Mercurial > repos > ecology > ecology_homogeneity_normality
changeset 1:3df8937fd6fd draft default tip
"planemo upload for repository https://github.com/Marie59/Data_explo_tools commit 60627aba07951226c8fd6bb3115be4bd118edd4e"
| author | ecology |
|---|---|
| date | Fri, 13 Aug 2021 18:16:46 +0000 |
| parents | 9f679060051a |
| children | |
| files | funct_anomy.r functions.r graph_lcbd.r graph_stat_presence_abs.r test-data/SCBD.txt |
| diffstat | 5 files changed, 291 insertions(+), 11 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/funct_anomy.r Fri Aug 13 18:16:46 2021 +0000 @@ -0,0 +1,39 @@ +#Rscript + +########################### +## Anonymization ## +########################### + +#####Packages : tangles + +#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] + latitude <- as.numeric(args[3]) + longitude <- as.numeric(args[4]) +} + +if (hr == "false") { + hr <- FALSE +}else{ + hr <- TRUE +} + +#####Import data +data <- read.table(table, sep = "\t", dec = ".", header = hr, fill = TRUE, encoding = "UTF-8") + +randomized_data <- tangles::tangles(data = as.matrix(data[, c(latitude, longitude)]), depth = 3, rasterdata = FALSE, raster_object = FALSE, saveTangles = FALSE, path = NULL) + +data[, c(latitude, longitude)] <- NULL + +tab_anon <- data.frame(longitude = randomized_data[[1]]$X, latitude = randomized_data[[1]]$Y) + +tab_anon <- cbind(data, tab_anon) + +write.table(tab_anon, "anonym_data.tabular", row.names = FALSE, quote = FALSE, sep = "\t", dec = ".", fileEncoding = "UTF-8")
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/functions.r Fri Aug 13 18:16:46 2021 +0000 @@ -0,0 +1,22 @@ +#Rscript + +######################################################################################### +####################### Exploration data tools function ################################# +######################################################################################### +#### Based on Romain Lorrillière R script +#### Modified by Alan Amosse, Benjamin Yguel and Marie Jossé for integrating within Galaxy-E + +######################################### start of the function makeTableAnalyse +##Species are placed in separated columns and addition of zero on plots where at least one selected species is present +make_table_analyse <- function(data, var, spe, var2, var3) { + tab <- reshape(data + , v.names = var + , idvar = c(var2, var3) + , timevar = spe + , direction = "wide") + tab[is.na(tab)] <- 0 ###### remplace les na par des 0 / replace NAs by 0 + + colnames(tab) <- sub(paste0(var, "."), "", colnames(tab))### remplace le premier pattern "abond." par le second "" / replace the column names "abond." by "" + return(tab) +} +######################################### end of the function makeTableAnalyse
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/graph_lcbd.r Fri Aug 13 18:16:46 2021 +0000 @@ -0,0 +1,225 @@ +#Rscript + +######################### +## Beta diversity ## +######################### + +#####Packages : ggplot2 +# vegan +# adespatial +# dplyr +# tibble +# tdyr + +#####Load arguments + +args <- commandArgs(trailingOnly = TRUE) + +if (length(args) < 2) { + stop("This tool needs at least 2 arguments") +}else{ + table <- args[1] + hr <- args[2] + abund <- as.numeric(args[3]) + loc <- as.numeric(args[4]) + spe <- as.numeric(args[5]) + date <- as.numeric(args[6]) + map <- as.logical(args[7]) + sepa <- as.logical(args[8]) + not <- as.logical(args[9]) + lat <- as.numeric(args[10]) + long <- as.numeric(args[11]) + var <- as.numeric(args[12]) + source(args[13]) +} + +if (hr == "false") { + hr <- FALSE +}else{ + hr <- TRUE +} + +#####Import data +data <- read.table(table, sep = "\t", dec = ".", header = hr, fill = TRUE, encoding = "UTF-8") +colabund <- colnames(data)[abund] +colloc <- colnames(data)[loc] +if (map) { + collat <- colnames(data)[lat] + collong <- colnames(data)[long] +} +colspe <- colnames(data)[spe] +coldate <- colnames(data)[date] +data[, coldate] <- as.factor(data[, coldate]) + +data <- data[grep("^$", data[, spe], invert = TRUE), ] + +if (sepa) { +colvar <- colnames(data)[var] +} + +# Data for species +data_num <- make_table_analyse(data, colabund, colspe, colloc, coldate) +nb_spe <- length(unique(data[, spe])) +nb_col <- ncol(data_num) - nb_spe + 1 + +#Data with coordinates and environmental +if (map) { + data_xy <- data_num[, c(collat, collong)] + colnames(data_xy) <- c("latitude", "longitude") + # Data for environment + data_env <- data_num[, c(colloc, collat, collong)] + colnames(data_env) <- c("site", "latitude", "longitude") +} + +# Data with only species and their abundance +data_spe <- data_num[, nb_col:ncol(data_num)] +rownames(data_spe) <- paste0(data_num[, colloc], " - ", data_num[, coldate]) + +#####Your analysis + +# Computation beta.div {adespatial} +# Beta.div on Hellinger-transformed species data +data_beta <- adespatial::beta.div(data_spe, method = "hellinger", nperm = 9999) + +save(data_beta, file = "beta_diversity.Rdata") +cat("##############################################################################", + "\n########################### Beta Diversity Summary ###########################", + "\n##############################################################################", + "\n\n### All data ###", + "\nBeta diversity: ", data_beta$beta[[2]], + "\nSum of Squares: ", data_beta$beta[[1]], + "\n\n### Vector of Local Contributions to Beta Diversity (LCBD) for the sites each date ###", + "\n", capture.output(data_beta$LCBD), + "\n\n### Vector of P-values associated with the LCBD indices ###", + "\n", capture.output(data_beta$p.LCBD), + "\n\n### Vector of Corrected P-values for the LCBD indices, Holm correction ###", + "\n", capture.output(data_beta$p.adj), + "\n\n### Vector of Species contributions to beta diversity (SCBD) ###", + "\n", capture.output(data_beta$SCBD), file = "LCBD.txt", fill = 1, append = TRUE) + +# Which species have a SCBD larger than the mean SCBD? +scbd <- capture.output(data_beta$SCBD[data_beta$SCBD >= mean(data_beta$SCBD)]) +write(scbd, "SCBD.txt") + +##1st fonction +beta_div_ext <- function(data_beta, data_xy, data_env) { + data_beta_ext <- data.frame(data_xy, data_env, LCBD = data_beta$LCBD * 100, p.LCBD = data_beta$p.LCBD, signif = data_beta$p.LCBD < 0.05) + + graph_beta_ext <- ggplot2::ggplot(data = data_beta_ext, ggplot2::aes(x = latitude, y = longitude, size = LCBD, col = signif)) + + ggplot2::geom_point() + + ggplot2::scale_colour_manual(values = c("#57bce0", "#ce0b0b"), labels = c("Non significant", "Significant"), name = "Significance at 0.05") + + ggplot2::xlab("Longitude") + ggplot2::ylab("Latitude") + + ggplot2::ggsave("Beta_diversity_through_space.png", graph_beta_ext) +} + +## Boyé et al. 2017 JSR Fig R +#################################################### + +####LCBD#### +lcbd_site <- adespatial::beta.div(data_spe, "hellinger", nperm = 999) + +compute_lcbd <- function(data_beta, data_spe, data_num) { + +############# + mat_lcbd_site <- data.frame(data_spe, LCBD = data_beta$LCBD * 100, p.LCBD = data_beta$p.LCBD, signif = data_beta$p.LCBD < 0.05, site = data_num[, colloc], date = data_num[, coldate]) + +## Map spatio-temp +################## + p1 <- ggplot2::qplot(date, site, size = LCBD, col = signif, data = mat_lcbd_site) + p1 <- p1 + ggplot2::scale_colour_manual(values = c("#57bce0", "#ce0b0b"), labels = c("Non significant", "Significant"), name = "Significance at 0.05") + p1 <- p1 + ggplot2::theme_bw() + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90)) + ggplot2::xlab("Date") + ggplot2::ylab("Site") + + ggplot2::ggsave("LCBD_sites_time.png", p1) + + +## Par années +############# + mean_time <- tapply(mat_lcbd_site$LCBD, mat_lcbd_site$date, mean) + sd_time <- tapply(mat_lcbd_site$LCBD, mat_lcbd_site$date, sd) + date <- unique(mat_lcbd_site$date) + + data <- data.frame(date, mean_time, sd_time) + + time <- ggplot2::ggplot() + ggplot2::geom_pointrange(ggplot2::aes(x = date, y = mean_time, ymin = mean_time - sd_time, ymax = mean_time + sd_time), data = data) + time <- time + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90), axis.line.y = ggplot2::element_line(size = 0.5)) + ggplot2::ylab("mean LCBD") + + ggplot2::ggsave("Mean_LCBD_through_time.png", time) +} + +## Choose another graph +####################### +compute_lcbd2 <- function(data_beta, data_spe, data_num) { + +############# + mat_lcbd_site <- data.frame(data_spe, LCBD = data_beta$LCBD * 100, p.LCBD = data_beta$p.LCBD, signif = data_beta$p.LCBD < 0.05, site = data_num[, colloc], date = data_num[, coldate], variable = data_num[, colvar]) + + p1 <- ggplot2::qplot(date, variable, size = LCBD, col = signif, data = mat_lcbd_site) + p1 <- p1 + ggplot2::scale_colour_manual(values = c("#57bce0", "#ce0b0b"), labels = c("Non significant", "Significant"), name = "Significance at 0.05") + p1 <- p1 + ggplot2::theme_bw() + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90)) + ggplot2::xlab("Date") + ggplot2::ylab(colvar) + + ggplot2::ggsave(paste0("LCBD_per_", colvar, "_through_time.png"), p1) +} + +####SCBD### +# Function to compute SCBD +library(dplyr) +make_scbd_uvc <- function(data_spe, z, data_beta) { + # Computation using beta.div {adespatial} on + # Hellinger-transformed species data + + # Which species have a SCBD larger than the mean SCBD? + spe_scbd <- data_beta$SCBD[data_beta$SCBD >= mean(data_beta$SCBD)] %>% + as.data.frame() %>% + tibble::rownames_to_column(var = "Taxon") %>% + dplyr::mutate("Methode" = z) + + return(spe_scbd) +} + +# Function to make a radar plot + +coord_radar <- function(theta = "x", start = 0, direction = 1) { + theta <- match.arg(theta, c("x", "y")) + r <- if (theta == "x") "y" else "x" + ggplot2::ggproto("CordRadar", ggplot2::coord_polar(theta = theta, start = start, + direction = sign(direction)), + is_linear = function(coord) TRUE) +} + +# Make the radar plot +radar_plot <- function(scbd_uvc_tc) { + uvc_rd_plot_data <- scbd_uvc_tc %>% + rename(scbd_score = ".") + + rad_uvc <- ggplot2::ggplot(uvc_rd_plot_data, ggplot2::aes(x = Taxon, y = scbd_score, group = Methode)) + + ggplot2::geom_line() + + ggplot2::geom_point(size = 3) + + coord_radar() + + ggplot2::theme_bw() + + ggplot2::theme(axis.text.x = ggplot2::element_text(size = 10), + legend.position = "bottom") + + ggplot2::ggsave("SCBD_Species_Radar_plot.png", rad_uvc) +} + +## LCBD + +if (map) { + #Beta diversity + beta_div_ext(data_beta, data_xy, data_env) +} + +#Lcbd per places and time +compute_lcbd(data_beta, data_spe, data_num) + +#Lcbd of your choice +if (sepa) { + compute_lcbd2(data_beta, data_spe, data_num) +} + +##SCBD + +scbd_uvc_tc <- make_scbd_uvc(data_spe, z = "TC", data_beta) + +radar_plot(scbd_uvc_tc)
--- a/graph_stat_presence_abs.r Tue Jul 27 16:56:15 2021 +0000 +++ b/graph_stat_presence_abs.r Fri Aug 13 18:16:46 2021 +0000 @@ -20,6 +20,7 @@ spe <- as.numeric(args[4]) loc <- as.numeric(args[5]) time <- as.numeric(args[6]) + source(args[7]) } if (hr == "false") { @@ -37,7 +38,6 @@ coltime <- colnames(data)[time] data <- data[grep("^$", data[, spe], invert = TRUE), ] -time <- as.integer(substring(data[, time], first = 1, last = 4)) #####Your analysis @@ -75,17 +75,7 @@ #### Zero problem in data #### #Put data in form -make_table_analyse <- function(data, var, spe, var2, var3) { - tab <- reshape(data - , v.names = var - , idvar = c(var2, var3) - , timevar = spe - , direction = "wide") - tab[is.na(tab)] <- 0 ###### remplace les na par des 0 / replace NAs by 0 - colnames(tab) <- sub(var, "", colnames(tab))### remplace le premier pattern "abond." par le second "" / replace the column names "abond." by "" - return(tab) -} data_num <- make_table_analyse(data, colvar, colspe, colloc, coltime) nb_spe <- length(unique(data[, spe])) nb_col <- ncol(data_num) - nb_spe + 1