From 107f82bb28bb7204d2fb670d42f52658047624f5 Mon Sep 17 00:00:00 2001 From: compta Date: Wed, 11 Sep 2024 00:36:52 -0400 Subject: [PATCH] Completed Assignment! --- .../Assignment02/compta-assignment2-f24.Rmd | 58 ++++ .../Assignment02/compta-assignment2-f24.html | 261 +++++++++++++++++- 2 files changed, 305 insertions(+), 14 deletions(-) diff --git a/StudentNotebooks/Assignment02/compta-assignment2-f24.Rmd b/StudentNotebooks/Assignment02/compta-assignment2-f24.Rmd index 75228ac..a7d6d4d 100644 --- a/StudentNotebooks/Assignment02/compta-assignment2-f24.Rmd +++ b/StudentNotebooks/Assignment02/compta-assignment2-f24.Rmd @@ -385,14 +385,72 @@ Each team has been assigned one of six datasets: **For the data set assigned to your team, perform the following steps.** Feel free to use the methods/code from Assignment 1 as desired. Communicate with your teammates. Make sure that you are doing different variations of below analysis so that no team member does the exact same analysis. If you want to use the same clustering for your team (which is okay but then vary rest), make sure you use the same random seeds. +I worked on H + 1. _Describe the data set contained in the data frame and matrix:_ How many rows does it have and how many features? Which features are measurements and which features are metadata about the samples? (3 pts) +There are 16 rows of data. 10 features are metadata, also they are duplicates. That leaves 89 features that are measurements. + 2. _Scale this data appropriately (you can choose the scaling method or decide to not scale data):_ Explain why you chose a scaling method or to not scale. (3 pts) +We should scale the data for clustering (maybe future PCA). Let's use scale() + +```{R} +sherloc_lithology_pixl_scaled.matrix <- scale(sherloc_lithology_pixl.matrix) +summary(sherloc_lithology_pixl_scaled.matrix) +#Prepare matrix for cluster plot +sherloc_lithology_pixl_scaled.matrix <- sherloc_lithology_pixl_scaled.matrix[, -16] +sherloc_lithology_pixl_scaled.matrix <- sherloc_lithology_pixl_scaled.matrix[, -60] + +# s_l_p_scaled.df <- data.frame(sherloc_lithology_pixl_scaled.matrix) +# Sample <- 1:16 +# s_l_p_scaled.df <- cbind(Sample,s_l_p_scaled.df) +# +# ggplot(sherloc_lithology_pixl_scaled.df, aes(x=Sample), colour = Sample) + +# geom_line() + +``` + 3. _Cluster the data using k-means or your favorite clustering method (like hierarchical clustering):_ Describe how you picked the best number of clusters. Indicate the number of points in each clusters. Coordinate with your team so you try different approaches. If you want to share results with your team mates, make sure to use the same random seeds. (6 pts) +```{R} +wssplot <- function(data, nc = 15, seed =10) { + wss <- data.frame(cluster=1:nc, quality=c(0)) + for (i in 1:nc){ + set.seed(seed) + wss[i,2] <- kmeans(data, centers=i)$tot.withinss} + ggplot(data=wss,aes(x=cluster,y=quality)) + + geom_line() + + ggtitle("Quality of k-means by Cluster") +} + +wssplot(sherloc_lithology_pixl_scaled.matrix, nc=8, seed=2469) + +#Select 4 clusters based on plot + +kmean <- kmeans(sherloc_lithology_pixl_scaled.matrix, centers=4) +``` + 4. _Perform a **creative analysis** that provides insights into what one or more of the clusters are and what they tell you about the MARS data: Alternatively do another creative analysis of your datasets that leads to one of more findings. Make sure to explain what your analysis and discuss your the results. +```{R} +#Make 2 heatmaps, look for connections +pheatmap(kmean$centers[,1:41],scale="none") +pheatmap(kmean$centers[,42:81],scale="none") +# H.pca <- prcomp(sherloc_lithology_pixl_scaled.matrix,scale=FALSE) +# +# ggbiplot::ggbiplot(H.pca, +# groups = as.factor(kmean$cluster),varname.size=1, var.axes = 0)+ +# xlim(-3,3) + ylim(-3,3) + + +#Determine Cluster sizes +kmean[["cluster"]] +#1: 3 Samples +#2: 2 Samples +#3: 1 Sample +#4: 10 Samples +``` # Preparation of Team Presentation (Part 4) diff --git a/StudentNotebooks/Assignment02/compta-assignment2-f24.html b/StudentNotebooks/Assignment02/compta-assignment2-f24.html index f4aa1dc..bc7dc41 100644 --- a/StudentNotebooks/Assignment02/compta-assignment2-f24.html +++ b/StudentNotebooks/Assignment02/compta-assignment2-f24.html @@ -11,7 +11,7 @@ - + Mars 2020 Mission Data Notebook: @@ -1624,7 +1624,7 @@

Mars 2020 Mission Data Notebook:

DAR Assignment 2 (Fall 2024)

Ashton Compton

-

04 September 2024

+

11 September 2024

@@ -2920,26 +2920,259 @@

4 Analysis of Data (Part exact same analysis. If you want to use the same clustering for your team (which is okay but then vary rest), make sure you use the same random seeds.

+

I worked on H

    -

  1. Describe the data set contained in the data frame and +

  2. Describe the data set contained in the data frame and matrix: How many rows does it have and how many features? Which features are measurements and which features are metadata about the -samples? (3 pts)

    3. -

  3. Scale this data appropriately (you can choose the scaling -method or decide to not scale data): Explain why you chose a -scaling method or to not scale. (3 pts)

    4. -

  4. Cluster the data using k-means or your favorite clustering +samples? (3 pts)

    5. +
+

There are 16 rows of data. 10 features are metadata, also they are +duplicates. That leaves 89 features that are measurements.

+
    +
  1. Scale this data appropriately (you can choose the scaling method +or decide to not scale data): Explain why you chose a scaling +method or to not scale. (3 pts)
    2. +
+

We should scale the data for clustering (maybe future PCA). Let’s use +scale()

+
sherloc_lithology_pixl_scaled.matrix <- scale(sherloc_lithology_pixl.matrix)
+summary(sherloc_lithology_pixl_scaled.matrix)
+
##   Plagioclase         Sulfate          Ca-sulfate      Hydrated Ca-sulfate
+##  Min.   :-0.4651   Min.   :-1.4133   Min.   :-0.7264   Min.   :-0.366     
+##  1st Qu.:-0.4651   1st Qu.:-1.0095   1st Qu.:-0.7264   1st Qu.:-0.366     
+##  Median :-0.4651   Median : 0.7403   Median :-0.7264   Median :-0.366     
+##  Mean   : 0.0000   Mean   : 0.0000   Mean   : 0.0000   Mean   : 0.000     
+##  3rd Qu.:-0.4651   3rd Qu.: 0.7403   3rd Qu.: 1.3867   3rd Qu.:-0.366     
+##  Max.   : 2.0156   Max.   : 0.7403   Max.   : 1.3867   Max.   : 2.562     
+##                                                                           
+##    Mg-sulfate      Hydrated Sulfates Hydrated Mg-Fe sulfate  Perchlorates  
+##  Min.   :-0.4651   Min.   :-0.366    Min.   :-0.4651        Min.   :-0.25  
+##  1st Qu.:-0.4651   1st Qu.:-0.366    1st Qu.:-0.4651        1st Qu.:-0.25  
+##  Median :-0.4651   Median :-0.366    Median :-0.4651        Median :-0.25  
+##  Mean   : 0.0000   Mean   : 0.000    Mean   : 0.0000        Mean   : 0.00  
+##  3rd Qu.:-0.4651   3rd Qu.:-0.366    3rd Qu.:-0.4651        3rd Qu.:-0.25  
+##  Max.   : 2.0156   Max.   : 2.562    Max.   : 2.0156        Max.   : 3.75  
+##                                                                            
+##  Na-perchlorate  Amorphous Silicate   Phosphate          Pyroxene      
+##  Min.   :-0.25   Min.   :-0.7733    Min.   :-0.5704   Min.   :-1.4361  
+##  1st Qu.:-0.25   1st Qu.:-0.7733    1st Qu.:-0.5704   1st Qu.:-1.4361  
+##  Median :-0.25   Median :-0.7733    Median :-0.5704   Median : 0.6528  
+##  Mean   : 0.00   Mean   : 0.0000    Mean   : 0.0000   Mean   : 0.0000  
+##  3rd Qu.:-0.25   3rd Qu.: 0.6014    3rd Qu.: 0.3071   3rd Qu.: 0.6528  
+##  Max.   : 3.75   Max.   : 1.9761    Max.   : 2.2376   Max.   : 0.6528  
+##                                                                        
+##     Olivine          Carbonate       Fe-Mg carbonate  Hydrated Carbonates
+##  Min.   :-1.0830   Min.   :-1.9823   Min.   :-0.366   Min.   : NA        
+##  1st Qu.:-1.0830   1st Qu.:-0.8014   1st Qu.:-0.366   1st Qu.: NA        
+##  Median : 0.1911   Median : 0.7170   Median :-0.366   Median : NA        
+##  Mean   : 0.0000   Mean   : 0.0000   Mean   : 0.000   Mean   :NaN        
+##  3rd Qu.: 0.9556   3rd Qu.: 0.7170   3rd Qu.:-0.366   3rd Qu.: NA        
+##  Max.   : 0.9556   Max.   : 0.7170   Max.   : 2.562   Max.   : NA        
+##                                                       NA's   :16         
+##  Disordered Silicates    Feldspar          Quartz          Apatite       
+##  Min.   :-0.366       Min.   :-0.366   Min.   :-0.366   Min.   :-0.4122  
+##  1st Qu.:-0.366       1st Qu.:-0.366   1st Qu.:-0.366   1st Qu.:-0.4122  
+##  Median :-0.366       Median :-0.366   Median :-0.366   Median :-0.4122  
+##  Mean   : 0.000       Mean   : 0.000   Mean   : 0.000   Mean   : 0.0000  
+##  3rd Qu.:-0.366       3rd Qu.:-0.366   3rd Qu.:-0.366   3rd Qu.:-0.4122  
+##  Max.   : 2.562       Max.   : 2.562   Max.   : 2.562   Max.   : 2.5188  
+##                                                                          
+##   FeTi oxides          Halite          Iron oxide      Hydrated Iron oxide
+##  Min.   :-0.4122   Min.   :-0.4651   Min.   :-0.7092   Min.   :-0.25      
+##  1st Qu.:-0.4122   1st Qu.:-0.4651   1st Qu.:-0.7092   1st Qu.:-0.25      
+##  Median :-0.4122   Median :-0.4651   Median :-0.7092   Median :-0.25      
+##  Mean   : 0.0000   Mean   : 0.0000   Mean   : 0.0000   Mean   : 0.00      
+##  3rd Qu.:-0.4122   3rd Qu.:-0.4651   3rd Qu.: 0.5516   3rd Qu.:-0.25      
+##  Max.   : 2.5188   Max.   : 2.0156   Max.   : 1.8123   Max.   : 3.75      
+##                                                                           
+##  Organic matter    Sulfate+Organic matter Other hydrated phases
+##  Min.   :-1.2319   Min.   :-0.55156       Min.   :-0.9139      
+##  1st Qu.:-1.2319   1st Qu.:-0.55156       1st Qu.:-0.9139      
+##  Median : 0.8429   Median :-0.55156       Median :-0.3917      
+##  Mean   : 0.0000   Mean   : 0.00000       Mean   : 0.0000      
+##  3rd Qu.: 0.8429   3rd Qu.: 0.07879       3rd Qu.: 1.1750      
+##  Max.   : 0.8429   Max.   : 1.96987       Max.   : 1.1750      
+##                                                                
+##  Phyllosilicates      Chlorite      Kaolinite (hydrous Al-clay)
+##  Min.   :-0.5217   Min.   :-0.366   Min.   :-0.4651            
+##  1st Qu.:-0.5217   1st Qu.:-0.366   1st Qu.:-0.4651            
+##  Median :-0.5217   Median :-0.366   Median :-0.4651            
+##  Mean   : 0.0000   Mean   : 0.000   Mean   : 0.0000            
+##  3rd Qu.:-0.1739   3rd Qu.:-0.366   3rd Qu.:-0.4651            
+##  Max.   : 2.2607   Max.   : 2.562   Max.   : 2.0156            
+##                                                                
+##     Chromite         Ilmenite      Zircon/Baddeleyite Fe-Mg-clay minerals
+##  Min.   :-0.366   Min.   :-0.366   Min.   :-0.366     Min.   :-0.4651    
+##  1st Qu.:-0.366   1st Qu.:-0.366   1st Qu.:-0.366     1st Qu.:-0.4651    
+##  Median :-0.366   Median :-0.366   Median :-0.366     Median :-0.4651    
+##  Mean   : 0.000   Mean   : 0.000   Mean   : 0.000     Mean   : 0.0000    
+##  3rd Qu.:-0.366   3rd Qu.:-0.366   3rd Qu.:-0.366     3rd Qu.:-0.4651    
+##  Max.   : 2.562   Max.   : 2.562   Max.   : 2.562     Max.   : 2.0156    
+##                                                                          
+##     Spinels          feldspar       plagioclase         pyroxene      
+##  Min.   :-0.366   Min.   :-0.366   Min.   :-0.4651   Min.   :-1.4361  
+##  1st Qu.:-0.366   1st Qu.:-0.366   1st Qu.:-0.4651   1st Qu.:-1.4361  
+##  Median :-0.366   Median :-0.366   Median :-0.4651   Median : 0.6528  
+##  Mean   : 0.000   Mean   : 0.000   Mean   : 0.0000   Mean   : 0.0000  
+##  3rd Qu.:-0.366   3rd Qu.:-0.366   3rd Qu.:-0.4651   3rd Qu.: 0.6528  
+##  Max.   : 2.562   Max.   : 2.562   Max.   : 2.0156   Max.   : 0.6528  
+##                                                                       
+##     olivine          quartz          apatite         FeTi_Oxides     
+##  Min.   :-1.25   Min.   :-0.366   Min.   :-0.4651   Min.   :-0.4651  
+##  1st Qu.:-1.25   1st Qu.:-0.366   1st Qu.:-0.4651   1st Qu.:-0.4651  
+##  Median : 0.75   Median :-0.366   Median :-0.4651   Median :-0.4651  
+##  Mean   : 0.00   Mean   : 0.000   Mean   : 0.0000   Mean   : 0.0000  
+##  3rd Qu.: 0.75   3rd Qu.:-0.366   3rd Qu.:-0.4651   3rd Qu.:-0.4651  
+##  Max.   : 0.75   Max.   : 2.562   Max.   : 2.0156   Max.   : 2.0156  
+##                                                                      
+##    Iron_Oxide         Sulfate        Perchlorates     Phosphate      
+##  Min.   :-0.8539   Min.   :-1.677   Min.   :-0.25   Min.   :-0.6528  
+##  1st Qu.:-0.8539   1st Qu.: 0.000   1st Qu.:-0.25   1st Qu.:-0.6528  
+##  Median :-0.8539   Median : 0.559   Median :-0.25   Median :-0.6528  
+##  Mean   : 0.0000   Mean   : 0.000   Mean   : 0.00   Mean   : 0.0000  
+##  3rd Qu.: 1.0979   3rd Qu.: 0.559   3rd Qu.:-0.25   3rd Qu.: 1.4361  
+##  Max.   : 1.0979   Max.   : 0.559   Max.   : 3.75   Max.   : 1.4361  
+##                                                                      
+##    Ca_Sulfate      Carbonate       Fe_Mg_clay      Fe_Mg_carbonate 
+##  Min.   :-0.75   Min.   :-3.75   Min.   :-0.4651   Min.   :-0.366  
+##  1st Qu.:-0.75   1st Qu.: 0.25   1st Qu.:-0.4651   1st Qu.:-0.366  
+##  Median :-0.75   Median : 0.25   Median :-0.4651   Median :-0.366  
+##  Mean   : 0.00   Mean   : 0.00   Mean   : 0.0000   Mean   : 0.000  
+##  3rd Qu.: 1.25   3rd Qu.: 0.25   3rd Qu.:-0.4651   3rd Qu.:-0.366  
+##  Max.   : 1.25   Max.   : 0.25   Max.   : 2.0156   Max.   : 2.562  
+##                                                                    
+##    Mg_sulfate      Phyllosilicates     Chlorite          Halite       
+##  Min.   :-0.4651   Min.   :-0.559   Min.   :-0.366   Min.   :-0.4651  
+##  1st Qu.:-0.4651   1st Qu.:-0.559   1st Qu.:-0.366   1st Qu.:-0.4651  
+##  Median :-0.4651   Median :-0.559   Median :-0.366   Median :-0.4651  
+##  Mean   : 0.0000   Mean   : 0.000   Mean   : 0.000   Mean   : 0.0000  
+##  3rd Qu.:-0.4651   3rd Qu.: 0.000   3rd Qu.:-0.366   3rd Qu.:-0.4651  
+##  Max.   : 2.0156   Max.   : 1.677   Max.   : 2.562   Max.   : 2.0156  
+##                                                                       
+##  Organic_matter    Hydrated_Ca_Sulfate Hydrated_Sulfates Hydrated_Mg_Fe_Sulfate
+##  Min.   :-1.4361   Min.   :-0.366      Min.   :-0.366    Min.   :-0.4651       
+##  1st Qu.:-1.4361   1st Qu.:-0.366      1st Qu.:-0.366    1st Qu.:-0.4651       
+##  Median : 0.6528   Median :-0.366      Median :-0.366    Median :-0.4651       
+##  Mean   : 0.0000   Mean   : 0.000      Mean   : 0.000    Mean   : 0.0000       
+##  3rd Qu.: 0.6528   3rd Qu.:-0.366      3rd Qu.:-0.366    3rd Qu.:-0.4651       
+##  Max.   : 0.6528   Max.   : 2.562      Max.   : 2.562    Max.   : 2.0156       
+##                                                                                
+##  Na_Perchlorate  Amorphous_Silicate Hydrated_Carbonates Disordered_Silicates
+##  Min.   :-0.25   Min.   :-0.8539    Min.   : NA         Min.   :-0.366      
+##  1st Qu.:-0.25   1st Qu.:-0.8539    1st Qu.: NA         1st Qu.:-0.366      
+##  Median :-0.25   Median :-0.8539    Median : NA         Median :-0.366      
+##  Mean   : 0.00   Mean   : 0.0000    Mean   :NaN         Mean   : 0.000      
+##  3rd Qu.:-0.25   3rd Qu.: 1.0979    3rd Qu.: NA         3rd Qu.:-0.366      
+##  Max.   : 3.75   Max.   : 1.0979    Max.   : NA         Max.   : 2.562      
+##                                     NA's   :16                              
+##  Hydrated_Iron_Oxide Sulfate+Organic_Matter Other_hydrated_phases
+##  Min.   :-0.25       Min.   :-0.6528        Min.   :-0.9682      
+##  1st Qu.:-0.25       1st Qu.:-0.6528        1st Qu.:-0.9682      
+##  Median :-0.25       Median :-0.6528        Median : 0.0000      
+##  Mean   : 0.00       Mean   : 0.0000        Mean   : 0.0000      
+##  3rd Qu.:-0.25       3rd Qu.: 1.4361        3rd Qu.: 0.9682      
+##  Max.   : 3.75       Max.   : 1.4361        Max.   : 0.9682      
+##                                                                  
+##    Kaolinite          Chromite         Ilmenite      Zircon/Baddeleyite
+##  Min.   :-0.4651   Min.   :-0.366   Min.   :-0.366   Min.   :-0.366    
+##  1st Qu.:-0.4651   1st Qu.:-0.366   1st Qu.:-0.366   1st Qu.:-0.366    
+##  Median :-0.4651   Median :-0.366   Median :-0.366   Median :-0.366    
+##  Mean   : 0.0000   Mean   : 0.000   Mean   : 0.000   Mean   : 0.000    
+##  3rd Qu.:-0.4651   3rd Qu.:-0.366   3rd Qu.:-0.366   3rd Qu.:-0.366    
+##  Max.   : 2.0156   Max.   : 2.562   Max.   : 2.562   Max.   : 2.562    
+##                                                                        
+##     Spinels            Na20              Mgo              Al203        
+##  Min.   :-0.366   Min.   :-1.1206   Min.   :-1.3765   Min.   :-0.8991  
+##  1st Qu.:-0.366   1st Qu.:-0.5494   1st Qu.:-1.1500   1st Qu.:-0.7605  
+##  Median :-0.366   Median :-0.5176   Median : 0.1404   Median :-0.3631  
+##  Mean   : 0.000   Mean   : 0.0000   Mean   : 0.0000   Mean   : 0.0000  
+##  3rd Qu.:-0.366   3rd Qu.: 1.2245   3rd Qu.: 0.9322   3rd Qu.: 0.5455  
+##  Max.   : 2.562   Max.   : 1.9280   Max.   : 1.3847   Max.   : 1.7407  
+##                                                                        
+##       Si02               P205              S03                Cl          
+##  Min.   :-1.44691   Min.   :-0.7945   Min.   :-0.6389   Min.   :-1.11146  
+##  1st Qu.:-0.66513   1st Qu.:-0.5999   1st Qu.:-0.5433   1st Qu.:-0.69646  
+##  Median : 0.02732   Median :-0.1820   Median :-0.3957   Median :-0.08165  
+##  Mean   : 0.00000   Mean   : 0.0000   Mean   : 0.0000   Mean   : 0.00000  
+##  3rd Qu.: 0.23751   3rd Qu.: 0.2720   3rd Qu.:-0.2354   3rd Qu.: 0.17964  
+##  Max.   : 1.68204   Max.   : 3.0392   Max.   : 2.1333   Max.   : 2.03944  
+##                                                                           
+##       K20               Cao               Ti02             Cr203        
+##  Min.   :-0.8190   Min.   :-1.1256   Min.   :-1.1321   Min.   :-0.5791  
+##  1st Qu.:-0.5931   1st Qu.:-0.5313   1st Qu.:-0.4192   1st Qu.:-0.5383  
+##  Median :-0.5366   Median :-0.2920   Median :-0.2182   Median :-0.3263  
+##  Mean   : 0.0000   Mean   : 0.0000   Mean   : 0.0000   Mean   : 0.0000  
+##  3rd Qu.: 0.3495   3rd Qu.: 0.3203   3rd Qu.: 0.3119   3rd Qu.:-0.1060  
+##  Max.   : 1.8640   Max.   : 2.1006   Max.   : 3.0535   Max.   : 2.5204  
+##                                                                         
+##       Mno              FeO-T        
+##  Min.   :-1.5772   Min.   :-1.3848  
+##  1st Qu.:-0.5678   1st Qu.:-0.7989  
+##  Median : 0.1051   Median : 0.4077  
+##  Mean   : 0.0000   Mean   : 0.0000  
+##  3rd Qu.: 0.6099   3rd Qu.: 0.7173  
+##  Max.   : 1.7314   Max.   : 1.4512  
+##
+
#Prepare matrix for cluster plot
+sherloc_lithology_pixl_scaled.matrix <- sherloc_lithology_pixl_scaled.matrix[, -16]
+sherloc_lithology_pixl_scaled.matrix <- sherloc_lithology_pixl_scaled.matrix[, -60]
+
+# s_l_p_scaled.df <- data.frame(sherloc_lithology_pixl_scaled.matrix)
+# Sample <- 1:16
+#  s_l_p_scaled.df <- cbind(Sample,s_l_p_scaled.df)
+#  
+# ggplot(sherloc_lithology_pixl_scaled.df, aes(x=Sample), colour = Sample) +
+#   geom_line()
+
    +
  1. Cluster the data using k-means or your favorite clustering method (like hierarchical clustering): Describe how you picked the best number of clusters. Indicate the number of points in each clusters. Coordinate with your team so you try different approaches. If you want to share results with your team mates, make sure to use the same random -seeds. (6 pts)

    2. -

  2. _Perform a creative analysis that provides -insights into what one or more of the clusters are and what they tell -you about the MARS data: Alternatively do another creative analysis of -your datasets that leads to one of more findings. Make sure to explain -what your analysis and discuss your the results.

    3. +seeds. (6 pts) +
+
wssplot <- function(data, nc = 15, seed =10) {
+  wss <- data.frame(cluster=1:nc, quality=c(0))
+  for (i in 1:nc){
+    set.seed(seed)
+    wss[i,2] <- kmeans(data, centers=i)$tot.withinss}
+  ggplot(data=wss,aes(x=cluster,y=quality)) + 
+    geom_line() + 
+    ggtitle("Quality of k-means by Cluster")
+}
+
+wssplot(sherloc_lithology_pixl_scaled.matrix, nc=8, seed=2469)
+

+
#Select 4 clusters based on plot
+
+kmean <- kmeans(sherloc_lithology_pixl_scaled.matrix, centers=4)
+
    +
  1. _Perform a creative analysis that provides insights +into what one or more of the clusters are and what they tell you about +the MARS data: Alternatively do another creative analysis of your +datasets that leads to one of more findings. Make sure to explain what +your analysis and discuss your the results.
+
#Make 2 heatmaps, look for connections
+pheatmap(kmean$centers[,1:41],scale="none")
+

+
pheatmap(kmean$centers[,42:81],scale="none")
+

+
# H.pca <- prcomp(sherloc_lithology_pixl_scaled.matrix,scale=FALSE)
+# 
+# ggbiplot::ggbiplot(H.pca,
+#                    groups = as.factor(kmean$cluster),varname.size=1, var.axes = 0)+
+#   xlim(-3,3) + ylim(-3,3) 
+
+
+#Determine Cluster sizes
+kmean[["cluster"]]
+
##  [1] 3 2 2 4 4 4 4 4 4 4 4 1 1 4 4 1
+
#1: 3 Samples
+#2: 2 Samples
+#3: 1 Sample
+#4: 10 Samples

5 Preparation of Team