Gap-filling on the temporal component of a cubble object

# S3 method for class 'temporal_cubble_df'
fill_gaps(.data, ..., .full = FALSE, .start = NULL, .end = NULL)

# S3 method for class 'temporal_cubble_df'
scan_gaps(.data, ...)

# S3 method for class 'temporal_cubble_df'
index_by(.data, ...)

Arguments

.data

A tsibble.

...

A set of name-value pairs. The values provided will only replace missing values that were marked as "implicit", and will leave previously existing NA untouched.

empty: filled with default NA.
filled by values or functions.

.full

FALSE inserts NA for each keyed unit within its own period.
TRUE fills NA over the entire time span of the data (a.k.a. fully balanced panel).
start() pad NA to the same starting point (i.e. min(<index>)) across units.
end() pad NA to the same ending point (i.e. max(<index>)) across units.

.start, .end

Set custom starting/ending time that allows to expand the existing time spans.

Value

a cubble object

Examples

library(tsibble)
climate_aus |> face_temporal() |> fill_gaps()
#> # cubble:   key: id [639], index: date, long form
#> # temporal: 2020-01-01 -- 2020-12-31 [1D], no gaps
#> # spatial:  long [dbl], lat [dbl], elev [dbl], name [chr], wmo_id [dbl]
#>    id          date        prcp  tmax  tmin
#>    <chr>       <date>     <dbl> <dbl> <dbl>
#>  1 ASN00001006 2020-01-01   164  38.3  25.3
#>  2 ASN00001006 2020-01-02     0  40.6  30.5
#>  3 ASN00001006 2020-01-03    16  39.7  27.2
#>  4 ASN00001006 2020-01-04     0  38.2  27.3
#>  5 ASN00001006 2020-01-05     2  39.3  26.7
#>  6 ASN00001006 2020-01-06    60  32.9  25.6
#>  7 ASN00001006 2020-01-07   146  34.1  25.5
#>  8 ASN00001006 2020-01-08    40  36.6  26.2
#>  9 ASN00001006 2020-01-09     0  38.2  27.6
#> 10 ASN00001006 2020-01-10     0  38.9  29.7
#> # ℹ 231,453 more rows
climate_aus |> face_temporal() |> scan_gaps()
#> # cubble:   key: id [639], index: date, long form, [tsibble]
#> # temporal: 2020-03-01 -- 2020-10-31 [1D], has gaps!
#> # spatial:  long [dbl], lat [dbl], elev [dbl], name [chr], wmo_id [dbl]
#>    id          date      
#>    <chr>       <date>    
#>  1 ASN00004090 2020-03-01
#>  2 ASN00004090 2020-03-02
#>  3 ASN00004090 2020-03-03
#>  4 ASN00004090 2020-03-04
#>  5 ASN00004090 2020-03-05
#>  6 ASN00004090 2020-03-06
#>  7 ASN00004090 2020-03-07
#>  8 ASN00004090 2020-03-08
#>  9 ASN00004090 2020-03-09
#> 10 ASN00004090 2020-03-10
#> # ℹ 693 more rows