Mercurial > repos > guerler > charts
view heatmap.r @ 65:48c591b9e917 draft
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author | guerler |
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date | Fri, 27 Feb 2015 02:43:07 -0500 |
parents | 680cf68098d5 |
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# load sparse matrix package suppressPackageStartupMessages(library('Matrix')) # access a numeric column get_numeric <- function(table, column_key) { column <- as.numeric(column_key) column_data <- suppressWarnings(as.numeric(as.character(table[column][[1]]))) return (c(column_data)) } # access a label column get_label <- function(table, column_key) { column <- as.numeric(column_key) column_data <- as.character(table[column][[1]]) return (c(column_data)) } # 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)))$order # reorder matrix my_matrix <- my_matrix[row_order, col_order] # transform back to three columns my_flatmatrix = flatten(my_matrix) # append to result list l <- append(l, list(my_flatmatrix$i)) l <- append(l, list(my_flatmatrix$j)) l <- append(l, list(my_flatmatrix$x)) } # return return (l) }