diff --git a/StudentNotebooks/Assignment01/balajy-assignment1-f24.Rmd b/StudentNotebooks/Assignment01/balajy-assignment1-f24.Rmd
index bdea1e9..41b8be9 100644
--- a/StudentNotebooks/Assignment01/balajy-assignment1-f24.Rmd
+++ b/StudentNotebooks/Assignment01/balajy-assignment1-f24.Rmd
@@ -4,11 +4,11 @@ subtitle: "DAR Assignment 1 (Fall 2024)"
 author: "Yashas Balaji"
 date: "`r format(Sys.time(), '%d %B %Y')`"
 output:
+  pdf_document: default
   html_document:
     toc: true
     number_sections: true
     df_print: paged
-  pdf_document: default
 ---
 ```{r setup, include=FALSE}
 # REQUIRE R PACKAGE INSTALLATIONS
@@ -217,9 +217,11 @@ Here is the MARS PIXL data. Take note of the variables, their types, and distriu
 # Saved LIBS data with locations added
 
 # NOTE: Use course directory version during the semester
+pixl.df<- readRDS("~/DAR-Mars-F24/Data/samples_pixl_wide.Rds")
+
 # pixl.df<- readRDS("/academics/MATP-4910-F24/DAR-Mars-F24/Data/samples_pixl_wide.Rds")
 # Use this version to  use downloaded data from github
-pixl.df <- readRDS("~/DAR-Mars-F24/Data/samples_pixl_wide.Rds")
+# pixl.df <- readRDS("~/DAR-Mars-F24/Data/samples_pixl_wide.Rds")
 
 # convert location to a number
 pixl.df$location <- as.numeric(pixl.df$location )
@@ -342,19 +344,16 @@ ggbiplot::ggbiplot(pixl_trim.mat.pca,
 
 Add a description of each cluster here in your own words.  
 
-Describe Cluster 1: _Your description here_
+Describe Cluster 1: Cluster 1, the smallest cluster seems to be mainly igneous rock and is highly composed of Silicone Dioxide (SiO2)
 
-Describe Cluster 2: _Your description here_
+Describe Cluster 2: Cluster 2, the largest cluster seems to be mainly comprised of sedimentary rock, and is highly composed of SiO2, SO3, Mgo, and FeO-T
 
-Describe Cluster 3: _Your description here_
+Describe Cluster 3: Cluster 3, is also composed of igneous rock and is highly composed of SiO2, Mgo and FeO-T
 
 
-What do the  clustering and  PCA results  tell us about the data detected by the M20 PIXL experiment? _Feel free to add graphs or analyses to support your conclusions._
+What do the  clustering and  PCA results  tell us about the data detected by the M20 PIXL experiment? 
 
-```{r}
-# Student's code for graphs and analysis here!
-
-```
+The clustering tells us what groups of rock has higher concentrations of certain molecules in the rock, and the pca analysis gives us the explained variance of the sample. Additionally the plot having the type of rock was helpful in telling us what rocks each cluster had.
 
 ## SAVE, COMMIT and PUSH YOUR CHANGES!
 
diff --git a/StudentNotebooks/Assignment01/balajy-assignment1-f24.html b/StudentNotebooks/Assignment01/balajy-assignment1-f24.html
index e9e6744..ed48ffa 100644
--- a/StudentNotebooks/Assignment01/balajy-assignment1-f24.html
+++ b/StudentNotebooks/Assignment01/balajy-assignment1-f24.html
@@ -11,7 +11,7 @@
 
 <meta name="author" content="Yashas Balaji" />
 
-<meta name="date" content="2024-08-28" />
+<meta name="date" content="2024-09-03" />
 
 <title>RPI github and Mars 2020 PIXL Example Notebook:</title>
 
@@ -167,7 +167,7 @@
 <h1 class="title toc-ignore">RPI github and Mars 2020 PIXL Example Notebook:</h1>
 <h3 class="subtitle">DAR Assignment 1 (Fall 2024)</h3>
 <h4 class="author">Yashas Balaji</h4>
-<h4 class="date">28 August 2024</h4>
+<h4 class="date">03 September 2024</h4>
 
 </div>
 
@@ -375,9 +375,11 @@ <h2><span class="header-section-number">3.1</span> Load the PIXL Data and displa
 <pre class="r"><code># Saved LIBS data with locations added
 
 # NOTE: Use course directory version during the semester
+pixl.df&lt;- readRDS(&quot;~/DAR-Mars-F24/Data/samples_pixl_wide.Rds&quot;)
+
 # pixl.df&lt;- readRDS(&quot;/academics/MATP-4910-F24/DAR-Mars-F24/Data/samples_pixl_wide.Rds&quot;)
 # Use this version to  use downloaded data from github
-pixl.df &lt;- readRDS(&quot;~/DAR-Mars-F24/Data/samples_pixl_wide.Rds&quot;)
+# pixl.df &lt;- readRDS(&quot;~/DAR-Mars-F24/Data/samples_pixl_wide.Rds&quot;)
 
 # convert location to a number
 pixl.df$location &lt;- as.numeric(pixl.df$location )
@@ -555,11 +557,11 @@ <h2><span class="header-section-number">4.4</span> Create a PCA Biplot using ggb
 <div id="answer-these-questions" class="section level2" number="4.5">
 <h2><span class="header-section-number">4.5</span> ANSWER THESE QUESTIONS!</h2>
 <p>Add a description of each cluster here in your own words.</p>
-<p>Describe Cluster 1: <em>Your description here</em></p>
-<p>Describe Cluster 2: <em>Your description here</em></p>
-<p>Describe Cluster 3: <em>Your description here</em></p>
-<p>What do the clustering and PCA results tell us about the data detected by the M20 PIXL experiment? <em>Feel free to add graphs or analyses to support your conclusions.</em></p>
-<pre class="r"><code># Student&#39;s code for graphs and analysis here!</code></pre>
+<p>Describe Cluster 1: Cluster 1, the smallest cluster seems to be mainly igneous rock and is highly composed of Silicone Dioxide (SiO2)</p>
+<p>Describe Cluster 2: Cluster 2, the largest cluster seems to be mainly comprised of sedimentary rock, and is highly composed of SiO2, SO3, Mgo, and FeO-T</p>
+<p>Describe Cluster 3: Cluster 3, is also composed of igneous rock and is highly composed of SiO2, Mgo and FeO-T</p>
+<p>What do the clustering and PCA results tell us about the data detected by the M20 PIXL experiment?</p>
+<p>The clustering tells us what groups of rock has higher concentrations of certain molecules in the rock, and the pca analysis gives us the explained variance of the sample. Additionally the plot having the type of rock was helpful in telling us what rocks each cluster had.</p>
 </div>
 <div id="save-commit-and-push-your-changes" class="section level2" number="4.6">
 <h2><span class="header-section-number">4.6</span> SAVE, COMMIT and PUSH YOUR CHANGES!</h2>
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