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 9 defined
calendars.formula_terms attribute.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)
# Easy access in understandable format to all the details
ds
#> <Dataset> ERA5land_Rwanda_20160101
#> Resource : /private/var/folders/gs/s0mmlczn4l7bjbmwfrrhjlt80000gn/T/RtmpUihQLG/temp_libpath68c95052b923/ncdfCF/extdata/ERA5land_Rwanda_20160101.nc
#> Format : offset64
#> 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
#>
#> Axes:
#> id axis name length unlim values
#> 0 T time 24 U [2016-01-01 00:00:00 ... 2016-01-01 23:00:00]
#> 1 X longitude 31 [28 ... 31]
#> 2 Y latitude 21 [-1 ... -3]
#> unit
#> hours since 1900-01-01 00:00:00.0
#> degrees_east
#> degrees_north
#>
#> Attributes:
#> id name type length
#> 0 CDI NC_CHAR 64
#> 1 Conventions NC_CHAR 6
#> 2 history NC_CHAR 482
#> 3 CDO NC_CHAR 64
#> value
#> Climate Data Interface version 2.4.1 (https://m...
#> CF-1.6
#> Tue May 28 18:39:12 2024: cdo seldate,2016-01-0...
#> Climate Data Operators version 2.4.1 (https://m...
# ...or very brief details
names(ds)
#> [1] "t2m" "pev" "tp"
dimnames(ds)
#> [1] "time" "longitude" "latitude"
# Variables can be accessed through standard list-type extraction syntax
t2m <- ds[["t2m"]]
t2m
#> <Variable> t2m
#> Long name: 2 metre temperature
#>
#> Axes:
#> id axis name length unlim values
#> 1 X longitude 31 [28 ... 31]
#> 2 Y latitude 21 [-1 ... -3]
#> 0 T time 24 U [2016-01-01 00:00:00 ... 2016-01-01 23:00:00]
#> unit
#> degrees_east
#> degrees_north
#> hours since 1900-01-01 00:00:00.0
#>
#> Attributes:
#> id name type length value
#> 0 long_name NC_CHAR 19 2 metre temperature
#> 1 units NC_CHAR 1 K
#> 2 add_offset NC_DOUBLE 1 292.664569285614
#> 3 scale_factor NC_DOUBLE 1 0.00045127252204996
#> 4 _FillValue NC_SHORT 1 -32767
#> 5 missing_value NC_SHORT 1 -32767
# Same with dimensions, but now without first attaching the object to a variable
ds[["longitude"]]
#> <Longitude axis> [1] longitude
#> Length : 31
#> Axis : X
#> Values : 28, 28.1, 28.2 ... 30.8, 30.9, 31 degrees_east
#> Bounds : (not set)
#>
#> Attributes:
#> id name type length value
#> 0 standard_name NC_CHAR 9 longitude
#> 1 long_name NC_CHAR 9 longitude
#> 2 units NC_CHAR 12 degrees_east
#> 3 axis NC_CHAR 1 X
# Regular base R operations simplify life further
dimnames(ds[["pev"]]) # A variable: list of dimension names
#> [1] "longitude" "latitude" "time"
dimnames(ds[["longitude"]]) # A dimension: vector of dimension element 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"]]$attribute("long_name")
#> [1] "Potential evaporation"There are three ways to read data for a variable from the resource:
data(): The data() method
returns all data of a variable, including its metadata, in a
CFData instance.[]: The usual R array operator gives
you access to the raw, non-interpreted data in the netCDF resource. This
uses index values into the dimensions and requires you to know the order
in which the dimensions are specified for the variable. With a bit of
tinkering and some helper functions in ncdfCF this is still
very easy to do.subset(): The subset()
method lets you specify what you want to extract from each dimension in
real-world coordinates and timestamps, in whichever order. This can also
rectify non-Cartesian grids to regular longitude-latitude grids.# Extract a timeseries for a specific location
ts <- t2m[5, 4, ]
str(ts)
#> num [1, 1, 1:24] 293 292 292 291 291 ...
#> - attr(*, "dimnames")=List of 3
#> ..$ : chr "28.4"
#> ..$ : chr "-1.3"
#> ..$ : chr [1:24] "2016-01-01 00:00:00" "2016-01-01 01:00:00" "2016-01-01 02:00:00" "2016-01-01 03:00:00" ...
#> - attr(*, "axis")= Named chr [1:3] "X" "Y" "T"
#> ..- attr(*, "names")= chr [1:3] "longitude" "latitude" "time"
#> - attr(*, "time")=List of 1
#> ..$ time:Formal class 'CFtime' [package "CFtime"] with 4 slots
#> .. .. ..@ datum :Formal class 'CFdatum' [package "CFtime"] with 5 slots
#> .. .. .. .. ..@ definition: chr "hours since 1900-01-01 00:00:00.0"
#> .. .. .. .. ..@ unit : int 3
#> .. .. .. .. ..@ origin :'data.frame': 1 obs. of 8 variables:
#> .. .. .. .. .. ..$ year : int 1900
#> .. .. .. .. .. ..$ month : num 1
#> .. .. .. .. .. ..$ day : num 1
#> .. .. .. .. .. ..$ hour : num 0
#> .. .. .. .. .. ..$ minute: num 0
#> .. .. .. .. .. ..$ second: num 0
#> .. .. .. .. .. ..$ tz : chr "+0000"
#> .. .. .. .. .. ..$ offset: num 0
#> .. .. .. .. ..@ calendar : chr "gregorian"
#> .. .. .. .. ..@ cal_id : int 1
#> .. .. ..@ resolution: num 1
#> .. .. ..@ offsets : num [1:24] 1016832 1016833 1016834 1016835 1016836 ...
#> .. .. ..@ bounds : logi FALSE
# 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
#> ..$ : chr [1:31] "28" "28.1" "28.2" "28.3" ...
#> ..$ : chr [1:21] "-1" "-1.1" "-1.2" "-1.3" ...
#> ..$ : chr "2016-01-01 11:00:00"
#> - attr(*, "axis")= Named chr [1:3] "X" "Y" "T"
#> ..- attr(*, "names")= chr [1:3] "longitude" "latitude" "time"
#> - attr(*, "time")=List of 1
#> ..$ time:Formal class 'CFtime' [package "CFtime"] with 4 slots
#> .. .. ..@ datum :Formal class 'CFdatum' [package "CFtime"] with 5 slots
#> .. .. .. .. ..@ definition: chr "hours since 1900-01-01 00:00:00.0"
#> .. .. .. .. ..@ unit : int 3
#> .. .. .. .. ..@ origin :'data.frame': 1 obs. of 8 variables:
#> .. .. .. .. .. ..$ year : int 1900
#> .. .. .. .. .. ..$ month : num 1
#> .. .. .. .. .. ..$ day : num 1
#> .. .. .. .. .. ..$ hour : num 0
#> .. .. .. .. .. ..$ minute: num 0
#> .. .. .. .. .. ..$ second: num 0
#> .. .. .. .. .. ..$ tz : chr "+0000"
#> .. .. .. .. .. ..$ offset: num 0
#> .. .. .. .. ..@ calendar : chr "gregorian"
#> .. .. .. .. ..@ cal_id : int 1
#> .. .. ..@ resolution: num NA
#> .. .. ..@ offsets : num 1016843
#> .. .. ..@ bounds : logi FALSENote 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
CFData, including axes and attributes:
# Extract a specific region, full time dimension
ts <- t2m$subset(list(X = 29:30, Y = -1:-2))
ts
#> <Data> t2m
#> Long name: 2 metre temperature
#>
#> Values: [283.0182 ... 299.917] K
#> NA: 0 (0.0%)
#>
#> Axes:
#> id axis name length unlim values
#> -1 X longitude 10 [29 ... 29.9]
#> -1 Y latitude 10 [-1.1 ... -2]
#> 0 T time 24 U [2016-01-01 00:00:00 ... 2016-01-01 23:00:00]
#> unit
#>
#>
#> hours since 1900-01-01 00:00:00.0
#>
#> Attributes:
#> id name type length value
#> 0 long_name NC_CHAR 19 2 metre temperature
#> 1 units NC_CHAR 1 K
#> 2 add_offset NC_DOUBLE 1 292.664569285614
#> 3 scale_factor NC_DOUBLE 1 0.00045127252204996
#> 4 _FillValue NC_SHORT 1 -32767
#> 5 missing_value NC_SHORT 1 -32767
# Extract specific time slices for a specific region
# Note that the dimensions are specified out of order and using alternative
# specifications: only the extreme values are used.
ts <- t2m$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)))
ts
#> <Data> t2m
#> Long name: 2 metre temperature
#>
#> Values: [288.2335 ... 299.917] K
#> NA: 0 (0.0%)
#>
#> Axes:
#> id axis name length values
#> -1 X longitude 8 [28.8 ... 29.5]
#> -1 Y latitude 10 [-1.1 ... -2]
#> -1 T time 6 [2016-01-01 09:00:00 ... 2016-01-01 14:00:00]
#>
#> Attributes:
#> id name type length value
#> 0 long_name NC_CHAR 19 2 metre temperature
#> 1 units NC_CHAR 1 K
#> 2 add_offset NC_DOUBLE 1 292.664569285614
#> 3 scale_factor NC_DOUBLE 1 0.00045127252204996
#> 4 _FillValue NC_SHORT 1 -32767
#> 5 missing_value NC_SHORT 1 -32767The latter two methods will read only as much data from the netCDF resource as is requested.
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 CFData::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 set with column and row names set as an array. 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 groups, variables, dimensions, user-defined data
types, attributes and data from netCDF resources in “classic” and
“netcdf4” formats. From the CF Metadata Conventions it supports
identification of dimension axes, interpretation of the “time”
dimension, name resolution when using groups, reading of “bounds”
information, parametric vertical coordinates, auxiliary coordinate
variables, and grid mapping information.
Development plans for the near future focus on supporting the below features:
CAUTION: 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.
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("pvanlaake/ncdfCF")