WARNING: Please note, this package was experimental and is no longer being developed. If you decide to use it, please be aware that no support is given. I recommend using the rOpenSci rerddap instead.
noaaErddap is an R interface to three NOAA marine data sources. The package is concerned with downloading the data and providing two methods to extract methods given a data.frame of geographical coordinates.
Functions to manipulate and extract environmental data need netCDF support. You'll need both the ncdf4 and raster packages. Installation of ncdf4 should be straightforward on Mac and Windows, but on Linux you may have issues.
You can use the function noaaErddap::dataTypeLimits to visualise this dataset within R.
| Dataset | Description | Lat Range | Lon Range | Start Date | End Date |
|---|---|---|---|---|---|
| NPP | 8 Day Composite | -90 to 90 | 0 to 360 | 1997-09-10 | 2010-12-07 |
| Chlorophyll-a | 8 Day Composite | -90 to 90 | -180 to 180 | 1997-09-02 | 2010-12-15 |
| SST | 1 Day Composite | -90 to 90 | 0 to 360 | 1981-09-01 | Present |
The noaaErddap package can be installed from github using the devtools package using devtools::install_github.
If you do not yet have devtools, install with install.packages("devtools").
Then install noaaErddap using the following:
library(devtools)
install_github('dbarneche/noaaErddap')
library(noaaErddap)
library(noaaErddap)
# check help page for main function
?noaaErddap::erddapDownload
# downloads chlorophyll data for Jan/2006
linkToCachedFile <- noaaErddap::erddapDownload(year = 2006, month = 1, type = 'chlorophyll', overwrite = TRUE)
library(ncdf4)
ncdf4::nc_open(filename = linkToCachedFile)
# downloads productivity data for all months in 1998 (might take a few hours)
library(plyr)
dat <- data.frame(year = 1998, month = 1:12)
nppFiles <- plyr::ddply(dat, .(year, month), function (x, type) {
data.frame(links = noaaErddap::erddapDownload(x$year, x$month, type, overwrite = TRUE), stringsAsFactors = FALSE)
}, type = 'productivity')
# get longitude and latitude for NPP files
envNc <- ncdf4::nc_open(filename = nppFiles$links[1])
longitude <- envNc$var[['productivity']]$dim[[1]]$vals
latitude <- envNc$var[['productivity']]$dim[[2]]$vals
ncdf4::nc_close(envNc)
# You can always recover cached files in a new session, e.g.
nppFiles <- noaaErddap::noaaErddapFiles('productivity', full.name = TRUE)
# extract NPP values for a given subset of coordinates (median value within a buffer of 20 km) across all files and take the mean
library(raster)
library(abind)
nppValues <- abind::abind(lapply(nppFiles, noaaErddap::openAndMatchNcdfData, method = 'raster', coordinates = data.frame(Longitude = c(330, 335, 340), Latitude = c(-27, -19, 0)), buffer = 2e4, fun = median, na.rm = TRUE), along = 3)
apply(nppValues, c(1, 2), mean, na.rm = TRUE)
# extract the average NPP values for the globe in 1998 (these steps are memory intensive)
nppValues1998 <- abind::abind(lapply(nppFiles[grep('-1998.nc', nppFiles, fixed = TRUE)], noaaErddap::openAndMatchNcdfData, method = 'ncdf4'), along = 3)
meanNPP1998 <- apply(nppValues1998, c(1, 2), mean, na.rm = TRUE)
I'd like to thank the rOpenSci project for providing source code via the rnoaa package from which I based this package.
Please report any issues or bugs.