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 tidync
hyper_array(
  x,
  select_var = NULL,
  ...,
  raw_datavals = FALSE,
  force = FALSE,
  drop = TRUE
)

# S3 method for character
hyper_array(x, select_var = NULL, ..., raw_datavals = FALSE, drop = TRUE)

Arguments

x

NetCDF file, connection object, or tidync object

select_var

optional vector of variable names to select

...

passed to hyper_filter()

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 TRUE

Details

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().

See also

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

Examples

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)
#> #> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’: #> #> filter, lag
#> The following objects are masked from ‘package:base’: #> #> intersect, setdiff, setequal, union
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)))
#> $lon #> [1] 99 #> #> $lat #> [1] 240 #>
## the selected axis coordinates should match in order and in size lapply(braw, dim)
#> $chlor_a #> [1] 99 240 #>