R/get_neighbors.R

In csqsiew/NFreq: Calculates the degree and neighborhood frequency of words

#' Outputs a list of 1-edit distance neighbors of a word
#'
#' @param stimuli A character vector containing words or nonwords.
#' @param database A dataframe that must contain a \code{Phono} column that contains phonological transcriptions.
#' @return A dataframe with \code{stimuli} and list of its neighbors (if any).
#' @examples
#' # load a database first
#' # get_neighbors(stimuli = c('xbet', 'gEt', 'hWs', 'xgEnst'), database = data)
#'
get_neighbors <- function(stimuli, database) {
  #stimuli = character vector of words to calculate degree
  #database = corpus

  #check that stimuli is a character vector
  if (class(stimuli) != 'character' || is.vector(stimuli) == F) {
    stop('Warning! Stimuli is either not of character type or is not a vector.')
  }

  #check that the database is correctly input
  if (is.data.frame(database) == F) {
    stop('Warning! Database is not a dataframe type.')
  }

  if ('Phono' %in% colnames(database) == F) {
    stop('Warning! Data does not contain a "Phono" column.')
  }

  #initialize a data frame to save data to
  output <- as.data.frame(matrix(0, ncol = 2, nrow = length(stimuli)))
  colnames(output) <- c('Stimuli', 'NeighborList')

  for (i in 1:length(stimuli)) {
    # save word to output
    output$Stimuli[i] <- stimuli[i]
    # save degree to output
    output$NeighborList[i] <- paste(levenshtein.neighbors(stimuli[i], data$Phono)[[1]], collapse = " ")
  }

  return(output)

}

# test pieces

#get_neighbors(stimuli = 'xbet', database = data)

#get_neighbors(stimuli = c('xbet', 'gEt', 'hWs', 'xgEnst'), database = data)