diff graph_homogeneity_normality.r @ 0:e5552099d0e3 draft

"planemo upload for repository https://github.com/Marie59/Data_explo_tools commit 2f883743403105d9cac6d267496d985100da3958"

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/graph_homogeneity_normality.r	Tue Jul 27 16:57:02 2021 +0000
@@ -0,0 +1,130 @@
+#Rscript
+
+#######################################
+##     Homogeneity and normality     ##
+#######################################
+
+#####Packages : car
+#               ggplot2
+#               ggpubr
+#               Cowplot
+
+#####Load arguments
+
+args <- commandArgs(trailingOnly = TRUE)
+
+if (length(args) == 0) {
+    stop("This tool needs at least one argument")
+}else{
+    table <- args[1]
+    hr <- args[2]
+    date <- as.numeric(args[3])
+    spe <- as.numeric(args[4])
+    var <- as.numeric(args[5])
+}
+
+if (hr == "false") {
+  hr <- FALSE
+}else{
+  hr <- TRUE
+}
+
+#####Import data
+data <- read.table(table, sep = "\t", dec = ".", header = hr, fill = TRUE, encoding = "UTF-8")
+data <- na.omit(data)
+coldate <- colnames(data)[date]
+colspe <- colnames(data)[spe]
+colvar <- colnames(data)[var]
+
+#####Your analysis
+
+####Homogeneity of the variance####
+
+##Test of Levene##
+testlevene <- function(data, col1, col2) {
+        data[, col1] <- as.numeric(data[, col1])
+        data[, col2] <- as.factor(data[, col2])
+    tb_levene <- car::leveneTest(y = data[, col1], group = data[, col2])
+
+    return(tb_levene)
+    }
+levene <- capture.output(testlevene(data = data, col1 = colvar, col2 = colspe))
+
+cat("\nwrite table with levene test. \n--> \"", paste(levene, "\"\n", sep = ""), file = "levene.txt", sep = "", append = TRUE)
+
+##Two boxplots to visualize it##
+
+homog_var <- function(data, col1, col2, col3, mult) {
+    data[, col1] <- as.factor(data[, col1])
+    if (mult) {
+            for (spe in unique(data[, col2])) {
+             data_cut <- data[data[, col2] == spe, ]
+             graph_2 <- ggplot2::ggplot(data_cut, ggplot2::aes_string(x = col1, y = col3, color = col1)) +
+             ggplot2::geom_boxplot() +
+             ggplot2::theme(legend.position = "none", axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5, hjust = 1), panel.background = ggplot2::element_rect(fill = "#d9d4c5", colour = "#d9d4c5", linetype = "solid"),
+              panel.grid.major = ggplot2::element_line(linetype = "solid", colour = "white"),
+              panel.grid.minor = ggplot2::element_line(linetype = "solid", colour = "white"))
+
+            ggplot2::ggsave(paste("Homogeneity_of_", spe, ".png"), graph_2, width = 16, height = 9, units = "cm")
+            }
+        }else{
+        graph_1 <- ggplot2::ggplot(data, ggplot2::aes_string(x = col1, y = col3, color = col1)) +
+          ggplot2::geom_boxplot() +
+          ggplot2::theme(legend.position = "none", axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5, hjust = 1))
+
+    #Put multiple panels
+    graph_2 <- graph_1 + ggplot2::facet_grid(rows = ggplot2::vars(data[, col2]), scales = "free") +
+      ggplot2::theme(panel.background = ggplot2::element_rect(fill = "#d9d4c5", colour = "#d9d4c5", linetype = "solid"),
+          panel.grid.major = ggplot2::element_line(linetype = "solid", colour = "white"),
+          panel.grid.minor = ggplot2::element_line(linetype = "solid", colour = "white"))
+
+    ggplot2::ggsave("Homogeneity.png", graph_2, width = 16, height = 9, units = "cm")
+        }
+}
+
+####Normality of the distribution####
+# Kolmogorov-Smirnov test
+
+ks <- capture.output(ks.test(x = data[, var], y = "pnorm", alternative = "two.sided"))
+
+cat("\nwrite table with Kolmogorov-Smirnov test. \n--> \"", paste(ks, "\"\n", sep = ""), file = "ks.txt", sep = "", append = TRUE)
+
+#Histogramm with distribution line
+graph_hist <- function(data, var1) {
+  graph_hist <- ggplot2::ggplot(data) +
+  ggplot2::geom_histogram(ggplot2::aes_string(x = var1), binwidth = 2, color = "black", fill = "white") +
+  ggplot2::geom_density(ggplot2::aes_string(var1), alpha = 0.12, fill = "red") +
+  ggplot2::ggtitle("Distribution histogram")
+
+return(graph_hist)
+}
+
+#Add the mean dashed line
+add_mean <- function(graph, var1) {
+  graph_mean <- graph + ggplot2::geom_vline(xintercept = mean(data[, var1]),
+              color = "midnightblue", linetype = "dashed", size = 1)
+
+return(graph_mean)
+}
+
+#Adding a QQplot
+graph_qqplot <- function(data, var1) {
+  graph2 <- ggpubr::ggqqplot(data, var1, color = "midnightblue") + ggplot2::ggtitle("Q-Q plot")
+
+return(graph2)
+}
+
+#On suppose que les données sont distribuées normalement lorsque les points suivent approximativement la ligne de référence à 45 degrés.
+
+graph_fin <- function(graph1, graph2) {
+  graph <- cowplot::plot_grid(graph1, graph2, ncol = 2, nrow = 1)
+
+  ggplot2::ggsave("Normal_distribution.png", graph, width = 10, height = 7, units = "cm")
+}
+
+mult <- ifelse(length(unique(data[, colspe])) == 2, FALSE, TRUE)
+homog_var(data, col1 = coldate, col2 = colspe, col3 = colvar, mult = mult)
+
+graph_hist1 <- graph_hist(data, var1 = colvar)
+graph_mean <- add_mean(graph = graph_hist1, var1 = colvar)
+graph_fin(graph1 = graph_mean, graph2 = graph_qqplot(data, var1 = colvar))