::rbind_all uses with dplyr::bind_rows (#46)

rerddap 0.3.4

MINOR IMPROVEMENTS

  • Removed use of ncdf package, which has been taken off CRAN. Using ncdf4 now for all NetCDF file manipulation. (#35)
  • Failing better now with custom error catching (#31)
  • Added many internal checks for parameter inputs, warning or stopping as necessary - ERDDAP servers silently drop with no informative messages (#32)

BUG FIXES

  • Using now file.info()$size instead of file.size() to be backwards compatible with R versions < 3.2

rerddap 0.3.0

NEW FEATURES

  • Cache functions accept the outputs of griddap() and tabledap() so that the user can easily see cache details or delete the file from the cache without having to manually get the file name. (#30)

MINOR IMPROVEMENTS

  • All package dependencies now use importFrom so we only import functions we need instead of their global namespaces.

BUG FIXES

  • Fixed bug in parsing data from netcdf files, affected the griddap() function (#28)

rerddap 0.2.0

NEW FEATURES

  • Added a suite of functions to manage local cached files (#17)

MINOR IMPROVEMENTS

  • Added new ERDDAP server to list of servers in the servers() function (#21)

BUG FIXES

  • Fixed a few cases across a number of functions in which an empty list passed to query parmaeter in httr::GET() caused an error (#23)
  • Fixed retrieval of path to file written to disk by httr::write_disk() (#24)
  • last is a value accepted by ERDDAP servers, but internal functions weren't checking correctly, fixed now. (#25)
  • as.info() wasn't passing on the url parameter to the info() function. fixed now. (#26)

rerddap 0.1.0

NEW FEATURES

  • released to CRAN

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("rerddap")

0.4.2 by Scott Chamberlain, a year ago


https://github.com/ropensci/rerddap


Report a bug at https://github.com/ropensci/rerddap/issues


Browse source code at https://github.com/cran/rerddap


Authors: Scott Chamberlain [aut, cre], Ben Tupper [ctb], Roy Mendelssohn [ctb]


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports utils, httr, dplyr, data.table, jsonlite, xml2, digest, ncdf4, tibble, hoardr

Suggests roxygen2, knitr, rmarkdown, testthat, akima, ggplot2, mapdata, plot3D

Enhances taxize


See at CRAN