Title: | A Tidy Approach to 'NetCDF' Data Exploration and Extraction |
---|---|
Description: | Tidy tools for 'NetCDF' data sources. Explore the contents of a 'NetCDF' source (file or URL) presented as variables organized by grid with a database-like interface. The hyper_filter() interactive function translates the filter value or index expressions to array-slicing form. No data is read until explicitly requested, as a data frame or list of arrays via hyper_tibble() or hyper_array(). |
Authors: | Michael Sumner [aut, cre], Simon Wotherspoon [ctb], Tomas Remenyi [ctb], Ben Raymond [ctb], Jakub Nowosad [ctb], Tim Lucas [ctb], Hadley Wickham [ctb], Adrian Odenweller [ctb], Patrick Van Laake [ctb], Fabian Bernhard [ctb] |
Maintainer: | Michael Sumner <[email protected]> |
License: | GPL-3 |
Version: | 0.4.0 |
Built: | 2024-08-17 08:18:09 UTC |
Source: | https://github.com/ropensci/tidync |
A grid in NetCDF is a particular shape and size available for array variables, and consists of sets of dimensions. To activate a grid is to set the context for downstream operations, for querying, summarizing and reading data. There's no sense in performing these operations on more than one grid at a time, but multiple variables may exist in a single grid. There may be only one significant grid in a source or many, individual dimensions are themselves grids.
activate(.data, what, ..., select_var = NULL) ## S3 method for class 'tidync' activate(.data, what, ..., select_var = NULL) ## S3 method for class 'tidync' active(x) active(x) ## Default S3 method: active(x) active(x) <- value ## Default S3 replacement method: active(x) <- value
activate(.data, what, ..., select_var = NULL) ## S3 method for class 'tidync' activate(.data, what, ..., select_var = NULL) ## S3 method for class 'tidync' active(x) active(x) ## Default S3 method: active(x) active(x) <- value ## Default S3 replacement method: active(x) <- value
.data |
NetCDF object |
what |
name of a grid or variable |
... |
reserved, currently ignored |
select_var |
optional argument to set selected state of variable/s by name |
x |
NetCDF object |
value |
name of grid or variable to be active |
There may be more than one grid and one is always activated by default. A
grid may be activated by name in the form of 'D1,D0' where one or more
numbered dimensions indicates the grid. The grid definition names are printed
as part of the summary of in the tidync object and may be obtained directly
with hyper_grids()
on the tidync object.
Activation of a grid sets the context for downstream operations (slicing and reading data) from NetCDF, and as there may be several grids in a single source activation allows a different choice of available variables. By default the largest grid is activated. Once activated, all downstream tasks apply to the set of variables that exist on that grid.
If activate()
is called with a variable name, it puts the variable first.
The function active()
gets and sets the active grid. To restrict ultimate
read to particular variables use the select_var
argument to
hyper_filter()
, hyper_tibble()
and hyper_tbl_cube()
.
Scalar variables are not currently available to tidync, and it's not obvious
how activation would occur for scalars, but in future perhaps activate("S")
could be the right way forward.
NetCDF object
hyper_filter hyper_tibble hyper_tbl_cube
if (!tolower(Sys.info()[["sysname"]]) == "sunos") { l3file <- "S20080012008031.L3m_MO_CHL_chlor_a_9km.nc" rnc <- tidync(system.file("extdata", "oceandata", l3file, package = "tidync")) activate(rnc, "palette") ## extract available grid names hyper_grids(rnc) }
if (!tolower(Sys.info()[["sysname"]]) == "sunos") { l3file <- "S20080012008031.L3m_MO_CHL_chlor_a_9km.nc" rnc <- tidync(system.file("extdata", "oceandata", l3file, package = "tidync")) activate(rnc, "palette") ## extract available grid names hyper_grids(rnc) }
Extract the raw array data as a list of one or more arrays. This can be the
entire variable/s or after dimension-slicing using hyper_filter()
expressions. This is a delay-breaking function and causes data to be read
from the source into R native arrays. This list of arrays is
lightly classed as tidync_data, with methods for print()
and tidync()
.
hyper_array( x, select_var = NULL, ..., raw_datavals = FALSE, force = FALSE, drop = TRUE ) hyper_slice( x, select_var = NULL, ..., raw_datavals = FALSE, force = FALSE, drop = TRUE ) ## S3 method for class 'tidync' hyper_array( x, select_var = NULL, ..., raw_datavals = FALSE, force = FALSE, drop = TRUE ) ## S3 method for class 'character' hyper_array( x, select_var = NULL, ..., raw_datavals = FALSE, force = FALSE, drop = TRUE )
hyper_array( x, select_var = NULL, ..., raw_datavals = FALSE, force = FALSE, drop = TRUE ) hyper_slice( x, select_var = NULL, ..., raw_datavals = FALSE, force = FALSE, drop = TRUE ) ## S3 method for class 'tidync' hyper_array( x, select_var = NULL, ..., raw_datavals = FALSE, force = FALSE, drop = TRUE ) ## S3 method for class 'character' hyper_array( x, select_var = NULL, ..., raw_datavals = FALSE, force = FALSE, drop = TRUE )
x |
NetCDF file, connection object, or tidync object |
select_var |
optional vector of variable names to select |
... |
passed to |
raw_datavals |
logical to control whether scaling in the NetCDF is applied or not |
force |
ignore caveats about large extraction and just do it |
drop |
collapse degenerate dimensions, defaults to |
The function hyper_array()
is used by hyper_tibble()
and hyper_tbl_cube()
to actually extract data arrays from NetCDF, if a result would be particularly large
there is a check made and user-opportunity to cancel. This is controllable as an
option getOption('tidync.large.data.check')
, and can be set to never check with
options(tidync.large.data.check = FALSE)
.
The function hyper_array()
will act on an existing tidync object or a source
string.
By default all variables in the active grid are returned, use select_var
to
specify one or more desired variables.
The transforms are stored as a list of tables in an attribute 'transforms“,
access these with hyper_transforms()
.
print.tidync_data for a description of the print summary,
hyper_tbl_cube()
and hyper_tibble()
which are also delay-breaking
functions that cause data to be read
f <- "S20080012008031.L3m_MO_CHL_chlor_a_9km.nc" l3file <- system.file("extdata/oceandata", f, package= "tidync") ## extract a raw list by filtered dimension library(dplyr) araw1 <- tidync(l3file) %>% hyper_filter(lat = between(lat, -78, -75.8), lon = between(lon, 165, 171)) %>% hyper_array() araw <- tidync(l3file) %>% hyper_filter(lat = abs(lat) < 10, lon = index < 100) %>% hyper_array() ## hyper_array will pass the expressions to hyper_filter braw <- tidync(l3file) %>% hyper_array(lat = abs(lat) < 10, lon = index < 100) ## get the transforms tables (the axis coordinates) lapply(attr(braw, "transforms"), function(x) nrow(dplyr::filter(x, selected))) ## the selected axis coordinates should match in order and in size lapply(braw, dim)
f <- "S20080012008031.L3m_MO_CHL_chlor_a_9km.nc" l3file <- system.file("extdata/oceandata", f, package= "tidync") ## extract a raw list by filtered dimension library(dplyr) araw1 <- tidync(l3file) %>% hyper_filter(lat = between(lat, -78, -75.8), lon = between(lon, 165, 171)) %>% hyper_array() araw <- tidync(l3file) %>% hyper_filter(lat = abs(lat) < 10, lon = index < 100) %>% hyper_array() ## hyper_array will pass the expressions to hyper_filter braw <- tidync(l3file) %>% hyper_array(lat = abs(lat) < 10, lon = index < 100) ## get the transforms tables (the axis coordinates) lapply(attr(braw, "transforms"), function(x) nrow(dplyr::filter(x, selected))) ## the selected axis coordinates should match in order and in size lapply(braw, dim)
The hyper_filter()
acts on a tidync object by matching one or more
filtering expressions like with dplyr::filter
. This allows us to lazily
specify a subset from a NetCDF array without pulling any data. The modified
object may be printed to see the effects of subsetting, or saved for further
use.
hyper_filter(.x, ...) ## S3 method for class 'tidync' hyper_filter(.x, ...)
hyper_filter(.x, ...) ## S3 method for class 'tidync' hyper_filter(.x, ...)
.x |
NetCDF file, connection object, or |
... |
currently ignored |
The function hyper_filter()
will act on an existing tidync object or a
source string.
Filter arguments must be named as per the dimensions in the variable in form
dimname = dimname < 10
. This is a restrictive variant of dplyr::filter()
,
with a syntax more like dplyr::mutate()
. This ensures that each element is
named, so we know which dimension to apply this to, but also that the
expression evaluated against can do some extra work for a nuanced test.
There are special columns provided with each axis, one is 'index' so that
exact matching can be done by position, or to ignore the actual value of the
coordinate. That means we can use a form like dimname = index < 10
to
subset by position in the array index, without necessarily knowing the
values along that dimension.
data frame
f <- "S20080012008031.L3m_MO_CHL_chlor_a_9km.nc" l3file <- system.file("extdata/oceandata", f, package= "tidync") ## filter by value tidync(l3file) %>% hyper_filter(lon = lon < 100) ## filter by index tidync(l3file) %>% hyper_filter(lon = index < 100) ## be careful that multiple comparisons must occur in one expression tidync(l3file) %>% hyper_filter(lon = lon < 100 & lon > 50) ## filter in combination/s tidync(l3file) %>% hyper_filter(lat = abs(lat) < 10, lon = index < 100)
f <- "S20080012008031.L3m_MO_CHL_chlor_a_9km.nc" l3file <- system.file("extdata/oceandata", f, package= "tidync") ## filter by value tidync(l3file) %>% hyper_filter(lon = lon < 100) ## filter by index tidync(l3file) %>% hyper_filter(lon = index < 100) ## be careful that multiple comparisons must occur in one expression tidync(l3file) %>% hyper_filter(lon = lon < 100 & lon > 50) ## filter in combination/s tidync(l3file) %>% hyper_filter(lat = abs(lat) < 10, lon = index < 100)
Produce a tbl_cube from NetCDF. This is a delay-breaking function and causes data to be read from the source into the tbl cube format defined in the dplyr package.
hyper_tbl_cube(x, ..., force = FALSE) ## S3 method for class 'tidync' hyper_tbl_cube(x, ..., force = FALSE) ## S3 method for class 'character' hyper_tbl_cube(x, ..., force = FALSE)
hyper_tbl_cube(x, ..., force = FALSE) ## S3 method for class 'tidync' hyper_tbl_cube(x, ..., force = FALSE) ## S3 method for class 'character' hyper_tbl_cube(x, ..., force = FALSE)
x |
tidync object |
... |
arguments for |
force |
ignore caveats about large extraction and just do it |
The size of an extraction is checked and if quite large there is an a user-controlled
prompt to proceed or cancel. This can be disabled with options(tidync.large.data.check = FALSE)
please see hyper_array()
for more details.
The tbl cube is a very general and arbitrarily-sized array that can be used with tidyverse functionality. Dimension coordinates are stored with the tbl cube, derived from the grid transforms.
tbl_cube
dplyr::tbl_cube
hyper_array()
and hyper_tibble()
which are also delay-breaking
functions that cause data to be read
f <- "S20080012008031.L3m_MO_CHL_chlor_a_9km.nc" l3file <- system.file("extdata/oceandata", f, package= "tidync") (cube <- hyper_tbl_cube(tidync(l3file) %>% activate(chlor_a), lon = lon > 107, lat = abs(lat) < 30)) ufile <- system.file("extdata", "unidata", "test_hgroups.nc", package = "tidync", mustWork = TRUE) ## some versions of NetCDF don't support this file ## (4.1.3 tidync/issues/82) group_nc <- try(tidync(ufile), silent = TRUE) if (!inherits(group_nc, "try-error")) { res <- hyper_tbl_cube(tidync(ufile)) print(res) } else { ## the error was writeLines(c(group_nc)) }
f <- "S20080012008031.L3m_MO_CHL_chlor_a_9km.nc" l3file <- system.file("extdata/oceandata", f, package= "tidync") (cube <- hyper_tbl_cube(tidync(l3file) %>% activate(chlor_a), lon = lon > 107, lat = abs(lat) < 30)) ufile <- system.file("extdata", "unidata", "test_hgroups.nc", package = "tidync", mustWork = TRUE) ## some versions of NetCDF don't support this file ## (4.1.3 tidync/issues/82) group_nc <- try(tidync(ufile), silent = TRUE) if (!inherits(group_nc, "try-error")) { res <- hyper_tbl_cube(tidync(ufile)) print(res) } else { ## the error was writeLines(c(group_nc)) }
Extract the raw array data as an expanded data frame. This can be the entire
variable/s or after dimension-slicing using hyper_filter()
expressions with
dimension values expanded appropriately for each element in the arrays (one
row per element).
hyper_tibble(x, ..., na.rm = TRUE, force = FALSE) ## S3 method for class 'character' hyper_tibble(x, ..., na.rm = TRUE, force = FALSE) ## S3 method for class 'tidync' hyper_tibble(x, ..., na.rm = TRUE, force = FALSE)
hyper_tibble(x, ..., na.rm = TRUE, force = FALSE) ## S3 method for class 'character' hyper_tibble(x, ..., na.rm = TRUE, force = FALSE) ## S3 method for class 'tidync' hyper_tibble(x, ..., na.rm = TRUE, force = FALSE)
x |
NetCDF file, connection object, or |
... |
arguments to 'hyper_filter“ |
na.rm |
if |
force |
ignore caveats about large extraction and just do it |
The size of an extraction is checked and if quite large there is an a user-controlled
prompt to proceed or cancel. This can be disabled with options(tidync.large.data.check = FALSE)
please see hyper_array()
for more details.
The function hyper_tibble()
will act on an existing tidync object or a source
string.
By default all variables in the active grid are returned, use select_var
to
limit.
a tbl_df
hyper_array()
and hyper_tbl_cube()
which are also delay-breaking
functions that cause data to be read
l3file <- "S20080012008031.L3m_MO_CHL_chlor_a_9km.nc" f <- system.file("extdata", "oceandata", l3file, package= "tidync") rnc <- tidync(f) hyper_filter(rnc) library(dplyr) lapply(hyper_array(f, lat = lat > 0, lon = index > 3000), dim) ht <- hyper_tibble(rnc) %>% filter(!is.na(chlor_a)) ht library(ggplot2) ggplot(ht %>% filter(!is.na(chlor_a)), aes(x = lon, y = lat, fill = chlor_a)) + geom_tile()
l3file <- "S20080012008031.L3m_MO_CHL_chlor_a_9km.nc" f <- system.file("extdata", "oceandata", l3file, package= "tidync") rnc <- tidync(f) hyper_filter(rnc) library(dplyr) lapply(hyper_array(f, lat = lat > 0, lon = index > 3000), dim) ht <- hyper_tibble(rnc) %>% filter(!is.na(chlor_a)) ht library(ggplot2) ggplot(ht %>% filter(!is.na(chlor_a)), aes(x = lon, y = lat, fill = chlor_a)) + geom_tile()
Axis 'transforms' are data frames of each dimension in a NetCDF source.
hyper_transforms
returns a list of the active transforms by default,
hyper_transforms(x, all = FALSE, ...) ## Default S3 method: hyper_transforms(x, all = FALSE, ...)
hyper_transforms(x, all = FALSE, ...) ## Default S3 method: hyper_transforms(x, all = FALSE, ...)
x |
tidync object |
all |
set to |
... |
ignored |
Each transform is available by name from a list, and each data frame has the
coordinate of the dimension, its index, and a 'selected' variable set by the
filtering expressions in hyper_filter
and used by the read-functions
hyper_array
and hyper_tibble
.
Use hyper_transforms
to interrogate and explore the available dimension
manually, or for development of custom functions.
list of axis transforms
l3file <- "S20080012008031.L3m_MO_CHL_chlor_a_9km.nc" f <- system.file("extdata", "oceandata", l3file, package = "tidync") ax <- tidync(f) %>% hyper_transforms() names(ax) lapply(ax, dim) ## this function returns the transforms tidync knows about for this source str(tidync(f)$transforms) names(hyper_transforms(tidync(f), all = TRUE))
l3file <- "S20080012008031.L3m_MO_CHL_chlor_a_9km.nc" f <- system.file("extdata", "oceandata", l3file, package = "tidync") ax <- tidync(f) %>% hyper_transforms() names(ax) lapply(ax, dim) ## this function returns the transforms tidync knows about for this source str(tidync(f)$transforms) names(hyper_transforms(tidync(f), all = TRUE))
Functions to report on the current status of the active
grid. Information
on the active dimensions and variables are listed in a data frame with
multiple columns.
hyper_vars(x, ...) hyper_dims(x, ...) hyper_grids(x, ...)
hyper_vars(x, ...) hyper_dims(x, ...) hyper_grids(x, ...)
x |
tidync object |
... |
ignored |
The dimensions and variables of the active grid are identified in the print method of the tidync object, these functions exist to provide that information directly.
hyper_vars()
will list the ids, data type, name, dimension number, number
of attributes and and coordinate status of the variables on the currently
active grid.
hyper_dims()
will list the names, lengths, start/count index, ids, and
status of dimensions on the currently active grid. records on the currently
active dimensions.
hyper_grids()
will list the names, number of dimension, and number of
variables and active status of each grid in the source.
data frame
f <- "S20080012008031.L3m_MO_CHL_chlor_a_9km.nc" l3file <- system.file("extdata/oceandata", f, package= "tidync") tnc <- tidync(l3file) hyper_vars(tnc) hyper_dims(tnc) hyper_dims(tnc %>% hyper_filter(lat = lat < 20))
f <- "S20080012008031.L3m_MO_CHL_chlor_a_9km.nc" l3file <- system.file("extdata/oceandata", f, package= "tidync") tnc <- tidync(l3file) hyper_vars(tnc) hyper_dims(tnc) hyper_dims(tnc %>% hyper_filter(lat = lat < 20))
This exists so we can (internally) use a file path, uri, or open NetCDF connection (ncdf4 or RNetCDF) in a simpler way.
nc_get(x, v, test = FALSE)
nc_get(x, v, test = FALSE)
x |
file path, uri, or NetCDF connection |
v |
variable name |
test |
if true we make sure the connection can be open, not applied for connections themselves |
This function just reads the whole array. It is equivalent to the angstroms package function 'rawdata(x, varname)'.
Provide a summary of variables and dimensions, organized by their 'grid' (or 'shape') and with a summary of any slicing operations provided as 'start' and 'count' summaries for each dimension in the active grid.
## S3 method for class 'tidync' print(x, ...)
## S3 method for class 'tidync' print(x, ...)
x |
NetCDF object |
... |
reserved |
See tidync for detail about the object, and hyper_vars for programmatic access to the active grid's variable and dimension information.
The print summary is organized in two sections, the first is available grids (sets of dimensions) and their associated variables, the second is the dimensions, separated into active and inactive. All dimensions may be active in some NetCDF sources.
Individual active dimensions include the following components: * 'dim' - dimension label, D0, D1, D2, ... * 'name' - dimension name * 'length' - size of the dimension * 'min' - minimum value of the dimension * 'max' - maximum value of the dimension * 'start' - start index of subsetting * 'count' - length of subsetting index * 'dmin' - minimum value of the subset dimension * 'dmax' - maximum value of the subset dimension * 'unlim'
indicates whether dimension is unlimited (spread across other files, usually the time-step) * 'coord_dim' - indicates whether dimension is a coordinate-dimension (i.e. listed as a 1-D grid)
The inactive dimension summary does not include 'start', 'count', 'dmin', 'dmax' as these are identical to the values of 1, 'length', 'min', 'max' when no array subsetting has been applied.
argofile <- system.file("extdata/argo/MD5903593_001.nc", package = "tidync") argo <- tidync(argofile) print(argo) ## the print is modified by choosing a new grid or running filters argo %>% activate("D7,D9,D11,D8") argo %>% hyper_filter(N_LEVELS = index > 300)
argofile <- system.file("extdata/argo/MD5903593_001.nc", package = "tidync") argo <- tidync(argofile) print(argo) ## the print is modified by choosing a new grid or running filters argo %>% activate("D7,D9,D11,D8") argo %>% hyper_filter(N_LEVELS = index > 300)
Print method for the 'tidync_data' list of arrays returned by hyper_array()
.
## S3 method for class 'tidync_data' print(x, ...)
## S3 method for class 'tidync_data' print(x, ...)
x |
'tidync_data' object (from |
... |
reserved args |
The output lists the variables and their dimensions of an object from a
previous call to tidync()
, and possibly hyper_filter()
. The available
data will differ from the source in terms of variables (via select_var
in
hyper_array) and the lengths of each dimension (via named expressions in
hyper_filter()
).
the input object invisibly
tidync_data
argofile <- system.file("extdata/argo/MD5903593_001.nc", package = "tidync") argodata <- tidync(argofile) %>% hyper_filter(N_LEVELS = index < 5) %>% hyper_array(select_var = c("TEMP_ADJUSTED", "PRES")) print(argodata)
argofile <- system.file("extdata/argo/MD5903593_001.nc", package = "tidync") argodata <- tidync(argofile) %>% hyper_filter(N_LEVELS = index < 5) %>% hyper_array(select_var = c("TEMP_ADJUSTED", "PRES")) print(argodata)
Connect to a NetCDF source and allow use of hyper_*
verbs for slicing with
hyper_filter()
, extracting data with hyper_array()
and [hyper_tibble()
from an activated grid. By default the largest grid encountered is
activated, seeactivate()
.
tidync(x, what, ...) ## S3 method for class 'character' tidync(x, what, ...) ## S3 method for class 'tidync_data' tidync(x, what, ...)
tidync(x, what, ...) ## S3 method for class 'character' tidync(x, what, ...) ## S3 method for class 'tidync_data' tidync(x, what, ...)
x |
path to a NetCDF file |
what |
(optional) character name of grid (see |
... |
reserved for arguments to methods, currently ignored |
The print method for tidync includes a lot of information about which
variables exist on which dimensions, and if any slicing (hyper_filter()
)
operations have occurred these are summarized as 'start' and 'count'
modifications relative to the dimension lengths. See print
for these details, and hyper_vars for programmatic access to
this information
Many NetCDF forms are supported and tidync tries to reduce the interpretation applied to a given source. The NetCDF system defines a 'grid' for storing array data, where 'grid' is the array 'shape', or 'set of dimensions'). There may be several grids in a single source and so we introduce the concept of grid 'activation'. Once activated, all downstream tasks apply to the set of variables that exist on that grid.
NetCDF sources with numeric types are chosen by default, even if existing 'NC_CHAR' type variables are on the largest grid. When read any 'NC_CHAR' type variables are exploded into single character elements so that dimensions match the source.
A grid is an instance of a particular set of dimensions, which can be shared by more than one variable. This is not the 'rank' of a variable (the number of dimensions) since a single data set may have many 3D variables composed of different sets of axes/dimensions. There's no formality around the concept of 'shape', as far as we know.
A dimension may have length zero, but this is a special case for a "measure" dimension, we think. (It doesn't mean the product of the dimensions is zero, for example).
Files with compound types are not yet supported and should fail gracefully. Groups are not yet supported.
We haven't yet explored 'HDF5' in detail, so any feedback is appreciated. Major use of compound types is made by https://github.com/sosoc/croc.
## a SeaWiFS (S) Level-3 Mapped (L3m) monthly (MO) chlorophyll-a (CHL) ## remote sensing product at 9km resolution (at the equator) ## from the NASA ocean colour group in NetCDF4 format (.nc) ## for 31 day period January 2008 (S20080012008031) f <- "S20080012008031.L3m_MO_CHL_chlor_a_9km.nc" l3file <- system.file("extdata/oceandata", f, package= "tidync") ## skip on Solaris if (!tolower(Sys.info()[["sysname"]]) == "sunos") { tnc <- tidync(l3file) print(tnc) } ## very simple Unidata example file, with one dimension ## Not run: uf <- system.file("extdata/unidata", "test_hgroups.nc", package = "tidync") recNum <- tidync(uf) %>% hyper_tibble() print(recNum) ## End(Not run) ## a raw grid of Southern Ocean sea ice concentration from IFREMER ## it is 12.5km resolution passive microwave concentration values ## on a polar stereographic grid, on 2 October 2017, displaying the ## "hole in the ice" made famous here: ## https://tinyurl.com/ycbchcgn ifr <- system.file("extdata/ifremer", "20171002.nc", package = "tidync") ifrnc <- tidync(ifr) ifrnc %>% hyper_tibble(select_var = "concentration")
## a SeaWiFS (S) Level-3 Mapped (L3m) monthly (MO) chlorophyll-a (CHL) ## remote sensing product at 9km resolution (at the equator) ## from the NASA ocean colour group in NetCDF4 format (.nc) ## for 31 day period January 2008 (S20080012008031) f <- "S20080012008031.L3m_MO_CHL_chlor_a_9km.nc" l3file <- system.file("extdata/oceandata", f, package= "tidync") ## skip on Solaris if (!tolower(Sys.info()[["sysname"]]) == "sunos") { tnc <- tidync(l3file) print(tnc) } ## very simple Unidata example file, with one dimension ## Not run: uf <- system.file("extdata/unidata", "test_hgroups.nc", package = "tidync") recNum <- tidync(uf) %>% hyper_tibble() print(recNum) ## End(Not run) ## a raw grid of Southern Ocean sea ice concentration from IFREMER ## it is 12.5km resolution passive microwave concentration values ## on a polar stereographic grid, on 2 October 2017, displaying the ## "hole in the ice" made famous here: ## https://tinyurl.com/ycbchcgn ifr <- system.file("extdata/ifremer", "20171002.nc", package = "tidync") ifrnc <- tidync(ifr) ifrnc %>% hyper_tibble(select_var = "concentration")