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.
Basic usage
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
#> Conventions NC_CHAR 6 CF-1.6
#> history NC_CHAR 34 Attributes simplified for example.
# ...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
#>
#> $attributes
#> name type length value
#> 1 Conventions NC_CHAR 6 CF-1.6
#> 2 history NC_CHAR 34 Attributes simplified for example.Extracting data
There are various ways to read data for a data variable from the resource:
-
[]: The usual R array operator gives you access to the raw, non-interpreted data in the netCDF resource. This uses index values into the dimensions and requires you to know the order in which the dimensions are specified for the variable. With a bit of tinkering and some helper functions inncdfCFthis is still very easy to do. -
raw(): This also gets the data in the layout of the file (or the data set) but with dimnames set. Importantly, you can call this after callingsubset()and you will get the raw data for the specific spatial and temporal domain that you are interested in. -
array(): Likeraw(), this extracts all the (subsetted) data, but now the data will be oriented in the standard R way of column-major order. Y coordinates will run from the top to the bottom (so latitude values, for instance, will be decreasing). -
subset(): Thesubset()method lets you specify what you want to extract from each dimension in real-world coordinates and timestamps, in whichever order. This can also rectify non-Cartesian grids to regular longitude-latitude grids. Subsetting is lazy: data is not loaded so long as a direct relationship to the data in the netCDF resource is maintained. -
profile(): Extract “profiles” from the data variable. This can take different forms, such as a temporal or depth profile for a single location, but it could also be a zonal field (such as a transect in latitude - atmospheric depth for a given longitude) or some other profile in the physical space of the data variable.
# Extract a timeseries for a specific location - see also the `profile()` method
ts <- t2m[5, 4, ]
str(ts)
#> num [1, 1, 1:24] 293 292 292 291 291 ...
#> - attr(*, "dimnames")=List of 3
#> ..$ 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.
Make a profile of data
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)
#> longitude latitude time .variable .value
#> <num> <num> <char> <char> <num>
#> 1: 30.07 -1.94 2016-01-01T00:00:00 Kigali 290.4055
#> 2: 30.07 -1.94 2016-01-01T01:00:00 Kigali 290.0088
#> 3: 30.07 -1.94 2016-01-01T02:00:00 Kigali 289.3608
#> 4: 30.07 -1.94 2016-01-01T03:00:00 Kigali 288.8414
#> 5: 30.07 -1.94 2016-01-01T04:00:00 Kigali 288.4713
#> 6: 30.07 -1.94 2016-01-01T05:00:00 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
#> X longitude 1 [29.74]
#> 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 2 286.539447, 298.96298Note that there is only a single longitude coordinate left, at exactly the specified longitude.
Summarising data over time
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$summarise("tmax", max, "day")
#> <Variable> tmax
#> Long name: 2 metre temperature
#>
#> Values: [290.0364 ... 302.0447] K
#> NA: 0 (0.0%)
#>
#> Axes:
#> axis name length values unit
#> T time 1 [2016-01-01T12:00:00] hours since 1900-01-01 00:00:00.0
#> X longitude 31 [28 ... 31] degrees_east
#> Y latitude 21 [-1 ... -3] degrees_north
#>
#> Attributes:
#> name type length value
#> long_name NC_CHAR 19 2 metre temperature
#> units NC_CHAR 1 K
#> actual_range NC_DOUBLE 2 290.036358, 302.04472A function may also return a vector of multiple values, in which case a list is returned with a new CFVariable object for each return value of the function. This allows you to calculate multiple results with a single call. You could write your own function to tailor the calculations to your needs. Rather than just calculating the daily maximum, you could get the daily maximum, minimum and diurnal range in one go:
# Function to calculate multiple daily stats
# It is good practice to include a `na.rm` argument in all your functions
daily_stats <- function(x, na.rm = TRUE) {
# x is the vector of values for one day
minmax <- range(x, na.rm = na.rm)
diurnal <- minmax[2L] - minmax[1L]
c(minmax, diurnal)
}
# Call summarise() with your own function
# The `name` argument should have as many names as the function returns results
(stats <- t2m$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
#> T time 1 [2016-01-01T12:00:00] hours since 1900-01-01 00:00:00.0
#> X longitude 31 [28 ... 31] degrees_east
#> Y latitude 21 [-1 ... -3] degrees_north
#>
#> 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
#> T time 1 [2016-01-01T12:00:00] hours since 1900-01-01 00:00:00.0
#> X longitude 31 [28 ... 31] degrees_east
#> Y latitude 21 [-1 ... -3] degrees_north
#>
#> 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
#> T time 1 [2016-01-01T12:00:00] hours since 1900-01-01 00:00:00.0
#> X longitude 31 [28 ... 31] degrees_east
#> Y latitude 21 [-1 ... -3] degrees_north
#>
#> 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.
Create new netCDF objects
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: [-1.845046 ... 2.907942]
#> 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 -1.845046, 2.907942Usable 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: [-1.845046 ... 2.907942]
#> 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 -1.845046, 2.907942
# Axes are of a specific type and have basic attributes set
obj$axes[["lat"]]
#> <Latitude axis> [-32] 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$axes[["time"]]
#> <Time axis> [-34] time
#> Length : 4
#> Axis : T
#> Calendar : standard
#> Range : 2025-07-01 ... 2025-07-04 (days)
#> Bounds : (not set)
#>
#> Attributes:
#> name type length value
#> actual_range NC_DOUBLE 2 20270, 20273
#> axis NC_CHAR 1 T
#> standard_name NC_CHAR 4 time
#> units NC_CHAR 30 days since 1970-01-01T00:00:00
#> calendar NC_CHAR 8 standardYou can further modify the resulting CFVariable by setting other properties, such as attributes or a coordinate reference system. Once the object is complete, you can export or save it.
Exporting and saving data
A CFVariable object can be exported to a data.table or to a terra::SpatRaster (3D) or terra::SpatRasterDataset (4D) for further processing. Obviously, these packages need to be installed to utilise these methods.
# install.packages("data.table")
library(data.table)
head(dt <- ts$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"]]$terra())
#> class : SpatRaster
#> size : 21, 31, 1 (nrow, ncol, nlyr)
#> resolution : 0.1, 0.1 (x, y)
#> extent : 27.95, 31.05, -3.05, -0.95 (xmin, xmax, ymin, ymax)
#> coord. ref. :
#> source(s) : memory
#> name : 2016-01-01T12:00:00
#> min value : 1.819982
#> max value : 11.273690
terra::plot(r)
A stars object can be created from a CFVariable or a CFDataset with multiple variables with the function stars::st_as_stars().
library(stars)
#> Loading required package: abind
#> Loading required package: sf
#> Linking to GEOS 3.13.0, GDAL 3.8.5, PROJ 9.5.1; sf_use_s2() is TRUE
(st <- st_as_stars(ts))
#> stars object with 3 dimensions and 1 attribute
#> attribute(s):
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> t2m [K] 288.2335 293.2838 294.7573 294.9324 296.6282 301.2081
#> dimension(s):
#> from to offset delta refsys x/y
#> longitude 1 7 28.85 0.1 OGC:CRS84 [x]
#> latitude 1 11 -0.95 -0.1 OGC:CRS84 [y]
#> time 1 6 2016-01-01 09:00:00 UTC 1 hours POSIXctA CFVariable object can also be written back to a netCDF file. The object will have all its relevant attributes and properties written together with the actual data: axes, bounds, attributes, CRS. The netCDF file is of version “netcdf4” and will have the axes oriented in such a way that the file has maximum portability (specifically, data will be stored in row-major order with increasing Y values).
# Save a CFVariable instance to a netCDF file on disk
stats[["diurnal_range"]]$save("~/path/file.nc")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 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:
Installation
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:
You can install the development version of ncdfCF from GitHub with: