rerddap tutorial
for v0.4.2
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 ncdf and ncdf4 packages make 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(). 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.
Installation
Stable version from CRAN
install.packages("rerddap")
Or, the development version from GitHub
devtools::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: 10 x 2
#> title
#> <chr>
#> 1 CalCOFI Larvae Sizes
#> 2 Channel Islands, Kelp Forest Monitoring, Size and Frequency, Natural Habita
#> 3 NWFSC Observer Fixed Gear Data, off West Coast of US, 2002-2006
#> 4 NWFSC Observer Trawl Data, off West Coast of US, 2002-2006
#> 5 CalCOFI Larvae Counts Positive Tows
#> 6 CalCOFI Tows
#> 7 GLOBEC NEP MOCNESS Plankton (MOC1) Data, 2000-2002
#> 8 GLOBEC NEP Vertical Plankton Tow (VPT) Data, 1997-2001
#> 9 OBIS - ARGOS Satellite Tracking of Animals
#> 10 AN EXPERIMENTAL DATASET: Underway Sea Surface Temperature and Salinity Aboa
#> # ... with 1 more variables: dataset_id <chr>
ed_search(query = 'size', which = "grid")
#> # A tibble: 290 x 2
#> title
#> <chr>
#> 1 COAWST Hindcast:MVCO/CBlast 2007:ripples with SWAN-40m res (00 dir roms) [t
#> 2 COAWST Hindcast:MVCO/CBlast 2007:ripples with SWAN-40m res (00 dir roms) [t
#> 3 COAWST Hindcast:MVCO/CBlast 2007:ripples with SWAN-40m res (00 dir roms) [t
#> 4 COAWST Hindcast:MVCO/CBlast 2007:ripples with SWAN-40m res (00 dir roms) [t
#> 5 COAWST Hindcast:MVCO/CBlast 2007:ripples with SWAN-40m res (00 dir roms) [t
#> 6 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0021) [time][
#> 7 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0021) [time][
#> 8 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0021) [time][
#> 9 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0021) [time][
#> 10 ROMS3.0 CBLAST2007 Ripples with SWAN-40m res (his case7 ar0fd 0021) [time][
#> # ... with 280 more rows, and 1 more variables: dataset_id <chr>
Information
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_1000m3:
#> Units: Fish larvae per 1,000 cubic meters of water sampled
#> larvae_10m2:
...
griddap (gridded) data
First, get information on a dataset to see time range, lat/long range, and variables.
(out <- info('noaa_esrl_027d_0fb5_5d38'))
#> <ERDDAP info> noaa_esrl_027d_0fb5_5d38
#> Dimensions (range):
#> time: (1850-01-01T00:00:00Z, 2014-05-01T00:00:00Z)
#> latitude: (87.5, -87.5)
#> longitude: (-177.5, 177.5)
#> Variables:
#> air:
#> Range: -20.9, 19.5
#> Units: degC
Then query for gridded data using the griddap() function
(res <- griddap(out,
time = c('2012-01-01', '2012-01-30'),
latitude = c(21, 10),
longitude = c(-80, -70)
))
#> <ERDDAP griddap> noaa_esrl_027d_0fb5_5d38
#> Path: [/Users/sacmac/Library/Caches/R/rdpla/1a664ce4d6af316b611ac5a74a68d704.nc]
#> Last updated: [2017-10-17 11:58:34]
#> File size: [0 mb]
#> Dimensions (dims/vars): [3 X 1]
#> Dim names: time, latitude, longitude
#> Variable names: CRUTEM3: Surface Air Temperature Monthly Anomaly
#> data.frame (rows/columns): [18 X 4]
#> # A tibble: 18 x 4
#> time lat lon air
#> <chr> <dbl> <dbl> <dbl>
#> 1 2012-01-01T00:00:00Z 22.5 -77.5 NA
#> 2 2012-01-01T00:00:00Z 22.5 -72.5 NA
#> 3 2012-01-01T00:00:00Z 22.5 -67.5 NA
#> 4 2012-01-01T00:00:00Z 17.5 -77.5 -0.10
#> 5 2012-01-01T00:00:00Z 17.5 -72.5 NA
#> 6 2012-01-01T00:00:00Z 17.5 -67.5 -0.20
#> 7 2012-01-01T00:00:00Z 12.5 -77.5 0.20
#> 8 2012-01-01T00:00:00Z 12.5 -72.5 NA
#> 9 2012-01-01T00:00:00Z 12.5 -67.5 0.30
#> 10 2012-02-01T00:00:00Z 22.5 -77.5 NA
#> 11 2012-02-01T00:00:00Z 22.5 -72.5 NA
#> 12 2012-02-01T00:00:00Z 22.5 -67.5 NA
#> 13 2012-02-01T00:00:00Z 17.5 -77.5 0.40
#> 14 2012-02-01T00:00:00Z 17.5 -72.5 NA
#> 15 2012-02-01T00:00:00Z 17.5 -67.5 0.20
#> 16 2012-02-01T00:00:00Z 12.5 -77.5 0.00
#> 17 2012-02-01T00:00:00Z 12.5 -72.5 NA
#> 18 2012-02-01T00:00:00Z 12.5 -67.5 0.32
The output of griddap() is a list that you can explore further. Get the summary
res$summary
#> $filename
#> [1] "/Users/sacmac/Library/Caches/R/rdpla/1a664ce4d6af316b611ac5a74a68d704.nc"
#>
#> $writable
#> [1] FALSE
#>
#> $id
#> [1] 65536
#>
#> $safemode
#> [1] FALSE
#>
#> $format
#> [1] "NC_FORMAT_CLASSIC"
#>
...
Get the dimension variables
names(res$summary$dim)
#> [1] "time" "latitude" "longitude"
Get the data.frame (beware: you may want to just look at the head of the data.frame if large)
res$data
#> time lat lon air
#> 1 2012-01-01T00:00:00Z 22.5 -77.5 NA
#> 2 2012-01-01T00:00:00Z 22.5 -72.5 NA
#> 3 2012-01-01T00:00:00Z 22.5 -67.5 NA
#> 4 2012-01-01T00:00:00Z 17.5 -77.5 -0.10
#> 5 2012-01-01T00:00:00Z 17.5 -72.5 NA
#> 6 2012-01-01T00:00:00Z 17.5 -67.5 -0.20
#> 7 2012-01-01T00:00:00Z 12.5 -77.5 0.20
#> 8 2012-01-01T00:00:00Z 12.5 -72.5 NA
#> 9 2012-01-01T00:00:00Z 12.5 -67.5 0.30
#> 10 2012-02-01T00:00:00Z 22.5 -77.5 NA
#> 11 2012-02-01T00:00:00Z 22.5 -72.5 NA
#> 12 2012-02-01T00:00:00Z 22.5 -67.5 NA
#> 13 2012-02-01T00:00:00Z 17.5 -77.5 0.40
#> 14 2012-02-01T00:00:00Z 17.5 -72.5 NA
#> 15 2012-02-01T00:00:00Z 17.5 -67.5 0.20
#> 16 2012-02-01T00:00:00Z 12.5 -77.5 0.00
#> 17 2012-02-01T00:00:00Z 12.5 -72.5 NA
#> 18 2012-02-01T00:00:00Z 12.5 -67.5 0.32
tabledap (tabular) data
(out <- info('erdCalCOFIlrvsiz'))
#> <ERDDAP info> erdCalCOFIlrvsiz
#> Variables:
#> calcofi_species_code:
#> Range: 19, 9760
#> common_name:
#> cruise:
#> itis_tsn:
#> larvae_1000m3:
#> Units: Fish larvae per 1,000 cubic meters of water sampled
#> larvae_10m2:
...
(dat <- tabledap(out, 'time>=2011-01-01', 'time<=2011-12-31', fields = c('longitude', 'latitude', 'larvae_size', 'scientific_name')))
#> <ERDDAP tabledap> erdCalCOFIlrvsiz
#> Path: [/Users/sacmac/Library/Caches/R/rdpla/c08c5d45ecceae143cd67258f5c9c5fa.csv]
#> Last updated: [2017-10-17 12:00:32]
#> File size: [0.05 mb]
#> # A tibble: 1,217 x 4
#> longitude latitude larvae_size scientific_name
#> * <chr> <chr> <chr> <chr>
#> 1 -117.305 32.956665 4.5 Engraulis mordax
#> 2 -117.305 32.956665 2.9 Doryteuthis opalescens
#> 3 -117.305 32.956665 2.7 Doryteuthis opalescens
#> 4 -117.305 32.956665 3.3 Doryteuthis opalescens
#> 5 -117.305 32.956665 3.0 Doryteuthis opalescens
#> 6 -117.305 32.956665 3.7 Doryteuthis opalescens
#> 7 -117.305 32.956665 3.4 Doryteuthis opalescens
#> 8 -117.305 32.956665 3.2 Doryteuthis opalescens
#> 9 -117.305 32.956665 2.8 Doryteuthis opalescens
#> 10 -117.305 32.956665 3.6 Doryteuthis opalescens
#> # ... with 1,207 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: 6 x 2
#> scientific_name mean_size
#> <chr> <dbl>
#> 1 Engraulis mordax 8.446067
#> 2 Sardinops sagax 5.828738
#> 3 Merluccius productus 5.512176
#> 4 Doryteuthis opalescens 3.653363
#> 5 Scomber japonicus 3.400000
#> 6 Trachurus symmetricus 3.264444
Citing
To cite rerddap in publications use:
Scott Chamberlain (2017). rerddap: General Purpose Client for ‘ERDDAP’ Servers. R package version 0.4.2. https://CRAN.R-project.org/package=rerddap
##E License and bugs
- License: MIT
- Report bugs at our Github repo for rerddap
