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, for two ocean
formulations.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/RtmpWtgooh/temp_libpath49db3636d842/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] "/private/var/folders/gs/s0mmlczn4l7bjbmwfrrhjlt80000gn/T/RtmpWtgooh/temp_libpath49db3636d842/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
#>
#> $attributes
#> id name type length
#> 1 0 CDI NC_CHAR 64
#> 2 1 Conventions NC_CHAR 6
#> 3 2 history NC_CHAR 482
#> 4 3 CDO NC_CHAR 64
#> value
#> 1 Climate Data Interface version 2.4.1 (https://mpimet.mpg.de/cdi)
#> 2 CF-1.6
#> 3 Tue May 28 18:39:12 2024: cdo seldate,2016-01-01,2016-01-01 /Users/patrickvanlaake/CC/ERA5land/Rwanda/ERA5land_Rwanda_t2m-pev-tp_2016-2018.nc ERA5land_Rwanda_20160101.nc\n2021-12-22 07:00:24 GMT by grib_to_netcdf-2.23.0: /opt/ecmwf/mars-client/bin/grib_to_netcdf -S param -o /cache/data5/adaptor.mars.internal-1640155821.967082-25565-12-0b19757d-da4e-4ea4-b8aa-d08ec89caf2c.nc /cache/tmp/0b19757d-da4e-4ea4-b8aa-d08ec89caf2c-adaptor.mars.internal-1640142203.3196251-25565-10-tmp.grib
#> 4 Climate Data Operators version 2.4.1 (https://mpimet.mpg.de/cdo)There are three ways to read data for a data variable from the resource:
[]: The usual R array operator gives
you access to the raw, non-interpreted data in the netCDF resource. This
uses index values into the dimensions and requires you to know the order
in which the dimensions are specified for the variable. With a bit of
tinkering and some helper functions in ncdfCF this is still
very easy to do.subset(): The subset()
method lets you specify what you want to extract from each dimension in
real-world coordinates and timestamps, in whichever order. This can also
rectify non-Cartesian grids to regular longitude-latitude grids.profile(): Extract “profiles” from the
data variable. This can take different forms, such as a temporal or
depth profile for a single location, but it could also be a zonal field
(such as a transect in latitude - atmospheric depth for a given
longitude) or some other profile in the physical space of the data
variable.# Extract a timeseries for a specific location - see also the `profile()` method
ts <- t2m[5, 4, ]
str(ts)
#> num [1, 1, 1:24] 293 292 292 291 291 ...
#> - attr(*, "dimnames")=List of 3
#> ..$ 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 KThe latter two methods will read only as much data from the netCDF resource as is requested.
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:
CFVariable
instance(s) or as a single data.table.CFVariable instance(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: [-3.054023 ... 2.954466]
#> 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 -3.054023, 2.954466Usable 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: [-3.054023 ... 2.954466]
#> 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 -3.054023, 2.954466
# 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
#> <num> <num> <char>
#> 1: 28.9 -1 2016-01-01T09:00:00
#> 2: 29.0 -1 2016-01-01T09:00:00
#> 3: 29.1 -1 2016-01-01T09:00:00
#> 4: 29.2 -1 2016-01-01T09:00:00
#> 5: 29.3 -1 2016-01-01T09:00:00
#> 6: 29.4 -1 2016-01-01T09:00:00
#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:
CFVariable instances can already be written to file).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")