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.
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.
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.
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.
Stable version from CRAN
install.packages("rerddap")
Or, the development version from GitHub
devtools::install_github("ropensci/rerddap")
library("rerddap")
First, you likely want to search for data, specify either griddadp or tabledap
ed_search(query = 'size', which = "table")
#> # A tibble: 9 x 2
#> title dataset_id
#> <chr> <chr>
#> 1 CalCOFI Larvae Sizes erdCalCOFIlrvsiz
#> 2 Channel Islands, Kelp Forest Monitoring, Size and Frequ… erdCinpKfmSFNH
#> 3 CalCOFI Larvae Counts Positive Tows erdCalCOFIlrvcn…
#> 4 CalCOFI Tows erdCalCOFItows
#> 5 NWFSC Observer Fixed Gear Data, off West Coast of US, 2… nwioosObsFixed2…
#> 6 NWFSC Observer Trawl Data, off West Coast of US, 2002-2… nwioosObsTrawl2…
#> 7 GLOBEC NEP MOCNESS Plankton (MOC1) Data, 2000-2002 erdGlobecMoc1
#> 8 GLOBEC NEP Vertical Plankton Tow (VPT) Data, 1997-2001 erdGlobecVpt
#> 9 AN EXPERIMENTAL DATASET: Underway Sea Surface Temperatu… nodcPJJU
ed_search(query = 'size', which = "grid")
#> # A tibble: 11 x 2
#> title dataset_id
#> <chr> <chr>
#> 1 Extended AVHRR Polar Pathfinder Fundamental Clima… noaa_ngdc_da08_dcdf_…
#> 2 Extended AVHRR Polar Pathfinder Fundamental Clima… noaa_ngdc_0fe5_a4b9_…
#> 3 Extended AVHRR Polar Pathfinder Fundamental Clima… noaa_ngdc_5253_bf9e_…
#> 4 Extended AVHRR Polar Pathfinder Fundamental Clima… noaa_ngdc_0f24_2f8c_…
#> 5 Archived Suite of NOAA Coral Reef Watch Operation… noaa_nodc_9f8b_ab7e_…
#> 6 Archived Suite of NOAA Coral Reef Watch Operation… noaa_nodc_da4e_3fc9_…
#> 7 USGS COAWST Forecast, US East Coast and Gulf of M… whoi_geoport_62d0_9d…
#> 8 USGS COAWST Forecast, US East Coast and Gulf of M… whoi_geoport_7dd7_db…
#> 9 USGS COAWST Forecast, US East Coast and Gulf of M… whoi_geoport_a4fb_2c…
#> 10 USGS COAWST Forecast, US East Coast and Gulf of M… whoi_geoport_ed12_89…
#> 11 USGS COAWST Forecast, US East Coast and Gulf of M… whoi_geoport_61c3_0b…
Then you can get information on a single dataset
info('erdCalCOFIlrvsiz')
#> <ERDDAP info> erdCalCOFIlrvsiz
#> Variables:
#> calcofi_species_code:
#> Range: 19, 9760
#> common_name:
#> cruise:
#> itis_tsn:
#> larvae_10m2:
#> Units: Fish larvae per ten meters squared of water sampled
#> larvae_count:
...
First, get information on a dataset to see time range, lat/long range, and variables.
(out <- info('erdMBchla1day'))
#> <ERDDAP info> erdMBchla1day
#> Dimensions (range):
#> time: (2006-01-01T12:00:00Z, 2019-01-30T12:00:00Z)
#> 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: [/Users/sckott/Library/Caches/R/rerddap/4d844aa48552049c3717ac94ced5f9b8.nc]
#> Last updated: [2019-01-31 13:39:37]
#> 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 4]
#> # A tibble: 5,043 x 4
#> time lat lon chlorophyll
#> <chr> <dbl> <dbl> <dbl>
#> 1 2015-01-01T12:00:00Z 14 125 NA
#> 2 2015-01-01T12:00:00Z 14 125. NA
#> 3 2015-01-01T12:00:00Z 14 125. NA
#> 4 2015-01-01T12:00:00Z 14 125. NA
#> 5 2015-01-01T12:00:00Z 14 125. NA
#> 6 2015-01-01T12:00:00Z 14 125. NA
#> 7 2015-01-01T12:00:00Z 14 125. NA
#> 8 2015-01-01T12:00:00Z 14 125. NA
#> 9 2015-01-01T12:00:00Z 14 125. NA
#> 10 2015-01-01T12:00:00Z 14 125. 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] "/Users/sckott/Library/Caches/R/rerddap/4d844aa48552049c3717ac94ced5f9b8.nc"
#>
#> $writable
#> [1] FALSE
#>
#> $id
#> [1] 65536
#>
#> $safemode
#> [1] FALSE
#>
#> $format
#> [1] "NC_FORMAT_CLASSIC"
#>
...
Get the dimension variables
names(res$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)
#> time lat lon chlorophyll
#> 1 2015-01-01T12:00:00Z 14 125.000 NA
#> 2 2015-01-01T12:00:00Z 14 125.025 NA
#> 3 2015-01-01T12:00:00Z 14 125.050 NA
#> 4 2015-01-01T12:00:00Z 14 125.075 NA
#> 5 2015-01-01T12:00:00Z 14 125.100 NA
#> 6 2015-01-01T12:00:00Z 14 125.125 NA
(out <- info('erdCalCOFIlrvsiz'))
#> <ERDDAP info> erdCalCOFIlrvsiz
#> Variables:
#> calcofi_species_code:
#> Range: 19, 9760
#> common_name:
#> cruise:
#> itis_tsn:
#> larvae_10m2:
#> Units: Fish larvae per ten meters squared of water sampled
#> larvae_count:
...
(dat <- tabledap('erdCalCOFIlrvsiz', fields=c('latitude','longitude','larvae_size',
'scientific_name'), 'time>=2011-01-01', 'time<=2011-12-31'))
#> <ERDDAP tabledap> erdCalCOFIlrvsiz
#> Path: [/Users/sckott/Library/Caches/R/rerddap/db7389db5b5b0ed9c426d5c13bc43d18.csv]
#> Last updated: [2019-01-31 13:40:29]
#> File size: [0.07 mb]
#> # A tibble: 1,725 x 4
#> latitude longitude larvae_size scientific_name
#> <chr> <chr> <chr> <chr>
#> 1 32.956665 -117.305 2.8 Doryteuthis opalescens
#> 2 32.956665 -117.305 3.4 Doryteuthis opalescens
#> 3 32.956665 -117.305 3.6 Doryteuthis opalescens
#> 4 32.956665 -117.305 3.1 Doryteuthis opalescens
#> 5 32.956665 -117.305 3.3 Doryteuthis opalescens
#> 6 32.956665 -117.305 3.7 Doryteuthis opalescens
#> 7 32.956665 -117.305 3.9 Doryteuthis opalescens
#> 8 32.956665 -117.305 2.7 Doryteuthis opalescens
#> 9 32.956665 -117.305 4.1 Doryteuthis opalescens
#> 10 32.956665 -117.305 2.8 Doryteuthis opalescens
#> # … with 1,715 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: 8 x 2
#> scientific_name mean_size
#> <chr> <dbl>
#> 1 Anoplopoma fimbria 23.3
#> 2 Engraulis mordax 9.26
#> 3 Sardinops sagax 7.29
#> 4 Merluccius productus 5.48
#> 5 Tactostoma macropus 5
#> 6 Doryteuthis opalescens 3.67
#> 7 Scomber japonicus 3.4
#> 8 Trachurus symmetricus 3.29