I am able to make a correlation matrix that is formatted with the significant values and stars indicating how significant but dont know how to make it so it focuses only on VOC.

Output I have:

                Greet    Connect  Shop     Membership  Solve    Recap&Re-Engage  Average  VOC     
Greet           "1.0   " "0.4***" "0.3***" "0.1***"    "0.2***" "0.0*  "         "0.5***" "0.1***"
Connect         "0.4***" "1.0   " "0.4***" "0.2***"    "0.4***" "0.3***"         "0.8***" "0.4***"
Shop            "0.3***" "0.4***" "1.0   " "0.3***"    "0.2***" "0.2***"         "0.7***" "0.2***"
Membership      "0.1***" "0.2***" "0.3***" "1.0   "    "0.2***" "0.2***"         "0.5***" "0.2***"
Solve           "0.2***" "0.4***" "0.2***" "0.2***"    "1.0   " "0.4***"         "0.6***" "0.4***"
Recap&Re-Engage "0.0*  " "0.3***" "0.2***" "0.2***"    "0.4***" "1.0   "         "0.5***" "0.2***"
Average         "0.5***" "0.8***" "0.7***" "0.5***"    "0.6***" "0.5***"         "1.0   " "0.4***"
VOC             "0.1***" "0.4***" "0.2***" "0.2***"    "0.4***" "0.2***"         "0.4***" "1.0   "

What I want:

            Voc
Greet.      0.1***
Connect.    0.4***
Shop.       0.2***
Membership. 0.2***
Solve       0.4***
Recap       0.2***
Average     0.4***
VOC         1.0

Below is my code currently:

#Selecting Greet, Connect, Shop, Membership, Solve, Recap & Re-Engage, Average, & VOC
QA_Behav_Overall <- select(NA_Work, c(88:94, 97))

#correlation_matrix
correlation_matrix <- function(QA_Behav_Overall, 
                               type = "pearson",
                               digits = 1, 
                               decimal.mark = ".",
                               use = "all", 
                               show_significance = TRUE, 
                               replace_diagonal = FALSE, 
                               replacement = ""){
  
  # check arguments
  stopifnot({
    is.numeric(digits)
    digits >= 0
    use %in% c("all", "upper", "lower")
    is.logical(replace_diagonal)
    is.logical(show_significance)
    is.character(replacement)
  })
  # we need the Hmisc package for this
  require(Hmisc)
  
  # retain only numeric and boolean columns
  isNumericOrBoolean = vapply(QA_Behav_Overall, function(x) is.numeric(x) | is.logical(x), logical(1))
  if (sum(!isNumericOrBoolean) > 0) {
    cat('Dropping non-numeric/-boolean column(s):', paste(names(isNumericOrBoolean)[!isNumericOrBoolean], collapse = ', '), 'nn')
  }
  QA_Behav_Overall = QA_Behav_Overall[isNumericOrBoolean]
  
  # transform input data frame to matrix
  x <- as.matrix(QA_Behav_Overall)
  
  # run correlation analysis using Hmisc package
  correlation_matrix <- Hmisc::rcorr(x, type = )
  R <- correlation_matrix$r # Matrix of correlation coeficients
  p <- correlation_matrix$P # Matrix of p-value 
  
  # transform correlations to specific character format
  Rformatted = formatC(R, format = 'f', digits = digits, decimal.mark = decimal.mark)
  
  # if there are any negative numbers, we want to put a space before the positives to align all
  if (sum(R < 0) > 0) {
    Rformatted = ifelse(R > 0, paste0(' ', Rformatted), Rformatted)
  }
  
  # add significance levels if desired
  if (show_significance) {
    # define notions for significance levels; spacing is important.
    stars <- ifelse(is.na(p), "   ", ifelse(p < .001, "***", ifelse(p < .01, "** ", ifelse(p < .05, "*  ", "   "))))
    Rformatted = paste0(Rformatted, stars)
  }
  # build a new matrix that includes the formatted correlations and their significance stars
  Rnew <- matrix(Rformatted, ncol = ncol(x))
  rownames(Rnew) <- colnames(x)
  colnames(Rnew) <- paste(colnames(x), "", sep =" ")
  
  # replace undesired values
  if (use == 'upper') {
    Rnew[lower.tri(Rnew, diag = replace_diagonal)] <- replacement
  } else if (use == 'lower') {
    Rnew[upper.tri(Rnew, diag = replace_diagonal)] <- replacement
  } else if (replace_diagonal) {
    diag(Rnew) <- replacement
  }
  
  return(Rnew)
}

#View matrix in console
correlation_matrix(QA_Behav_Overall)

How do I go about focusing only on the VOC values while keeping the asterisk to indicate how significant a value is?