#' Calculates the degree or neighborhood density of a word based on Levenshtein edit distance of 1
#'
#' @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 corresponding degree.
#' @examples
#' # load a database first
#' # get_degree(stimuli = c('xbet', 'gEt', 'hWs', 'xgEnst'), database = data)
#'
get_degree <- 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', 'Degree')
for (i in 1:length(stimuli)) {
# save word to output
output$Stimuli[i] <- stimuli[i]
# save degree to output
output$Degree[i] <- length(levenshtein.neighbors(stimuli[i], data$Phono)[[1]])
}
return(output)
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.