Verbs supported for both nested and long cubble include:
dplyr::mutate(), dplyr::filter(), dplyr::arrange(), dplyr::select(),
dplyr::group_by(), dplyr::ungroup(), dplyr::summarise(),.
dplyr::rename(), dplyr::bind_cols(), dplyr::rowwise(),
dplyr::slice_*(), dplyr::*_join(), dplyr::relocate(),
dplyr::pull()
# S3 method for class 'temporal_cubble_df'
arrange(.data, ...)
# S3 method for class 'spatial_cubble_df'
select(.data, ...)
# S3 method for class 'temporal_cubble_df'
select(.data, ...)
# S3 method for class 'spatial_cubble_df'
group_by(.data, ..., .add, .drop)
# S3 method for class 'temporal_cubble_df'
group_by(.data, ..., .add, .drop)
# S3 method for class 'spatial_cubble_df'
ungroup(x, ...)
# S3 method for class 'temporal_cubble_df'
ungroup(x, ...)
# S3 method for class 'spatial_cubble_df'
summarise(.data, ..., .by = NULL, .groups = NULL)
# S3 method for class 'temporal_cubble_df'
summarise(.data, ..., .by = key_vars(.data), .groups = NULL)
# S3 method for class 'spatial_cubble_df'
rename(.data, ...)
# S3 method for class 'temporal_cubble_df'
rename(.data, ...)
bind_rows.temporal_cubble_df(..., .id = NULL)
bind_cols.spatial_cubble_df(..., .name_repair)
bind_cols.temporal_cubble_df(..., .name_repair)
# S3 method for class 'spatial_cubble_df'
rowwise(data, ...)
# S3 method for class 'temporal_cubble_df'
rowwise(data, ...)
# S3 method for class 'cubble_df'
dplyr_col_modify(data, cols)
# S3 method for class 'spatial_cubble_df'
dplyr_row_slice(data, i, ...)
# S3 method for class 'temporal_cubble_df'
dplyr_row_slice(data, i, ...)
# S3 method for class 'spatial_cubble_df'
dplyr_reconstruct(data, template)
# S3 method for class 'temporal_cubble_df'
dplyr_reconstruct(data, template)
# S3 method for class 'spatial_cubble_df'
mutate(.data, ...)
# S3 method for class 'temporal_cubble_df'
mutate(.data, ...)
# S3 method for class 'spatial_cubble_df'
filter(.data, ...)
# S3 method for class 'spatial_cubble_df'
arrange(.data, ...)Arguments
- ...
In
group_by(), variables or computations to group by. Computations are always done on the ungrouped data frame. To perform computations on the grouped data, you need to use a separatemutate()step before thegroup_by(). Computations are not allowed innest_by(). Inungroup(), variables to remove from the grouping.- .add
When
FALSE, the default,group_by()will override existing groups. To add to the existing groups, use.add = TRUE.This argument was previously called
add, but that prevented creating a new grouping variable calledadd, and conflicts with our naming conventions.- .drop
Drop groups formed by factor levels that don't appear in the data? The default is
TRUEexcept when.datahas been previously grouped with.drop = FALSE. Seegroup_by_drop_default()for details.- x
A
tbl()- .by
![[Experimental]](figures/lifecycle-experimental.svg)
<
tidy-select> Optionally, a selection of columns to group by for just this operation, functioning as an alternative togroup_by(). For details and examples, see ?dplyr_by.- .groups
- Grouping structure of the
result.
"drop_last": dropping the last level of grouping. This was the only supported option before version 1.0.0.
"drop": All levels of grouping are dropped.
"keep": Same grouping structure as
.data."rowwise": Each row is its own group.
When
.groupsis not specified, it is chosen based on the number of rows of the results:If all the results have 1 row, you get "drop_last".
If the number of rows varies, you get "keep" (note that returning a variable number of rows was deprecated in favor of
reframe(), which also unconditionally drops all levels of grouping).
In addition, a message informs you of that choice, unless the result is ungrouped, the option "dplyr.summarise.inform" is set to
FALSE, or whensummarise()is called from a function in a package. - .id
The name of an optional identifier column. Provide a string to create an output column that identifies each input. The column will use names if available, otherwise it will use positions.
- .name_repair
One of
"unique","universal", or"check_unique". Seevctrs::vec_as_names()for the meaning of these options.- data, .data
a cubble object of class
spatial_cubble_dfortemporal_cubble_df- cols
A named list used to modify columns. A
NULLvalue should remove an existing column.- i
A numeric or logical vector that indexes the rows of
data.- template
Template data frame to use for restoring attributes.
Details
You may find not all the verbs have a verb.spatial_cubble_df or
verb.temporal_cubble_df implemented. These verbs call
the dplyr extending trios: dplyr_row_slice, dplyr_col_modify,
and dplyr_reconstruct under the hood.
See https://dplyr.tidyverse.org/reference/dplyr_extending.html
Examples
library(dplyr)
cb_nested <- climate_mel
cb_long <- face_temporal(climate_mel)
# filter - currently filter.spatial_cubble_df, dply_row_slice
cb_nested |> filter(elev > 40)
#> # cubble: key: id [2], index: date, nested form
#> # spatial: [144.83, -37.73, 144.91, -37.67], Missing CRS!
#> # temporal: date [date], prcp [dbl], tmax [dbl], tmin [dbl]
#> id long lat elev name wmo_id ts
#> <chr> <dbl> <dbl> <dbl> <chr> <dbl> <list>
#> 1 ASN00086038 145. -37.7 78.4 essendon airport 95866 <tibble [10 × 4]>
#> 2 ASN00086282 145. -37.7 113. melbourne airport 94866 <tibble [10 × 4]>
cb_long |> filter(prcp > 0)
#> # cubble: key: id [3], index: date, long form
#> # temporal: 2020-01-05 -- 2020-01-07 [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 ASN00086038 2020-01-05 18 16.1 12.5
#> 2 ASN00086038 2020-01-06 104 17.5 11.1
#> 3 ASN00086038 2020-01-07 14 20.7 12.1
#> 4 ASN00086077 2020-01-05 20 17.4 12.7
#> 5 ASN00086077 2020-01-06 122 17.8 11.8
#> 6 ASN00086077 2020-01-07 6 20.3 12.6
#> 7 ASN00086282 2020-01-05 16 15.7 12
#> 8 ASN00086282 2020-01-06 90 17.3 11.5
#> 9 ASN00086282 2020-01-07 6 19.9 11.8
# mutate - curerntly mutate.spatial_cubble_df, dply_col_modify
cb_nested |> mutate(elev2 = elev + 10)
#> # cubble: key: id [3], index: date, nested form
#> # spatial: [144.83, -37.98, 145.1, -37.67], Missing CRS!
#> # temporal: date [date], prcp [dbl], tmax [dbl], tmin [dbl]
#> id long lat elev name wmo_id ts elev2
#> <chr> <dbl> <dbl> <dbl> <chr> <dbl> <list> <dbl>
#> 1 ASN00086038 145. -37.7 78.4 essendon airport 95866 <tibble [10 × 4]> 88.4
#> 2 ASN00086077 145. -38.0 12.1 moorabbin airport 94870 <tibble [10 × 4]> 22.1
#> 3 ASN00086282 145. -37.7 113. melbourne airport 94866 <tibble [10 × 4]> 123.
cb_long |> mutate(prcp2 = prcp + 10)
#> # cubble: key: id [3], index: date, long form
#> # temporal: 2020-01-01 -- 2020-01-10 [1D], no gaps
#> # spatial: long [dbl], lat [dbl], elev [dbl], name [chr], wmo_id [dbl]
#> id date prcp tmax tmin prcp2
#> <chr> <date> <dbl> <dbl> <dbl> <dbl>
#> 1 ASN00086038 2020-01-01 0 26.8 11 10
#> 2 ASN00086038 2020-01-02 0 26.3 12.2 10
#> 3 ASN00086038 2020-01-03 0 34.5 12.7 10
#> 4 ASN00086038 2020-01-04 0 29.3 18.8 10
#> 5 ASN00086038 2020-01-05 18 16.1 12.5 28
#> 6 ASN00086038 2020-01-06 104 17.5 11.1 114
#> 7 ASN00086038 2020-01-07 14 20.7 12.1 24
#> 8 ASN00086038 2020-01-08 0 26.4 16.4 10
#> 9 ASN00086038 2020-01-09 0 33.1 17.4 10
#> 10 ASN00086038 2020-01-10 0 34 19.6 10
#> # ℹ 20 more rows
# arrange - currently arrange.spatial_cubble_df, arrange.temporal_cubble_df
cb_nested |> arrange(wmo_id)
#> # cubble: key: id [3], index: date, nested form
#> # spatial: [144.83, -37.98, 145.1, -37.67], Missing CRS!
#> # temporal: date [date], prcp [dbl], tmax [dbl], tmin [dbl]
#> id long lat elev name wmo_id ts
#> <chr> <dbl> <dbl> <dbl> <chr> <dbl> <list>
#> 1 ASN00086282 145. -37.7 113. melbourne airport 94866 <tibble [10 × 4]>
#> 2 ASN00086077 145. -38.0 12.1 moorabbin airport 94870 <tibble [10 × 4]>
#> 3 ASN00086038 145. -37.7 78.4 essendon airport 95866 <tibble [10 × 4]>
cb_long |> arrange(prcp)
#> # cubble: key: id [3], index: date, long form
#> # temporal: 2020-01-01 -- 2020-01-10 [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 ASN00086038 2020-01-01 0 26.8 11
#> 2 ASN00086038 2020-01-02 0 26.3 12.2
#> 3 ASN00086038 2020-01-03 0 34.5 12.7
#> 4 ASN00086038 2020-01-04 0 29.3 18.8
#> 5 ASN00086038 2020-01-08 0 26.4 16.4
#> 6 ASN00086038 2020-01-09 0 33.1 17.4
#> 7 ASN00086038 2020-01-10 0 34 19.6
#> 8 ASN00086077 2020-01-01 0 24.7 10
#> 9 ASN00086077 2020-01-02 0 24.8 11.8
#> 10 ASN00086077 2020-01-03 0 35 12.2
#> # ℹ 20 more rows
# summarise - summarise.spatial_cubble_df, summarise.temporal_cubble_df
cb_long |>
group_by(first_5 = ifelse(lubridate::day(date) <=5, 1, 2 )) |>
summarise(tmax = mean(tmax))
#> # cubble: key: id [3], index: first_5, long form, groups: first_5 [2]
#> # temporal: 1 -- 2 [1], no gaps
#> # spatial: long [dbl], lat [dbl], elev [dbl], name [chr], wmo_id [dbl]
#> first_5 id tmax
#> <dbl> <chr> <dbl>
#> 1 1 ASN00086038 26.6
#> 2 1 ASN00086077 25.5
#> 3 1 ASN00086282 27.1
#> 4 2 ASN00086038 26.3
#> 5 2 ASN00086077 25.9
#> 6 2 ASN00086282 26.2
cb_long |>
mutate(first_5 = ifelse(lubridate::day(date) <=5, 1, 2)) |>
summarise(t = mean(tmax), .by = first_5)
#> # cubble: key: id [3], index: date, long form
#> # temporal: 2020-01-01 -- 2020-01-10 [1D], no gaps
#> # spatial: long [dbl], lat [dbl], elev [dbl], name [chr], wmo_id [dbl]
#> id date t
#> <chr> <date> <dbl>
#> 1 ASN00086038 2020-01-01 26.8
#> 2 ASN00086038 2020-01-02 26.3
#> 3 ASN00086038 2020-01-03 34.5
#> 4 ASN00086038 2020-01-04 29.3
#> 5 ASN00086038 2020-01-05 16.1
#> 6 ASN00086038 2020-01-06 17.5
#> 7 ASN00086038 2020-01-07 20.7
#> 8 ASN00086038 2020-01-08 26.4
#> 9 ASN00086038 2020-01-09 33.1
#> 10 ASN00086038 2020-01-10 34
#> # ℹ 20 more rows
# select - select.spatial_cubble_df, select.temporal_cubble_df
cb_nested |> select(name)
#> ℹ Missing attribute `id`, `long`, `lat`, and `ts`, add it back.
#> # cubble: key: id [3], index: date, nested form
#> # spatial: [144.83, -37.98, 145.1, -37.67], Missing CRS!
#> # temporal: date [date], prcp [dbl], tmax [dbl], tmin [dbl]
#> id long lat ts name
#> <chr> <dbl> <dbl> <list> <chr>
#> 1 ASN00086038 145. -37.7 <tibble [10 × 4]> essendon airport
#> 2 ASN00086077 145. -38.0 <tibble [10 × 4]> moorabbin airport
#> 3 ASN00086282 145. -37.7 <tibble [10 × 4]> melbourne airport
cb_nested |> select(-id, -name)
#> ℹ Missing attribute `id`, add it back.
#> # cubble: key: id [3], index: date, nested form
#> # spatial: [144.83, -37.98, 145.1, -37.67], Missing CRS!
#> # temporal: date [date], prcp [dbl], tmax [dbl], tmin [dbl]
#> id long lat elev wmo_id ts
#> <chr> <dbl> <dbl> <dbl> <dbl> <list>
#> 1 ASN00086038 145. -37.7 78.4 95866 <tibble [10 × 4]>
#> 2 ASN00086077 145. -38.0 12.1 94870 <tibble [10 × 4]>
#> 3 ASN00086282 145. -37.7 113. 94866 <tibble [10 × 4]>
cb_long |> select(prcp)
#> ℹ Missing attribute `id` and `date`, add it back.
#> # cubble: key: id [3], index: date, long form
#> # temporal: 2020-01-01 -- 2020-01-10 [1D], no gaps
#> # spatial: long [dbl], lat [dbl], elev [dbl], name [chr], wmo_id [dbl]
#> id date prcp
#> <chr> <date> <dbl>
#> 1 ASN00086038 2020-01-01 0
#> 2 ASN00086038 2020-01-02 0
#> 3 ASN00086038 2020-01-03 0
#> 4 ASN00086038 2020-01-04 0
#> 5 ASN00086038 2020-01-05 18
#> 6 ASN00086038 2020-01-06 104
#> 7 ASN00086038 2020-01-07 14
#> 8 ASN00086038 2020-01-08 0
#> 9 ASN00086038 2020-01-09 0
#> 10 ASN00086038 2020-01-10 0
#> # ℹ 20 more rows
cb_long |> select(-prcp, -date)
#> ℹ Missing attribute `date`, add it back.
#> # cubble: key: id [3], index: date, long form
#> # temporal: 2020-01-01 -- 2020-01-10 [1D], no gaps
#> # spatial: long [dbl], lat [dbl], elev [dbl], name [chr], wmo_id [dbl]
#> date id tmax tmin
#> <date> <chr> <dbl> <dbl>
#> 1 2020-01-01 ASN00086038 26.8 11
#> 2 2020-01-02 ASN00086038 26.3 12.2
#> 3 2020-01-03 ASN00086038 34.5 12.7
#> 4 2020-01-04 ASN00086038 29.3 18.8
#> 5 2020-01-05 ASN00086038 16.1 12.5
#> 6 2020-01-06 ASN00086038 17.5 11.1
#> 7 2020-01-07 ASN00086038 20.7 12.1
#> 8 2020-01-08 ASN00086038 26.4 16.4
#> 9 2020-01-09 ASN00086038 33.1 17.4
#> 10 2020-01-10 ASN00086038 34 19.6
#> # ℹ 20 more rows
# rename - rename.spatial_cubble_df, rename.temporal_cubble_df
cb_nested |> rename(elev2 = elev)
#> # cubble: key: id [3], index: date, nested form
#> # spatial: [144.83, -37.98, 145.1, -37.67], Missing CRS!
#> # temporal: date [date], prcp [dbl], tmax [dbl], tmin [dbl]
#> id long lat elev2 name wmo_id ts
#> <chr> <dbl> <dbl> <dbl> <chr> <dbl> <list>
#> 1 ASN00086038 145. -37.7 78.4 essendon airport 95866 <tibble [10 × 4]>
#> 2 ASN00086077 145. -38.0 12.1 moorabbin airport 94870 <tibble [10 × 4]>
#> 3 ASN00086282 145. -37.7 113. melbourne airport 94866 <tibble [10 × 4]>
cb_long |> rename(prcp2 = prcp)
#> # cubble: key: id [3], index: date, long form
#> # temporal: 2020-01-01 -- 2020-01-10 [1D], no gaps
#> # spatial: long [dbl], lat [dbl], elev [dbl], name [chr], wmo_id [dbl]
#> id date prcp2 tmax tmin
#> <chr> <date> <dbl> <dbl> <dbl>
#> 1 ASN00086038 2020-01-01 0 26.8 11
#> 2 ASN00086038 2020-01-02 0 26.3 12.2
#> 3 ASN00086038 2020-01-03 0 34.5 12.7
#> 4 ASN00086038 2020-01-04 0 29.3 18.8
#> 5 ASN00086038 2020-01-05 18 16.1 12.5
#> 6 ASN00086038 2020-01-06 104 17.5 11.1
#> 7 ASN00086038 2020-01-07 14 20.7 12.1
#> 8 ASN00086038 2020-01-08 0 26.4 16.4
#> 9 ASN00086038 2020-01-09 0 33.1 17.4
#> 10 ASN00086038 2020-01-10 0 34 19.6
#> # ℹ 20 more rows
# rename on key attributes
cb_nested |> rename(id2 = id)
#> # cubble: key: id2 [3], index: date, nested form
#> # spatial: [144.83, -37.98, 145.1, -37.67], Missing CRS!
#> # temporal: date [date], prcp [dbl], tmax [dbl], tmin [dbl]
#> id2 long lat elev name wmo_id ts
#> <chr> <dbl> <dbl> <dbl> <chr> <dbl> <list>
#> 1 ASN00086038 145. -37.7 78.4 essendon airport 95866 <tibble [10 × 4]>
#> 2 ASN00086077 145. -38.0 12.1 moorabbin airport 94870 <tibble [10 × 4]>
#> 3 ASN00086282 145. -37.7 113. melbourne airport 94866 <tibble [10 × 4]>
cb_long |> rename(date2 = date)
#> # cubble: key: id [3], index: date2, long form
#> # temporal: 2020-01-01 -- 2020-01-10 [1D], no gaps
#> # spatial: long [dbl], lat [dbl], elev [dbl], name [chr], wmo_id [dbl]
#> id date2 prcp tmax tmin
#> <chr> <date> <dbl> <dbl> <dbl>
#> 1 ASN00086038 2020-01-01 0 26.8 11
#> 2 ASN00086038 2020-01-02 0 26.3 12.2
#> 3 ASN00086038 2020-01-03 0 34.5 12.7
#> 4 ASN00086038 2020-01-04 0 29.3 18.8
#> 5 ASN00086038 2020-01-05 18 16.1 12.5
#> 6 ASN00086038 2020-01-06 104 17.5 11.1
#> 7 ASN00086038 2020-01-07 14 20.7 12.1
#> 8 ASN00086038 2020-01-08 0 26.4 16.4
#> 9 ASN00086038 2020-01-09 0 33.1 17.4
#> 10 ASN00086038 2020-01-10 0 34 19.6
#> # ℹ 20 more rows
# join - mutate_join - dplyr_reconstruct()
# join - filter_join - dplyr_row_slice()
df1 <- cb_nested |> as_tibble() |> select(id, name) |> head(2)
nested <- cb_nested |> select(-name)
nested |> left_join(df1, by = "id")
#> # cubble: key: id [3], index: date, nested form
#> # spatial: [144.83, -37.98, 145.1, -37.67], Missing CRS!
#> # temporal: date [date], prcp [dbl], tmax [dbl], tmin [dbl]
#> id long lat elev wmo_id ts name
#> <chr> <dbl> <dbl> <dbl> <dbl> <list> <chr>
#> 1 ASN00086038 145. -37.7 78.4 95866 <tibble [10 × 4]> essendon airport
#> 2 ASN00086077 145. -38.0 12.1 94870 <tibble [10 × 4]> moorabbin airport
#> 3 ASN00086282 145. -37.7 113. 94866 <tibble [10 × 4]> NA
nested |> right_join(df1, by = "id")
#> # cubble: key: id [2], index: date, nested form
#> # spatial: [144.91, -37.98, 145.1, -37.73], Missing CRS!
#> # temporal: date [date], prcp [dbl], tmax [dbl], tmin [dbl]
#> id long lat elev wmo_id ts name
#> <chr> <dbl> <dbl> <dbl> <dbl> <list> <chr>
#> 1 ASN00086038 145. -37.7 78.4 95866 <tibble [10 × 4]> essendon airport
#> 2 ASN00086077 145. -38.0 12.1 94870 <tibble [10 × 4]> moorabbin airport
nested |> inner_join(df1, by = "id")
#> # cubble: key: id [2], index: date, nested form
#> # spatial: [144.91, -37.98, 145.1, -37.73], Missing CRS!
#> # temporal: date [date], prcp [dbl], tmax [dbl], tmin [dbl]
#> id long lat elev wmo_id ts name
#> <chr> <dbl> <dbl> <dbl> <dbl> <list> <chr>
#> 1 ASN00086038 145. -37.7 78.4 95866 <tibble [10 × 4]> essendon airport
#> 2 ASN00086077 145. -38.0 12.1 94870 <tibble [10 × 4]> moorabbin airport
nested |> full_join(df1, by = "id")
#> # cubble: key: id [3], index: date, nested form
#> # spatial: [144.83, -37.98, 145.1, -37.67], Missing CRS!
#> # temporal: date [date], prcp [dbl], tmax [dbl], tmin [dbl]
#> id long lat elev wmo_id ts name
#> <chr> <dbl> <dbl> <dbl> <dbl> <list> <chr>
#> 1 ASN00086038 145. -37.7 78.4 95866 <tibble [10 × 4]> essendon airport
#> 2 ASN00086077 145. -38.0 12.1 94870 <tibble [10 × 4]> moorabbin airport
#> 3 ASN00086282 145. -37.7 113. 94866 <tibble [10 × 4]> NA
nested |> anti_join(df1, by = "id")
#> # cubble: key: id [1], index: date, nested form
#> # spatial: [144.83, -37.67, 144.83, -37.67], Missing CRS!
#> # temporal: date [date], prcp [dbl], tmax [dbl], tmin [dbl]
#> id long lat elev wmo_id ts
#> <chr> <dbl> <dbl> <dbl> <dbl> <list>
#> 1 ASN00086282 145. -37.7 113. 94866 <tibble [10 × 4]>
# bind_rows - dplyr_reconstruct, bind_rows.temporal_cubble_df
df1 <- cb_nested |> head(1)
df2 <- cb_nested |> tail(2)
bind_rows(df1, df2)
#> # cubble: key: id [3], index: date, nested form
#> # spatial: [144.83, -37.98, 145.1, -37.67], Missing CRS!
#> # temporal: date [date], prcp [dbl], tmax [dbl], tmin [dbl]
#> id long lat elev name wmo_id ts
#> <chr> <dbl> <dbl> <dbl> <chr> <dbl> <list>
#> 1 ASN00086038 145. -37.7 78.4 essendon airport 95866 <tibble [10 × 4]>
#> 2 ASN00086077 145. -38.0 12.1 moorabbin airport 94870 <tibble [10 × 4]>
#> 3 ASN00086282 145. -37.7 113. melbourne airport 94866 <tibble [10 × 4]>
df1 <- cb_long |> head(10)
df2 <- cb_long |> tail(20)
bind_rows(df1, df2)
#> # cubble: key: id [1], index: date, long form
#> # temporal: 2020-01-01 -- 2020-01-10 [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 ASN00086038 2020-01-01 0 26.8 11
#> 2 ASN00086038 2020-01-02 0 26.3 12.2
#> 3 ASN00086038 2020-01-03 0 34.5 12.7
#> 4 ASN00086038 2020-01-04 0 29.3 18.8
#> 5 ASN00086038 2020-01-05 18 16.1 12.5
#> 6 ASN00086038 2020-01-06 104 17.5 11.1
#> 7 ASN00086038 2020-01-07 14 20.7 12.1
#> 8 ASN00086038 2020-01-08 0 26.4 16.4
#> 9 ASN00086038 2020-01-09 0 33.1 17.4
#> 10 ASN00086038 2020-01-10 0 34 19.6
#> # ℹ 20 more rows
# relocate - dplyr_col_select, dplyr_col_select
cb_nested |> relocate(ts, .before = name)
#> # cubble: key: id [3], index: date, nested form
#> # spatial: [144.83, -37.98, 145.1, -37.67], Missing CRS!
#> # temporal: date [date], prcp [dbl], tmax [dbl], tmin [dbl]
#> id long lat elev ts name wmo_id
#> <chr> <dbl> <dbl> <dbl> <list> <chr> <dbl>
#> 1 ASN00086038 145. -37.7 78.4 <tibble [10 × 4]> essendon airport 95866
#> 2 ASN00086077 145. -38.0 12.1 <tibble [10 × 4]> moorabbin airport 94870
#> 3 ASN00086282 145. -37.7 113. <tibble [10 × 4]> melbourne airport 94866
cb_nested |> face_temporal() |> relocate(tmin)
#> # cubble: key: id [3], index: date, long form
#> # temporal: 2020-01-01 -- 2020-01-10 [1D], no gaps
#> # spatial: long [dbl], lat [dbl], elev [dbl], name [chr], wmo_id [dbl]
#> tmin id date prcp tmax
#> <dbl> <chr> <date> <dbl> <dbl>
#> 1 11 ASN00086038 2020-01-01 0 26.8
#> 2 12.2 ASN00086038 2020-01-02 0 26.3
#> 3 12.7 ASN00086038 2020-01-03 0 34.5
#> 4 18.8 ASN00086038 2020-01-04 0 29.3
#> 5 12.5 ASN00086038 2020-01-05 18 16.1
#> 6 11.1 ASN00086038 2020-01-06 104 17.5
#> 7 12.1 ASN00086038 2020-01-07 14 20.7
#> 8 16.4 ASN00086038 2020-01-08 0 26.4
#> 9 17.4 ASN00086038 2020-01-09 0 33.1
#> 10 19.6 ASN00086038 2020-01-10 0 34
#> # ℹ 20 more rows
# slice - all the slice_* uses dplyr::slice(), which uses dplyr_row_slice()
cb_nested |> slice_head(n = 2)
#> # cubble: key: id [2], index: date, nested form
#> # spatial: [144.91, -37.98, 145.1, -37.73], Missing CRS!
#> # temporal: date [date], prcp [dbl], tmax [dbl], tmin [dbl]
#> id long lat elev name wmo_id ts
#> <chr> <dbl> <dbl> <dbl> <chr> <dbl> <list>
#> 1 ASN00086038 145. -37.7 78.4 essendon airport 95866 <tibble [10 × 4]>
#> 2 ASN00086077 145. -38.0 12.1 moorabbin airport 94870 <tibble [10 × 4]>
cb_nested |> slice_tail(n = 2)
#> # cubble: key: id [2], index: date, nested form
#> # spatial: [144.83, -37.98, 145.1, -37.67], Missing CRS!
#> # temporal: date [date], prcp [dbl], tmax [dbl], tmin [dbl]
#> id long lat elev name wmo_id ts
#> <chr> <dbl> <dbl> <dbl> <chr> <dbl> <list>
#> 1 ASN00086077 145. -38.0 12.1 moorabbin airport 94870 <tibble [10 × 4]>
#> 2 ASN00086282 145. -37.7 113. melbourne airport 94866 <tibble [10 × 4]>
cb_nested |> slice_max(elev)
#> # cubble: key: id [1], index: date, nested form
#> # spatial: [144.83, -37.67, 144.83, -37.67], Missing CRS!
#> # temporal: date [date], prcp [dbl], tmax [dbl], tmin [dbl]
#> id long lat elev name wmo_id ts
#> <chr> <dbl> <dbl> <dbl> <chr> <dbl> <list>
#> 1 ASN00086282 145. -37.7 113. melbourne airport 94866 <tibble [10 × 4]>
cb_nested |> slice_min(elev)
#> # cubble: key: id [1], index: date, nested form
#> # spatial: [145.1, -37.98, 145.1, -37.98], Missing CRS!
#> # temporal: date [date], prcp [dbl], tmax [dbl], tmin [dbl]
#> id long lat elev name wmo_id ts
#> <chr> <dbl> <dbl> <dbl> <chr> <dbl> <list>
#> 1 ASN00086077 145. -38.0 12.1 moorabbin airport 94870 <tibble [10 × 4]>
cb_nested |> slice_sample(n = 2)
#> # cubble: key: id [2], index: date, nested form
#> # spatial: [144.83, -37.98, 145.1, -37.67], Missing CRS!
#> # temporal: date [date], prcp [dbl], tmax [dbl], tmin [dbl]
#> id long lat elev name wmo_id ts
#> <chr> <dbl> <dbl> <dbl> <chr> <dbl> <list>
#> 1 ASN00086077 145. -38.0 12.1 moorabbin airport 94870 <tibble [10 × 4]>
#> 2 ASN00086282 145. -37.7 113. melbourne airport 94866 <tibble [10 × 4]>
# rowwise - rowwise.spatial_cubble_df, rowwise.temporal_cuble_df
cb_nested |> rowwise()
#> # cubble: key: id [3], index: date, nested form, groups: rowwise
#> # spatial: [144.83, -37.98, 145.1, -37.67], Missing CRS!
#> # temporal: date [date], prcp [dbl], tmax [dbl], tmin [dbl]
#> id long lat elev name wmo_id ts
#> <chr> <dbl> <dbl> <dbl> <chr> <dbl> <list>
#> 1 ASN00086038 145. -37.7 78.4 essendon airport 95866 <tibble [10 × 4]>
#> 2 ASN00086077 145. -38.0 12.1 moorabbin airport 94870 <tibble [10 × 4]>
#> 3 ASN00086282 145. -37.7 113. melbourne airport 94866 <tibble [10 × 4]>
cb_long |> rowwise()
#> # cubble: key: id [3], index: date, long form, groups: rowwise
#> # temporal: 2020-01-01 -- 2020-01-10 [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 ASN00086038 2020-01-01 0 26.8 11
#> 2 ASN00086038 2020-01-02 0 26.3 12.2
#> 3 ASN00086038 2020-01-03 0 34.5 12.7
#> 4 ASN00086038 2020-01-04 0 29.3 18.8
#> 5 ASN00086038 2020-01-05 18 16.1 12.5
#> 6 ASN00086038 2020-01-06 104 17.5 11.1
#> 7 ASN00086038 2020-01-07 14 20.7 12.1
#> 8 ASN00086038 2020-01-08 0 26.4 16.4
#> 9 ASN00086038 2020-01-09 0 33.1 17.4
#> 10 ASN00086038 2020-01-10 0 34 19.6
#> # ℹ 20 more rows
# group_by & ungroup -
(res <- cb_nested |> mutate(group1 = c(1, 1, 2)) |> group_by(group1))
#> # cubble: key: id [3], index: date, nested form, groups: group1 [2]
#> # spatial: [144.83, -37.98, 145.1, -37.67], Missing CRS!
#> # temporal: date [date], prcp [dbl], tmax [dbl], tmin [dbl]
#> id long lat elev name wmo_id ts group1
#> <chr> <dbl> <dbl> <dbl> <chr> <dbl> <list> <dbl>
#> 1 ASN00086038 145. -37.7 78.4 essendon airport 95866 <tibble> 1
#> 2 ASN00086077 145. -38.0 12.1 moorabbin airport 94870 <tibble> 1
#> 3 ASN00086282 145. -37.7 113. melbourne airport 94866 <tibble> 2
res |> ungroup()
#> # cubble: key: id [3], index: date, nested form
#> # spatial: [144.83, -37.98, 145.1, -37.67], Missing CRS!
#> # temporal: date [date], prcp [dbl], tmax [dbl], tmin [dbl]
#> id long lat elev name wmo_id ts group1
#> <chr> <dbl> <dbl> <dbl> <chr> <dbl> <list> <dbl>
#> 1 ASN00086038 145. -37.7 78.4 essendon airport 95866 <tibble> 1
#> 2 ASN00086077 145. -38.0 12.1 moorabbin airport 94870 <tibble> 1
#> 3 ASN00086282 145. -37.7 113. melbourne airport 94866 <tibble> 2
(res2 <- res |> face_temporal())
#> Adding missing grouping variables: `group1`
#> # cubble: key: id [3], index: date, long form, groups: group1 [2]
#> # temporal: 2020-01-01 -- 2020-01-10 [1D], no gaps
#> # spatial: long [dbl], lat [dbl], elev [dbl], name [chr], wmo_id [dbl], group1
#> # [dbl]
#> group1 id date prcp tmax tmin
#> <dbl> <chr> <date> <dbl> <dbl> <dbl>
#> 1 1 ASN00086038 2020-01-01 0 26.8 11
#> 2 1 ASN00086038 2020-01-02 0 26.3 12.2
#> 3 1 ASN00086038 2020-01-03 0 34.5 12.7
#> 4 1 ASN00086038 2020-01-04 0 29.3 18.8
#> 5 1 ASN00086038 2020-01-05 18 16.1 12.5
#> 6 1 ASN00086038 2020-01-06 104 17.5 11.1
#> 7 1 ASN00086038 2020-01-07 14 20.7 12.1
#> 8 1 ASN00086038 2020-01-08 0 26.4 16.4
#> 9 1 ASN00086038 2020-01-09 0 33.1 17.4
#> 10 1 ASN00086038 2020-01-10 0 34 19.6
#> # ℹ 20 more rows
res2 |> ungroup()
#> # cubble: key: id [3], index: date, long form
#> # temporal: 2020-01-01 -- 2020-01-10 [1D], no gaps
#> # spatial: long [dbl], lat [dbl], elev [dbl], name [chr], wmo_id [dbl], group1
#> # [dbl]
#> group1 id date prcp tmax tmin
#> <dbl> <chr> <date> <dbl> <dbl> <dbl>
#> 1 1 ASN00086038 2020-01-01 0 26.8 11
#> 2 1 ASN00086038 2020-01-02 0 26.3 12.2
#> 3 1 ASN00086038 2020-01-03 0 34.5 12.7
#> 4 1 ASN00086038 2020-01-04 0 29.3 18.8
#> 5 1 ASN00086038 2020-01-05 18 16.1 12.5
#> 6 1 ASN00086038 2020-01-06 104 17.5 11.1
#> 7 1 ASN00086038 2020-01-07 14 20.7 12.1
#> 8 1 ASN00086038 2020-01-08 0 26.4 16.4
#> 9 1 ASN00086038 2020-01-09 0 33.1 17.4
#> 10 1 ASN00086038 2020-01-10 0 34 19.6
#> # ℹ 20 more rows
res2 |> mutate(first_5 = ifelse(lubridate::day(date) <= 5, 1, 6)) |>
group_by(first_5)
#> # cubble: key: id [3], index: date, long form, groups: first_5 [2]
#> # temporal: 2020-01-01 -- 2020-01-10 [1D], no gaps
#> # spatial: long [dbl], lat [dbl], elev [dbl], name [chr], wmo_id [dbl], group1
#> # [dbl]
#> group1 id date prcp tmax tmin first_5
#> <dbl> <chr> <date> <dbl> <dbl> <dbl> <dbl>
#> 1 1 ASN00086038 2020-01-01 0 26.8 11 1
#> 2 1 ASN00086038 2020-01-02 0 26.3 12.2 1
#> 3 1 ASN00086038 2020-01-03 0 34.5 12.7 1
#> 4 1 ASN00086038 2020-01-04 0 29.3 18.8 1
#> 5 1 ASN00086038 2020-01-05 18 16.1 12.5 1
#> 6 1 ASN00086038 2020-01-06 104 17.5 11.1 6
#> 7 1 ASN00086038 2020-01-07 14 20.7 12.1 6
#> 8 1 ASN00086038 2020-01-08 0 26.4 16.4 6
#> 9 1 ASN00086038 2020-01-09 0 33.1 17.4 6
#> 10 1 ASN00086038 2020-01-10 0 34 19.6 6
#> # ℹ 20 more rows