Mercurial > repos > ecology > pampa_communitymetrics

annotate FunctExeCalcCommIndexesGalaxy.r @ 5:5bd7ddd7601f draft

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"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
author ecology
date Mon, 16 Nov 2020 11:02:24 +0000
parents ddd5b2e74b8b
children 2cd0a5a321c2
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1 #Rscript
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3 #####################################################################################################################
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4 #####################################################################################################################
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5 ################################# Calculate community indexes from observation data #################################
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6 #####################################################################################################################
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7 #####################################################################################################################
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9 ###################### Packages R
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11 suppressMessages(library(tidyr))
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13 ###################### Load arguments and declaring variables
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15 args <- commandArgs(trailingOnly = TRUE)
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17 if (length(args) < 4) {
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18 stop("At least one argument must be supplied, an input dataset file (.tabular).", call. = FALSE) # if no args -> error and exit1
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20 } else {
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21 import_data <- args[1] ###### Nom du fichier importé avec son extension / file name imported with the file type ".filetype"
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22 index <- args[2] ###### List of selected metrics to calculate
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23 source(args[3]) ###### Import functions
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24 }
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25 #### 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")
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28 #Import des données / Import data
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29 obs <- read.table(import_data, sep = "\t", dec = ".", header = TRUE, encoding = "UTF-8") #
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30 obs[obs == -999] <- NA
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31 factors <- fact_det_f(obs = obs)
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32 obs_type <- def_typeobs_f(obs = obs)
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33 obs <- create_unitobs(data = obs)
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35 vars_data <- c("observation.unit", "species.code", "number")
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36 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"
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37 check_file(obs, err_msg_data, vars_data, 3)
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41 ####################################################################################################
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42 ################# create community metrics table ## Function : calc_biodiv_f #######################
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43 ####################################################################################################
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45 ########################################################################################################################
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46 calc_biodiv_f <- function(d_ata, unitobs = "observation.unit", code_species = "species.code", nombres = "number",
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47 indices = index) {
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48 ## Purpose: compute biodiversity indexes
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49 ## ----------------------------------------------------------------------
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50 ## Arguments: d_ata : input observation file
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51 ## unitobs : name of column observation unit
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52 ## code_species : name of species column
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53 ## nombres : name of abundance column
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54 ## indices : list of indexes to compute
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55 ## ----------------------------------------------------------------------
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56 ## Author: Yves Reecht, Date: 29 oct. 2010, 08:58 modified by Coline ROYAUX in june 2020
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57
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58 ## Supress lines that doesn't represent a species :
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59
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60 notspline <- grep("(sp\\.)$|([1-9])$|^(Absencemacrofaune)$|^(NoID)$|^(Acrobranc)$|^(Acrodigit)$|^(Acroencr)$|^(Acrosubm)$|^(Acrotabu)$|^(Adredure)$|^(Adremoll)$|^(Algaturf)$|^(Balimona)$|^(Corablan)$|^(CoradurV)$|^(Coraenal)$|^(Coramor1)$|^(Coramor2)$|^(Coramou)$|^( Dallcora)$|^(Debrcora)$|^(Debris)$|^(Hare)$|^(HexaChar)$|^(MuraCong)$|^(Nacrbran)$|^(Nacrcham)$|^(Nacrencr)$|^(Nacrfoli)$|^(Nacrmass)$|^(Nacrsubm)$|^(Recrcora)$|^(Roche)$|^(Sable)$|^(Vase)$", d_ata[, code_species], value = FALSE)
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61 if (length(notspline) != 0) {
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62 d_ata <- d_ata[-notspline, ]
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63 }
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64
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65 ## Suppress unused factor levels :
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66 d_ata <- .GlobalEnv$drop_levels_f(df = d_ata)
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67
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69 ## aggregation of data if not already done :
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70 if (nrow(d_ata) > nrow(expand.grid(unique(d_ata[, unitobs]), unique(d_ata[, code_species])))) {
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71 d_ata <- agregations_generic_f(d_ata = d_ata, metrics = nombres,
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72 factors = c(unitobs, code_species),
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73 list_fact = NULL)
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74 }
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76 df_biodiv <- as.data.frame(as.table(tapply(d_ata[, nombres],
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77 d_ata[, unitobs],
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78 sum, na.rm = TRUE)))
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79
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80 colnames(df_biodiv) <- c(unitobs, nombres)
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81
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82 ## ##################################################
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83 ## species richness :
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84 d_ata$presence_absence <- .GlobalEnv$pres_abs_f(nombres = d_ata[, nombres], logical = FALSE)
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85
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86 df_biodiv$species_richness <- as.vector(tapply(d_ata$presence_absence,
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87 d_ata[, unitobs], sum, na.rm = TRUE),
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88 "integer")
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89 ## ... as.vector to avoid the class "array".
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90
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91 ## ##################################################
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92 ## Simpson, Shannon indexes and derivatives :
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94 mat_nb <- tapply(d_ata[, nombres], # Matrix of individual count /species/unitobs.
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95 list(d_ata[, unitobs], d_ata[, code_species]),
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96 sum, na.rm = TRUE)
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97
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98 mat_nb[is.na(mat_nb)] <- 0 # Vrais zéros
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99
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100 ## each species individual proportion in the dataset :
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101 prop_indiv <- sweep(mat_nb, 1,
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102 apply(mat_nb, 1, sum, na.rm = TRUE), # individual count / unitobs ; equiv df_biodiv$nombre.
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103 FUN = "/")
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104
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105 ## Simpson indexes :
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106 df_biodiv$simpson <- apply(prop_indiv^2, 1, sum, na.rm = TRUE)
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107
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108 if (any(is.element(c("all", "simpson.l"), indices))) {
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109 df_biodiv$simpson_l <- 1 - df_biodiv$simpson
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110 }
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111
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112 ## Shannon index :
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113 df_biodiv$shannon <- -1 * apply(prop_indiv * log(prop_indiv), 1, sum, na.rm = TRUE)
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114
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115 ## Pielou index :
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116 if (any(is.element(c("all", "pielou"), indices))) {
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117 df_biodiv$pielou <- df_biodiv$shannon / log(df_biodiv$species_richness)
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118 }
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119
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120 ## Hill index :
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121 if (any(is.element(c("all", "hill"), indices))) {
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122 df_biodiv$hill <- (1 - df_biodiv$simpson) / exp(df_biodiv$shannon) # equiv df_biodiv$l.simpson / exp(df_biodiv$shannon)
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123 }
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124
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125
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126 return(df_biodiv)
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127 }
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128
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129 ################# Analysis
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130
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131 res <- calc_numbers_f(obs, obs_type = obs_type, factors = factors, nb_name = "number")
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132
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133 table_comm_indexes <- calc_biodiv_f(res, unitobs = "observation.unit", code_species = "species.code", nombres = "number",
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134 indices = index)
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135 table_comm_indexes <- create_year_location(table_comm_indexes)
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136 #Save dataframe in a tabular format
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137
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138 filename_comm <- "TabCommunityIndexes.tabular"
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139 write.table(table_comm_indexes, filename_comm, row.names = FALSE, sep = "\t", dec = ".", fileEncoding = "UTF-8")
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140 cat(paste("\nWrite table with Community indexes. \n--> \"", filename_comm, "\"\n", sep = ""))