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rerddap introduction
In rerddap: General Purpose Client for 'ERDDAP' Servers
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")
Search
First, you likely want to search for data, specify either griddadp or tabledap
ed_search(query = 'size', which = "table")
#> # A tibble: 36 × 2
#> title dataset_id
#> <chr> <chr>
#> 1 CCE Prey Size and Hard Part Size Regressions mmtdPreyS…
#> 2 CCE Teleost Prey Size and Hard Part Size Measurements mmtdTeleo…
#> 3 CalCOFI Larvae Sizes erdCalCOF…
#> 4 CCE Non-Teleost Prey Size and Hard Part Size Measurements mmtdNonTe…
#> 5 Channel Islands, Kelp Forest Monitoring, Size and Frequency, Natu… erdCinpKf…
#> 6 File Names from the AWS S3 noaa-goes16 Bucket awsS3Noaa…
#> 7 File Names from the AWS S3 noaa-goes17 Bucket awsS3Noaa…
#> 8 PacIOOS Beach Camera 001: Waikiki, Oahu, Hawaii BEACHCAM-…
#> 9 PacIOOS Beach Camera 003: Waimea Bay, Oahu, Hawaii BEACHCAM-…
#> 10 PacIOOS Beach Camera 004: Waimea Bay (Offshore), Oahu, Hawaii BEACHCAM-…
#> # ℹ 26 more rows
ed_search(query = 'size', which = "grid")
#> # A tibble: 103 × 2
#> title dataset_id
#> <chr> <chr>
#> 1 Audio data from a local source. testGridW…
#> 2 Main Hawaiian Islands Multibeam Bathymetry Synthesis: 50-m Bathym… hmrg_bath…
#> 3 Main Hawaiian Islands Multibeam Bathymetry Synthesis: 50-m Bathym… hmrg_bath…
#> 4 Coastal Upwelling Transport Index (CUTI), Daily erdCUTIda…
#> 5 SST smoothed frontal gradients FRD_SSTgr…
#> 6 Coastal Upwelling Transport Index (CUTI), Monthly erdCUTImo…
#> 7 SST smoothed frontal gradients, Lon0360 FRD_SSTgr…
#> 8 Biologically Effective Upwelling Transport Index (BEUTI), Daily erdBEUTId…
#> 9 Biologically Effective Upwelling Transport Index (BEUTI), Monthly erdBEUTIm…
#> 10 Daily averaged and put on grid 4x daily NCEP reanalysis (psi.2012) noaa_psl_…
#> # ℹ 93 more rows
There is now a convenience function to search over a list of ERDDAP servers, designed to work with the function servers()
server_list <- c(
emodnet_physics = 'https://erddap.emodnet-physics.eu/erddap/',
irish_marine_institute = 'https://erddap.marine.ie/erddap/'
)
global_search(query = 'size', server_list, 'griddap')
#> title
#> 1 EMODnet Physics - Total Suspended Matter - GridSeriesObservation - Concentration of total suspended matter - BALTIC SEA
#> 2 EMODnet Physics - Total Suspended Matter - GridSeriesObservation - Concentration of total suspended matter - MEDITERRANEAN SEA
#> 3 EMODnet Physics - Total Suspended Matter - GridSeriesObservation - Concentration of total suspended matter - MEDITERRANEAN SEA - LOW RESOLUTION
#> 4 EMODnet Physics - TEMPERATURE YEARLY RECORDING DENSITY
#> 5 EMODPACE - Monthly sea level derived from CMEMS-DUACS (DT-2018) satellite altimetry (1993-2019)
#> 6 EMODnet Physics - Total Suspended Matter - GridSeriesObservation - Concentration of total suspended matter - NORTH SEA
#> 7 EMODPACE - Absolute sea level trend (1993 – 2019) - derived from CMEMS-DUACS (DT-2018) satellite altimetry
#> 8 EMODPACE - Sea Level monthly mean, EurAsia. This product is based, uses and reprocess the CMEMS product id. SEALEVEL_GLO_PHY_CLIMATE_L4_REP_OBSERVATIONS_008_057
#> 9 COMPASS-NEATL Hindcast 2016-2020
#> dataset_id
#> 1 TSM_BALTICSEA
#> 2 TSM_MBSEA
#> 3 TSM_MBSEA_LOWRESOLUTION
#> 4 ERD_EP_TEMP_DNS
#> 5 EMODPACE_SLEV_MONTHLY_MEAN_DESEASONALIZED
#> 6 TSM_NORTHSEA
#> 7 EMODPACE_SLEV_TREND
#> 8 EMODPACE_SLEV_MONTHLY_MEAN
#> 9 compass_neatl_hindcast_grid
#> url
#> 1 https://erddap.emodnet-physics.eu/erddap/
#> 2 https://erddap.emodnet-physics.eu/erddap/
#> 3 https://erddap.emodnet-physics.eu/erddap/
#> 4 https://erddap.emodnet-physics.eu/erddap/
#> 5 https://erddap.emodnet-physics.eu/erddap/
#> 6 https://erddap.emodnet-physics.eu/erddap/
#> 7 https://erddap.emodnet-physics.eu/erddap/
#> 8 https://erddap.emodnet-physics.eu/erddap/
#> 9 https://erddap.marine.ie/erddap/
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
Try the rerddap package in your browser
Any scripts or data that you put into this service are public.
rerddap documentation built on Aug. 23, 2023, 9:06 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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")
Search
First, you likely want to search for data, specify either griddadp or tabledap
ed_search(query = 'size', which = "table") #> # A tibble: 36 × 2 #> title dataset_id #> <chr> <chr> #> 1 CCE Prey Size and Hard Part Size Regressions mmtdPreyS… #> 2 CCE Teleost Prey Size and Hard Part Size Measurements mmtdTeleo… #> 3 CalCOFI Larvae Sizes erdCalCOF… #> 4 CCE Non-Teleost Prey Size and Hard Part Size Measurements mmtdNonTe… #> 5 Channel Islands, Kelp Forest Monitoring, Size and Frequency, Natu… erdCinpKf… #> 6 File Names from the AWS S3 noaa-goes16 Bucket awsS3Noaa… #> 7 File Names from the AWS S3 noaa-goes17 Bucket awsS3Noaa… #> 8 PacIOOS Beach Camera 001: Waikiki, Oahu, Hawaii BEACHCAM-… #> 9 PacIOOS Beach Camera 003: Waimea Bay, Oahu, Hawaii BEACHCAM-… #> 10 PacIOOS Beach Camera 004: Waimea Bay (Offshore), Oahu, Hawaii BEACHCAM-… #> # ℹ 26 more rows
ed_search(query = 'size', which = "grid") #> # A tibble: 103 × 2 #> title dataset_id #> <chr> <chr> #> 1 Audio data from a local source. testGridW… #> 2 Main Hawaiian Islands Multibeam Bathymetry Synthesis: 50-m Bathym… hmrg_bath… #> 3 Main Hawaiian Islands Multibeam Bathymetry Synthesis: 50-m Bathym… hmrg_bath… #> 4 Coastal Upwelling Transport Index (CUTI), Daily erdCUTIda… #> 5 SST smoothed frontal gradients FRD_SSTgr… #> 6 Coastal Upwelling Transport Index (CUTI), Monthly erdCUTImo… #> 7 SST smoothed frontal gradients, Lon0360 FRD_SSTgr… #> 8 Biologically Effective Upwelling Transport Index (BEUTI), Daily erdBEUTId… #> 9 Biologically Effective Upwelling Transport Index (BEUTI), Monthly erdBEUTIm… #> 10 Daily averaged and put on grid 4x daily NCEP reanalysis (psi.2012) noaa_psl_… #> # ℹ 93 more rows
There is now a convenience function to search over a list of ERDDAP servers, designed to work with the function servers()
server_list <- c( emodnet_physics = 'https://erddap.emodnet-physics.eu/erddap/', irish_marine_institute = 'https://erddap.marine.ie/erddap/' ) global_search(query = 'size', server_list, 'griddap') #> title #> 1 EMODnet Physics - Total Suspended Matter - GridSeriesObservation - Concentration of total suspended matter - BALTIC SEA #> 2 EMODnet Physics - Total Suspended Matter - GridSeriesObservation - Concentration of total suspended matter - MEDITERRANEAN SEA #> 3 EMODnet Physics - Total Suspended Matter - GridSeriesObservation - Concentration of total suspended matter - MEDITERRANEAN SEA - LOW RESOLUTION #> 4 EMODnet Physics - TEMPERATURE YEARLY RECORDING DENSITY #> 5 EMODPACE - Monthly sea level derived from CMEMS-DUACS (DT-2018) satellite altimetry (1993-2019) #> 6 EMODnet Physics - Total Suspended Matter - GridSeriesObservation - Concentration of total suspended matter - NORTH SEA #> 7 EMODPACE - Absolute sea level trend (1993 – 2019) - derived from CMEMS-DUACS (DT-2018) satellite altimetry #> 8 EMODPACE - Sea Level monthly mean, EurAsia. This product is based, uses and reprocess the CMEMS product id. SEALEVEL_GLO_PHY_CLIMATE_L4_REP_OBSERVATIONS_008_057 #> 9 COMPASS-NEATL Hindcast 2016-2020 #> dataset_id #> 1 TSM_BALTICSEA #> 2 TSM_MBSEA #> 3 TSM_MBSEA_LOWRESOLUTION #> 4 ERD_EP_TEMP_DNS #> 5 EMODPACE_SLEV_MONTHLY_MEAN_DESEASONALIZED #> 6 TSM_NORTHSEA #> 7 EMODPACE_SLEV_TREND #> 8 EMODPACE_SLEV_MONTHLY_MEAN #> 9 compass_neatl_hindcast_grid #> url #> 1 https://erddap.emodnet-physics.eu/erddap/ #> 2 https://erddap.emodnet-physics.eu/erddap/ #> 3 https://erddap.emodnet-physics.eu/erddap/ #> 4 https://erddap.emodnet-physics.eu/erddap/ #> 5 https://erddap.emodnet-physics.eu/erddap/ #> 6 https://erddap.emodnet-physics.eu/erddap/ #> 7 https://erddap.emodnet-physics.eu/erddap/ #> 8 https://erddap.emodnet-physics.eu/erddap/ #> 9 https://erddap.marine.ie/erddap/
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
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