Tidyverse methods for csquares objects (drop the 'csquares'-suffix)

Tidyverse methods for csquares objects that inherit from data.frame, tibble, sf, or in some cases stars. Load the tidyverse package containing the generic implementation (dplyr or tidyr), and call the function without the .csquares suffix. See examples and vignette("tidy") for more details. The methods implemented here ensure that the csquare class is preserved.

filter.csquares(.data, ..., .dots)

select.csquares(.data, ...)

as_tibble.csquares(x, ...)

arrange.csquares(.data, ..., .dots)

group_by.csquares(.data, ..., add = FALSE)

ungroup.csquares(.data, ...)

rowwise.csquares(.data, ...)

mutate.csquares(.data, ..., .dots)

rename.csquares(.data, ...)

rename_with.csquares(.data, .fn, .cols, ...)

slice.csquares(.data, ..., .dots)

distinct.csquares(.data, ..., .keep_all = FALSE)

summarise.csquares(.data, ..., .dots)

pivot_longer.csquares(
  data,
  cols,
  ...,
  cols_vary = "fastest",
  names_to = "name",
  names_prefix = NULL,
  names_sep = NULL,
  names_pattern = NULL,
  names_ptypes = NULL,
  names_transform = NULL,
  names_repair = "check_unique",
  values_to = "value",
  values_drop_na = FALSE,
  values_ptypes = NULL,
  values_transform = NULL
)

pivot_wider.csquares(
  data,
  ...,
  id_cols = NULL,
  id_expand = FALSE,
  names_from = NULL,
  names_prefix = "",
  names_sep = "_",
  names_glue = NULL,
  names_sort = FALSE,
  names_vary = "fastest",
  names_expand = FALSE,
  names_repair = "check_unique",
  values_from = NULL,
  values_fill = NULL,
  values_fn = NULL,
  unused_fn = NULL
)

group_split.csquares(.tbl, ..., .keep = TRUE)

nest.csquares(.data, ...)

unite.csquares(data, col, ..., sep = "_", remove = TRUE)

unnest.csquares(data, ..., .preserve = NULL)

unnest.csquares_nested(data, cols, ...)

drop_na.csquares(x, ...)

Arguments

.data, ..., .dots, data, x, add, .fn, .cols, .keep_all, cols, cols_vary, names_to, names_prefix, names_sep, names_pattern, names_ptypes, names_transform, names_repair, values_to, values_drop_na, values_ptypes, values_transform, id_cols, id_expand, names_from, names_glue, names_sort, names_vary, names_expand, values_from, values_fill, values_fn, unused_fn, .tbl, .keep, col, sep, remove, .preserve

Passed to tidyverse generic methods. Consult their documentation.

Details

Note that the implementation of summarise.csquares has changed since version 0.0.5.002, to better reflect the dplyr generic implementation. To get results similar to the earlier implementation please use resample_csquares().

Author

Pepijn de Vries

Examples

if (requireNamespace(c("dplyr", "tidyr"))) {
  library(dplyr)
  library(tidyr)
  
  ## Create a csquares object from the orca dataset:
  orca_csq <- as_csquares(orca, csquares = "csquares")
  
  ## Filter values that belong to the killer whale realm:
  orca2 <- filter(orca_csq, orcinus_orca == TRUE)
  
  ## Mutate the object to hold information on the quadrant:
  orca_csq <- mutate(orca_csq, quadrant = csquares |> as.character() |> substr(1,1))
  
  ## Select the quadrant column:
  orca2 <- select(orca_csq, quadrant)
  
  ## Convert it into a tibble:
  orca_csq <- as_tibble(orca_csq)
  
  ## Arrange by quadrant:
  orca2 <- arrange(orca_csq, quadrant)
  
  ## Group by quadrant:
  orca_csq <- group_by(orca_csq, quadrant)
  
  ## Summarise per quadrant:
  summarise(orca_csq, realm_frac = sum(orcinus_orca)/n())
  
  #' Introduce a group split:
  orca2 <- group_split(orca_csq)
  
  ## Ungroup the object:
  orca_csq <- ungroup(orca_csq)
  
  ## Take a slice of the first three rows:
  slice(orca_csq, 1:3)
  
  ## Take a sample of 10 rows with replacement:
  slice_sample(orca_csq, n = 10, replace = TRUE)
  
  ## Rename a column:
  rename(orca_csq, quad = "quadrant")
  rename_with(orca_csq, toupper, starts_with("quad"))
  
  ## Distinct will remove any duplicated rows:
  orca_csq[c(1, 1, 1),] |> distinct()
  
  ## Pivot to a wide format:
  pivot_wider(orca_csq, names_from = "quadrant", values_from = "orcinus_orca")
  pivot_wider(orca_csq, names_from = "orcinus_orca", values_from = "orcinus_orca",
              id_cols = "quadrant", values_fn = length)
  
  ## Pivot to a long format (note that you can't pivot the csquares column to long)
  tibble(csq = "1000", a = 1, b = 2, d = 3) |>
    as_csquares(csquares = "csq") |>
    pivot_longer(c("a", "b", "d"), names_to = "letter", values_to = "numeric")
  
  ## Unite two columns into one:
  unite(orca_csq, "quad_realm", any_of(c("quadrant", "orcinus_orca")))
  
  ## As the csquares column gets nested in the example below,
  ## the resulting object is no longer of class csquares:
  orca_nest <- nest(orca_csq, nested_data = c("csquares", "orcinus_orca"))
  
  ## Unnest it:
  unnest(orca_nest, "nested_data")
}
#> # A tibble: 2,058 × 3
#>    quadrant csquares   orcinus_orca
#>  * <chr>    <csquares> <lgl>       
#>  1 3        3603:3     FALSE       
#>  2 3        3413:4     TRUE        
#>  3 3        3109:3     FALSE       
#>  4 3        3016:4     FALSE       
#>  5 3        3602:2     FALSE       
#>  6 3        3700:1     FALSE       
#>  7 3        3015:3     FALSE       
#>  8 3        3106:4     FALSE       
#>  9 3        3317:4     FALSE       
#> 10 3        3507:3     FALSE       
#> # ℹ 2,048 more rows