This article demonstrates how to create a cubble from various types of data. We will create a cubble from:
tibble objectstars objectsftime objectIn many cases, spatio-temporal data arrive in separate tables for analysis. For example, in climate data, analysts may initially receive station data containing geographic location information, recorded variables and their recording periods. They can then query the temporal variables using the stations of interest to obtain the relevant temporal data. Alternatively, analyses may begin as purely spatial or temporal, and analysts may obtain additional temporal or spatial data to expand the result to spatio-temporal.
The function make_cubble() composes a cubble object from
a spatial table (spatial) and a temporal table
(temporal), along with the three attributes
key, index, and coords introduced
in 1. The cubble class. The following code
creates the nested cubble:
make_cubble(spatial = stations, temporal = meteo,
key = id, index = date, coords = c(long, lat))
#> # 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]>The coords argument can be safely omitted if the spatial
data is an sf object (e.g. stations_sf) . Similarly, if the
temporal object is a tsibble (i.e. meteo_ts), you don’t
need to specify the key and index arguments.
The class attributes from sf and tsibble will be carried over to the
nested and long cubble:
(res <- make_cubble(spatial = stations_sf, temporal = meteo_ts))
#> # cubble: key: id [3], index: date, nested form, [sf]
#> # spatial: [144.83, -37.98, 145.1, -37.67], WGS 84
#> # temporal: date [date], prcp [dbl], tmax [dbl], tmin [dbl]
#> id elev name wmo_id long lat geometry ts
#> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <POINT [°]> <list>
#> 1 ASN00086038 78.4 essen… 95866 145. -37.7 (144.9066 -37.7276) <tbl_ts>
#> 2 ASN00086077 12.1 moora… 94870 145. -38.0 (145.0964 -37.98) <tbl_ts>
#> 3 ASN00086282 113. melbo… 94866 145. -37.7 (144.8321 -37.6655) <tbl_ts>
class(res)
#> [1] "spatial_cubble_df" "cubble_df" "sf"
#> [4] "tbl_df" "tbl" "data.frame"
class(res$ts[[1]])
#> [1] "tbl_ts" "tbl_df" "tbl" "data.frame"The vignette 3. Compatibility with tsibble and sf will introduce more on the cubble’s compatibility with tsibble and sf.
tibble objectsThe dataset climate_flat combines the spatial data,
stations, with the temporal data, meteo, into
a single tibble object. It can be coerced into a cubble using:
climate_flat |> as_cubble(key = id, index = date, coords = c(long, lat))
#> # 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]>In R, there are several packages available for wrangling
NetCDF data, including ncdf4, RNetCDF, and
tidync. The code below converts a NetCDF object of class
ncdf4 into a cubble object:
path <- system.file("ncdf/era5-pressure.nc", package = "cubble")
raw <- ncdf4::nc_open(path)
as_cubble(raw)
#> # cubble: key: id [26565], index: time, nested form
#> # spatial: [113, -53, 153, -12], Missing CRS!
#> # temporal: time [date], q [dbl], z [dbl]
#> id long lat ts
#> <int> <dbl> <dbl> <list>
#> 1 1 113 -12 <tibble [6 × 3]>
#> 2 2 113. -12 <tibble [6 × 3]>
#> 3 3 114. -12 <tibble [6 × 3]>
#> 4 4 114. -12 <tibble [6 × 3]>
#> 5 5 114 -12 <tibble [6 × 3]>
#> 6 6 114. -12 <tibble [6 × 3]>
#> 7 7 114. -12 <tibble [6 × 3]>
#> 8 8 115. -12 <tibble [6 × 3]>
#> 9 9 115 -12 <tibble [6 × 3]>
#> 10 10 115. -12 <tibble [6 × 3]>
#> # ℹ 26,555 more rowsSometimes, analysts may choose to read only a subset of the NetCDF
data. In such cases, the vars, long_range and
lat_range arguments can be used to subset the data based on
the variable and the grid resolution:
stars objectstif <- system.file("tif/L7_ETMs.tif", package = "stars")
x <- stars::read_stars(tif)
as_cubble(x, index = band)
#> # cubble: key: id [122848], index: band, nested form
#> # spatial: [288790.5, 9110743, 298708.5, 9120746.5], SIRGAS 2000 / UTM zone
#> # 25S
#> # temporal: band [int], L7_ETMs.tif [dbl]
#> x y id ts
#> <dbl> <dbl> <int> <list>
#> 1 288791. 9120747. 352 <tibble [6 × 2]>
#> 2 288819. 9120747. 704 <tibble [6 × 2]>
#> 3 288848. 9120747. 1056 <tibble [6 × 2]>
#> 4 288876. 9120747. 1408 <tibble [6 × 2]>
#> 5 288905. 9120747. 1760 <tibble [6 × 2]>
#> 6 288933. 9120747. 2112 <tibble [6 × 2]>
#> 7 288962. 9120747. 2464 <tibble [6 × 2]>
#> 8 288990. 9120747. 2816 <tibble [6 × 2]>
#> 9 289019. 9120747. 3168 <tibble [6 × 2]>
#> 10 289047. 9120747. 3520 <tibble [6 × 2]>
#> # ℹ 122,838 more rowsWhen the dimensions object is too complex for the
cubble package to handle, a warning message will be
generated.
sftime objectsdt <- climate_flat |>
sf::st_as_sf(coords = c("long", "lat"), crs = sf::st_crs("OGC:CRS84")) |>
sftime::st_as_sftime()
dt |> as_cubble(key = id, index = date)
#> # cubble: key: id [3], index: date, nested form, [sf]
#> # spatial: [144.83, -37.98, 145.1, -37.67], WGS 84
#> # temporal: prcp [dbl], tmax [dbl], tmin [dbl], date [date]
#> id elev name wmo_id geometry long lat ts
#> <chr> <dbl> <chr> <dbl> <POINT [°]> <dbl> <dbl> <list>
#> 1 ASN00086038 78.4 essen… 95866 (144.9066 -37.7276) 145. -37.7 <tibble>
#> 2 ASN00086077 12.1 moora… 94870 (145.0964 -37.98) 145. -38.0 <tibble>
#> 3 ASN00086282 113. melbo… 94866 (144.8321 -37.6655) 145. -37.7 <tibble>