R/as_cell_df.R

In tidycells: Read Tabular Data from Diverse Sources and Easily Make Them Tidy

#' Transform data into Cell-DF Structure
#'
#' @description Transform an R object (mostly matrix or data.frame) into a [`cell_df`][cell_df-class]
#' for further processing in other `tidycells` functions.
#'
#' @param d the data (either a matrix with column name or a data.frame)
#' @param take_row_names consider row names as separate cells
#' (applicable only for data with no (row, col) information). Default is \code{FALSE}.
#' @param take_col_names consider column names as separate cells
#' (applicable only for data with no (row, col) information). Default is \code{FALSE}.
#'
#' @return An object of class [`cell_df`][cell_df-class].
#'
#' **Note**: After this, you may like to do [`Value Attribute Classification`][value_attribute_classify()].
#'
#' @export
#' @rdname as_cell_df
#' @seealso
#' * [`validate_cells`][validate_cells()] which is used to validate `cell_df`.
#' * [`as_cells`][unpivotr::as_cells()] from `unpivotr` package.
#'
#' @examples
#'
#' as_cell_df(iris)
#'
#' # consider column name as cell
#' as_cell_df(iris, take_col_names = TRUE)
#'
#' # if the data is already in a similar format it will not further transform
#' # which is not true for ---> unpivotr::as_cells
#' # check ---> unpivotr::as_cells(iris) %>% unpivotr::as_cells()
#' unpivotr::as_cells(iris) %>% as_cell_df()
as_cell_df <- function(d, take_row_names = FALSE, take_col_names = FALSE) {
  UseMethod("as_cell_df")
}

#' @export
as_cell_df.data.frame <- function(d, ...) {
  d %>%
    attach_intermediate_class() %>%
    as_cell_df_internal(...)
}

#' @export
as_cell_df.matrix <- function(d, ...) {
  d %>%
    as.data.frame() %>%
    attach_intermediate_class() %>%
    as_cell_df_internal(...)
}