R/mi_clean_data.R

In MantaID: A Machine-Learning Based Tool to Automate the Identification of Biological Database IDs

#' Reshape data and delete meaningless rows.
#' @param data A dataframe or tibble or data.table or matrix. Names of the column will be regard as the class of ID included in column.
#' @param placeholder Character vectors. IDs included in `placeholder` will be omitted.
#' @param cols Character vectors. Columns of `data` that contain the IDs
#' @importFrom tibble as_tibble
#' @importFrom dplyr select mutate_at pull slice
#' @importFrom tidyselect everything all_of
#' @importFrom tidyr pivot_longer
#' @importFrom purrr map
#' @importFrom magrittr %<>%
#' @export
#' @return A tibble with two columns("ID" and "class")
#' @examples
#' data <- tibble::tibble(
#'   "class1" = c("A", "B", "C", "D"),
#'   "class2" = c("E", "F", "G", "H"),
#'   "class3" = c("L", "M", "-", "O")
#' )
#' mi_clean_data(data)
mi_clean_data <- function(data, cols = everything(), placeholder = c("-")) {
  #Create a 2-column tibble dataframe with the column names "ID" and "class", and convert the elements to characters.
  data %<>% as_tibble() %>%
    select(all_of(cols)) %>%
    mutate_at(colnames(.), ~ as.character(.x)) %>%
    pivot_longer(
      cols = everything(),
      names_to = "class",
      values_to = "ID",
      names_repair = "minimal"
    ) %>%
    select("ID", "class")
  #Re-arrange the tibble data box according to the ID column, eliminating rows with "-" in the ID.
  index <- map(data %>% pull("ID"), ~ !.x %in% placeholder)
  data %>% slice(which(index == TRUE))
}