--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/heatmap.r	Fri May 09 00:59:20 2014 -0400
@@ -0,0 +1,106 @@
+# limits
+min_limit = 20
+
+# load sparse matrix package
+suppressPackageStartupMessages(library('Matrix'))
+
+# access a numeric column
+get_numeric <- function(table, column_key) {
+    column <- as.numeric(column_key)
+    column_data <- sapply( table[column], as.numeric )
+    return (c(column_data))
+}
+
+# access a label column
+get_label <- function(table, column_key) {
+    column <- as.numeric(column_key)
+    return (c(table[column]))
+}
+
+# inflate three columns into matrix
+matrify <- function (data) {
+    if (ncol(data) != 3)
+        stop('Data frame must have three column format')
+    plt <- data[, 1]
+    spc <- data[, 2]
+    abu <- data[, 3]
+    plt.codes <- levels(factor(plt))
+    spc.codes <- levels(factor(spc))
+    taxa <- Matrix(0, nrow=length(plt.codes), ncol=length(spc.codes), sparse=TRUE)
+    row <- match(plt, plt.codes)
+    col <- match(spc, spc.codes)
+    for (i in 1:length(abu)) {
+        taxa[row[i], col[i]] <- abu[i]
+    }
+    colnames(taxa) <- spc.codes
+    rownames(taxa) <- plt.codes
+    taxa
+}
+
+# flatten data.frame into three column format
+flatten <- function(my_matrix) {
+    summ <-summary(my_matrix)
+    summ <- data.frame(i=rownames(my_matrix)[summ$i], j=colnames(my_matrix)[summ$j], x=summ$x)
+    summ
+}
+
+# wrapper
+wrapper <- function(table, columns, options) {
+
+    # initialize output list
+    l <- list()
+
+    # get number of columns
+    n = length(columns)
+    
+    # consistency check
+    if (n %% 3 != 0) {
+        print ('heatmap::wrapper() - Data not consistent (n mod 3 != 0)')
+        return (l)
+    }
+    
+    # create index sequence
+    index = seq(1, n, by=3)
+    
+    # get keys
+    keys = names(columns)
+    
+    # loop through blocks
+    for (i in index) {
+        # create columns
+        ci <- get_label(table, columns[keys[i]])
+        cj <- get_label(table, columns[keys[i+1]])
+        cx <- get_numeric(table, columns[keys[i+2]])
+        
+        # create a frame from columns
+        my_frame <- data.frame(ci=ci, cj=cj, cx=cx)
+        
+        # create matrix out of the frame
+        my_matrix <- matrify(my_frame)
+        
+        # create/cluster matrix
+        row_order <- hclust(dist(my_matrix))$order
+        col_order <- hclust(dist(t(my_matrix)))$order
+        
+        # reorder matrix
+        my_matrix <- my_matrix[row_order, col_order]
+        
+        # get min size
+        min_limit = max(as.integer(options$limit), min_limit)
+        
+        # get max size
+        max_row = min(length(row_order), min_limit)
+        max_col = min(length(col_order), min_limit)
+        
+        # transform back to three columns
+        my_flatmatrix = flatten(my_matrix[1:max_row, 1:max_col])
+        
+        # append to result list
+        l <- append(l, list(my_flatmatrix$i))
+        l <- append(l, list(my_flatmatrix$j))
+        l <- append(l, list(my_flatmatrix$x))
+    }
+    
+    # return
+    return (l)
+}