baoz3-assignment2-f24.Rmd

---
title: "baoz3-assignment2-f24:"
subtitle: "DAR Assignment 2 (Fall 2024)"
author: "Ziyi Bao"
date: "`r format(Sys.time(), '%d %B %Y')`"
output:
  html_document:
    toc: true
    number_sections: true
    df_print: paged
  pdf_document: default
---
```{r setup, include=FALSE}

# Required R package installation; RUN THIS BLOCK BEFORE ATTEMPTING TO KNIT THIS NOTEBOOK!!!
# This section  install packages if they are not already installed. 
# This block will not be shown in the knit file.
knitr::opts_chunk$set(echo = TRUE)

# Set the default CRAN repository
local({r <- getOption("repos")
       r["CRAN"] <- "http://cran.r-project.org" 
       options(repos=r)
})

if (!require("pandoc")) {
  install.packages("pandoc")
  library(pandoc)
}

# Required packages for M20 LIBS analysis
if (!require("rmarkdown")) {
  install.packages("rmarkdown")
  library(rmarkdown)
}
if (!require("tidyverse")) {
  install.packages("tidyverse")
  library(tidyverse)
}
if (!require("stringr")) {
  install.packages("stringr")
  library(stringr)
}

if (!require("ggbiplot")) {
  install.packages("ggbiplot")
  library(ggbiplot)
}

if (!require("pheatmap")) {
  install.packages("pheatmap")
  library(pheatmap)
}

```

# DAR ASSIGNMENT 2 (Introduction): Introductory DAR Notebook

This notebook is broken into two main parts:

* **Part 1:** Preparing your local repo for **DAR Assignment 2**
* **Part 2:** Loading and some analysis of the Mars 2020 (M20) Datasets
   * Lithology: _Summarizes the mineral characteristics of samples collected at certain sample locations._
   * PIXL: Planetary Instrument for X-ray Lithochemistry. _Measures elemental chemistry of samples at sub-millimeter scales of samples._
   * SHERLOC: Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals. _Uses cameras, a spectrometer, and a laser of samples to search for organic compounds and minerals that have been altered in watery environments and may be signs of past microbial life._
   * LIBS: Laser-induced breakdown spectroscopy. _Uses a laser beam to help identify minerals in samples and other areas that are beyond the reach of the rover's robotic arm or in areas too steep for the rover to travel._

* **Part 3:** Individual analysis of your team's dataset

* **Part 4:** Preparation of Team Presentation 


**NOTE:** The RPI github repository for all the code and data required for this notebook may be found at:

* https://github.rpi.edu/DataINCITE/DAR-Mars-F24


# DAR ASSIGNMENT 2 (Part 1): Preparing your local repo for Assignment 2 

In this assignment you'll start by making a copy of the Assignment 2 template notebook, then you'll add to your copy with your original work. The instructions which follow explain how to accomplish this. 

**NOTE:** You already cloned the `DAR-Mars-F24` repository for Assignment 1; you **do not** need to make another clone of the repo, but you must begin by updating your copy as instructed below:

## Updating your local clone of the `DAR-Mars-F24` repository

* Access RStudio Server on the IDEA Cluster at http://lp01.idea.rpi.edu/rstudio-ose/
    * REMINDER: You must be on the RPI VPN!!
* Access the Linux shell on the IDEA Cluster by clicking the **Terminal** tab of RStudio Server (lower left panel). 
    * You now see the Linux shell on the IDEA Cluster
    * `cd` (change directory) to enter your home directory using: `cd ~`
    * Type `pwd` to confirm where you are
* In the Linux shell, `cd` to `DAR-Mars-F24`
    * Type `git pull origin main` to pull any updates
    * Always do this when you being work; we might have added or changed something!
* In the Linux shell, `cd` into `Assignment02`
    * Type `ls -al` to list the current contents
    * Don't be surprised if you see many files!
* In the Linux shell, type `git branch` to verify your current working branch
    * If it is not `dar-yourrcs`, type `git checkout dar-yourrcs` (where `yourrcs` is your RCS id)
    * Re-type `git branch` to confirm
* Now in the RStudio Server UI, navigate to the `DAR-Mars-F24/StudentNotebooks/Assignment02` directory via the **Files** panel (lower right panel)
    * Under the **More** menu, set this to be your R working directory
    * Setting the correct working directory is essential for interactive R use!

You're now ready to start coding Assignment 2!

## Creating your copy of the Assignment 2 notebook

1. In RStudio, make a **copy** of `dar-f24-assignment2-template.Rmd` file using a *new, original, descriptive* filename that **includes your RCS ID!**
    * Open `dar-f24-assignment2-template.Rmd`
    * **Save As...** using a new filename that includes your RCS ID
    * Example filename for user `erickj4`: `erickj4-assignment2-f24.Rmd`
    * POINTS OFF IF:
       * You don't create a new filename!
       * You don't include your RCS ID!
       * You include `template` in your new filename!
2. Edit your new notebook using RStudio and save
    * Change the `title:` and `subtitle:` headers (at the top of the file)
    * Change the `author:` 
    * Don't bother changing the `date:`; it should update automagically...
    * **Save** your changes
3. Use the RStudio `Knit` command to create an PDF file; repeat as necessary
    * Use the down arrow next to the word `Knit` and select **Knit to PDF**
    * You may also knit to HTML...
4. In the Linux terminal, use `git add` to add each new file you want to add to the repository
    * Type: `git add yourfilename.Rmd` 
    * Type: `git add yourfilename.pdf` (created when you knitted)
    * Add your HTML if you also created one...
5. When you're ready, in Linux commit your changes: 
    * Type: `git commit -m "some comment"` where "some comment" is a useful comment describing your changes
    * This commits your changes to your local repo, and sets the stage for your next operation.
6. Finally, push your commits to the RPI github repo
    * Type: `git push origin dar-yourrcs` (where `dar-yourrcs` is the branch you've been working in)
    * Your changes are now safely on the RPI github. 
7. **REQUIRED:** On the RPI github, **submit a pull request.**
    * In a web browser, navigate to https://github.rpi.edu/DataINCITE/DAR-Mars-F24
    * In the branch selector drop-down (by default says **master**), select your branch
    * **Submit a pull request for your branch**
    * One of the DAR instructors will merge your branch, and your new files will be added to the master branch of the repo. _Do not merge your branch yourself!_

# DAR ASSIGNMENT 2 (Part 2): Loading the Mars 2020 (M20) Datasets

In this assignment there are four datasets from separate instruments on the Mars Perserverance rover available for analysis:

* **Lithology:** Summarizes the mineral characteristics of samples collected at certain sample locations
* **PIXL:** Planetary Instrument for X-ray Lithochemistry of collected samples
* **SHERLOC:** Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals for collected samples
* **LIBS:** Laser-induced breakdown spectroscopy which are measured in many areas (not just samples)

Each dataset provides data about the mineralogy of the surface of Mars. Based on the purpose and nature of the instrument, the data is collected at different intervals along the path of Perseverance as it makes it way across the Jezero crater. Some of the data (esp. LIBS) is collected almost every Martian day, or _sol_. Some of the data (PIXL and SHERLOC) is only collected at certain sample locations of interest 

Your objective is to perform an analysis of the your team's assigned dataset in order to learn all you can about these Mars samples. 

NOTES: 

   * All of these datasets can be found in `/academics/MATP-4910-F24/DAR-Mars-F24/Data`
   * We have included a comprehensive `samples.Rds` dataset that includes useful details about the sample locations, including Martian latitude and longitude and the sol that individual samples were collected. 
   * Also included is `rover.waypoints.Rds` that provides detailed location information (lat/lon) for the Perseverance rover throughout its journey, up to the present. This can be updated when necessary using the included `roverStatus-f24.R` script. 
   * A general guide to the available Mars 2020 data is available here: https://pds-geosciences.wustl.edu/missions/mars2020/index.htm
   * Other useful MARS 2020 sites
   https://science.nasa.gov/mission/mars-2020-perseverance/mars-rock-samples/ and https://an.rsl.wustl.edu/m20/AN/an3.aspx?AspxAutoDetectCookieSupport=1
   * Note that PIXL, SHERLOC, and Lithology describe 16  sample that were physically collected. There will eventually be 38 samples.   These datasets can be merged by sample.   The LIBS data includes observations collected at many more locations so how to combine the LIBS data with the other datasets is an open research question.   

## Data Set A: Load the Lithology Data

The first five features of the dataset describe twenty-four (24) rover sample locations.  

The remaining features  provides a simple binary (`1` or `0`) summary of presence or absence of 35 minerals at the 24 rover sample locations. 

Only the first sixteen (16) samples are maintained, as the remaining are missing the mineral descriptors. 

The following code "cleans" the dataset to prepare for analysis.  It first creates a dataframe with metadata and measurements for samples, and then creates a matrix containing only numeric measurements for later analysis. 

```{r}
# Load the saved lithology data with locations added
lithology.df<- readRDS("/academics/MATP-4910-F24/DAR-Mars-F24/Data/mineral_data_static.Rds")

# Cast samples as numbers
lithology.df$sample <- as.numeric(lithology.df$sample)

# Convert rest into factors
lithology.df[sapply(lithology.df, is.character)] <-
  lapply(lithology.df[sapply(lithology.df, is.character)], 
                                       as.factor)

# Keep only first 16 samples because the data for the rest of the samples is not available yet
lithology.df<-lithology.df[1:16,]

# Look at summary of cleaned data frame
summary(lithology.df)

# Create a matrix containing only the numeric measurements.  The remaining features are metadata about the sample. 
lithology.matrix <- sapply(lithology.df[,6:40],as.numeric)-1            

# Review the structure of our matrix
str(lithology.matrix)
```


## Data Set B: Load the PIXL Data

The PIXL data provides summaries of the mineral compositions measured at selected sample sites by the PIXL instrument.  Note that here we scale pixl.mat so features have mean 0 and standard deviation so results will be different than in Assignment 1. 

```{r}
# Load the saved PIXL data with locations added
pixl.df <- readRDS("/academics/MATP-4910-F24/DAR-Mars-F24/Data/samples_pixl_wide.Rds")

# Convert to factors
pixl.df[sapply(pixl.df, is.character)] <- lapply(pixl.df[sapply(pixl.df, is.character)], 
                                       as.factor)

# Review our dataframe
summary(pixl.df)

# Make the matrix of just mineral percentage measurements
pixl.matrix <- pixl.df[,2:14] %>% scale()

# Review the structure
str(pixl.matrix)
```

## Data Set C: Load the LIBS Data

The LIBS data provides summaries of the mineral compositions measured at selected sample sites by the LIBS instrument, part of the Perseverance SuperCam.   

```{r}
# Load the saved LIBS data with locations added
libs.df <- readRDS("/academics/MATP-4910-F24/DAR-Mars-F24/Data/supercam_libs_moc_loc.Rds")

#Drop  features that are not to be used in the analysis for this notebook
libs.df <- libs.df %>% 
  select(!(c(distance_mm,Tot.Em.,SiO2_stdev,TiO2_stdev,Al2O3_stdev,FeOT_stdev,
             MgO_stdev,Na2O_stdev,CaO_stdev,K2O_stdev,Total)))

# Convert the points to numeric
libs.df$point <- as.numeric(libs.df$point)

# Review what we have
summary(libs.df)

# Make the a matrix contain only the libs measurements for each mineral
libs.matrix <- as.matrix(libs.df[,6:13]) 

# Check to see scaling
str(libs.matrix)
```



## Dataset D: Load the SHERLOC Data

The SHERLOC data you will be using for this lab is the result of scientists' interpretations of extensive spectral analysis of abrasion samples provided by the SHERLOC instrument.

**NOTE:** This dataset presents minerals as rows and sample sites as columns. You'll probably want to rotate the dataset for easier analysis....

```{r}

# Read in data as provided.  
sherloc_abrasion_raw <- readRDS("/academics/MATP-4910-F24/DAR-Mars-F24/Data/abrasions_sherloc_samples.Rds")

# Clean up data types
sherloc_abrasion_raw$Mineral<-as.factor(sherloc_abrasion_raw$Mineral)
sherloc_abrasion_raw[sapply(sherloc_abrasion_raw, is.character)] <- lapply(sherloc_abrasion_raw[sapply(sherloc_abrasion_raw, is.character)], 
                                       as.numeric)
# Transform NA's to 0
sherloc_abrasion_raw <- sherloc_abrasion_raw %>% replace(is.na(.), 0)

# Reformat data so that rows are "abrasions" and columns list the presence of minerals. 
# Do this by "pivoting" to a long format, and then back to the desired wide format.  

sherloc_long <- sherloc_abrasion_raw %>%
  pivot_longer(!Mineral, names_to = "Name", values_to = "Presence")

# Make abrasion a factor 
sherloc_long$Name <- as.factor(sherloc_long$Name)

# Make it a matrix
sherloc.matrix <- sherloc_long %>%
  pivot_wider(names_from = Mineral, values_from = Presence)

# Get sample information from PIXL and add to measurements -- assumes order is the same

sherloc.df <- cbind(pixl.df[,c("sample","type","campaign","abrasion")],sherloc.matrix)

# Review what we have
summary(sherloc.df)

# Measurements are everything except first column
sherloc.matrix<-as.matrix(sherloc.matrix[,-1])

# Sherlock measurement matrix
# Review the structure 
str(sherloc.matrix)
```
## Data Set E: PIXL + Sherloc 
```{r}
# Combine PIXL and SHERLOC dataframes 
pixl_sherloc.df <- cbind(pixl.df,sherloc.df )

# Review what we have
summary(pixl_sherloc.df)

# Combine PIXL and SHERLOC matrices
pixl_sherloc.matrix<-cbind(pixl.matrix,sherloc.matrix)

# Review the structure of our matrix
str(pixl_sherloc.matrix)

```
 

## Data Set F: PIXL + Lithography 

Create data and matrix from prior datasets 

```{r}
# Combine our PIXL and Lithology dataframes
pixl_lithology.df <- cbind(pixl.df,lithology.df )

# Review what we have
summary(pixl_lithology.df)

# Combine PIXL and Lithology matrices
pixl_lithology.matrix<-cbind(pixl.matrix,lithology.matrix)

# Review the structure
str(pixl_lithology.matrix)

```

## Data Set G: Sherloc + Lithology 

Create Data and matrix from prior datasets by taking on appropriate combinations.

```{r}
# Combine the Lithology and SHERLOC dataframes
sherloc_lithology.df <- cbind(sherloc.df,lithology.df )

# Review what we have
summary(sherloc_lithology.df)

# Combine the Lithology and SHERLOC matrices
sherloc_lithology.matrix<-cbind(sherloc.matrix,lithology.matrix)

# Review the resulting matrix
str(sherloc_lithology.matrix)

```
## Data Set H: Sherloc + Lithology + PIXL 

Create data frame and matrix from prior datasets by making on appropriate combinations.

```{r}
# Combine the Lithology and SHERLOC dataframes
sherloc_lithology_pixl.df <- cbind(sherloc.df,lithology.df, pixl.df )

# Review what we have
summary(sherloc_lithology_pixl.df)

# Combine the Lithology, SHERLOC and PIXLmatrices
sherloc_lithology_pixl.matrix<-cbind(sherloc.matrix,lithology.matrix,pixl.matrix)

# Review the resulting matrix
str(sherloc_lithology_pixl.matrix)

```

# Analysis of Data (Part 3)

Each team has been assigned one of six datasets:   

1. Dataset B: PIXL: The PIXL team's goal is to understand and explain how scaling changes results from Assignment 1.   The matrix version was scaled above but not in Assignment 1.  

2. Dataset C: LIBS (with appropriate scaling as necessary. Not scaled yet.) 

3. Dataset D: Sherloc (with appropriate scaling as necessary. Not scaled yet.)

4. Dataset E: PIXL + Sherloc (with appropriate scaling as necessary. Not scaled yet.)

5. Dataset F: PIXL + Lithography (with appropriate scaling as necessary. Not scaled yet.) 

6. Dataset G: Sherloc + Lithograpy (with appropriate scaling as necessary. Not scaled yet.)

7. Dataset H: PIXL + Sherloc + Lithograpy  (with appropriate scaling as necessary. Not scaled yet.)

**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. 

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)

The Sherloc + Lithology dataset contains 16 rows, each representing a sample from Mars, and 99 features. These features include both measurements and metadata.

The measurements include the presence or absence of minerals such as "Plagioclase," "Sulfate," "Fe-Mg carbonate," and others, indicating the sample's mineralogical composition. The metadata provides context about each sample, including features like "sample," "type," "campaign," and "abrasion," which describe where and how the sample was collected.

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) 

```{r}
str(sherloc_lithology.df) 
numeric_data <- sherloc_lithology.df[, sapply(sherloc_lithology.df, is.numeric)]
scaled_data <- scale(numeric_data)
scaled_data_with_metadata <- cbind(sherloc_lithology.df[, 1:4], scaled_data)
```

I chose z-score scaling because it ensures that each feature in the dataset has a mean of 0 and a standard deviation of 1. This method is particularly important when the dataset contains features  that are measured on different scales. In the case of the Sherloc + Lithology dataset, the presence or concentration of various minerals might vary significantly in magnitude. By applying z-score scaling, we standardize the features so that they all contribute equally to the analysis, preventing any one feature with larger values from dominating the clustering or other analyses.



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}
library(knitr)

scaled_data[is.na(scaled_data)] <- 0
scaled_data[is.nan(scaled_data)] <- 0
scaled_data[is.infinite(scaled_data)] <- 0

# Function to plot WSS (within-cluster sum of squares)
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")
}

# Apply wssplot to scaled data
wssplot(scaled_data, nc=8, seed=2)

# Run k-means on the cleaned scaled data
set.seed(2)
k <- 3
km <- kmeans(scaled_data, centers=k)

# Visualize cluster centers using pheatmap
pheatmap(km$centers, scale="none") 

# Create a data frame to show the size of each cluster
cluster.df <- data.frame(cluster=1:k, size=km$size)
kable(cluster.df, caption="Samples per cluster")


```


To determine the optimal number of clusters for the k-means clustering, I utilized the elbow method. This approach involves plotting the within-cluster sum of squares (WSS) for a range of cluster numbers and identifying the point where the reduction in WSS becomes less pronounced, forming an "elbow" in the plot. In this case, I plotted the WSS for 1 to 8 clusters, and the elbow was clearly visible at 3 clusters. Up to this point, the WSS decreased significantly, but adding more clusters after 3 led to diminishing returns, with minimal improvement in WSS. Hence, I selected 3 as the optimal number of clusters.

Upon executing the k-means clustering with 3 clusters, the distribution of points was as follows: Cluster 1 contained 7 points, Cluster 2 had 2 points, and Cluster 3 also contained 7 points. This distribution reflects how the samples were grouped based on the feature similarities.

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}



scaled_data_clean <- na.omit(scaled_data)  
k <- min(2, nrow(scaled_data_clean))  

invalid_rows <- numeric_data[!complete.cases(numeric_data), ]

numeric_data[is.na(numeric_data)] <- apply(numeric_data, 2, function(x) mean(x, na.rm = TRUE))

# 1. Scaling the data (ensure to handle NA, NaN, Inf)
numeric_data <- sherloc_lithology.df[, sapply(sherloc_lithology.df, is.numeric)]
scaled_data <- scale(numeric_data)



# 3. Perform k-means clustering
set.seed(123)  
k <- 3  

# Run k-means clustering on scaled data
km <- kmeans(scaled_data_clean, centers = k)

# 4. Feature selection: Calculate mean of each feature by cluster
cluster_means <- aggregate(scaled_data_clean, by = list(cluster = km$cluster), FUN = mean)

# Look at the means for Cluster 1
cluster_1_means <- cluster_means[cluster_means$cluster == 1, ]

# View key features (Fe-Mg carbonate and Chromite) for Cluster 1
cluster_1_fe_mg_carbonate <- cluster_1_means[,"Fe-Mg carbonate"]  
cluster_1_chromite <- cluster_1_means[,"Chromite"]

print(paste("Cluster 1 Fe-Mg Carbonate Mean:", cluster_1_fe_mg_carbonate))
print(paste("Cluster 1 Chromite Mean:", cluster_1_chromite))

# 5. Visualize key features (Fe-Mg Carbonate and Chromite) across clusters

feature_plot_data <- data.frame(
  Cluster = as.factor(km$cluster), 
  Fe_Mg_Carbonate = scaled_data_clean[,"Fe-Mg carbonate"],  
  Chromite = scaled_data_clean[,"Chromite"]  
)

# Plot Fe-Mg Carbonate by cluster
ggplot(feature_plot_data, aes(x = Cluster, y = Fe_Mg_Carbonate, fill = Cluster)) +
  geom_boxplot() +
  ggtitle("Fe-Mg Carbonate Concentration by Cluster") +
  xlab("Cluster") + ylab("Fe-Mg Carbonate")

# Plot Chromite by cluster
ggplot(feature_plot_data, aes(x = Cluster, y = Chromite, fill = Cluster)) +
  geom_boxplot() +
  ggtitle("Chromite Concentration by Cluster") +
  xlab("Cluster") + ylab("Chromite")

```
In this analysis, k-means clustering was applied to the MARS dataset, focusing on mineralogical content, particularly Fe-Mg Carbonate and Chromite. The objective was to uncover patterns or distinct groupings within the dataset and draw insights into Mars' geological history. After identifying three clusters, the analysis examined the concentrations of Fe-Mg Carbonate and Chromite in each cluster, revealing significant differences across them.

Fe-Mg Carbonate concentrations were notably high in Cluster 1, while Clusters 2 and 3 exhibited almost no presence of this mineral. This suggests that Cluster 1 represents regions on Mars that are rich in Fe-Mg carbonates, potentially indicating historical geological processes involving water interaction or specific atmospheric conditions that supported carbonate formation. The presence of Fe-Mg Carbonate may imply that the areas corresponding to Cluster 1 experienced conditions that were conducive to the formation of carbonates, a potential marker for ancient Martian environments with liquid water.

Similarly, Chromite concentrations were also found to be high in Cluster 1, while Clusters 2 and 3 showed very little to no Chromite. Chromite is commonly associated with igneous rock formations, suggesting that Cluster 1 might represent older, volcanic terrains on Mars. The high concentration of both Chromite and Fe-Mg Carbonate in Cluster 1 points toward a unique geological history in these regions, possibly involving significant volcanic activity and the presence of water during Mars' early history.

Overall,this analysis shows that Cluster 1 stands out for its higher concentrations of Fe-Mg Carbonate and Chromite, pointing toward a unique geological history that could be crucial for understanding Mars' past environments. This suggests that regions in Cluster 1 are potential sites for further exploration to uncover Mars' volcanic history and its past interaction with water. By contrast, Clusters 2 and 3 likely represent different lithological types or regions, with little to no presence of these key minerals.

# Preparation of Team Presentation (Part 4) 

Prepare a presentation of your teams result to present in class on **September 11** starting at 9am in AE217 (20 pts) 
The presentation should include the following elements

0.Your teams names and members
1. A **Description** of the data set that you analyzed including how many observations and how many features. (<= 1.5 mins)
2. Each team member gets **three minutes** to explain their analysis: 
  * what analysis they performed
  * the results of that analysis
  * a brief discussion of their interpretation of these results
  * <= 18 mins _total!_
3. A **Conclusion** slide indicating major findings of the teams (<= 1.5 mins)
4. Thoughts on **potential next steps** for the MARS team (<= 1.5 mins) 

* A template for your team presentation is included here: https://bit.ly/dar-template-f24

* The rubric for the presentation is here: 

https://docs.google.com/document/d/1-4o1O4h2r8aMjAplmE-ItblQnyDAKZwNs5XCnmwacjs/pub

 
* Post a link to your teams presentation in the MARS webex chat before class.  You can continue to edit until the last minute.   




# When you're done: SAVE, COMMIT and PUSH YOUR CHANGES!

When you are satisfied with your edits and your notebook knits successfully, remember to push your changes to the repo using the following steps:

* `git branch`
   * To double-check that you are in your working branch
* `git add <your changed files>`
* `git commit -m "Some useful comments"`
* `git push origin <your branch name>`
*  do a pull request





# APPENDIX: Accessing RStudio Server on the IDEA Cluster

The IDEA Cluster provides seven compute nodes (4x 48 cores, 3x 80 cores, 1x storage server)

* The Cluster requires RCS credentials, enabled via registration in class
    * email John Erickson for problems `erickj4@rpi.edu`
* RStudio, Jupyter, MATLAB, GPUs (on two nodes); lots of storage and computes
* Access via RPI physical network or VPN only

# More info about Rstudio on our Cluster  

## RStudio GUI Access:

* Use: 
   * http://lp01.idea.rpi.edu/rstudio-ose/
   * http://lp01.idea.rpi.edu/rstudio-ose-3/ 
   * http://lp01.idea.rpi.edu/rstudio-ose-6/ 
   * http://lp01.idea.rpi.edu/rstudio-ose-7/ 
* Linux terminal accessible from within RStudio "Terminal" or via ssh (below)