Correlation Matrix

corstars1.R

corstarsl <- function(xx){ 
    require(Hmisc) 
    x <- as.matrix(xx) 
    R <- rcorr(x)$r 
    p <- rcorr(x)$P 

    ## define notions for significance levels; spacing is important.
    mystars <- ifelse(p < .001, "***", ifelse(p < .01, "** ", ifelse(p < .05, "* ", " ")))

    ## trunctuate the matrix that holds the correlations to two decimal
    R <- format(round(cbind(rep(-1.11, ncol(x)), R), 2))[,-1] 

    ## build a new matrix that includes the correlations with their apropriate stars 
    Rnew <- matrix(paste(R, mystars, sep=""), ncol=ncol(x)) 
    diag(Rnew) <- paste(diag(R), " ", sep="") 
    rownames(Rnew) <- colnames(x) 
    colnames(Rnew) <- paste(colnames(x), "", sep="") 

    ## remove upper triangle
    Rnew <- as.matrix(Rnew)
    Rnew[upper.tri(Rnew, diag = TRUE)] <- ""
    Rnew <- as.data.frame(Rnew) 

    ## remove last column and return the matrix (which is now a data frame)
    Rnew <- cbind(Rnew[1:length(Rnew)-1])

    ## add summary columns
    not.na = function(x, ...) sum(!is.na(x))

    stats <- xx %>% 
    summarise_all(c("mean","sd", "not.na"),na.rm = TRUE) %>% 
    unlist() %>%  
    matrix(ncol = 3 ) %>% 
    as.tibble() %>% 
    rename( "Mean" = V1, "SD" = V2, "N" = V3)

    Rnew <- cbind(Rnew, stats)

    return(Rnew) 
}