rerddap introduction

Scott Chamberlain

2023-06-29

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")
library("rerddap")

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, 2023-06-27T12:00:00Z) 
#>      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: [/var/folders/xw/mcmsdzzx4mgbttplylgs7ysh0000gp/T//RtmpoME6FV/R/rerddap/4d844aa48552049c3717ac94ced5f9b8.nc]
#>    Last updated: [2023-06-29 13:18:53.945082]
#>    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[1d]> <dbl[1d]> <dbl[1d]> <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
#> # ℹ 5,033 more rows

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

res$summary
#> $filename
#> [1] "/var/folders/xw/mcmsdzzx4mgbttplylgs7ysh0000gp/T//RtmpoME6FV/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: [/var/folders/xw/mcmsdzzx4mgbttplylgs7ysh0000gp/T//RtmpoME6FV/R/rerddap/db7389db5b5b0ed9c426d5c13bc43d18.csv]
#>    Last updated: [2023-06-29 13:18:57.579066]
#>    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    
#> # ℹ 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