| Next changeset 1:fd0687e91bf6 (2020-05-11) |
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Commit message:
"planemo upload for repository https://github.com/Alanamosse/Galaxy-E/tree/stoctool/tools/stoc commit f82f897ab22464de40c878e17616333855814e25" |
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added:
ExeFilteringRareLowabundSPGalaxy.r FunctTrendSTOCGalaxy.r filteringSp.xml stoceps_macros.xml test-data/Datafilteredfortrendanalysis.tabular test-data/Datatransformedforfiltering_trendanalysis.tabular |
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| diff -r 000000000000 -r 2e45ae3b297a ExeFilteringRareLowabundSPGalaxy.r --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/ExeFilteringRareLowabundSPGalaxy.r Thu Apr 02 03:34:37 2020 -0400 |
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| @@ -0,0 +1,50 @@ +#!/usr/bin/env Rscript + +##################################################################################################################### +############## FILTERING RARE AND LOW-ABUNDANCE SPECIES function:filtreEspeceRare ############################## +##################################################################################################################### + +#### Based on Romain Lorrillière R script +#### Modified by Alan Amosse and Benjamin Yguel for integrating within Galaxy-E + + +suppressMessages(library(reshape2)) + + +########### +#delcaration des arguments et variables/ declaring some variables and load arguments + +args = commandArgs(trailingOnly=TRUE) + +if (length(args)==0) { + stop("At least one argument must be supplied, dataset transformed by make table function (.tabular).", call.=FALSE) #si pas d'arguments -> affiche erreur et quitte / if no args -> error and exit1 +} else { + Datatransformedforfiltering_trendanalysis<-args[1] ###### Nom du fichier peut provenir de la fonction "MakeTableAnalyse" / file name , may result from the function "MakeTableAnalys" + source(args[2])### chargement des fonctions / load the functions +} + +##### Le tableau de données doit posséder 3 variables en colonne minimum avec 1 seule espèce et autant de colonne en plus que d'espèces en plus: les carrés ou sont réalisés les observatiosn ("carre"), la ou les années des observations ("annee"), 1 colonne par espèce renseignée avec les abondances correspondantes +##### Data must be a dataframe with 3 variables in column: plots where observation where made ("carre"), year(s) of the different sampling ("annee"), and one column per species with its abundance + + +#Import des données / Import data +tab <- read.table(Datatransformedforfiltering_trendanalysis,sep="\t",dec=".",header=TRUE) # + +err_msg_tab="\nThe input dataset doesn't have the right format. It need to have the following 2 variables : \"carre\" and \"annee\" followed by at least one species" +if(ncol(tab)<3 || !("carre" %in% names(tab)) || !("annee" %in% names(tab))){ + stop(err_msg_tab,call.=FALSE) +} + + + + +#Do your analysis +tab_filtred1<-filter_absent_species(tab) +tab_filtred2<-filter_rare_species(tab) + +#save the data in a output file in a tabular format +filename <- "Datafilteredfortrendanalysis.tabular" +write.table(tab_filtred2, filename,row.names=FALSE,sep="\t",dec=".") +cat(paste("\nWrite table with data filtered for trend analysis. \n--> \"",filename,"\"\n")) + + |
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| diff -r 000000000000 -r 2e45ae3b297a FunctTrendSTOCGalaxy.r --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/FunctTrendSTOCGalaxy.r Thu Apr 02 03:34:37 2020 -0400 |
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| b'@@ -0,0 +1,776 @@\n+#!/usr/bin/env Rscript\n+\n+\n+##################################################################################################################################\n+############## FUNCTION TO CALCULATE AND PLOT EVOLUTION OF SPECIES POPULATION function:main.glm ##############################\n+##################################################################################################################################\n+\n+#### Based on Romain Lorrilli\xc3\xa8re R script\n+#### Modified by Alan Amosse and Benjamin Yguel for integrating within Galaxy-E\n+\n+##### workes with the R version 3.5.1 (2018-07-02)\n+##### Package used with the version:\n+#suppressMessages(library(lme4)) version 1.1.18.1\n+#suppressMessages(library(ggplot2)) version 3.0.0\n+#suppressMessages(library(speedglm)) version 0.3.2\n+#suppressMessages(library(arm)) version 1.10.1\n+#suppressMessages(library(reshape)) version 0.8.8\n+#suppressMessages(library(data.table)) version 1.12.0\n+#suppressMessages(library(reshape2)) version 1.4.3\n+\n+\n+\n+######################################### debut de la fonction makeTableAnalyse / stard of the function makeTableAnalyse\n+## mise en colonne des especes et rajout de zero mais sur la base des carr\xc3\xa9s selectionn\xc3\xa9 sans l\'import / Species are placed in separated columns and addition of zero on plots where at least one selected species is present \n+\n+makeTableAnalyse <- function(data) {\n+ tab <- reshape(data\n+ ,v.names="abond"\n+ ,idvar=c("carre","annee") \n+ ,timevar="espece"\n+ ,direction="wide")\n+ tab[is.na(tab)] <- 0 ###### remplace les na par des 0 / replace NAs by 0 \n+\n+ colnames(tab) <- sub("abond.","",colnames(tab))### remplace le premier pattern "abond." par le second "" / replace the column names "abond." by ""\n+ return(tab)\n+}\n+\n+######################################### fin de la fonction makeTableAnalyse / end of the function makeTableAnalyse\n+\n+\n+\n+\n+\n+############################################# les fonctions qui filtrent les donn\xc3\xa9es pas suffisantes pour analyses fiables / The filtering functions removing species with not enough data to perform accurate analyses\n+\n+filter_absent_species<-function(tab){\n+##################### Filtre les esp\xc3\xa8ces jamais pr\xc3\xa9sentes (abondance=0) / Filter of species with 0 abundance\n+################################################################################# PARTIE POTENTIELLEMENT ISOLABLE ET INSERABLE AVANT LA BOUCLE = permet de gagner du temps sur la boucle car supprime sps pas vu, donc pas repris par la boucle\n+ \n+ ## Fait la somme des abondances totales par esp\xc3\xa8ce / calculate the sum of all abundance per species\n+ if(ncol(tab)==3) {\n+\ttabSum <- sum(tab[,3])## cas d\'une seule especes (probl\xc3\xa8me de format et manip un peu differente) / when selecting only one species, use a different method\n+\tnames(tabSum) <- colnames(tab)[3]\n+ } else { ## cas de plusieurs esp\xc3\xa8ce/ when selecting more than one species\n+ tabSum <- colSums(tab[,-(1:2)])\n+ }\n+ ## colNull= espece(s) toujours absente /species with 0 total abundance\n+ colNull <- names(which(tabSum==0))\n+ ## colconserve= espece(s) au moins presente 1 fois/ species at least with 1 presence\n+ colConserve <- names(which(tabSum>0))\n+ ## Affichage des esp\xc3\xa8ces rejetees / show species eliminated for the analyses\n+ if(length(colNull)>0){\n+ cat("\\n",length(colNull)," Species removed from the analysis, abundance is always 0.\\n\\n",sep="") #Esp\xc3\xa8ces enlev\xc3\xa9es de l\'analyse car abondance toujours \xc3\xa9gale a 0\\n\\n",sep="")\n+ #tabNull <- data.frame(Code_espece = colNull, nom_espece = tabsp[colNull,"nom"])\n+ #cat("\\n\\n",sep="")\n+ tab <- tab[,c("carre","annee",colConserve)]\n+ }\n+################################################################################ FIN DE LA PARTIE ISOLABLE\n+ return(tab) \n+}\n+\n+\n+\n+\n+###################### Filtre les especes trop ra'..b' ggplot2 / plots with ggplot2\n+ titre <- paste("Variation de l\'indicateur groupe de specialisation",sep="")\n+\n+ vecCouleur <- setNames(groupeCouleur,groupeNom)\n+ #browser()\n+ p <- ggplot(data = da, mapping = aes(x = annee, y = abondance_relative, colour=groupe,fill=groupe))\n+ p <- p + geom_hline(aes(yintercept = 1), colour="white", alpha=1,size=1.2) \n+ if(ICfigureGroupeSp)\n+ p <- p + geom_ribbon(mapping=aes(ymin=IC_inferieur,ymax=IC_superieur),linetype=2,alpha=.1,size=0.1) \n+ p <- p + geom_line(size=1.5)\n+ p <- p + ylab("") + xlab("Annee")+ ggtitle(titre) \n+ if(!is.null(groupeNom)) p <- p + scale_colour_manual(values=vecCouleur, name = "" )+\n+ scale_x_continuous(breaks=unique(da$annee))\n+ if(!is.null(groupeNom)) p <- p + scale_fill_manual(values=vecCouleur, name="")\n+ p <- p + theme(panel.grid.minor=element_blank(), panel.grid.major.y=element_blank()) \n+ ggsave(nameFileSpepng, p,width=17,height=10,units="cm")\n+\n+ # cat(" <==",nameFileSpepng,"\\n")\n+ \n+ ## calul pour chaque groupe une pente de regression d\'evolution des abondances sur la periode \xc3\xa9tudi\xc3\xa9e / calculating for each group the regression slope for the abundance evolution on the studied period\n+ vecSpe <- unique(da$groupe)\n+ datasum <- data.frame(groupe=NULL,tendance=NULL,pourcentage_variation=NULL)\n+ for(spe in 1:4){\n+ # print(spe)\n+ subtab <- subset(da,groupe==vecSpe[spe])\n+ if(nrow(subtab)>1) {\n+ sumlm <- summary(lm(abondance_relative~annee,data=subtab)) ##### recup\xc3\xa8re les resultats du mod\xc3\xa8le lin\xc3\xa9aire / retrieve the results of the linear model\n+ subdatasum <- data.frame(groupe=vecSpe[spe],\n+ tendance=round(sumlm$coefficients[2,1],3),\n+ pourcentage_variation=round(sumlm$coefficients[2,1]*(nrow(subtab)-1)*100,3)) #### assemble les resultats pour en faire une sortie / bring together the results for an output file\n+ datasum <- rbind(datasum,subdatasum)\n+ \n+ }\n+ \n+ }\n+ datasum <- merge(datasum,tIncert,by="groupe") #### \n+ datasum <- data.frame(id,datasum)\n+ #datasum$cat_tendance_EBCC <- affectCatEBCC(trend,pVal,ICinf,ICsup\n+ namefilesum <- paste("Output/",id,"/tendancesGlobalesGroupes_",id,\n+ ".tabular",sep="" )\n+ write.table(datasum,file=namefilesum,row.names=FALSE,quote=FALSE,sep="\\t",dec=".",fileEncoding="UTF-8")\n+ cat("-->",namefilesum,"\\n")\n+}\n+\n+################################################################################################################## fin de la fonction analyseGroupe / end of the function analyseGroupe\n+\n+\n+\n+\n+\n+\n+\n+################################################################################################################### debut de la fonction check_file / start of the function check_file\n+# Fonction pour verifier les donn\xc3\xa9es d\'entr\xc3\xa9e / General function to check integrity of input file. Will check numbers and contents of variables(colnames). \n+#return an error message and exit if mismatch detected\n+#Faut rentrer le nom du jeu de donn\xc3\xa9es, le nbre et le nom des variables / Enter dataset name, expected number and names of variables. + an exit error message to guide user.\n+\n+check_file<-function(dataset,err_msg,vars,nb_vars){\n+ if(ncol(dataset)!=nb_vars){ #Verifiction de la pr\xc3\xa9sence du bon nb de colonnes, si c\'est pas le cas= message d\'erreur / checking for right number of columns in the file if not = error message\n+ cat("\\nerr nb var\\n") \n+ stop(err_msg, call.=FALSE)\n+ }\n+\n+ for(i in vars){\n+ if(!(i %in% names(dataset))){\n+ stop(err_msg,call.=FALSE)\n+ }\n+ }\n+}\n+\n+#####################################################################################################################\n+\n' |
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| diff -r 000000000000 -r 2e45ae3b297a filteringSp.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/filteringSp.xml Thu Apr 02 03:34:37 2020 -0400 |
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| @@ -0,0 +1,71 @@ +<tool id="stoceps_filteringsp" name="Filter species" version="@VERSION@"> + <description>with rare and low abundances</description> + <macros> + <import>stoceps_macros.xml</import> + </macros> + <expand macro="reshape2_requirements"/> + <command detect_errors="exit_code"><![CDATA[ + Rscript + '$__tool_directory__/ExeFilteringRareLowabundSPGalaxy.r' + '$input' + '$__tool_directory__/FunctTrendSTOCGalaxy.r' + '$output' + ]]> + </command> + <inputs> + <param name="input" type="data" format="tabular" label="Preprocessed Stoc input file" help="Output file from the 'Preprocess population data tool'"/> + </inputs> + <outputs> + <data name="output" from_work_dir="Datafilteredfortrendanalysis.tabular" format="tabular"/> + </outputs> + <tests> + <test> + <param name="input" value="Datatransformedforfiltering_trendanalysis.tabular"/> + <output name="output" file="Datafilteredfortrendanalysis.tabular"/> + </test> + </tests> + <help><![CDATA[ +================================================= +STOC Filter species with rare and low abundances +================================================= + +**What it does** + +Reshape the data for the next steps of STOC analyzes by removing species with not enough data to perform the analysis i.e. with low abundance or not observed at all. + +The format of the file from the STOC data can be obtained after running the tool "Preprocess population data". + +| + +**Input description** + +A tabular file with each species abundance in different column, and one column to indicate the year, and another one indicating the site. This file may come from the tools preprocess population data that use the function MakeTableAnalysis . + +The table needs the following structure (at least these 4 four columns, if one species, and as much additional columns as there are additional species) : + ++-----------+---------+--------------+--------------+--------------+ +| carre | annee | speciesId1 | speciesId2 | speciesIdN | ++===========+=========+==============+==============+==============+ +| carreId1 | 2019 | abund | abund | abund | ++-----------+---------+--------------+--------------+--------------+ +| ... | ... | ... | ... | ... | ++-----------+---------+--------------+--------------+--------------+ + +| + +**Output** + +A tabular file transformed, with only one column for the abundance, one column indicating the species, one indicating the site and one indicating the year. + +This file is ready for its analysis with "Estimate temporal population evolution" + +| + +**Source** + +UnPublished script available at http://www.vigienature.fr/sites/vigienature/files/atoms/files/analysestoceps_0.zip + +First version written by romain.lorrilliere@mnhn.fr + ]]></help> + <expand macro="stoceps_bibref" /> +</tool> |
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| diff -r 000000000000 -r 2e45ae3b297a stoceps_macros.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/stoceps_macros.xml Thu Apr 02 03:34:37 2020 -0400 |
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| @@ -0,0 +1,73 @@ +<macros> + <token name="@VERSION@">0.0.1</token> + <xml name="reshape_requirements"> + <requirements> + <requirement type="package" version="0.8.8">r-reshape</requirement> + <requirement type="package" version="1.12.0">r-data.table</requirement> + </requirements> + </xml> + <xml name="reshape2_requirements"> + <requirements> + <requirement type="package" version="1.4.3">r-reshape2</requirement> + </requirements> + </xml> + <xml name="mainglm_requirements"> + <requirements> + <requirement type="package" version="3.0.0">r-ggplot2</requirement> + <requirement type="package" version="0.3_2">r-speedglm</requirement> + <requirement type="package" version="1.10_1">r-arm</requirement> + <requirement type="package" version="1.12.0">r-data.table</requirement> + <requirement type="package" version="1.4.3">r-reshape2</requirement> + </requirements> + </xml> + <xml name="temp_indic_requirements"> + <requirements> + <requirement type="package" version="1.3_15">r-rodbc</requirement> + <requirement type="package" version="0.8.8">r-reshape</requirement> + <requirement type="package" version="1.12.0">r-data.table</requirement> + <requirement type="package" version="1.4_3">r-rgdal</requirement> + <requirement type="package" version="1.7.4">r-lubridate</requirement> + <requirement type="package" version="4.6_2">r-doby</requirement> + <requirement type="package" version="1.10_1">r-arm</requirement> + <requirement type="package" version="3.1.0">r-ggplot2</requirement> + <requirement type="package" version="1.0.0">r-scales</requirement> + <requirement type="package" version="1.8_24">r-mgcv</requirement> + <requirement type="package" version="1.8.4">r-plyr</requirement> + <requirement type="package" version="0.3_2">r-speedglm</requirement> + <requirement type="package" version="3.1_0">r-lmertest</requirement> + <requirement type="package" version="0.2.3">r-glmmtmb</requirement> + </requirements> + </xml> + <xml name="stoceps_input_filtered"> + <param name="input" type="data" format="tabular" label="Stoc filtered input" help="Input Stoc count file, shaped and filtered with the 'preprocess population data' and 'filter species' tools." /> + </xml> + <xml name="stoceps_advanced_params_select"> + <param name="advanced" type="select" label="Specify advanced parameters"> + <option value="simple" selected="true">No, use program defaults.</option> + <option value="advanced">Yes, see full parameter list.</option> + </param> + <when value="simple"> + </when> + </xml> + <xml name="stoceps_compute_ic"> + <param name="compute_ic" type="boolean" truevalue="TRUE" falsevalue="FALSE" checked="yes" label="Compute confidence intervals"/> + </xml> + <xml name="stoceps_filter_glmmtmb"> + <filter> settings['advanced'] == 'advanced'</filter> + <filter> settings['method'] == 'glmmtmb'</filter> + </xml> + <xml name="stoceps_filter_gam"> + <filter> settings['method'] == 'gam'</filter> + </xml> + <xml name="stoceps_bibref"> + <citations> + <citation type="bibtex"> + @unpublished{stocepsromain, + title={Vigie-Nature STOC unpublished scripts}, + author={Lorrilliere, R}, + url={http://www.vigienature.fr/sites/vigienature/files/atoms/files/analysestoceps_0.zip} + } + </citation> + </citations> + </xml> +</macros> |
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| diff -r 000000000000 -r 2e45ae3b297a test-data/Datafilteredfortrendanalysis.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/Datafilteredfortrendanalysis.tabular Thu Apr 02 03:34:37 2020 -0400 |
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| b'@@ -0,0 +1,851 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| b |
| diff -r 000000000000 -r 2e45ae3b297a test-data/Datatransformedforfiltering_trendanalysis.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/Datatransformedforfiltering_trendanalysis.tabular Thu Apr 02 03:34:37 2020 -0400 |
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de"\t2018\t3\t7\n+"Le Mas"\t2018\t0\t7\n' |