Update loop_categorization_20191128.R

Quantile_categorisation

+#Function
+#create groups according quantile distribution
+#b matrix with results of analysis
+#n number of seps for quantiles analysis
+
+category <- function(file,n=0.2,name)
+{
+b<-read.table(file, header = T,stringsAsFactors = F,sep="\t")
+output<-b
+for(j in 1:dim(b)[2])
+  {
+  if(is.numeric(b[1,j]))
+	{
+	quant <- quantile(b[,j],na.rm=T,probs=seq(0,1,n))
+	for (i in 1:length(quant))
+		{
+		a <- which(b[j]>=quant[i]&b[j]<=quant[i+1])
+		output[a,j] <- paste(colnames(output)[j],round(quant[i],2),round(quant[i+1],2),sep="_")
+		}
+	}
+	else
+	{
+	output[,j]<-b[,j]
+	}
+	}
+write.table(output,file=name,quote=F,sep="\t",row.names = F)
+}
+

loop_categorization_20191128.R

-
-
-setwd("//pommard/agro-users$/evanschaik/Mes documents/Solca/Amplicon/16s")
-b=read.csv("Solca_reference_1.3_DIRTY.csv", header = T,stringsAsFactors = F)
-
-
-category <- function(b,n)
-{
-n=0.2
-output <- b
-for(j in 7:dim(b)[2])
-  {
-  quant <- quantile(b[,j],na.rm=T,probs=seq(0,1,n))
-  for (i in 1:length(quant))
-    {
-    a <- which(b[j]>=quant[i]&b[j]<=quant[i+1])
-    output[a,j] <- paste(colnames(output)[j],round(quant[i],2),round(quant[i+1],2),sep="_")
-    }
-  }
-return(output)
-}
-
-temp <- category(b,0.2)
-write.table(temp,file="essai_b1ca_1.0.txt",quote=F,sep="\t",row.names = F)