ghrsst

Access OPeNDAP GHRSST data from R. Extract point or bounded boxes (as raster).

Requirements

• R v4.1+

Packages from CRAN.

• rlang
• dplyr
• sf
• stars
• tidyr
• ncdf4

Packages from Github

• xyzt

Installation

remotes::install_github("BigelowLab/ghrsst")

Usage

suppressPackageStartupMessages({
  library(dplyr)
  library(sf)
  library(ghrsst)
  library(xyzt)
  library(stars)
})

Working with points.

See the xyzt package for more details on the example Gulf of Maine data.

# read in example GOM points
x <- xyzt::read_gom() |>
  dplyr::select(-time, -depth) |>
  xyzt::as_POINT()

# generate a MUR url for a given date
url <- mur_url("2018-12-18")

# open the resource
X <- ncdf4::nc_open(url)

# extract the data 
covars <- ghrsst::extract(x, X, varname = mur_vars(X))

# bind to the input
(y <- dplyr::bind_cols(x, covars))

## Simple feature collection with 5 features and 7 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: -70.17 ymin: 40.5 xmax: -66.59 ymax: 43.5
## Geodetic CRS:  WGS 84
## # A tibble: 5 × 8
##   id    name           geometry analysed_sst analysis_er…¹ dt_1k…²  mask sea_i…³
## * <chr> <chr>       <POINT [°]>        <dbl>         <dbl>   <int> <int>   <dbl>
## 1 44098 Jeffrey… (-70.17 42.81)         281.          0.37      NA     1      NA
## 2 44005 Cashes … (-69.22 43.17)         281.          0.37     -55     1      NA
## 3 44037 Jordan …  (-67.87 43.5)         281.          0.39      45     1      NA
## 4 44011 Georges… (-66.59 41.09)         282.          0.39      NA     1      NA
## 5 44008 Nantuck…  (-69.24 40.5)         283.          0.38      18     1      NA
## # … with abbreviated variable names ¹​analysis_error, ²​dt_1km_data,
## #   ³​sea_ice_fraction

Working with bounding boxes (from points or polygons).

Learn more about working with stars objects in the vignettes.

# read in example GOM points
x <- xyzt::read_gom() |>
  dplyr::select(-time, -depth) |>
  xyzt::as_BBOX()

(covars <- ghrsst::extract(x, X, varnames = ghrsst::mur_vars(X)))

## stars object with 2 dimensions and 5 attributes
## attribute(s):
##                      Min. 1st Qu.  Median        Mean 3rd Qu.    Max.   NA's
## analysed_sst      277.878 280.896 281.174 281.1748818 281.471 289.056    459
## analysis_error      0.370   0.380   0.380   0.3836834   0.390   0.390    459
## mask                1.000   1.000   1.000   1.0055922   1.000   2.000      0
## sea_ice_fraction       NA      NA      NA         NaN      NA      NA 109080
## dt_1km_data       -55.000  18.000  41.000  22.4352115  45.000  45.000  72484
## dimension(s):
##   from  to offset       delta refsys point values x/y
## x    1 360 -70.17  0.00994444 WGS 84    NA   NULL [x]
## y    1 303   43.5 -0.00990099 WGS 84    NA   NULL [y]

Now let’s see what it looks like.

x <- xyzt::read_gom() |>
  dplyr::select(-time, -depth) |>
  xyzt::as_POINT()
par(mfrow = c(1,2))
plot(covars, attr = 'analysed_sst', axes = TRUE, reset = FALSE)
plot(sf::st_geometry(x), add = TRUE, col = "orange", pch = 19, cex = 2)

# cleanup
ncdf4::nc_close(X)