diff graph_pres_abs_abund.r @ 0:c7dd4706f982 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_pres_abs_abund.r	Tue Jul 27 16:55:49 2021 +0000
@@ -0,0 +1,195 @@
+#Rscript
+
+#########################################################
+##    Presence abscence and abundance in environment   ##
+#########################################################
+
+#####Packages : ggplot2
+#               vegan
+
+#####Load arguments
+
+args <- commandArgs(trailingOnly = TRUE)
+
+if (length(args) < 5) {
+    stop("This tool needs at least 5 arguments")
+}else{
+    table <- args[1]
+    hr <- args[2]
+    abundance <- as.logical(args[3])
+    presabs <- as.logical(args[4])
+    rarefaction <- as.logical(args[5])
+    lat <- as.numeric(args[6])
+    long <- as.numeric(args[7])
+    ind <- as.character(args[8])
+    loc <- as.numeric(args[9])
+    num <- as.character(args[10])
+    spe <- as.numeric(args[11])
+    abond <- as.numeric(args[12])
+}
+
+if (hr == "false") {
+  hr <- FALSE
+}else{
+  hr <- TRUE
+}
+
+#####Import data
+data <- read.table(table, sep = "\t", dec = ".", header = hr, fill = TRUE, encoding = "UTF-8")
+
+if (abundance) {
+collat <- colnames(data)[lat]
+collong <- colnames(data)[long]
+}
+
+if (presabs) {
+colloc <- colnames(data)[loc]
+}
+
+if (presabs | rarefaction | abundance) {
+colabond <- colnames(data)[abond]
+colspe <- colnames(data)[spe]
+data <- data[grep("^$", data[, colspe], invert = TRUE), ]
+}
+
+#####Your analysis
+
+####The abundance in the environment####
+
+##Representation of the environment##
+
+## Mapping
+graph_map <- function(data, collong, collat, colabond, ind, colspe) {
+  cat("\nCoordinates range\n\nLatitude from ", min(data[, collat], na.rm = TRUE), " to ", max(data[, collat], na.rm = TRUE), "\nLongitude from ", min(data[, collong], na.rm = TRUE), " to ", max(data[, collong], na.rm = TRUE), file = "Data_abund.txt", fill = 1, append = TRUE)
+  if (mult0) {
+    mappy <- ggplot2::ggplot(data, ggplot2::aes_string(x = collong, y = collat, cex = colabond, color = colspe)) +
+    ggplot2::geom_point() + ggplot2::ggtitle(paste("Abundance of", ind, "in the environment")) + ggplot2::xlab("Longitude") + ggplot2::ylab("Latitude")  + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5, hjust = 1), legend.text = ggplot2::element_text(size = 8)) + ggplot2::guides(cex = ggplot2::guide_legend(reverse = TRUE))
+
+  }else{
+    mappy <- ggplot2::ggplot(data, ggplot2::aes_string(x = collong, y = collat, cex = colabond, color = colabond)) +
+    ggplot2::geom_point() + ggplot2::ggtitle(paste("Abundance of", ind, "in the environment")) + ggplot2::xlab("Longitude") + ggplot2::ylab("Latitude")  + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5, hjust = 1), legend.text = ggplot2::element_text(size = 8)) + ggplot2::guides(cex = ggplot2::guide_legend(reverse = TRUE))
+  }
+  ggplot2::ggsave("mappy.png", mappy, width = 20, height = 9, units = "cm")
+}
+
+####Presence absence abundance####
+
+## Histogram
+graph_hist <- function(data, col1, col2, col3) {
+  cat("\nLocations\n", unique(data[, col1]), file = "Locations.txt", fill = 1, append = TRUE)
+  if (mult1) {
+    for (loc in unique(data[, col1])) {
+      data_cut <- data[data[, col1] == loc, ]
+      data_cut <- data_cut[data_cut[, col3] > 0, ]
+      if (length(unique(data_cut[, col2])) <= 40) {
+        top <- nrow(data_cut)
+        var <- nchar(as.character(round(top * 0.1, digits = 0)))
+        step <- round(top * 0.1, digits = ifelse(var == 1, 1, -var + 1))
+        graph <- ggplot2::ggplot(data_cut) +
+        ggplot2::geom_bar(ggplot2::aes_string(x = col1, fill = col2)) +
+        ggplot2::scale_y_continuous(breaks = seq(from = 0, to = top, by = step)) +
+        ggplot2::theme(plot.title = ggplot2::element_text(color = "black", size = 12, face = "bold")) +
+        ggplot2::ggtitle("Number of presence")
+
+        ggplot2::ggsave(paste("number_in_", loc, ".png"), graph)
+      }else{
+        cat(paste0("\n", loc, " had more than 40 species and plot isn't readable please select a higher taxon level or cut your data"))
+      }
+    }
+  }else{
+  top <- nrow(data)
+  var <- nchar(as.character(round(top * 0.1, digits = 0)))
+  step <- round(top * 0.1, digits = ifelse(var == 1, 1, -var + 1))
+  graph <- ggplot2::ggplot(data) +
+  ggplot2::geom_bar(ggplot2::aes_string(x = col1, fill = col2)) +
+  ggplot2::scale_y_continuous(breaks = seq(from = 0, to = top, by = step)) +
+  ggplot2::theme(plot.title = ggplot2::element_text(color = "black", size = 12, face = "bold")) +
+  ggplot2::ggtitle("Number of individuals")
+
+  ggplot2::ggsave("number.png", graph)
+  }
+}
+
+rare <- function(data, spe, abond, rare, num) {
+# Put the data in form
+  new_data <- as.data.frame(data[, spe])
+  colnames(new_data) <- c("Species")
+  new_data$total <- data[, abond]
+
+  new_data$rarefaction <- as.integer(rare)
+
+  end <- length(unique(new_data$Species))
+  out <- vegan::rarecurve(new_data[, 2:3], step = 10, sample = rarefy_sample, label = FALSE)
+  names(out) <- paste(unique(new_data$Species), sep = "")
+
+# Coerce data into "long" form.
+  protox <- mapply(FUN = function(x, y) {
+    mydf <- as.data.frame(x)
+    colnames(mydf) <- "value"
+    mydf$species <- y
+    mydf$subsample <- attr(x, "Subsample")
+    mydf <- na.omit(mydf)
+    mydf
+  }, x = out, y = as.list(names(out)), SIMPLIFY = FALSE)
+
+  xy <- do.call(rbind, protox)
+  rownames(xy) <- NULL  # pretty
+
+# Plot.
+
+  if (mult2) {
+    for (spe in unique(data[, spe])) {
+      xy_cut <- xy[xy$species == spe, ]
+      top <- max(xy_cut$subsample)
+      var <- nchar(as.character(round(top * 0.1, digits = 0)))
+      step <- round(top * 0.1, digits = ifelse(var == 1, 1, -var + 1))
+      courbe <- ggplot2::ggplot(xy_cut, ggplot2::aes(x = subsample, y = value)) +
+      ggplot2::theme_gray() +
+      ggplot2::geom_line(size = 1) +
+      ggplot2::scale_x_continuous(breaks = seq(from = 0, to = top, by = step)) +
+      ggplot2::xlab("Abundance") + ggplot2::ylab("Value") +
+      ggplot2::ggtitle("rarefaction curve")
+
+      ggplot2::ggsave(paste("rarefaction_of_", spe, ".png"), courbe)
+    }
+  }else{
+    top <- max(xy$subsample)
+    var <- nchar(as.character(round(top * 0.1, digits = 0)))
+    step <- round(top * 0.1, digits = ifelse(var == 1, 1, -var + 1))
+    courbe <- ggplot2::ggplot(xy, ggplot2::aes(x = subsample, y = value, color = species)) +
+    ggplot2::theme_gray() +
+    ggplot2::geom_line(size = 1) +
+    ggplot2::scale_x_continuous(breaks = seq(from = 0, to = top, by = step)) +
+    ggplot2::xlab("Subsample") + ggplot2::ylab("Value") +
+    ggplot2::ggtitle("rarefaction curves")
+
+    ggplot2::ggsave("rarefaction.png", courbe)
+  }
+}
+
+if (abundance) {
+#Maps
+mult0 <- ifelse(length(unique(data[, colspe])) > 10, FALSE, TRUE)
+graph_map(data, collong = collong, collat = collat, colabond = colabond, ind = ind, colspe = colspe)
+}
+
+if (presabs) {
+#Presence absence count
+mult1 <- ifelse(length(unique(data[, colloc])) <= 10, FALSE, TRUE)
+graph_hist(data, col1 = colloc, col2 = colspe, col3 = colabond)
+}
+
+if (rarefaction) {
+#rarefaction
+
+#### rarefaction indice ####
+rarefy_sample <- as.numeric(num)
+
+## Calcul de la rarefaction
+rarefaction <- vegan::rarefy(data[, abond], rarefy_sample)
+
+write.table(rarefaction, "rare.tabular")
+
+mult2 <- ifelse(length(unique(data[, colspe])) <= 30, FALSE, TRUE)
+rare(data, spe = colspe, abond = colabond, rare = rarefaction, num = rarefy_sample)
+}