Mercurial > repos > ecology > pampa_presabs
view FunctExeCalcPresAbsGalaxy.r @ 4:07b081730994 draft
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit cd938a05cb066b16f2518159fa0ff5e3ed8a2b2b"
author | ecology |
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date | Mon, 26 Apr 2021 16:04:47 +0000 |
parents | 8d8aec182fb1 |
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#Rscript ##################################################################################################################### ##################################################################################################################### ############################ Calculate presence absence table from observation data ################################# ##################################################################################################################### ##################################################################################################################### ###################### Packages suppressMessages(library(tidyr)) ###################### Load arguments and declaring variables args <- commandArgs(trailingOnly = TRUE) if (length(args) < 2) { stop("At least one argument must be supplied, an input dataset file (.tabular).", call. = FALSE) # if no args -> error and exit1 } else { import_data <- args[1] ###### Nom du fichier importé avec son extension / file name imported with the file type ".filetype" source(args[2]) ###### Import functions } #### d_ata must be a dataframe with at least 3 variables : unitobs representing location and year ("observation.unit"), species code ("species.code") and abundance ("number") #Import des données / Import data obs <- read.table(import_data, sep = "\t", dec = ".", header = TRUE, encoding = "UTF-8") # obs[obs == -999] <- NA factors <- fact_det_f(obs = obs) obs_type <- def_typeobs_f(obs = obs) obs <- create_unitobs(data = obs) vars_data <- c("observation.unit", "species.code", "number") err_msg_data <- "The input dataset doesn't have the right format. It need to have at least the following 3 variables :\n- observation.unit (or location and year)\n- species.code\n- number\n" check_file(obs, err_msg_data, vars_data, 3) #################################################################################################### #################### Create presence/absence table ## Function : calc_pres_abs_f #################### #################################################################################################### calc_pres_abs_f <- function(d_ata, nb_name = "number") { ## Purpose: Compute presence absence ## ---------------------------------------------------------------------- ## Arguments: d_ata : temporary metrics table ## nb_name : name of abundance column ## ## Output: presence absence vector ## ---------------------------------------------------------------------- ## Author: Yves Reecht, Date: 20 déc. 2011, 12:04 modified by Coline ROYAUX 04 june 2020 ## Presence - absence : pres_abs <- integer(nrow(d_ata)) pres_abs[d_ata[, nb_name] > 0] <- as.integer(1) pres_abs[d_ata[, nb_name] == 0] <- as.integer(0) return(pres_abs) } ################# Analysis res <- calc_numbers_f(obs, obs_type = obs_type, factors = factors, nb_name = "number") res$presence_absence <- calc_pres_abs_f(res, nb_name = "number") res <- create_year_location(res) #Save dataframe in a tabular format filename_pres_abs <- "TabPresAbs.tabular" write.table(res, filename_pres_abs, row.names = FALSE, quote = FALSE, sep = "\t", dec = ".", fileEncoding = "UTF-8") cat(paste("\nWrite table with presence/absence. \n--> \"", filename_pres_abs, "\"\n", sep = ""))