R/kmeRs_statistics.R

In kmeRs: K-Mers Similarity Score Matrix

#' @title Calculate And Add Basic Statistics To The K-mers Similarity Matrix
#'
#' @description
#' The \code{kmeRs_statistics} function calculates basic statistics and returns the similarity matrix
#' with calculated results or summarized table with statistics only when summary_statistics_only
#' is set to TRUE
#'
#' @aliases kmeRs_statistics
#'
#' @param kmeRs_similarity_matrix matrix calculated by \code{kmeRs_similarity_matrix} function
#' @param summary_statistics_only when parameter is set to TRUE only the summarized table with
#' statistics is returned
#'
#' @return data.frame with results
#'
#' @examples
#' # Simple BLOSUM62 similarity matrix for DNA nucleotides
#' example  <- kmeRs_similarity_matrix(kmers_given = c("A", "T", "C", "G"), submat = "BLOSUM62")
#'
#' # Result as a full matrix
#' kmeRs_statistics(example)
#'
#' # Result a summary statistics table
#' kmeRs_statistics(example, summary_statistics_only = TRUE)
#'
#' @export


  kmeRs_statistics <- function(kmeRs_similarity_matrix, summary_statistics_only = FALSE){

      kmeRs_similarity_matrix_temp <- kmeRs_similarity_matrix

      # Ignore score column if exists

      score_column_number <- -1

      if ('score_total' %in% colnames(kmeRs_similarity_matrix_temp)){

        score_column_number <- which(colnames(kmeRs_similarity_matrix) == 'score_total')
        kmeRs_similarity_matrix_temp <- kmeRs_similarity_matrix[ , - score_column_number]

      }

      # Calculate the stats for rows and add columns for min, max, mean etc.

      Min  <- apply(kmeRs_similarity_matrix_temp, 1, min)
      Max  <- apply(kmeRs_similarity_matrix_temp, 1, max)
      Mean <- apply(kmeRs_similarity_matrix_temp, 1, mean)
      Sd   <- apply(kmeRs_similarity_matrix_temp, 1, stats::sd)

      kmeRs_similarity_matrix <- cbind(kmeRs_similarity_matrix, data.frame(Min, Max, Mean, Sd))

      # Calculate the stats for cols and add rows for min, max, mean etc.

      Min  <- apply(kmeRs_similarity_matrix, 2, min)
      Max  <- apply(kmeRs_similarity_matrix, 2, max)
      Mean <- apply(kmeRs_similarity_matrix, 2, mean)
      Sd   <- apply(kmeRs_similarity_matrix, 2, stats::sd)

      kmeRs_similarity_matrix <- rbind(kmeRs_similarity_matrix, t(data.frame(Min, Max, Mean, Sd)))
      kmeRs_similarity_matrix <- round(kmeRs_similarity_matrix, 2)

      # Add results to matrix or prepare a summary table

      if (summary_statistics_only == TRUE){

        # start table from score or Min column and Min row

        col <- which(colnames(kmeRs_similarity_matrix) == 'Min')
        if (score_column_number > 0){ col <- score_column_number }

        row <- which(rownames(kmeRs_similarity_matrix) == 'Min')

        kmeRs_similarity_matrix <- kmeRs_similarity_matrix[ row:length(rownames(kmeRs_similarity_matrix)), col:length(colnames(kmeRs_similarity_matrix))]

      }

    return(kmeRs_similarity_matrix)

 }