Cubble
Cubble provides a new data structure to manipulate spatio-temporal
vector data. It arranges variables into two forms: nested form and long
form. The nested form shows each site in a row and time invariant
variables as columns. The time varying variables are nested into a ts
column. In the long form, each row is cross-identified by the site and
time, time varying variables are presented, and time invariant variables
are stored as an attribute. The two forms can be switched back and forth
for manipulation on the spatial and temporal dimension of the data.
Installation
You can install the released version of cubble from CRAN with:
install.packages("cubble")And the development version from GitHub with:
# install.packages("remotes")
remotes::install_github("huizezhang-sherry/cubble")Example
as_cubble() creates a cubble in the nested form by supplying the
spatial identifier, key, temporal identifier, index, and the spatial
coordinates that defines the site, coords.
library(cubble)
library(dplyr)
#> Warning: package 'dplyr' was built under R version 4.1.2
nested <- climate_flat |>
as_cubble(key = id, index = date, coords = c(long, lat))
nested
#> # cubble: id [5]: nested form
#> # bbox: [115.97, -32.94, 133.55, -12.42]
#> # temporal: date [date], prcp [dbl], tmax [dbl], tmin [dbl]
#> id lat long elev name wmo_id ts
#> <chr> <dbl> <dbl> <dbl> <chr> <dbl> <list>
#> 1 ASN00009021 -31.9 116. 15.4 perth airport 94610 <tibble [366 × 4]>
#> 2 ASN00010311 -31.9 117. 179 york 94623 <tibble [366 × 4]>
#> 3 ASN00010614 -32.9 117. 338 narrogin 94627 <tibble [366 × 4]>
#> 4 ASN00014015 -12.4 131. 30.4 darwin airport 94120 <tibble [366 × 4]>
#> 5 ASN00015131 -17.6 134. 220 elliott 94236 <tibble [366 × 4]>face_temporal() switches a cubble from the nested form to the long
form. The long form cubble is for operations whose output is
cross-identified by key and index, for example, filtering January
records:
long <- nested |>
face_temporal() |>
filter(lubridate::month(date) == 1)
long
#> # cubble: date, id [5]: long form
#> # bbox: [115.97, -32.94, 133.55, -12.42]
#> # spatial: lat [dbl], long [dbl], elev [dbl], name [chr], wmo_id [dbl]
#> id date prcp tmax tmin
#> <chr> <date> <dbl> <dbl> <dbl>
#> 1 ASN00009021 2020-01-01 0 31.9 15.3
#> 2 ASN00009021 2020-01-02 0 24.9 16.4
#> 3 ASN00009021 2020-01-03 6 23.2 13
#> 4 ASN00009021 2020-01-04 0 28.4 12.4
#> 5 ASN00009021 2020-01-05 0 35.3 11.6
#> 6 ASN00009021 2020-01-06 0 34.8 13.1
#> 7 ASN00009021 2020-01-07 0 32.8 15.1
#> 8 ASN00009021 2020-01-08 0 30.4 17.4
#> 9 ASN00009021 2020-01-09 0 28.7 17.3
#> 10 ASN00009021 2020-01-10 0 32.6 15.8
#> # … with 145 more rowsface_spatial() switches the long cubble back to the nested cubble. The
nested form is for operations whose output is only identified by the
key, for example, mutating the average maximum temperature in January:
long |>
face_spatial() |>
mutate(avg_max = mean(ts$tmax, na.rm = TRUE))
#> # cubble: id [5]: nested form
#> # bbox: [115.97, -32.94, 133.55, -12.42]
#> # temporal: date [date], prcp [dbl], tmax [dbl], tmin [dbl]
#> id lat long elev name wmo_id ts avg_max
#> <chr> <dbl> <dbl> <dbl> <chr> <dbl> <list> <dbl>
#> 1 ASN00009021 -31.9 116. 15.4 perth airport 94610 <tibble [31 × 4]> 31.6
#> 2 ASN00010311 -31.9 117. 179 york 94623 <tibble [31 × 4]> 34.6
#> 3 ASN00010614 -32.9 117. 338 narrogin 94627 <tibble [31 × 4]> 31.4
#> 4 ASN00014015 -12.4 131. 30.4 darwin airport 94120 <tibble [31 × 4]> 32.8
#> 5 ASN00015131 -17.6 134. 220 elliott 94236 <tibble [31 × 4]> 38.5Misc
- Naming: Cubble stands for “cubical tibble” and you can think of multivariate spatio-temporal data as a cube with three axes: variable, location, and time.