diff --git a/AppRelated/LIBS_wireframe.R b/AppRelated/LIBS_wireframe.R
index 7ee38a4..584fc48 100644
--- a/AppRelated/LIBS_wireframe.R
+++ b/AppRelated/LIBS_wireframe.R
@@ -187,6 +187,46 @@ server <- function(input, output) {
   
   # Placeholder for Ternary Plot
   output$ternaryPlot <- renderPlot({# Need to figure out how to show ggtern
+    
+    #Get rid of all earth samples(already done)
+    libswe <- libs
+    
+    #Sum the elements we are looking at
+    libswe <- libswe %>%
+      mutate(y = (FeOT + MgO) / 100, z = (CaO+Na2O+K2O) / 100, x = (SiO2 + Al2O3) / 100)
+    
+    # PIXL data added
+    pixl.df <- pixl_v1
+    pixl.df[sapply(pixl.df, is.character)] <- lapply(pixl.df[sapply(pixl.df, is.character)], 
+                                                     as.factor)
+    pixl.df <- pixl.df[2:16,] #Excluding first, atmospheric sample
+    
+    #PIXL data, with identically reflected compositions
+    new_pixl_trim <- pixl.df %>% 
+      dplyr::select(c("Na20","Mgo","Al203","Si02", "K20","Cao","FeO-T", campaign, type)) %>%
+      rename("Na2O"="Na20","MgO"="Mgo","Al2O3"="Al203","SiO2"="Si02","K2O"="K20",
+             "CaO"="Cao","FeOT"="FeO-T")
+    
+    #take the sums of the specific elements, and rename type column
+    pixl_ternary <- new_pixl_trim %>%
+      mutate(x=(SiO2+Al2O3)/100,y=(FeOT+MgO)/100,z=(CaO+Na2O+K2O)/100, PIXL_Rock_Type = type) %>%
+      select(-c(SiO2,Al2O3,FeOT,MgO,CaO,Na2O,K2O)) %>%
+      drop_na()
+    
+    #This is for the labels on the Ternary Plot below
+    pixl_ternary <- cbind(pixl_ternary, Sample_display=
+                            c("2","3","4,6,7","5,8,9","","","","",
+                              "10,11","","12,13","","14,15","","16"))
+    
+    libs_ternplot <- libswe %>% select(c(x,y,z))
+    
+    set.seed(1234)
+    
+    #kmeans on the original data
+    tern.km <- kmeans(libs_ternplot, centers=4)
+    
+    libs_ternplot <- cbind(libs_ternplot, LIBS_Cluster=as.factor(tern.km$cluster))
+    
     #ternary plot for LIBS data
     #I saw that wrapping a ternary plot in a print statement helps to display the graph in r shiny, idk if it works
     print(ggtern(libs_ternplot, ggtern::aes(x=x, y=y, z=z, cluster=LIBS_Cluster)) +
@@ -216,34 +256,32 @@ server <- function(input, output) {
   
   # MAKE LIBS PLOTS HERE ==============================================
   output$alkaliSilica_plot <- renderPlot({
-    suppressWarnings(
-      libs.uniquetar <- 
-        aggregate(libs, list(Target = libs$Target), mean))
     
-    #Aggregate data
-    libs.uniquetar <- libs.uniquetar %>% select(!c(Target,Point))
-    libs.uniquetar <- libs.uniquetar %>% 
-      mutate(y = (FeOT + MgO) / 100, z = (CaO+Na2O+K2O) / 100, x = (SiO2 + Al2O3) / 100)
-    libs_ternplot2 <- libs.uniquetar %>% select(c(x,y,z))
+    #Get rid of all earth samples (already done)
+    libswe <- libs
     
-    tern.km2 <- kmeans(as.matrix(libs_ternplot2), centers=4)
+    #Data Processing for the Alk Sil Plot
     
-    #Same fro aggregate data
-    libs_alksilag <- libs.uniquetar %>%
+    #Add the total alkali column (y)
+    libs_alksil <- libswe %>%
       select(c(SiO2, TiO2, Al2O3, FeOT, MgO, CaO, Na2O, K2O)) %>%
       mutate(y = Na2O + K2O)
     
-    libs_alksilag <- libs_alksilag %>%
+    #Drop every column except Silicon and Alkali content
+    libs_alksil <- libs_alksil %>%
       select(c(SiO2, y))
     
     #Cluster them according to the kmeans from the Ternary plot
-    libs_alksilag <- cbind(libs_alksilag, cluster=as.factor(tern.km2$cluster))
+    #MIGHT HAVE TO REDO THIS KMEANS, KEEP IN MIND------------------------------------------------
+    libs_alksil <- cbind(libs_alksil, cluster=as.factor(tern.km$cluster))
     
     #Plot for original LIBS data
-    plot <- ggplot() +
-      geom_point(data = libs_alksilag,
+    ggplot() +
+      geom_point(data = libs_alksil,
                  mapping = aes(x=SiO2, y=y, color=as.character(cluster)),
-                 alpha = 0.3) +
+                 #add alpha so that labels on graph are visible
+                 alpha = 0.3) + 
+      #Add Line segments and labels for the igneous rocks reference
       geom_segment(aes(x=41,y=0, xend=41, yend=7)) +
       geom_segment(aes(x=45,y=0, xend=45, yend=5)) +
       geom_segment(aes(x=52,y=0, xend=52, yend=5)) +
@@ -284,11 +322,11 @@ server <- function(input, output) {
       annotate("text",x=62.5,y=11.5,label="Trachydacite", size = 2) +
       annotate("text",x=56.5,y=14.5,label="Phonolite", size = 2) +
       theme_minimal() +
-      xlim(0,78) +
-      ylim(-5,38) +
-      labs(title = "Total Alkali vs. Silica Plot for LIBS Aggregated by Target",
-           x = "Si",
-           y = "Na + K",
+      xlim(0,78) + 
+      ylim(-1,20) + 
+      labs(title = "Total Alkali vs. Silica Plot for LIBS \nWith Earth Reference Data Removed",
+           x = "Si", 
+           y = "Na + K", 
            color="Cluster")
       
       plot