ncdfCF

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.

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.

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: these will become the names of the axes
arr <- array(rnorm(120), dim = c(6, 5, 4))
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_new_CF_object", arr))
#> <Variable> a_new_CF_object 
#> 
#> Values: [-2.59647 ... 2.487776] 
#>     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      -2.59647, 2.487776

# Axes are of a specific type and have basic attributes set
obj$axes[["lat"]]
#> <Latitude axis> [-2] 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> [-4] 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     standard

You 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.

More detailed operations and options are given on the package web site.

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:

netCDF

• Writing data to an unlimited dimension of a data variable.

CF Metadata Conventions

• Cell methods.
• Aggregation.
• Support for discrete sampling geometries.
• Compliance with CMIP5 / CMIP6 requirements.

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:

install.packages("ncdfCF")

You can install the development version of ncdfCF from GitHub with:

# install.packages("devtools")
devtools::install_github("R-CF/ncdfCF")