diff --git a/Archetypal Analysis.R b/Archetypal Analysis.R
new file mode 100644
index 0000000..9d4ecb5
--- /dev/null
+++ b/Archetypal Analysis.R	
@@ -0,0 +1,57 @@
+library("archetypes")
+library("readxl")
+
+td <- read_excel("dataset_java_25.xlsx")
+td <- data.frame(td)
+str(td)
+td$project <- factor(td$project)
+DifProjects <- levels(td$project)
+
+for (i in 1:length(DifProjects)){
+  
+  dataset <- td[td$project==paste(DifProjects[i]),]
+  
+  dataset$sonar_td_minutes.Ranks <- c(rank(dataset$sonar_td_minutes, ties.method="average"))
+  dataset$squore_td_minutes.Ranks <- c(rank(dataset$squore_td_minutes, ties.method="average"))
+  dataset$cast_td_minutes.Ranks <- c(rank(dataset$cast_td_minutes, ties.method="average"))
+  dataset$file <- factor(dataset$file) 
+  dat <- dataset[,c("file","sonar_td_minutes.Ranks","squore_td_minutes.Ranks","cast_td_minutes.Ranks")]
+  colnames(dat) <- c("file","Sonar","Squore","Cast")
+  mat <- as.matrix(subset(dat, select = c(Sonar,Squore,Cast)))
+  rownames(mat) <- NULL
+  as <- stepArchetypes(mat, k = 1:10,nrep=50)
+  rss.vector <- vector("numeric", 10)
+  
+  a1 <- bestModel(as[[1]])
+  a2 <- bestModel(as[[2]])
+  a3 <- bestModel(as[[3]])
+  a4 <- bestModel(as[[4]])
+  a5 <- bestModel(as[[5]])
+  a6 <- bestModel(as[[6]])
+  a7 <- bestModel(as[[7]])
+  a8 <- bestModel(as[[8]])
+  a9 <- bestModel(as[[9]])
+  a10 <- bestModel(as[[10]])
+  
+  
+  rss.vector <- c(rss(a1),rss(a2),rss(a3),rss(a4),rss(a5),rss(a6),rss(a7), rss(a8), rss(a9), rss(a10))
+  rss.vector.reduction <- c(100*(rss.vector[1]/rss.vector[1]),
+                            100*(rss.vector[2]/rss.vector[1]),
+                            100*(rss.vector[3]/rss.vector[1]),
+                            100*(rss.vector[4]/rss.vector[1]),
+                            100*(rss.vector[5]/rss.vector[1]),
+                            100*(rss.vector[6]/rss.vector[1]),
+                            100*(rss.vector[7]/rss.vector[1]),
+                            100*(rss.vector[8]/rss.vector[1]),
+                            100*(rss.vector[9]/rss.vector[1]),
+                            100*(rss.vector[10]/rss.vector[1]))
+  
+  archetypes.vector <- seq(1:10)
+  
+  data.RSS <- data.frame(archetypes.vector, rss.vector,rss.vector.reduction)
+  
+  abest <- bestModel(as[[8]])
+  rm(a1,a2,a3,a4,a5,a6,a7,a9,a10)
+  
+  save.image(file = paste0(DifProjects[i],".RData"))
+}