Pulling data from ERDDAP™
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Many publicly available scientific datasets from around the world can be found on servers supported by ERDDAP™ (Environmental Research Division’s Data Access Program). Many organizations, like government agencies and universities, run their own ERDDAP™ servers and host oceanographic and environmental datasets.
The data are served up in a consistent way, allowing users to visualize and download data in various standard data formats, like netCDF (nc), csv, txt, JSON.
While it is perfectly reasonable to interact with these ERDDAP™ servers through a web interface, and often preferable initially to identify data sources, read metadata, and visualize the data, pulling and working with the data is often best accomplished using a language like python or R. Since i mostly use R for my work, we’ll go that route.
For R users there is a wonderful package called rerddap designed to help search, connect, and download data from ERDDAP™ servers. Lets go through an example to demonstrate how to get started. The steps involved are
- List all servers (
rerddap::servers()) - List all datasets on a specific server (
rerddap::ed_datasets()) - Select and explore dataset fields (
rerddap::info()) - Pull data (
rerddap::tabledap()orrerddap::griddap())
List servers
First, let’s see the list of server names available using the servers() function
rerddap::servers()
#> # A tibble: 63 × 4
#> name short_name url public
#> <chr> <chr> <chr> <lgl>
#> 1 Voice of the Ocean VOTO http… TRUE
#> 2 St. Lawrence Global Observatory - CIOOS | Observatoi… SLGO-OGSL http… TRUE
#> 3 CoastWatch West Coast Node CSWC http… TRUE
#> 4 ERDDAP at the Asia-Pacific Data-Research Center APDRC http… TRUE
#> 5 NOAA's National Centers for Environmental Informatio… NCEI http… TRUE
#> 6 Biological and Chemical Oceanography Data Management… BCODMO http… TRUE
#> 7 European Marine Observation and Data Network (EMODne… EMODnet http… TRUE
#> 8 European Marine Observation and Data Network (EMODne… EMODnet P… http… TRUE
#> 9 Marine Institute - Ireland MII http… TRUE
#> 10 CoastWatch Caribbean/Gulf of Mexico Node CSCGOM http… TRUE
#> # ℹ 53 more rows
Created on 2025-12-08 with reprex v2.1.1
List datasets
The “CoastWatch West Coast Node” server looks interesting, lets explore that. We’ll need to grab the url field to explore the datasets hosted on this server.
servers <- rerddap::servers()
servers |>
dplyr::filter(short_name == "CSWC") |>
dplyr::select(url)
#> # A tibble: 1 × 1
#> url
#> <chr>
#> 1 https://coastwatch.pfeg.noaa.gov/erddap/
Created on 2025-12-08 with reprex v2.1.1
Now, at this point you can either copy this url into your browser and explore the contents of the server from there, or you can use rerddap to list the data sets. At this point we should mention that there are often two types of data hosted on these servers
- Gridded data, termed
griddap, in NetCDF (nc) format - Tabular data, termed
tabledap, often in csv format
Let’s search for all of the tabular data on this server.
servers <- rerddap::servers()
url <- servers |>
dplyr::filter(short_name == "CSWC") |>
dplyr::select(url)
rerddap::ed_datasets(which = "tabledap", url = url)
#> # A tibble: 293 × 17
#> griddap Subset tabledap Make.A.Graph wms files Accessible Title Summary
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 "" https://c… https:/… https://coa… "" "" public * Th… "This …
#> 2 "" https://c… https:/… https://coa… "" "htt… public Audi… "Audio…
#> 3 "" https://c… https:/… https://coa… "" "htt… public CalC… "Hydro…
#> 4 "" https://c… https:/… https://coa… "" "htt… public CalC… "Sampl…
#> 5 "" https://c… https:/… https://coa… "" "htt… public CalC… "Cruis…
#> 6 "" https://c… https:/… https://coa… "" "htt… public CalC… "Fish …
#> 7 "" https://c… https:/… https://coa… "" "htt… public CalC… "Egg m…
#> 8 "" https://c… https:/… https://coa… "" "htt… public CalC… "Fish …
#> 9 "" https://c… https:/… https://coa… "" "htt… public CalC… "Size …
#> 10 "" https://c… https:/… https://coa… "" "htt… public CalC… "Devel…
#> # ℹ 283 more rows
#> # ℹ 8 more variables: FGDC <chr>, ISO.19115 <chr>, Info <chr>,
#> # Background.Info <chr>, RSS <chr>, Email <chr>, Institution <chr>,
#> # Dataset.ID <chr>
Created on 2025-12-08 with reprex v2.1.1
We can see that there are 293 datasets available, each with its own url field (tabldap) and ID (Dataset.ID) and other accompanying metadata. As mentioned earlier it may be easier to look at these from within a web browser. But for the sake of this example let’s just look at the Summary metadata
servers <- rerddap::servers()
url <- servers |>
dplyr::filter(short_name == "CSWC") |>
dplyr::select(url)
datasets <- rerddap::ed_datasets(which = "tabledap", url = url)
datasets |>
dplyr::select(Dataset.ID,Summary)
#> # A tibble: 293 × 2
#> Dataset.ID Summary
#> <chr> <chr>
#> 1 allDatasets "This dataset is a table which has a row of information…
#> 2 testTableWav "Audio data from a local source.\n\ncdm_data_type = Oth…
#> 3 erdCalCOFINOAAhydros "Hydrographic data collected by CTD as part of CalCOFI …
#> 4 erdCalCOFIcufes "Samples collected using the Continuous Underway Fish-E…
#> 5 erdCalCOFIcruises "Cruises using one or more ships conducted as part of t…
#> 6 erdCalCOFIeggcnt "Fish egg counts and standardized counts for eggs captu…
#> 7 erdCalCOFIeggstg "Egg morphological developmental stage for eggs of sele…
#> 8 erdCalCOFIlrvcnt "Fish larvae counts and standardized counts for eggs ca…
#> 9 erdCalCOFIlrvsiz "Size data for selected larval fish captured in CalCOFI…
#> 10 erdCalCOFIlrvstg "Developmental stages (yolk sac, preflexion, flexion, p…
#> # ℹ 283 more rows
Created on 2025-12-08 with reprex v2.1.1
Explore dataset
Now, from experience i know there is a dataset hosted here containing data from the National Data Buoy Center (NDBC). The data has a Dataset.ID = “cwwcNDBCMet”.
datasets |>
dplyr::filter(grepl("NDBC",Summary)) |>
dplyr::pull(Dataset.ID)
[1] "cwwcNDBCMet"
Once we have this id, we are ready to explore and pull the data. So lets grab all of the information relating to this dataset. We’ll use the function info()
data_info <- rerddap::info(datasetid = "cwwcNDBCMet", url = "https://coastwatch.pfeg.noaa.gov/erddap/")
data_info is a list object containing metadata, like the url of the server, variables describing the variable names, variable type, and variable data range. This list object is what you’ll pass to the function, tabledap() to pull the data.
Pull the dataset
Even though we have identified the data set we want, we don’t really know how much data there is! If you tried to pull all of the data at once, there is a good chance your computer will crash! So in prep, we can explore the set of available variables using a couple of methods that involve the data_info list object:
rerddap::browse(data_info)- will open a webpage on ERDDAP™ describing the data setdata_info$variableswill list the variables available anddata_info$alldata[[variableName]]will show more detailed information about each variable.
erddap_url <- 'https://coastwatch.pfeg.noaa.gov/erddap/'
datasetid <- "cwwcNDBCMet"
data_info <- suppressMessages(rerddap::info(datasetid, url = erddap_url))
data_info$variables
#> variable_name data_type actual_range
#> 1 apd float 0.0, 95.0
#> 2 atmp float -153.4, 50.0
#> 3 bar float 800.7, 1198.8
#> 4 dewp float -99.9, 48.7
#> 5 dpd float 0.0, 64.0
#> 6 gst float 0.0, 75.5
#> 7 latitude float -55.0, 71.758
#> 8 longitude float -177.75, 179.001
#> 9 mwd short 0, 359
#> 10 ptdy float -13.1, 14.9
#> 11 station String
#> 12 tide float -9.37, 6.15
#> 13 time double 4910400.0, 1.7697828E9
#> 14 vis float 0.0, 66.7
#> 15 wd short 0, 359
#> 16 wspd float 0.0, 96.0
#> 17 wspu float -98.7, 97.5
#> 18 wspv float -98.7, 97.5
#> 19 wtmp float -98.7, 50.0
#> 20 wvht float 0.0, 92.39
Created on 2026-01-30 with reprex v2.1.1
What jumps out here is the station variable and the latitude and longitude variables. We’ll now use these to pull the list of stations available
erddap_url <- 'https://coastwatch.pfeg.noaa.gov/erddap/'
datasetid <- "cwwcNDBCMet"
data_info <- suppressMessages(rerddap::info(datasetid, url = erddap_url))
rerddap::tabledap(
data_info,
fields = c("station", "longitude", "latitude"),
distinct = TRUE
)
#> info() output passed to x; setting base url to: https://coastwatch.pfeg.noaa.gov/erddap
#> <ERDDAP tabledap> cwwcNDBCMet
#> # A tibble: 1,329 × 3
#> station longitude latitude
#> <chr> <dbl> <dbl>
#> 1 0Y2W3 -87.3 44.8
#> 2 18CI3 -86.9 41.7
#> 3 20CM4 -86.5 42.1
#> 4 23020 38.5 22.2
#> 5 31201 -48.1 -27.7
#> 6 32012 -85.4 -19.6
#> 7 32301 -105. -9.9
#> 8 32302 -85.1 -18
#> 9 32487 -77.7 3.52
#> 10 32488 -77.5 6.26
#> # ℹ 1,319 more rows
Created on 2026-01-30 with reprex v2.1.1
Now that you have identified the set of buoy stations available you can now either pull individual stations data or pull a collection of stations within a geographic region. Either of these tasks will require using the erddap::tabledap() function. For example:
Get data from a single station
Select the station(s) of interest, then get the data. In this example we’ll pull all of the data associated with buoy 32012
erddap_url <- 'https://coastwatch.pfeg.noaa.gov/erddap/'
datasetid <- "cwwcNDBCMet"
data_info <- suppressMessages(rerddap::info(datasetid, url = erddap_url))
variables <- data_info$variables$variable_name
rerddap::tabledap(
datasetid,
fields = variables,
query = paste0('station="', 32012, '"')
)
#> <ERDDAP tabledap> cwwcNDBCMet
#> File size: [9.37 mb]
#> # A tibble: 84,918 × 20
#> apd atmp bar dewp dpd gst latitude longitude mwd ptdy station
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <int>
#> 1 6.88 NaN NaN NaN 13.8 NaN -19.6 -85.4 215 NaN 32012
#> 2 7.01 NaN NaN NaN 13.8 NaN -19.6 -85.4 253 NaN 32012
#> 3 6.8 NaN NaN NaN 11.4 NaN -19.6 -85.4 202 NaN 32012
#> 4 7.31 NaN NaN NaN 11.4 NaN -19.6 -85.4 200 NaN 32012
#> 5 7.32 NaN NaN NaN 10.8 NaN -19.6 -85.4 190 NaN 32012
#> 6 7.09 NaN NaN NaN 11.4 NaN -19.6 -85.4 204 NaN 32012
#> 7 7.68 NaN NaN NaN 10.8 NaN -19.6 -85.4 207 NaN 32012
#> 8 7.07 NaN NaN NaN 12.9 NaN -19.6 -85.4 235 NaN 32012
#> 9 7.09 NaN NaN NaN 11.4 NaN -19.6 -85.4 219 NaN 32012
#> 10 6.94 NaN NaN NaN 10 NaN -19.6 -85.4 201 NaN 32012
#> # ℹ 84,908 more rows
#> # ℹ 9 more variables: tide <dbl>, time <dttm>, vis <dbl>, wd <int>, wspd <dbl>,
#> # wspu <dbl>, wspv <dbl>, wtmp <dbl>, wvht <dbl>
Created on 2026-01-30 with reprex v2.1.1
Now you can work with this data in R for whatever purpose you’d like.
Get data by geographic region
If you want to simply pull all of the buoys in a specific region you can do this too. ERDDAP™ has a few server side functions that let you narrow your search. For example, lets pull all stations within a region along the Northeast USA seaboard, (around Cape cod, MA) between latitudes [41.6,41.8] and longitudes [-70.5, -69.5]
erddap_url <- 'https://coastwatch.pfeg.noaa.gov/erddap/'
datasetid <- "cwwcNDBCMet"
data_info <- suppressMessages(rerddap::info(datasetid, url = erddap_url))
variables <- data_info$variables$variable_name
rerddap::tabledap(
datasetid,
fields = variables,
'latitude>=41.6','latitude<=41.8','longitude<=-69.5','longitude>=-70.5'
)
#> <ERDDAP tabledap> cwwcNDBCMet
#> Path: [C:\Users\ANDREW~1.BEE\AppData\Local\Temp\RtmpSQAjbB\R\rerddap\c9f6726fc0a4f89d39c0c92913fabd90.csv]
#> Last updated: [2026-01-30 11:00:00.736641]
#> File size: [54.57 mb]
#> # A tibble: 501,476 × 20
#> apd atmp bar dewp dpd gst latitude longitude mwd ptdy station
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <chr>
#> 1 NaN 4.9 998. NaN NaN 9.8 41.7 -70.0 NA NaN CHTM3
#> 2 NaN 4.9 998. NaN NaN 11.9 41.7 -70.0 NA NaN CHTM3
#> 3 NaN 4.9 998 NaN NaN 11.3 41.7 -70.0 NA NaN CHTM3
#> 4 NaN 4.9 998 NaN NaN 10.8 41.7 -70.0 NA NaN CHTM3
#> 5 NaN 5 998. NaN NaN 10 41.7 -70.0 NA NaN CHTM3
#> 6 NaN 5 998. NaN NaN 11.4 41.7 -70.0 NA NaN CHTM3
#> 7 NaN 5.1 998. NaN NaN 9.9 41.7 -70.0 NA NaN CHTM3
#> 8 NaN 5 998. NaN NaN 11.6 41.7 -70.0 NA NaN CHTM3
#> 9 NaN 5 998. NaN NaN 8.9 41.7 -70.0 NA NaN CHTM3
#> 10 NaN 4.9 998. NaN NaN 10.6 41.7 -70.0 NA NaN CHTM3
#> # ℹ 501,466 more rows
#> # ℹ 9 more variables: tide <dbl>, time <dttm>, vis <dbl>, wd <int>, wspd <dbl>,
#> # wspu <dbl>, wspv <dbl>, wtmp <dbl>, wvht <dbl>
Created on 2026-01-30 with reprex v2.1.1
From this narrow geographic region there is only one station CHMT3 which is a buoy off Chatham, MA
Summary
Getting the data from ERDDAP™ into R requires a little work. You can use a combination of the R package rerddap and the ERDDAP™ website to help identify the data you are interested in.
Fortunately for the buoy station data used in the example, there is an R package called buoydata available to help with the identification of buoy station availability with tools to pull station data hassle free.
Enjoy exploring the masses of data on ERDDAP™