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 : /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
#> 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
#>
#> 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$var_names
#> t2m pev tp
#> "t2m" "pev" "tp"
ds$axis_names
#> time longitude latitude
#> "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
#>
#> Axes:
#> axis name length unlim 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 coordinates
#> [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 #> #> $attributes #> id name type length #> 1 0 CDI NC_CHAR 64 #> 2 1 Conventions NC_CHAR 6 #> 3 2 history NC_CHAR 482 #> 4 3 CDO NC_CHAR 64 #> value #> 1 Climate Data Interface version 2.4.1 (https://mpimet.mpg.de/cdi) #> 2 CF-1.6 #> 3 Tue May 28 18:39:12 2024: cdo seldate,2016-01-01,2016-01-01 /Users/patrickvanlaake/CC/ERA5land/Rwanda/ERA5land_Rwanda_t2m-pev-tp_2016-2018.nc ERA5land_Rwanda_20160101.nc\n2021-12-22 07:00:24 GMT by grib_to_netcdf-2.23.0: /opt/ecmwf/mars-client/bin/grib_to_netcdf -S param -o /cache/data5/adaptor.mars.internal-1640155821.967082-25565-12-0b19757d-da4e-4ea4-b8aa-d08ec89caf2c.nc /cache/tmp/0b19757d-da4e-4ea4-b8aa-d08ec89caf2c-adaptor.mars.internal-1640142203.3196251-25565-10-tmp.grib #> 4 Climate Data Operators version 2.4.1 (https://mpimet.mpg.de/cdo)
There are four ways to read data for a data variable from the resource:
data(): The data() method returns all data of a variable, including its metadata, in a CFArray instance.[]: The usual R array operator gives you access to the raw, non-interpreted data in the netCDF resource. This uses index values into the dimensions and requires you to know the order in which the dimensions are specified for the variable. With a bit of tinkering and some helper functions in ncdfCF this is still very easy to do.subset(): The subset() method lets you specify what you want to extract from each dimension in real-world coordinates and timestamps, in whichever order. This can also rectify non-Cartesian grids to regular longitude-latitude grids.profile(): Extract “profiles” from the data variable. This can take different forms, such as a temporal or depth profile for a single location, but it could also be a zonal field (such as a transect in latitude - atmospheric depth for a given longitude) or some other profile in the physical space of the data variable.# Extract a timeseries for a specific location ts <- t2m[5, 4, ] str(ts) #> num [1, 1, 1:24] 293 292 292 291 291 ... #> - attr(*, "dimnames")=List of 3 #> ..$ 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 values
Note 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$subset(list(X = 29:30, Y = -1:-2)))
#> <Data array> t2m
#> Long name: 2 metre temperature
#>
#> Values: [283.0182 ... 302.0447] K
#> NA: 0 (0.0%)
#>
#> Axes:
#> axis name length unlim values
#> X longitude 31 [28 ... 31]
#> Y latitude 21 [-1 ... -3]
#> T time 24 U [2016-01-01T00:00:00 ... 2016-01-01T23:00:00]
#> unit
#> degrees_east
#> degrees_north
#> hours since 1900-01-01 00:00:00.0
#>
#> Attributes:
#> name type length value
#> long_name NC_CHAR 19 2 metre temperature
#> units NC_CHAR 1 K
#> actual_range NC_DOUBLE 2 283.018168, 302.04472
# Extract specific time slices for a specific region
# Note that the dimensions are specified out of order and using alternative
# specifications: only the extreme values are used.
(ts <- t2m$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: [283.0182 ... 302.0447] K
#> NA: 0 (0.0%)
#>
#> Axes:
#> axis name length unlim 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
#> actual_range NC_DOUBLE 2 283.018168, 302.04472
The 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:
CFArray instance(s) or as a single data.table.CFArray 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"
#>
#> $units
#> [1] "K"
Some critical metadata is recorded in the “value” attribute: original long name and the physical unit.
When you provide coordinates for all axes but one, you get a profile of values along the remaining axis, as shown above. If you provide fewer axis coordinates you get progressively higher-order results. To get a latitudinal transect, for instance, provide only a longitude coordinate:
(trans30 <- t2m$profile(longitude = 30, .names = "lon_30")) #> <Data array> lon_30 #> Long name: 2 metre temperature #> #> Values: [286.4614 ... 300.0948] K #> NA: 0 (0.0%) #> #> Axes: #> axis name length unlim values #> Y latitude 21 [-1 ... -3] #> T time 24 U [2016-01-01T00:00:00 ... 2016-01-01T23:00:00] #> X longitude 1 [30] #> 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 #> coordinates NC_CHAR 9 longitude #> actual_range NC_DOUBLE 2 286.461377, 300.094771
Note that there is only a single longitude coordinate left, at exactly the specified longitude.
Summarising data over timeWith 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$summarise("tmax", max, "day")
#> <Data array> tmax
#> Long name: 2 metre temperature
#>
#> Values: [290.0364 ... 302.0447] K
#> NA: 0 (0.0%)
#>
#> Axes:
#> axis name length values unit
#> X longitude 31 [28 ... 31] degrees_east
#> Y latitude 21 [-1 ... -3] degrees_north
#> T time 1 [2016-01-01T12:00:00] hours since 1900-01-01 00:00:00.0
#>
#> Attributes:
#> name type length value
#> long_name NC_CHAR 19 2 metre temperature
#> units NC_CHAR 1 K
#> coordinates NC_CHAR 4 time
#> actual_range NC_DOUBLE 2 290.036358, 302.04472
A function may also return a vector of multiple values, in which case a list is returned with a new CFArray object for each return value of the function. This allows you to calculate multiple results with a single call. You could write your own function to tailor the calculations to your needs. Rather than just calculating the daily maximum, you could get the daily maximum, minimum and diurnal range in one go:
# Function to calculate multiple daily stats
# It is good practice to include a `na.rm` argument in all your functions
daily_stats <- function(x, na.rm = TRUE) {
# x is the vector of values for one day
minmax <- range(x, na.rm = na.rm)
diurnal <- minmax[2L] - minmax[1L]
c(minmax, diurnal)
}
# Call summarise() with your own function
# The `name` argument should have as many names as the function returns results
(stats <- t2m$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:
#> 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
#> coordinates NC_CHAR 4 time
#> actual_range NC_DOUBLE 2 283.018168, 293.865857
#>
#> $tmax
#> <Data array> 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
#> coordinates NC_CHAR 4 time
#> actual_range NC_DOUBLE 2 290.036358, 302.04472
#>
#> $diurnal_range
#> <Data array> 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
#> coordinates NC_CHAR 4 time
#> actual_range NC_DOUBLE 2 1.819982, 11.27369
Note 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.
You can convert a suitable R object into a CFArray 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_CFArray("my_first_CF_object", arr)
#> <Data array> my_first_CF_object
#>
#> Values: [-2.416518 ... 2.849734]
#> 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 -2.416518, 2.849734
Usable 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_CFArray("a_better_CF_object", arr))
#> <Data array> a_better_CF_object
#>
#> Values: [-2.416518 ... 2.849734]
#> 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 -2.416518, 2.849734
# Axes are of a specific type and have basic attributes set
obj$axes[["lat"]]
#> <Latitude axis> [-1] lat
#> Length : 6
#> Axis : Y
#> Coordinates: 45, 44, 43, 42, 41, 40 (degrees_north)
#> Bounds : (not set)
#>
#> Attributes:
#> name type length value
#> axis NC_CHAR 1 Y
#> actual_range NC_DOUBLE 2 40, 45
#> standard_name NC_CHAR 8 latitude
#> units NC_CHAR 13 degrees_north
obj$axes[["time"]]
#> <Time axis> [-1] time
#> Length : 4
#> Axis : T
#> Calendar : standard
#> Range : 2025-07-01 ... 2025-07-04 (days)
#> Bounds : (not set)
#>
#> Attributes:
#> name type length value
#> axis NC_CHAR 1 T
#> units NC_CHAR 30 days since 1970-01-01T00:00:00
#> calendar NC_CHAR 8 standard
#> standard_name NC_CHAR 4 time
#> actual_range NC_DOUBLE 2 20270, 20273
You can further modify the resulting CFArray by setting other properties, such as attributes or a coordinate reference system. Once the object is complete, you can export or save it.
A CFData object can be exported to a data.table or to a terra::SpatRaster (3D) or terra::SpatRasterDataset (4D) for further processing. Obviously, these packages need to be installed to utilise these methods.
# install.packages("data.table")
library(data.table)
head(dt <- ts$data.table())
#> longitude latitude time t2m
#> <num> <num> <char> <num>
#> 1: 28.0 -1 2016-01-01T00:00:00 293.8875
#> 2: 28.1 -1 2016-01-01T00:00:00 294.4015
#> 3: 28.2 -1 2016-01-01T00:00:00 294.4972
#> 4: 28.3 -1 2016-01-01T00:00:00 293.9426
#> 5: 28.4 -1 2016-01-01T00:00:00 293.6339
#> 6: 28.5 -1 2016-01-01T00:00:00 293.0206
#install.packages("terra")
suppressMessages(library(terra))
(r <- stats[["diurnal_range"]]$terra())
#> class : SpatRaster
#> dimensions : 21, 31, 1 (nrow, ncol, nlyr)
#> resolution : 0.1, 0.1 (x, y)
#> extent : 27.95, 31.05, -3.05, -0.95 (xmin, xmax, ymin, ymax)
#> coord. ref. : lon/lat WGS 84 (EPSG:4326)
#> source(s) : memory
#> name : 2016-01-01T12:00:00
#> min value : 1.819982
#> max value : 11.273690
terra::plot(r)
A CFData object can also be written back to a netCDF file. The object will have all its relevant attributes and properties written together with the actual data: axes, bounds, attributes, CRS. The netCDF file is of version “netcdf4” and will have the axes oriented in such a way that the file has maximum portability (specifically, data will be stored in row-major order with increasing Y values).
# Save a CFData instance to a netCDF file on disk
stats[["diurnal_range"]]$save("~/path/file.nc") A note on Discrete Sampling Geometries
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 CFArray::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 CFArray object for further processing, possibly converting it of a 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 in the early phases of development. It supports reading of all data objects from netCDF resources in “classic” and “netcdf4” formats; and can write single data arrays 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 grid cell boundary information, auxiliary coordinate variables, labels, cell measures, attributes and grid mapping information.
Development plans for the near future focus on supporting the below features:
CFArray instances can already be written to file).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. 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")
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