diff --git a/perf.R b/perf.R index 2dce9208149434acc2efc3d044e0ccaade9311d4..5c03b0370c1f66921ba8a4f26264ab450a013888 100644 --- a/perf.R +++ b/perf.R @@ -1,6 +1,10 @@ library(microbenchmark) mb <- microbenchmark( + "clear" = { + rm(list = ls()) + lapply(paste('package:', names(sessionInfo()$otherPkgs), sep=""), detach, character.only=TRUE, unload=TRUE) + }, "load packages" = { library(shinydashboard) library(glue) @@ -14,23 +18,23 @@ mb <- microbenchmark( "barplot" = { p <- plot_bar(physeq = data, fill = "Phylum", x = "Description", title = "OTU abundance barplot") p <- p + facet_grid(". ~ EnvType", scales = "free_x") - plot(p) + # plot(p) }, "filtered plot" = { p <- plot_composition(physeq = data, taxaRank1 = "Kingdom", taxaSet1 = "Bacteria", taxaRank2 = "Phylum", numberOfTaxa = 10, fill = "Phylum", x = "Description") p <- p + facet_grid(". ~ EnvType", scales = "free_x") - plot(p) + # plot(p) }, "heatmap" = { p <- plot_heatmap(prune_taxa(names(sort(taxa_sums(data), decreasing = TRUE)[1:250]), data), distance = "bray", method = "NMDS", low = "yellow", high = "red", na.value = "white", sample.order = "Description", title = "Taxa heatmap by samples") p <- p + facet_grid(". ~ EnvType", scales = "free_x") - plot(p) + # plot(p) }, "alpha" = { p <- plot_richness(physeq = data, measures = c("Observed", "Chao1", "ACE", "Shannon", "Simpson", "InvSimpson", "Fisher"), x = "EnvType", color = "EnvType", shape = "FoodType", title = "Alpha diversity graphics") p <- p + geom_boxplot() p <- p + geom_point() - plot(p) + # plot(p) }, "beta" = { beta <- melt(as(distance(data, method = "bray"), "matrix")) @@ -46,29 +50,32 @@ mb <- microbenchmark( p1 <- p1 + geom_tile() p1 <- p1 + ggtitle("Beta diversity heatmap") p1 <- p1 + theme(axis.text.x = element_text(angle = 90, hjust = 1, color = tipColor), axis.text.y = element_text(color = tipColor), axis.title.x = element_blank(), axis.title.y = element_blank()) - plot(p1 + scale_fill_gradient2()) + # plot(p1 + scale_fill_gradient2()) }, "rarefaction" = { p <- ggrare(physeq = data, step = 100, se = FALSE, color = "EnvType", label = "Description") p <- p + facet_grid(". ~ FoodType") p <- p + geom_vline(xintercept = min(sample_sums(data)), color = "gray60") p <- p + ggtitle("Rarefaction curves") - plot(p) + # plot(p) }, "acp" = { p <- plot_samples(physeq = data, ordination = ordinate(data, method = "MDS", distance = "unifrac"), axes = c(1, 2), color = "EnvType", shape = "FoodType", replicate = "EnvType", label = "Description", title = "Samples ordination graphic") p <- p + stat_ellipse(aes_string(group = "EnvType")) - plot(p + theme_bw()) + # plot(p + theme_bw()) }, "tree" = { p <- plot_tree(physeq = prune_taxa(names(sort(taxa_sums(data), decreasing = TRUE)[1:20]), data), method = "sampledodge", color = "EnvType", size = "abundance", label.tips = "taxa_names", sizebase = 5, ladderize = "left", plot.margin = 0, title = "Phylogenetic tree") - plot(p) + # plot(p) }, "clustering" = { p <- plot_clust(physeq = data, dist = "unifrac", method = "ward.D2", color = "EnvType") - plot(p) + # plot(p) }, - times = 100, unit = 's', order = 'inorder', warmup = 0) + times = 100, unit = "s", control = list(order="inorder")) mb autoplot(mb) + +save(mb, file = "benchmark.RData") +ggsave("benchmark.png")