finished final notebook

StudentNotebooks/Assignment07_DraftFinalProjectNotebook/wangx53_final_draft.Rmd

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+---
+title: "Data Analytics Research Individual Final Project Report"
+author: "Evangeline Wang"
+date: "Fall 2024"
+output:
+  pdf_document:
+    toc: yes
+    toc_depth: '3'
+  html_notebook: default
+  html_document:
+    toc: yes
+    toc_depth: 3
+    toc_float: yes
+    number_sections: yes
+    theme: united
+---
+
+# DAR Project and Group Members
+
+* Project name: MARS 
+* Project team members: 
+  - Xuanting Wang (Primary Contributor)
+
+# 0.0 Preliminaries
+
+This report includes the analysis and findings on Martian sample composition patterns using data from the Perseverance rover’s PIXL and LIBS instruments. Required R packages include:
+
+* `ggplot2`
+* `tidyverse`
+* `ggtern`
+* Additional packages are installed and loaded as necessary.
+
+```{r, include=FALSE}
+# Install required packages if not already installed
+packages <- c("ggplot2", "tidyverse", "dplyr", "ggtern")
+for (pkg in packages) {
+  if (!require(pkg, character.only = TRUE)) {
+    install.packages(pkg, dependencies = TRUE)
+    library(pkg, character.only = TRUE)
+  }
+}
+```
+
+# 1.0 Project Introduction
+
+This project investigates the chemical composition of Martian samples across campaigns using PIXL and LIBS datasets. Key objectives include:
+
+- Identifying patterns in cation group compositions (Si-Al, Fe-Mg, Ca-Na-K).
+- Assessing variations across campaigns using statistical analysis.
+- Comparing insights derived from PIXL and LIBS data.
+
+Data analysis involved methods such as ANOVA, post-hoc tests, and logistic regression for campaign classification based on cation group compositions.
+
+# 2.0 Organization of Report
+
+This report is organized as follows:
+
+- **Section 3.0:** PIXL Data Analysis – Findings and visualizations.
+- **Section 4.0:** LIBS Data Analysis – Results and comparisons.
+- **Section 5.0:** Conclusions, limitations, and future directions.
+- **Section 6.0:** Appendix – Supplementary materials.
+
+# 3.0 PIXL Data Analysis
+
+## 3.1 Data and Methods
+
+PIXL datasets were processed to calculate the cation group sums:
+
+- **Si-Al:** Sum of \( SiO_2 \) and \( Al_2O_3 \).
+- **Fe-Mg:** Sum of \( FeO-T \) and \( MgO \).
+- **Ca-Na-K:** Sum of \( CaO \), \( Na_2O \), and \( K_2O \).
+
+Samples were classified based on the largest cation group proportion. Statistical methods included:
+
+- ANOVA for campaign-based differences.
+- Dunn’s post-hoc tests for pairwise comparisons.
+- Logistic regression for campaign classification.
+
+## 3.2 Findings
+
+1. **Classification Results:**
+   - **Si-Al rich:** Majority of samples (11).
+   - **Fe-Mg rich:** Fewer samples (5).
+   - **Ca-Na-K rich:** Minimal samples.
+
+2. **Statistical Results:**
+   - ANOVA indicated significant differences in Si-Al (p = 0.0014) and Ca-Na-K (p = 0.0136) across campaigns.
+   - Fe-Mg showed marginal significance (p = 0.0791).
+
+3. **Post-hoc Test Results:**
+   - Significant differences in Si-Al and Ca-Na-K between Crater Floor and Delta Front.
+
+4. **Logistic Regression:**
+   - Limited predictive power for campaign classification using cation compositions.
+
+## 3.3 Visualizations
+
+- **Ternary Plot:** Proportional distribution of cation groups.
+- **Density Plots:** Distribution patterns of Si-Al, Fe-Mg, and Ca-Na-K.
+- **Box Plots:** Campaign-specific variations in cation concentrations.
+
+# 4.0 LIBS Data Analysis
+
+## 4.1 Data and Methods
+
+LIBS data followed the same processing pipeline as PIXL. The analysis included:
+
+- Campaign-based classification.
+- Statistical tests (ANOVA and Dunn’s test).
+- Comparisons between LIBS and PIXL results.
+
+## 4.2 Findings
+
+1. **Classification Results:**
+   - Si-Al rich (majority), Fe-Mg rich, and Ca-Na-K rich distributions mirrored PIXL trends.
+
+2. **Statistical Results:**
+   - Significant variations were observed in all cation groups (p < 0.0001) across campaigns.
+
+3. **Post-hoc Test Results:**
+   - Clear differences between Campaign 3 and the other campaigns.
+
+4. **Logistic Regression:**
+   - Fe-Mg showed some predictive strength in distinguishing campaigns.
+
+## 4.3 Visualizations
+
+- **Ternary Plot:** Similar trends as PIXL.
+- **Box Plots:** Campaign-specific distributions.
+
+# 5.0 Conclusions, Limitations, and Future Work
+
+## 5.1 Conclusions
+
+- Both datasets showed consistent compositional trends, with Campaign 3 exhibiting distinct patterns.
+- Significant differences were noted in Si-Al, Fe-Mg, and Ca-Na-K across campaigns.
+
+## 5.2 Limitations
+
+- Limited predictive power in logistic regression models.
+- Variability in sample sizes may affect statistical robustness.
+
+## 5.3 Recommendations
+
+- Incorporate additional datasets (e.g., SHERLOC) for broader insights.
+- Explore machine learning models for improved classification accuracy.
+
+# 6.0 Appendix
+
+## Supplementary Figures
+
+- Extended ternary plots, density plots, and box plots.
+- Statistical tables summarizing ANOVA and post-hoc results.
+
+## References
+
+1. PIXL and LIBS Data Documentation.
+2. R Documentation for ggplot2 and tidyverse.
+