diff --git a/StudentNotebooks/Assignment02/roberd10-dar-assignment2-f24.Rmd b/StudentNotebooks/Assignment02/roberd10-dar-assignment2-f24.Rmd new file mode 100755 index 0000000..b236354 --- /dev/null +++ b/StudentNotebooks/Assignment02/roberd10-dar-assignment2-f24.Rmd @@ -0,0 +1,545 @@ +--- +title: "Mars 2020 Mission Data Notebook group C:" +subtitle: "DAR Assignment 2" +author: "Doña Roberts" +date: "`r format(Sys.time(), '%d %B %Y')`" +output: + pdf_document: default + html_document: + toc: true + number_sections: true + df_print: paged +--- +```{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) +} + +if (!require("knitr")) { + install.packages("knitr") + library(knitr) +} + +``` + +# 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) + +I'm looking at the __Laser-induced breakdown spectroscopy__ (*LIBS*) data, i.e Dataset C. The data has 1,932 rows, i.e 1,932 samples. It has 13 features, the last 8 of these features represent the percentage of various molecules/minerals present (measurements), and the first 5 features are metadata such as time (sol), position (lat, lon), target, and point. + +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) + +I chose _not_ to scale or center the LIBS data at this point. +I'm not centering, because it makes the most sense for "0" to be the minimum and there isn't any benefit to centering it. +I'm not scaling the data (at this moment) because it wouldn't effect the cluster formation and it'd have to be scaled again after the cluster averages had been determined anyways. I do scale it after cluster averages have been calculated though, as without scaling the large average percentage of SiO2 completely conceals the variation in other molecules. + +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) + +We will be clustering not by mineral presence but by *location*. That is, by the latitude and longitude of the samples. + +```{r} +# Setting seed +set.seed(25) + +# A user-defined function to examine clusters and plot the results +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 our LIBS data +libs.location <- as.data.frame(cbind(libs.df$lon,libs.df$lat)) +wssplot(libs.location, nc=8, seed=25) +``` +Based on the plot above, we will chose the elbow, k = 3, for our number of clusters + +```{r} +# Use our chosen 'k' to perform k-means clustering +set.seed(15) +k <- 3 +km.loc <- kmeans(libs.location,k) + +cluster.df<-data.frame(cluster= 1:3, size=km.loc$size) +kable(cluster.df,caption="Samples per cluster") +``` + +From above we can see that the clusters are actually pretty evenly distributed. Each having between 550 and 760 samples. + +Now that we have the clusters, we'll look at what we can learn from them. + +4. _Perform a **creative analysis** that provides insights into what one or more of the clusters are and what they tell you about the MARS data: Alternatively do another creative analysis of your datasets that leads to one of more findings. Make sure to explain what your analysis and discuss your the results. + +First, we'll look at how exactly the data was clustered by plotting based on location (x = lon, y = lat) and coloring based on cluster. +```{r} +cluster <- factor(km.loc$cluster) +ggplot(data = libs.df, + aes(lon,lat) + ) + + ggtitle("Map of samples based on location") + + geom_point(aes(colour = cluster)) +``` + +From the plot above we can see that k-means clustering did an extremely good job at splitting based on location, and we can also visualize how far apart the sample locations between clusters are. + +Now we'll actually look at the mean percentage of minerals present in each cluster. We _will_ be scaling the data after the averages have been calculated, so that we can see which locations had more of which minerals instead of just that they all had a high concentration of SiO2 (which is all we'd be seeing without scaling). + +It's important to remember that since this is the **averages** in each area (cluster) the variation between *samples in an area* is not visible. We are abstracting out from that to see if there is any relation in concentrations and areas, but in the process we will be losing *a lot* of detail. + +```{r} +# Finding averages for each cluster +c1 <- libs.matrix[km.loc$cluster == 1,] +cluster1_avg <- colMeans(c1) +c2 <- libs.matrix[km.loc$cluster == 2,] +cluster2_avg <- colMeans(c2) +c3 <- libs.matrix[km.loc$cluster == 3,] +cluster3_avg <- colMeans(c3) +avg <- rbind(cluster1_avg,cluster2_avg,cluster3_avg) +avg <- avg %>% scale(center=FALSE) + +pheatmap(avg,scale="none",center=FALSE,cluster_cols = FALSE,cluster_rows=FALSE,main="Heatmap of average molecule concentrations for each cluster") +``` + +From the heatmap above, we can see that the first area (cluster 1) had a higher concentration of K2O, TiO2, Na2O, and CaO concentration than the other areas. We also see that the second area was about average on _all_ of the minerals*, with slightly lower concentrations of K2O and Na2O. Finally, area 3 seems to have the highest concentration of MgO. + +Also interesting to note is that the average concentration for both SiO2 and FeOT between the different areas seem to remain about the same. This might relate to these two minerals being the two with the highest concentrations. + +*It's __very__ important to note though that since this is the _averages_ of the samples in each area that this could mean each individual sample in this area is also about average on each mineral __or__ it could mean that the samples in this area are all wildly varied and that they happen to average out to between those in area 1 and area 2's averages. + +One thing we could speculate from this is that the concentration of MgO increases and that K2O, TiO2, Na2O, and CaO concentrations decrease as you head north west. This would match with cluster 2, which is in the middle, being between cluster 1 and cluster 3 averages. It's important to remember this is just *speculation* though, because we have abstracted out from the variation between samples within clusters. + +# 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 ` +* `git commit -m "Some useful comments"` +* `git push origin ` +* 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) + diff --git a/StudentNotebooks/Assignment02/roberd10-dar-assignment2-f24.pdf b/StudentNotebooks/Assignment02/roberd10-dar-assignment2-f24.pdf new file mode 100644 index 0000000..fcbad32 Binary files /dev/null and b/StudentNotebooks/Assignment02/roberd10-dar-assignment2-f24.pdf differ