Audit the CMR revision/production lifecycle of a collection's granules.

Usage

cmr_audit_revisions(
  short_name,
  from = as.Date("2024-01-01"),
  to = Sys.Date(),
  provider = NULL,
  gran_date_fun = .default_gran_date_fun,
  bounding_box = NULL,
  polygon = NULL
)

Arguments

short_name: CMR collection short name
from, to: Date range to sweep, chunked into 3-month CMR requests
provider: Optional CMR provider id (see cmr_find_granules())
gran_date_fun: Function of the assembled tibble (with columns granule_ur, temporal_start, data_url, ...) that returns the granule's nominal date. Defaults to parsing a leading YYYYMMDD out of granule_ur, which holds for GHRSST-style products (MUR, AVHRR_OI, ...); for collections with opaque GranuleURs, pass e.g. function(df) as.Date(df$temporal_start).
bounding_box, polygon: Optional spatial filters, passed through to every chunked cmr_find_granules() call – see cmr_find_granules() for their formats. Mutually exclusive.

Value

A tibble::tibble() with one row per granule (deduplicated across chunk boundaries), including gran_date and the lag columns described in this file's header comment. Warns if gran_date comes back entirely NA (see the MOD021KM example in the README).

Examples

# \donttest{
rev <- cmr_audit_revisions("MUR-JPL-L4-GLOB-v4.1", from = as.Date("2024-01-01"))
#> 2024-01-01 .. 2024-03-31 : 92 granules
#> 2024-04-01 .. 2024-06-30 : 92 granules
#> 2024-07-01 .. 2024-09-30 : 93 granules
#> 2024-10-01 .. 2024-12-31 : 93 granules
#> 2025-01-01 .. 2025-03-31 : 91 granules
#> 2025-04-01 .. 2025-06-30 : 92 granules
#> 2025-07-01 .. 2025-09-30 : 93 granules
#> 2025-10-01 .. 2025-12-31 : 93 granules
#> 2026-01-01 .. 2026-03-31 : 91 granules
#> 2026-04-01 .. 2026-06-30 : 92 granules
#> 2026-07-01 .. 2026-09-02 : 62 granules
plot_cmr_lifecycle(rev)



# opaque GranuleUR (e.g. LAADS swath products) needs an explicit gran_date_fun
cmr_audit_revisions("MOD021KM",
                     from = as.Date("2026-08-01"), to = as.Date("2026-08-02"),
                     gran_date_fun = function(df) as.Date(df$temporal_start))
#> 2026-08-01 .. 2026-08-02 : 1128 granules
#> # A tibble: 1,128 × 16
#>    granule_ur       concept_id n_rev rev_date insert_date update_date production
#>    <chr>            <chr>      <int> <chr>    <chr>       <chr>       <chr>     
#>  1 LAADS:9975745835 G42694858…     1 2026-08… 2026-08-04… 2026-08-04… 2026-08-0…
#>  2 LAADS:9980651746 G42733712…     1 2026-08… 2026-08-07… 2026-08-07… 2026-08-0…
#>  3 LAADS:9975746411 G42694858…     1 2026-08… 2026-08-04… 2026-08-04… 2026-08-0…
#>  4 LAADS:9980653360 G42733715…     1 2026-08… 2026-08-07… 2026-08-07… 2026-08-0…
#>  5 LAADS:9975746412 G42694858…     1 2026-08… 2026-08-04… 2026-08-04… 2026-08-0…
#>  6 LAADS:9980649184 G42733683…     1 2026-08… 2026-08-07… 2026-08-07… 2026-08-0…
#>  7 LAADS:9975746413 G42694858…     1 2026-08… 2026-08-04… 2026-08-04… 2026-08-0…
#>  8 LAADS:9980651297 G42733712…     1 2026-08… 2026-08-07… 2026-08-07… 2026-08-0…
#>  9 LAADS:9975746414 G42694858…     1 2026-08… 2026-08-04… 2026-08-04… 2026-08-0…
#> 10 LAADS:9980652514 G42733714…     1 2026-08… 2026-08-07… 2026-08-07… 2026-08-0…
#> # ℹ 1,118 more rows
#> # ℹ 9 more variables: temporal_start <chr>, data_url <chr>, s3_url <chr>,
#> #   nc_bytes <dbl>, gran_date <date>, insert_lag <dbl>, update_lag <dbl>,
#> #   rev_lag <dbl>, prod_lag <dbl>
# }

Audit the CMR revision/production lifecycle of a collection's granules.

Usage

Arguments

Value

See also

Examples