Merge branch 'master' of gitlab-ssh.irstea.fr:cedric.midoux/easy16S

Conflicts:
	perf.R

perf.R

 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")