The ncdfCF package provides an easy to use interface to
netCDF resources in R, either in local files or remotely on a THREDDS
server. It is built on the RNetCDF package which, like
package ncdf4, provides a basic interface to the
netcdf library, but which lacks an intuitive user
interface. Package ncdfCF provides a high-level interface
using functions and methods that are familiar to the R user. It reads
the structural metadata and also the attributes upon opening the
resource. In the process, the ncdfCF package also applies
CF Metadata Conventions to interpret the data. This currently applies
to:
CFtime package these
offsets can be turned into intelligible dates and times, for all defined
calendars.dimnames for the axis. (Note that this also applies to
generic numeric axes with labels defined.)formula_terms attribute.coordinates attribute of axes, are read, including when
multiple sets of labels are defined for a single axis. Users can select
which set of labels to make active for display, selection and
processing.Opening and inspecting the contents of a netCDF resource is very straightforward:
library(ncdfCF)
# Get any netCDF file
fn <- system.file("extdata", "ERA5land_Rwanda_20160101.nc", package = "ncdfCF")
# Open the file, all metadata is read
(ds <- open_ncdf(fn))
#> <Dataset> ERA5land_Rwanda_20160101
#> Resource : /private/var/folders/gs/s0mmlczn4l7bjbmwfrrhjlt80000gn/T/RtmpLl1tMa/temp_libpath16666fe3c1d4/ncdfCF/extdata/ERA5land_Rwanda_20160101.nc
#> Format : offset64
#> Type : generic netCDF data
#> Conventions: CF-1.6
#> Keep open : FALSE
#>
#> Variables:
#> name long_name units data_type axes
#> t2m 2 metre temperature K NC_SHORT longitude, latitude, time
#> pev Potential evaporation m NC_SHORT longitude, latitude, time
#> tp Total precipitation m NC_SHORT longitude, latitude, time
#>
#> Axes:
#> id axis name length unlim values
#> 0 T time 24 U [2016-01-01T00:00:00 ... 2016-01-01T23:00:00]
#> 1 X longitude 31 [28 ... 31]
#> 2 Y latitude 21 [-1 ... -3]
#> unit
#> hours since 1900-01-01 00:00:00.0
#> degrees_east
#> degrees_north
#>
#> Attributes:
#> id name type length
#> 0 CDI NC_CHAR 64
#> 1 Conventions NC_CHAR 6
#> 2 history NC_CHAR 482
#> 3 CDO NC_CHAR 64
#> value
#> Climate Data Interface version 2.4.1 (https://m...
#> CF-1.6
#> Tue May 28 18:39:12 2024: cdo seldate,2016-01-0...
#> Climate Data Operators version 2.4.1 (https://m...
# ...or very brief details
ds$var_names
#> [1] "t2m" "pev" "tp"
ds$axis_names
#> [1] "time" "longitude" "latitude"
# Variables and dimensions can be accessed through standard list-type extraction syntax
(t2m <- ds[["t2m"]])
#> <Variable> t2m
#> Long name: 2 metre temperature
#>
#> Axes:
#> id axis name length unlim values
#> 1 X longitude 31 [28 ... 31]
#> 2 Y latitude 21 [-1 ... -3]
#> 0 T time 24 U [2016-01-01T00:00:00 ... 2016-01-01T23:00:00]
#> unit
#> degrees_east
#> degrees_north
#> hours since 1900-01-01 00:00:00.0
#>
#> Attributes:
#> id name type length value
#> 0 long_name NC_CHAR 19 2 metre temperature
#> 1 units NC_CHAR 1 K
ds[["longitude"]]
#> <Longitude axis> [1] longitude
#> Length : 31
#> Axis : X
#> Values : 28, 28.1, 28.2 ... 30.8, 30.9, 31
#> Bounds : (not set)
#>
#> Attributes:
#> id name type length value
#> 0 standard_name NC_CHAR 9 longitude
#> 1 long_name NC_CHAR 9 longitude
#> 2 units NC_CHAR 12 degrees_east
#> 3 axis NC_CHAR 1 X
#> 4 actual_range NC_FLOAT 2 28, 31
# Regular base R operations simplify life further
dimnames(ds[["pev"]]) # A variable: list of dimension names
#> [1] "longitude" "latitude" "time"
dimnames(ds[["longitude"]]) # A dimension: vector of dimension element values
#> [1] 28.0 28.1 28.2 28.3 28.4 28.5 28.6 28.7 28.8 28.9 29.0 29.1 29.2 29.3 29.4
#> [16] 29.5 29.6 29.7 29.8 29.9 30.0 30.1 30.2 30.3 30.4 30.5 30.6 30.7 30.8 30.9
#> [31] 31.0
# Access attributes
ds[["pev"]]$attribute("long_name")
#> [1] "Potential evaporation"If you just want to inspect what data is included in the netCDF
resource, use the peek_ncdf() function:
peek_ncdf(fn)
#> $uri
#> [1] "/private/var/folders/gs/s0mmlczn4l7bjbmwfrrhjlt80000gn/T/RtmpLl1tMa/temp_libpath16666fe3c1d4/ncdfCF/extdata/ERA5land_Rwanda_20160101.nc"
#>
#> $type
#> [1] "generic netCDF data"
#>
#> $variables
#> id name long_name standard_name units axes
#> t2m 3 t2m 2 metre temperature NA K longitude, latitude, time
#> pev 4 pev Potential evaporation NA m longitude, latitude, time
#> tp 5 tp Total precipitation NA m longitude, latitude, time
#>
#> $axes
#> class id axis name long_name standard_name
#> time CFAxisTime 0 T time time time
#> longitude CFAxisLongitude 1 X longitude longitude longitude
#> latitude CFAxisLatitude 2 Y latitude latitude latitude
#> units length unlimited
#> time hours since 1900-01-01 00:00:00.0 24 TRUE
#> longitude degrees_east 31 FALSE
#> latitude degrees_north 21 FALSE
#> values has_bounds
#> time [2016-01-01T00:00:00 ... 2016-01-01T23:00:00] FALSE
#> longitude [28 ... 31] FALSE
#> latitude [-1 ... -3] FALSE
#> coordinate_sets
#> time 1
#> longitude 1
#> latitude 1
#>
#> $attributes
#> id name type length
#> 1 0 CDI NC_CHAR 64
#> 2 1 Conventions NC_CHAR 6
#> 3 2 history NC_CHAR 482
#> 4 3 CDO NC_CHAR 64
#> value
#> 1 Climate Data Interface version 2.4.1 (https://mpimet.mpg.de/cdi)
#> 2 CF-1.6
#> 3 Tue May 28 18:39:12 2024: cdo seldate,2016-01-01,2016-01-01 /Users/patrickvanlaake/CC/ERA5land/Rwanda/ERA5land_Rwanda_t2m-pev-tp_2016-2018.nc ERA5land_Rwanda_20160101.nc\n2021-12-22 07:00:24 GMT by grib_to_netcdf-2.23.0: /opt/ecmwf/mars-client/bin/grib_to_netcdf -S param -o /cache/data5/adaptor.mars.internal-1640155821.967082-25565-12-0b19757d-da4e-4ea4-b8aa-d08ec89caf2c.nc /cache/tmp/0b19757d-da4e-4ea4-b8aa-d08ec89caf2c-adaptor.mars.internal-1640142203.3196251-25565-10-tmp.grib
#> 4 Climate Data Operators version 2.4.1 (https://mpimet.mpg.de/cdo)There are three ways to read data for a variable from the resource:
data(): The data() method
returns all data of a variable, including its metadata, in a
CFArray instance.[]: The usual R array operator gives
you access to the raw, non-interpreted data in the netCDF resource. This
uses index values into the dimensions and requires you to know the order
in which the dimensions are specified for the variable. With a bit of
tinkering and some helper functions in ncdfCF this is still
very easy to do.subset(): The subset()
method lets you specify what you want to extract from each dimension in
real-world coordinates and timestamps, in whichever order. This can also
rectify non-Cartesian grids to regular longitude-latitude grids.# Extract a timeseries for a specific location
ts <- t2m[5, 4, ]
str(ts)
#> num [1, 1, 1:24] 293 292 292 291 291 ...
#> - attr(*, "dimnames")=List of 3
#> ..$ : chr "28.4"
#> ..$ : chr "-1.3"
#> ..$ : chr [1:24] "2016-01-01T00:00:00" "2016-01-01T01:00:00" "2016-01-01T02:00:00" "2016-01-01T03:00:00" ...
#> - attr(*, "axis")= Named chr [1:3] "X" "Y" "T"
#> ..- attr(*, "names")= chr [1:3] "longitude" "latitude" "time"
#> - attr(*, "time")=List of 1
#> ..$ time:CFTime with origin [hours since 1900-01-01 00:00:00.0] using calendar [gregorian] having 24 offset values
# Extract the full spatial extent for one time step
ts <- t2m[, , 12]
str(ts)
#> num [1:31, 1:21, 1] 300 300 300 300 300 ...
#> - attr(*, "dimnames")=List of 3
#> ..$ : chr [1:31] "28" "28.1" "28.200001" "28.299999" ...
#> ..$ : chr [1:21] "-1" "-1.1" "-1.2" "-1.3" ...
#> ..$ : chr "2016-01-01T11:00:00"
#> - attr(*, "axis")= Named chr [1:3] "X" "Y" "T"
#> ..- attr(*, "names")= chr [1:3] "longitude" "latitude" "time"
#> - attr(*, "time")=List of 1
#> ..$ time:CFTime with origin [hours since 1900-01-01 00:00:00.0] using calendar [gregorian] having 1 offset valuesNote that the results contain degenerate dimensions (of length 1).
This by design when using basic [] data access because it
allows attributes to be attached in a consistent manner. When using the
subset() method, the data is returned as an instance of
CFArray, including axes and attributes:
# Extract a specific region, full time dimension
(ts <- t2m$subset(list(X = 29:30, Y = -1:-2)))
#> <Data array> t2m
#> Long name: 2 metre temperature
#>
#> Values: [283.0182 ... 299.917] K
#> NA: 0 (0.0%)
#>
#> Axes:
#> id axis name length unlim values
#> -1 X longitude 10 [29 ... 29.9]
#> -1 Y latitude 10 [-1.1 ... -2]
#> 0 T time 24 U [2016-01-01T00:00:00 ... 2016-01-01T23:00:00]
#> unit
#>
#>
#> hours since 1900-01-01 00:00:00.0
#>
#> Attributes:
#> id name type length value
#> 0 long_name NC_CHAR 19 2 metre temperature
#> 1 units NC_CHAR 1 K
#> 2 actual_range NC_DOUBLE 2 283.01816785, 299.91696999
# Extract specific time slices for a specific region
# Note that the dimensions are specified out of order and using alternative
# specifications: only the extreme values are used.
(ts <- t2m$subset(list(T = c("2016-01-01 09:00", "2016-01-01 15:00"),
X = c(29.6, 28.8),
Y = seq(-2, -1, by = 0.05))))
#> <Data array> t2m
#> Long name: 2 metre temperature
#>
#> Values: [288.2335 ... 299.917] K
#> NA: 0 (0.0%)
#>
#> Axes:
#> id axis name length values
#> -1 X longitude 7 [28.9 ... 29.5]
#> -1 Y latitude 10 [-1.1 ... -2]
#> -1 T time 6 [2016-01-01T09:00:00 ... 2016-01-01T14:00:00]
#> unit
#>
#>
#> hours since 1900-01-01 00:00:00.0
#>
#> Attributes:
#> id name type length value
#> 0 long_name NC_CHAR 19 2 metre temperature
#> 1 units NC_CHAR 1 K
#> 2 actual_range NC_DOUBLE 2 288.23352439, 299.91696999The latter two methods will read only as much data from the netCDF resource as is requested.
With the summarise() method, available for both
CFVariable and CFArray, you can apply a
function over the data to generate summaries. You could, for instance,
summarise daily data to monthly means. These methods use the specific
calendar of the “time” axis. The return value is a new
CFArray object.
# Summarising hourly temperature data to calculate the daily maximum temperature
t2m$summarise("tmax", max, "day")
#> <Data array> tmax
#> Long name: 2 metre temperature
#>
#> Values: [290.0364 ... 302.0447] K
#> NA: 0 (0.0%)
#>
#> Axes:
#> id axis name length values
#> 1 X longitude 31 [28 ... 31]
#> 2 Y latitude 21 [-1 ... -3]
#> T time 1 [2016-01-01T12:00:00]
#> unit
#> degrees_east
#> degrees_north
#> hours since 1900-01-01 00:00:00.0
#>
#> Attributes:
#> id name type length value
#> 0 long_name NC_CHAR 19 2 metre temperature
#> 1 units NC_CHAR 1 K
#> 2 actual_range NC_DOUBLE 2 290.03635812, 302.04471993A function may also return a vector of multiple values, in which case
a list is returned with a new CFArray object for each
return value of the function. This allows you to calculate multiple
results with a single call. You could write your own function to tailor
the calculations to your needs. Rather than just calculating the daily
maximum, you could get the daily maximum, minimum and diurnal range in
one go:
# Function to calculate multiple daily stats
# It is good practice to include a `na.rm` argument in all your functions
daily_stats <- function(x, na.rm = TRUE) {
# x is the vector of values for one day
minmax <- range(x, na.rm = na.rm)
diurnal <- minmax[2L] - minmax[1L]
c(minmax, diurnal)
}
# Call summarise() with your own function
# The `name` argument should have as many names as the function returns results
(stats <- t2m$summarise(c("tmin", "tmax", "diurnal_range"), daily_stats, "day"))
#> $tmin
#> <Data array> tmin
#> Long name: 2 metre temperature
#>
#> Values: [283.0182 ... 293.8659] K
#> NA: 0 (0.0%)
#>
#> Axes:
#> id axis name length values
#> 1 X longitude 31 [28 ... 31]
#> 2 Y latitude 21 [-1 ... -3]
#> T time 1 [2016-01-01T12:00:00]
#> unit
#> degrees_east
#> degrees_north
#> hours since 1900-01-01 00:00:00.0
#>
#> Attributes:
#> id name type length value
#> 0 long_name NC_CHAR 19 2 metre temperature
#> 1 units NC_CHAR 1 K
#> 2 actual_range NC_DOUBLE 2 283.01816785, 293.86585674
#>
#> $tmax
#> <Data array> tmax
#> Long name: 2 metre temperature
#>
#> Values: [290.0364 ... 302.0447] K
#> NA: 0 (0.0%)
#>
#> Axes:
#> id axis name length values
#> 1 X longitude 31 [28 ... 31]
#> 2 Y latitude 21 [-1 ... -3]
#> T time 1 [2016-01-01T12:00:00]
#> unit
#> degrees_east
#> degrees_north
#> hours since 1900-01-01 00:00:00.0
#>
#> Attributes:
#> id name type length value
#> 0 long_name NC_CHAR 19 2 metre temperature
#> 1 units NC_CHAR 1 K
#> 2 actual_range NC_DOUBLE 2 290.03635812, 302.04471993
#>
#> $diurnal_range
#> <Data array> diurnal_range
#> Long name: 2 metre temperature
#>
#> Values: [1.819982 ... 11.27369] K
#> NA: 0 (0.0%)
#>
#> Axes:
#> id axis name length values
#> 1 X longitude 31 [28 ... 31]
#> 2 Y latitude 21 [-1 ... -3]
#> T time 1 [2016-01-01T12:00:00]
#> unit
#> degrees_east
#> degrees_north
#> hours since 1900-01-01 00:00:00.0
#>
#> Attributes:
#> id name type length value
#> 0 long_name NC_CHAR 19 2 metre temperature
#> 1 units NC_CHAR 1 K
#> 2 actual_range NC_DOUBLE 2 1.81998208, 11.27369015Note that you may have to update some attributes after calling
summarise(). You can use the set_attribute()
method on the CFArray objects to do that.
A CFData object can be exported to a
data.table or to a terra::SpatRaster (3D) or
terra::SpatRasterDataset (4D) for further processing.
Obviously, these packages need to be installed to utilise these
methods.
# install.packages("data.table")
library(data.table)
head(dt <- ts$data.table())
#> longitude latitude time t2m
#> <num> <num> <char> <num>
#> 1: 28.9 -1.1 2016-01-01T09:00:00 294.9227
#> 2: 29.0 -1.1 2016-01-01T09:00:00 295.8135
#> 3: 29.1 -1.1 2016-01-01T09:00:00 297.0929
#> 4: 29.2 -1.1 2016-01-01T09:00:00 297.4697
#> 5: 29.3 -1.1 2016-01-01T09:00:00 298.5419
#> 6: 29.4 -1.1 2016-01-01T09:00:00 299.8894
#install.packages("terra")
suppressMessages(library(terra))
(r <- stats[["diurnal_range"]]$terra())
#> class : SpatRaster
#> dimensions : 21, 31, 1 (nrow, ncol, nlyr)
#> resolution : 0.1, 0.1 (x, y)
#> extent : 27.95, 31.05, -3.05, -0.95 (xmin, xmax, ymin, ymax)
#> coord. ref. : lon/lat WGS 84 (EPSG:4326)
#> source(s) : memory
#> name : 2016-01-01T12:00:00
#> min value : 1.819982
#> max value : 11.273690
terra::plot(r)
A CFData object can also be written back to a netCDF
file. The object will have all its relevant attributes and properties
written together with the actual data: axes, bounds, attributes,
CRS.
# Save a CFData instance to a netCDF file on disk
stats[["diurnal_range"]]$save("~/path/file.nc")Discrete Sampling Geometries (DSG) map almost directly to the
venerable data.frame in R (with several exceptions). In
that sense, they are rather distinct from array-based data sets. At the
moment there is no specific code for DSG, but the simplest layouts can
currently already be read (without any warranty). Various methods, such
as CFVariable::subset() or CFData::array()
will fail miserably, and you are well-advised to try no more than the
empty array indexing operator CFVariable::[] which will
yield the full data variable with column and row names set as an array,
of CFVariable::data() to get the whole data variable as a
CFData object for further processing. You can identify a
DSG data set by the featureType attribute of the
CFDataset.
More comprehensive support for DSG is in the development plan.
Package ncdfCF is in the early phases of development. It
supports reading of groups, variables, dimensions, user-defined data
types, attributes and data from netCDF resources in “classic” and
“netcdf4” formats; and can write single data variables back to a netCDF
file. From the CF Metadata Conventions it supports identification of
axes, interpretation of the “time” axis, name resolution when using
groups, reading of “bounds” information, parametric vertical
coordinates, auxiliary coordinate variables, labels, cell measures and
grid mapping information.
Development plans for the near future focus on supporting the below features:
CAUTION: Package
ncdfCF is still in the early phases of development. While
extensively tested on multiple well-structured datasets, errors may
still occur, particularly in datasets that do not adhere to the CF
Metadata Conventions.
Installation from CRAN of the latest release:
install.packages("ncdfCF")You can install the development version of ncdfCF from
GitHub with:
# install.packages("devtools")
devtools::install_github("pvanlaake/ncdfCF")