Skip to contents

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.

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(). The list of ERDDAP servers is drawn from the Awesome ERDDAP page maintained by the Irish Marine Institute . If you know of more ERDDAP servers, follow the instructions on that page to add the server.

Install

Stable version from CRAN

install.packages("rerddap")

Or, the development version from GitHub

remotes::install_github("ropensci/rerddap")

First, you likely want to search for data, specify either griddadp or tabledap

ed_search(query = 'size', which = "table")
#> # A tibble: 41 × 2
#>    title                                                                 datas…¹
#>    <chr>                                                                 <chr>  
#>  1 CCE Prey Size and Hard Part Size Regressions                          mmtdPr…
#>  2 CCE Teleost Prey Size and Hard Part Size Measurements                 mmtdTe…
#>  3 CalCOFI Larvae Sizes                                                  erdCal…
#>  4 Seabird Prey Size                                                     cciea_…
#>  5 CCE Non-Teleost Prey Size and Hard Part Size Measurements             mmtdNo…
#>  6 Channel Islands, Kelp Forest Monitoring, Size and Frequency, Natural… erdCin…
#>  7 File Names from the AWS S3 noaa-goes16 Bucket                         awsS3N…
#>  8 File Names from the AWS S3 noaa-goes17 Bucket                         awsS3N…
#>  9 PacIOOS Beach Camera 001: Waikiki, Oahu, Hawaii                       BEACHC…
#> 10 PacIOOS Beach Camera 003: Waimea Bay, Oahu, Hawaii                    BEACHC…
#> # … with 31 more rows, and abbreviated variable name ¹​dataset_id
ed_search(query = 'size', which = "grid")
#> # A tibble: 54 × 2
#>    title                                                                 datas…¹
#>    <chr>                                                                 <chr>  
#>  1 Audio data from a local source.                                       testGr…
#>  2 Main Hawaiian Islands Multibeam Bathymetry Synthesis: 50-m Bathymetry hmrg_b…
#>  3 Main Hawaiian Islands Multibeam Bathymetry Synthesis: 50-m Bathymetr… hmrg_b…
#>  4 Coastal Upwelling Transport Index (CUTI), Daily                       erdCUT…
#>  5 SST smoothed frontal gradients                                        FRD_SS…
#>  6 Coastal Upwelling Transport Index (CUTI), Monthly                     erdCUT…
#>  7 SST smoothed frontal gradients, Lon0360                               FRD_SS…
#>  8 Biologically Effective Upwelling Transport Index (BEUTI), Daily       erdBEU…
#>  9 Biologically Effective Upwelling Transport Index (BEUTI), Monthly     erdBEU…
#> 10 monthly mean psi from the NCEP Reanalysis (psi.mon.ltm), 0001         noaa_p…
#> # … with 44 more rows, and abbreviated variable name ¹​dataset_id

There is now a convenience function to search over a list of ERDDAP servers, designed to work with the function servers()

global_search(query, server_list, which_service)
#> Error in check_arg(query, "character"): object 'query' not found

Information

Then you can get information on a single dataset

info('erdCalCOFIlrvsiz')
#> <ERDDAP info> erdCalCOFIlrvsiz 
#>  Base URL: https://upwell.pfeg.noaa.gov/erddap 
#>  Dataset Type: tabledap 
#>  Variables:  
#>      calcofi_species_code: 
#>          Range: 19, 946 
#>      common_name: 
#>      cruise: 
#>      itis_tsn: 
#>      larvae_10m2: 
...

griddap (gridded) data

First, get information on a dataset to see time range, lat/long range, and variables.

(out <- info('erdMBchla1day'))
#> <ERDDAP info> erdMBchla1day 
#>  Base URL: https://upwell.pfeg.noaa.gov/erddap 
#>  Dataset Type: griddap 
#>  Dimensions (range):  
#>      time: (2006-01-01T12:00:00Z, 2022-09-28T12:00:00Z) 
#>      altitude: (0.0, 0.0) 
#>      latitude: (-45.0, 65.0) 
#>      longitude: (120.0, 320.0) 
#>  Variables:  
#>      chlorophyll: 
#>          Units: mg m-3

Then query for gridded data using the griddap() function

(res <- griddap(out,
  time = c('2015-01-01','2015-01-03'),
  latitude = c(14, 15),
  longitude = c(125, 126)
))
#> <ERDDAP griddap> erdMBchla1day
#>    Path: [~/Library/Caches/R/rerddap/4d844aa48552049c3717ac94ced5f9b8.nc]
#>    Last updated: [2022-09-30 09:34:02]
#>    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 × 5
#>    longitude latitude altitude time                 chlorophyll
#>        <dbl>    <dbl>    <dbl> <chr>                      <dbl>
#>  1      125        14        0 2015-01-01T12:00:00Z          NA
#>  2      125.       14        0 2015-01-01T12:00:00Z          NA
#>  3      125.       14        0 2015-01-01T12:00:00Z          NA
#>  4      125.       14        0 2015-01-01T12:00:00Z          NA
#>  5      125.       14        0 2015-01-01T12:00:00Z          NA
#>  6      125.       14        0 2015-01-01T12:00:00Z          NA
#>  7      125.       14        0 2015-01-01T12:00:00Z          NA
#>  8      125.       14        0 2015-01-01T12:00:00Z          NA
#>  9      125.       14        0 2015-01-01T12:00:00Z          NA
#> 10      125.       14        0 2015-01-01T12:00:00Z          NA
#> # … with 5,033 more rows

The output of griddap() is a list that you can explore further. Get the summary

res$summary
#> $filename
#> [1] "~/Library/Caches/R/rerddap/4d844aa48552049c3717ac94ced5f9b8.nc"
#> 
#> $writable
#> [1] FALSE
#> 
#> $id
#> [1] 65536
#> 
#> $error
#> [1] FALSE
#> 
#> $safemode
#> [1] FALSE
#> 
...

Get the dimension variables

names(res$summary$dim)
#> [1] "time"      "altitude"  "latitude"  "longitude"

Get the data.frame (beware: you may want to just look at the head of the data.frame if large)

head(res$data)
#>   longitude latitude altitude                 time chlorophyll
#> 1   125.000       14        0 2015-01-01T12:00:00Z          NA
#> 2   125.025       14        0 2015-01-01T12:00:00Z          NA
#> 3   125.050       14        0 2015-01-01T12:00:00Z          NA
#> 4   125.075       14        0 2015-01-01T12:00:00Z          NA
#> 5   125.100       14        0 2015-01-01T12:00:00Z          NA
#> 6   125.125       14        0 2015-01-01T12:00:00Z          NA

tabledap (tabular) data

(out <- info('erdCalCOFIlrvsiz'))
#> <ERDDAP info> erdCalCOFIlrvsiz 
#>  Base URL: https://upwell.pfeg.noaa.gov/erddap 
#>  Dataset Type: tabledap 
#>  Variables:  
#>      calcofi_species_code: 
#>          Range: 19, 946 
#>      common_name: 
#>      cruise: 
#>      itis_tsn: 
#>      larvae_10m2: 
...
(dat <- tabledap('erdCalCOFIlrvsiz', fields=c('latitude','longitude','larvae_size',
  'scientific_name'), 'time>=2011-01-01', 'time<=2011-12-31'))
#> <ERDDAP tabledap> erdCalCOFIlrvsiz
#>    Path: [~/Library/Caches/R/rerddap/db7389db5b5b0ed9c426d5c13bc43d18.csv]
#>    Last updated: [2022-09-30 09:34:05]
#>    File size:    [0.05 mb]
#> # A tibble: 1,304 × 4
#>    latitude  longitude  larvae_size scientific_name     
#>    <chr>     <chr>      <chr>       <chr>               
#>  1 32.956665 -117.305   4.5         Engraulis mordax    
#>  2 32.91     -117.4     5.0         Merluccius productus
#>  3 32.511665 -118.21167 2.0         Merluccius productus
#>  4 32.511665 -118.21167 3.0         Merluccius productus
#>  5 32.511665 -118.21167 5.5         Merluccius productus
#>  6 32.511665 -118.21167 6.0         Merluccius productus
#>  7 32.511665 -118.21167 2.8         Merluccius productus
#>  8 32.511665 -118.21167 3.0         Sardinops sagax     
#>  9 32.511665 -118.21167 5.0         Sardinops sagax     
#> 10 32.511665 -118.21167 2.5         Engraulis mordax    
#> # … with 1,294 more rows

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$larvae_size <- as.numeric(dat$larvae_size)
dat %>%
  group_by(scientific_name) %>%
  summarise(mean_size = mean(larvae_size)) %>%
  arrange(desc(mean_size))
#> # A tibble: 7 × 2
#>   scientific_name       mean_size
#>   <chr>                     <dbl>
#> 1 Anoplopoma fimbria        23.3 
#> 2 Engraulis mordax           9.26
#> 3 Sardinops sagax            7.28
#> 4 Merluccius productus       5.48
#> 5 Tactostoma macropus        5   
#> 6 Scomber japonicus          3.4 
#> 7 Trachurus symmetricus      3.29