Env<-read.csv("Filename.csv") ##--Loads file into R--
Env2<-Env[,-1] ##--Removes "Filename" column from file, enabling R to work on
the matrix--
Env20000<-log10(Env2) ##--Logs the resulting matrix--

EnvM<-data.matrix(Env20000)
rm(Env20000)
BREAKS<-c(0,0.001,0.01,0.1,1,1000000)
counts<-hist(x=EnvM,breaks=BREAKS)$count
rm(BREAKS)

RND<-runif(counts[1],0.0001,0.0099)
ind<-which(EnvM==0,arr.ind=TRUE)
for (i in 1:counts[1]) {
EnvM[ind[i,1],ind[i,2]]<-RND[i]
}

rm(counts) ##--Replaces zeroes in matrix with random numbers (quicker!)--

merged20000.res<-EMclust(EnvM,G=1:10) ##--performs MBC operation on matrix--
plot(merged20000.res) ##--plots BIC vs cluster number--

merged20000.resF<-EMclust(EnvM,G=SelectedClusterNumber,"Modelname") ##--Performs MBC operation on the chosen model name and number of clusters--
merged20000.resG<-summary(merged20000.resF,EnvM) ##--Shows the number of spectra included in each cluster and creates matrix table--
merged20000.resG ##--prints number of spectra in each cluster
mz20000a<-merged20000.resG$mu ##--Creates a table of mass/charge values and cluster numbers--
write.table(mz20000a, file = "mz2000a.csv", sep = ",") ##--Writes the resulting values to a CSV file to be viewed in Excel--