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:
- The axis designation. The three mechanisms to identify the axis each dimension represents are applied until an axis is determined.
- The time coordinate. Time is usually encoded as an offset from an
origin. Using the
CFtimepackage these offsets can be turned into intelligible dates and times, for all defined calendars. - Bounds information. When present, bounds are read and used in analyses.
- Discrete coordinates, optionally with character labels. When
labels are provided, these will be used as
dimnamesfor the axis. (Note that this also applies to generic numeric axes with labels defined.) - Parametric vertical coordinates are computed using the
formula_termsattribute, for two ocean formulations. - Auxiliary coordinates are identified and read. This applies also to scalar axes and auxiliary longitude-latitude grids. Auxiliary coordinates can be activated by the user and then used in display, selection and processing. Data on non-Cartesian grids can be automatically rectified to a longitude-latitude grid if an auxiliary grid is present in the resource.
- The cell measure variables are read and linked to any data variables referencing them. Cell measure variables that are external to the netCDF resource with the referring data variable can be linked to the data set and then they are immediately available to the referring data variables.
- Labels, as separate variables identified through the
coordinatesattribute 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. - The grid_mapping variables, providing the coordinate reference system (CRS) of the data, with support for all defined objects in the latest EPSG database as well as “manual” construction of CRSs.
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 : /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/library/ncdfCF/extdata/ERA5land_Rwanda_20160101.nc
#> Format : offset64
#> Collection : Generic netCDF data
#> Conventions: CF-1.6
#>
#> Variables:
#> name long_name units data_type axes
#> t2m 2 metre temperature K NC_DOUBLE longitude, latitude, time
#> pev Potential evaporation m NC_DOUBLE longitude, latitude, time
#> tp Total precipitation m NC_DOUBLE longitude, latitude, time
#>
#> Attributes:
#> name type length value
#> CDI NC_CHAR 64 Climate Data Interface version 2.4.1 (https://m...
#> Conventions NC_CHAR 6 CF-1.6
#> history NC_CHAR 482 Tue May 28 18:39:12 2024: cdo seldate,2016-01-0...
#> CDO NC_CHAR 64 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 axes can be accessed through standard list-type extraction syntax
(t2m <- ds[["t2m"]])
#> <Variable> t2m
#> Long name: 2 metre temperature
#>
#> Values: (not loaded)
#>
#> Axes:
#> axis name length values
#> X longitude 31 [28 ... 31]
#> Y latitude 21 [-1 ... -3]
#> 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:
#> name type length value
#> long_name NC_CHAR 19 2 metre temperature
#> units NC_CHAR 1 K
ds[["longitude"]]
#> <Longitude axis> [1] longitude
#> Length : 31
#> Axis : X
#> Coordinates: 28, 28.1, 28.2 ... 30.8, 30.9, 31 (degrees_east)
#> Bounds : (not set)
#>
#> Attributes:
#> name type length value
#> standard_name NC_CHAR 9 longitude
#> long_name NC_CHAR 9 longitude
#> units NC_CHAR 12 degrees_east
#> axis NC_CHAR 1 X
#> actual_range NC_FLOAT 2 28, 31
# Regular base R operations simplify life further
dimnames(ds[["pev"]]) # A variable: list of axis names
#> [1] "longitude" "latitude" "time"
dimnames(ds[["longitude"]]) # An axis: vector of axis coordinate 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] "/Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/library/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 various ways to read data for a data variable from the resource:
[]: 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 inncdfCFthis is still very easy to do.raw(): This also gets the data in the layout of the file (or the data set) but with dimnames set. Importantly, you can call this after callingsubset()and you will get the raw data for the specific spatial and temporal domain that you are interested in.array(): Likeraw(), this extracts all the (subsetted) data, but now the data will be oriented in the standard R way of column-major order. Y coordinates will run from the top to the bottom (so latitude values, for instance, will be decresing).subset(): Thesubset()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. Subsetting is lazy: data is not loaded so long as a direct relationship to the data in the netCDF resource is maintained.profile(): Extract “profiles” from the data variable. This can take different forms, such as a temporal or depth profile for a single location, but it could also be a zonal field (such as a transect in latitude - atmospheric depth for a given longitude) or some other profile in the physical space of the data variable.
# Extract a timeseries for a specific location - see also the `profile()` method
ts <- t2m[5, 4, ]
str(ts)
#> num [1, 1, 1:24] 293 292 292 291 291 ...
#> - attr(*, "dimnames")=List of 3
#> ..$ longitude: chr "28.4"
#> ..$ latitude : chr "-1.3"
#> ..$ time : 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 [standard] 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
#> ..$ longitude: chr [1:31] "28" "28.1" "28.200001" "28.299999" ...
#> ..$ latitude : chr [1:21] "-1" "-1.1" "-1.2" "-1.3" ...
#> ..$ time : 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 [standard] 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 CFVariable, including axes and
attributes:
# Extract a specific region, full time dimension
(ts <- t2m$subset(list(X = 29:30, Y = -1:-2)))
#> <Variable> t2m
#> Long name: 2 metre temperature
#>
#> Values: (not loaded)
#>
#> Axes:
#> axis name length values
#> X longitude 11 [29 ... 30]
#> Y latitude 11 [-1 ... -2]
#> T time 24-U [2016-01-01T00:00:00 ... 2016-01-01T23:00:00]
#> unit
#> degrees_east
#> degrees_north
#> hours since 1900-01-01 00:00:00.0
#>
#> Attributes:
#> name type length value
#> long_name NC_CHAR 19 2 metre temperature
#> units NC_CHAR 1 K
# Extract specific time slices for a specific region
# Note that the dimensions are specified out of order and using alternative
# specifications: only the extreme values are used.
(ts <- t2m$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))))
#> <Variable> t2m
#> Long name: 2 metre temperature
#>
#> Values: (not loaded)
#>
#> Axes:
#> axis name length values
#> X longitude 7 [28.9 ... 29.5]
#> Y latitude 11 [-1 ... -2]
#> T time 6-U [2016-01-01T09:00:00 ... 2016-01-01T14:00:00]
#> unit
#> degrees_east
#> degrees_north
#> hours since 1900-01-01 00:00:00.0
#>
#> Attributes:
#> name type length value
#> long_name NC_CHAR 19 2 metre temperature
#> units NC_CHAR 1 KData loading is lazy. In the examples above, you can see that data did
not yet get loaded. This is intentional: you can subset your data in
multiple ways before actually reading the data from the resource. This
is particularly important when getting data from an online location,
such as a remote THREDDS server. Use raw() or array() to get the
arrays.
It is often useful to extract a “profile” of data for a given location
or zone, such as a timeseries of data. The profile() method has some
flexible options to support this:
- Profile specific locations, with multiple locations specified per
call, returning the data as a (set of)
CFVariableinstance(s) or as a singledata.table. - Profile zones, such as a latitude band or an atmospheric level. Data
is returned as a new
CFVariableinstance(s).
In all cases, you can profile over any of the axes and over any number of axes.
Note that the profile() method returns data for the grid cells closest
to the specified location. That is different from the subset() method,
which will return data as it is recorded in the netCDF resource.
rwa <- t2m$profile(longitude = c(30.07, 30.07, 29.74), latitude = c(-1.94, -1.58, -2.60),
.names = c("Kigali", "Byumba", "Butare"), .as_table = TRUE)
head(rwa)
#> time longitude latitude .variable .value
#> <char> <num> <num> <char> <num>
#> 1: 2016-01-01T00:00:00 30.07 -1.94 Kigali 290.4055
#> 2: 2016-01-01T01:00:00 30.07 -1.94 Kigali 290.0088
#> 3: 2016-01-01T02:00:00 30.07 -1.94 Kigali 289.3608
#> 4: 2016-01-01T03:00:00 30.07 -1.94 Kigali 288.8414
#> 5: 2016-01-01T04:00:00 30.07 -1.94 Kigali 288.4713
#> 6: 2016-01-01T05:00:00 30.07 -1.94 Kigali 289.9276
attr(rwa, "value")
#> $name
#> [1] "2 metre temperature"
#>
#> $units
#> [1] "K"Some critical metadata is recorded in the “value” attribute: original long name and the physical unit.
When you provide coordinates for all axes but one, you get a profile of values along the remaining axis, as shown above. If you provide fewer axis coordinates you get progressively higher-order results. To get a latitudinal transect, for instance, provide only a longitude coordinate:
(trans29_74 <- t2m$profile(longitude = 29.74, .names = "lon_29_74"))
#> <Variable> lon_29_74
#> Long name: 2 metre temperature
#>
#> Values: [286.5394 ... 298.963] K
#> NA: 0 (0.0%)
#>
#> Axes:
#> axis name length values
#> Y latitude 21 [-1 ... -3]
#> T time 24-U [2016-01-01T00:00:00 ... 2016-01-01T23:00:00]
#> X longitude 1 [29.74]
#> unit
#> degrees_north
#> hours since 1900-01-01 00:00:00.0
#> degrees_east
#>
#> Attributes:
#> name type length value
#> long_name NC_CHAR 19 2 metre temperature
#> units NC_CHAR 1 K
#> actual_range NC_DOUBLE 2 286.539447, 298.96298
#> coordinates NC_CHAR 9 longitudeNote that there is only a single longitude coordinate left, at exactly the specified longitude.
With the summarise() method 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 CFVariable object.
# Summarising hourly temperature data to calculate the daily maximum temperature
t2m$summarise("tmax", max, "day")
#> <Variable> tmax
#> Long name: 2 metre temperature
#>
#> Values: [290.0364 ... 302.0447] K
#> NA: 0 (0.0%)
#>
#> Axes:
#> axis name length values unit
#> X longitude 31 [28 ... 31] degrees_east
#> Y latitude 21 [-1 ... -3] degrees_north
#> T time 1 [2016-01-01T12:00:00] hours since 1900-01-01 00:00:00.0
#>
#> Attributes:
#> name type length value
#> long_name NC_CHAR 19 2 metre temperature
#> units NC_CHAR 1 K
#> actual_range NC_DOUBLE 2 290.036358, 302.04472A function may also return a vector of multiple values, in which case a
list is returned with a new CFVariable object for each return value of
the function. This allows you to calculate multiple results with a
single call. You could write your own function to tailor the
calculations to your needs. Rather than just calculating the daily
maximum, you could get the daily maximum, minimum and diurnal range in
one go:
# Function to calculate multiple daily stats
# It is good practice to include a `na.rm` argument in all your functions
daily_stats <- function(x, na.rm = TRUE) {
# x is the vector of values for one day
minmax <- range(x, na.rm = na.rm)
diurnal <- minmax[2L] - minmax[1L]
c(minmax, diurnal)
}
# Call summarise() with your own function
# The `name` argument should have as many names as the function returns results
(stats <- t2m$summarise(c("tmin", "tmax", "diurnal_range"), daily_stats, "day"))
#> $tmin
#> <Variable> tmin
#> Long name: 2 metre temperature
#>
#> Values: [283.0182 ... 293.8659] K
#> NA: 0 (0.0%)
#>
#> Axes:
#> axis name length values unit
#> X longitude 31 [28 ... 31] degrees_east
#> Y latitude 21 [-1 ... -3] degrees_north
#> T time 1 [2016-01-01T12:00:00] hours since 1900-01-01 00:00:00.0
#>
#> Attributes:
#> name type length value
#> long_name NC_CHAR 19 2 metre temperature
#> units NC_CHAR 1 K
#> actual_range NC_DOUBLE 2 283.018168, 293.865857
#>
#> $tmax
#> <Variable> tmax
#> Long name: 2 metre temperature
#>
#> Values: [290.0364 ... 302.0447] K
#> NA: 0 (0.0%)
#>
#> Axes:
#> axis name length values unit
#> X longitude 31 [28 ... 31] degrees_east
#> Y latitude 21 [-1 ... -3] degrees_north
#> T time 1 [2016-01-01T12:00:00] hours since 1900-01-01 00:00:00.0
#>
#> Attributes:
#> name type length value
#> long_name NC_CHAR 19 2 metre temperature
#> units NC_CHAR 1 K
#> actual_range NC_DOUBLE 2 290.036358, 302.04472
#>
#> $diurnal_range
#> <Variable> diurnal_range
#> Long name: 2 metre temperature
#>
#> Values: [1.819982 ... 11.27369] K
#> NA: 0 (0.0%)
#>
#> Axes:
#> axis name length values unit
#> X longitude 31 [28 ... 31] degrees_east
#> Y latitude 21 [-1 ... -3] degrees_north
#> T time 1 [2016-01-01T12:00:00] hours since 1900-01-01 00:00:00.0
#>
#> Attributes:
#> name type length value
#> long_name NC_CHAR 19 2 metre temperature
#> units NC_CHAR 1 K
#> actual_range NC_DOUBLE 2 1.819982, 11.27369Note that you may have to update some attributes after calling
summarise(). You can use the set_attribute() method on the
CFVariable objects to do that.
You can convert a suitable R object into a CFVariable instance quite
easily. R objects that are supported include arrays, matrices and
vectors of type logical, integer, numeric or logical.
arr <- array(rnorm(120), dim = c(6, 5, 4))
as_CF("my_first_CF_object", arr)
#> <Variable> my_first_CF_object
#>
#> Values: [-1.925378 ... 2.716479]
#> NA: 0 (0.0%)
#>
#> Axes:
#> name length values
#> axis_1 6 [1 ... 6]
#> axis_2 5 [1 ... 5]
#> axis_3 4 [1 ... 4]
#>
#> Attributes:
#> name type length value
#> actual_range NC_DOUBLE 2 -1.925378, 2.716479Usable but not very impressive. The axes have dull names without any meaning and the coordinates are just a sequence along the axis.
If the R object has dimnames set, these will be used to create more
informed axes. More interestingly, if your array represents some spatial
data you can give your dimnames appropriate names (“lat”, “lon”,
“latitude”, “longitude”, case-insensitive) and the corresponding axis
will be created (if the coordinate values in the dimnames are within
the domain of the axis type). For “time” coordinates, these are
automatically detected irrespective of the name.
# Note the use of named dimnames here - these will become the names of the axes
dimnames(arr) <- list(lat = c(45, 44, 43, 42, 41, 40), lon = c(0, 1, 2, 3, 4),
time = c("2025-07-01", "2025-07-02", "2025-07-03", "2025-07-04"))
(obj <- as_CF("a_better_CF_object", arr))
#> <Variable> a_better_CF_object
#>
#> Values: [-1.925378 ... 2.716479]
#> NA: 0 (0.0%)
#>
#> Axes:
#> axis name length values unit
#> Y lat 6 [45 ... 40] degrees_north
#> X lon 5 [0 ... 4] degrees_east
#> T time 4 [2025-07-01 ... 2025-07-04] days since 1970-01-01T00:00:00
#>
#> Attributes:
#> name type length value
#> actual_range NC_DOUBLE 2 -1.925378, 2.716479
# Axes are of a specific type and have basic attributes set
obj$axes[["lat"]]
#> <Latitude axis> [-22] lat
#> Length : 6
#> Axis : Y
#> Coordinates: 45, 44, 43, 42, 41, 40 (degrees_north)
#> Bounds : (not set)
#>
#> Attributes:
#> name type length value
#> actual_range NC_DOUBLE 2 40, 45
#> axis NC_CHAR 1 Y
#> standard_name NC_CHAR 8 latitude
#> units NC_CHAR 13 degrees_north
obj$axes[["time"]]
#> <Time axis> [-24] time
#> Length : 4
#> Axis : T
#> Calendar : standard
#> Range : 2025-07-01 ... 2025-07-04 (days)
#> Bounds : (not set)
#>
#> Attributes:
#> name type length value
#> actual_range NC_DOUBLE 2 20270, 20273
#> axis NC_CHAR 1 T
#> standard_name NC_CHAR 4 time
#> units NC_CHAR 30 days since 1970-01-01T00:00:00
#> calendar NC_CHAR 8 standardYou can further modify the resulting CFVariable by setting other
properties, such as attributes or a coordinate reference system. Once
the object is complete, you can export or save it.
A CFVariable object can be exported to a data.table or to a
terra::SpatRaster (3D) or terra::SpatRasterDataset (4D) for further
processing. Obviously, these packages need to be installed to utilise
these methods.
# install.packages("data.table")
library(data.table)
head(dt <- ts$data.table())
#> longitude latitude time t2m
#> <num> <num> <char> <num>
#> 1: 28.9 -1 2016-01-01T09:00:00 295.7120
#> 2: 29.0 -1 2016-01-01T09:00:00 296.1809
#> 3: 29.1 -1 2016-01-01T09:00:00 297.6046
#> 4: 29.2 -1 2016-01-01T09:00:00 298.8195
#> 5: 29.3 -1 2016-01-01T09:00:00 300.1376
#> 6: 29.4 -1 2016-01-01T09:00:00 300.8583
#install.packages("terra")
suppressMessages(library(terra))
(r <- stats[["diurnal_range"]]$terra())
#> class : SpatRaster
#> size : 21, 31, 1 (nrow, ncol, nlyr)
#> resolution : 0.1, 0.1 (x, y)
#> extent : 27.95, 31.05, -3.05, -0.95 (xmin, xmax, ymin, ymax)
#> coord. ref. :
#> source(s) : memory
#> name : 2016-01-01T12:00:00
#> min value : 1.819982
#> max value : 11.273690
terra::plot(r)
A CFVariable object can also be written back to a netCDF file. The
object will have all its relevant attributes and properties written
together with the actual data: axes, bounds, attributes, CRS. The netCDF
file is of version “netcdf4” and will have the axes oriented in such a
way that the file has maximum portability (specifically, data will be
stored in row-major order with increasing Y values).
# Save a CFVariable instance to a netCDF file on disk
stats[["diurnal_range"]]$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 CFVariable::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.table for a
format that matches the structure of a typical table closest. 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 still being developed. It supports reading of all
data objects 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, cell boundary
information, auxiliary coordinate variables, labels, cell measures,
attributes and grid mapping information, among others.
Development plans for the near future focus on supporting the below features:
- Support for writing of complex data sets (single
CFVariableinstances can already be written to file). - Writing data to an unlimited dimension of a data variable.
- Cell methods
- Aggregation, using the CFA convention.
- Support for discrete sampling geometries.
- Compliance with CMIP5 / CMIP6 requirements.
Package ncdfCF is still being developed. While extensively tested on
multiple well-structured data sets, errors may still occur, particularly
in data sets that do not adhere to the CF Metadata Conventions. The API
may still change and although care is taken not to make breaking
changes, sometimes this is unavoidable.
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("R-CF/ncdfCF")