no column names with flattenCorrMatrix (rquery.cormat)

Dear all,

Hope my question is not too trivial but I've really searched everywhere for an answer...

I'm trying to get the flatten table (with the name of variable tested, the correlation and the p value) but I unfortunately can't get the column names, they are all noted "NA", and the result looks like this:

row column cor p
Coul NA 0.1700 2.0e-01
Coul NA 0.1600 2.2e-01
Min_N NA 0.9800 2.6e-40
Coul NA 0.0810 5.4e-01
etc...
With this message (translated from french...) "only the first element of the 'length.out' argument is used$r"

Here is the rquery function I'm using:

#+++++++++++++++++++++++++
# Computing of correlation matrix
#+++++++++++++++++++++++++

# x : matrix
# type: possible values are "lower" (default), "upper", "full" or "flatten";
  #display lower or upper triangular of the matrix, full  or flatten matrix.
# graph : if TRUE, a correlogram or heatmap is plotted
# graphType : possible values are "correlogram" or "heatmap"
# col: colors to use for the correlogram
# ... : Further arguments to be passed to cor or cor.test function
# Result is a list including the following components :
  # r : correlation matrix, p :  p-values
  # sym : Symbolic number coding of the correlation matrix
rquery.cormat<-function(x,
                        type=c('lower', 'upper', 'full', 'flatten'),
                        graph=TRUE,
                        graphType=c("correlogram", "heatmap"),
                        col=NULL, cor.method = "pearson", ...)
{
  library(corrplot)
  # Helper functions
  #+++++++++++++++++
  # Compute the matrix of correlation p-values
  cor.pmat <- function(x, ...) {
    mat <- as.matrix(x)
    n <- ncol(mat)
    p.mat<- matrix(NA, n, n)
    diag(p.mat) <- 0
    for (i in 1:(n - 1)) {
      for (j in (i + 1):n) {
        tmp <- cor.test(mat[, i], mat[, j], ...)
        p.mat[i, j] <- p.mat[j, i] <- tmp$p.value
      }
    }
    colnames(p.mat) <- rownames(p.mat) <- colnames(mat)
    p.mat
  }
  # Get lower triangle of the matrix
  getLower.tri<-function(mat){
    upper<-mat
    upper[upper.tri(mat)]<-""
    mat<-as.data.frame(upper)
    mat
  }
  # Get upper triangle of the matrix
  getUpper.tri<-function(mat){
    lt<-mat
    lt[lower.tri(mat)]<-""
    mat<-as.data.frame(lt)
    mat
  }
  # Get flatten matrix
  flattenCorrMatrix <- function(cormat, pmat) {
    ut <- upper.tri(cormat)
    data.frame(
      row = rownames(cormat)[row(cormat)[ut]],
      column = rownames(cormat)[col(cormat)[ut]],
      cor  =(cormat)[ut],
      p = pmat[ut]
    )
  }
  # Define color
  if (is.null(col)) {
    col <- colorRampPalette(
            c("#67001F", "#B2182B", "#D6604D", "#F4A582",
              "#FDDBC7", "#FFFFFF", "#D1E5F0", "#92C5DE", 
             "#4393C3", "#2166AC", "#053061"))(200)
    col<-rev(col)
  }
  
  # Correlation matrix
  cormat<-signif(cor(x, use = "complete.obs", ...),2)
  pmat<-signif(cor.pmat(x, ...),2)
  # Reorder correlation matrix
  ord<-corrMatOrder(cormat, order="hclust")
  cormat<-cormat[ord, ord]
  pmat<-pmat[ord, ord]
  # Replace correlation coeff by symbols
  sym<-symnum(cormat, abbr.colnames=FALSE)
  # Correlogram
  if(graph & graphType[1]=="correlogram"){
    corrplot(cormat, type=ifelse(type[1]=="flatten", "lower", type[1]),
             tl.col="black", tl.srt=45,col=col,...)
  }
  else if(graphType[1]=="heatmap")
    heatmap(cormat, col=col, symm=TRUE)
  # Get lower/upper triangle
  if(type[1]=="lower"){
    cormat<-getLower.tri(cormat)
    pmat<-getLower.tri(pmat)
  }
  else if(type[1]=="upper"){
    cormat<-getUpper.tri(cormat)
    pmat<-getUpper.tri(pmat)
    sym=t(sym)
  }
  else if(type[1]=="flatten"){
    cormat<-flattenCorrMatrix(cormat, pmat)
    pmat=NULL
    sym=NULL
  }
  list(r=cormat, p=pmat, sym=sym)
}

And how I "call" it:
Nb: don is my dataframe, I want every variable to be tested with the other:

don2 <- as.matrix(don)
 rquery.cormat(don, type="flatten", graph=FALSE, cor.method = "pearson")

I also tried this with the same result:

flattenCorrMatrix <- function(DF) {
  DF <- DF %>% as.matrix() %>% Hmisc::rcorr()
  ut <- upper.tri(DF$r)
  flat <- data.frame(row = rownames(DF$r)[row(DF$r)[ut]], column = rownames(DF$r)[col(DF$r)[ut]], 
                     cor = (DF$r)[ut], p = DF$P[ut], n = DF$n[ut])
  return(flat)
}

#using the function and filtering out the y variable and correlations higher than abs(0.7)
flattenCorrMatrix(don) %>% 
          filter(!grepl("y", row)) %>% 
          filter(cor > abs(0.7)&p<0.05)

I already shortened the variable names, just in case ...

Thanks ,

Best regards,

Fran├žoise

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