combined libs/pixl work file so everything is in one place

StudentNotebooks/Assignment05/MatchingLIBSandPIXL.Rmd

@@ -0,0 +1,212 @@
+---
+title: "Combining LIBS and PIXL Data"
+author: "Margo VanEsselstyn"
+date: "`r Sys.Date()`"
+output:
+  pdf_document:
+    toc: yes
+  html_document:
+    toc: yes
+subtitle: "DAR Mars"
+---
+
+```{r setup, include=FALSE}
+# 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)
+}
+
+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("knitr")){
+  install.packages("knitr")
+  library(knitr)
+}
+
+if(!require("cluster")){
+  install.packages("cluster")
+  library(cluster)
+}
+
+if(!require("ggtern")){
+  install.packages("ggtern")
+  library(ggtern)
+}
+
+if(!require("caret")){
+  install.packages("caret")
+  library(caret)
+}
+
+if(!require("geosphere")){
+  install.packages("geosphere")
+  library(geosphere)
+}
+
+knitr::opts_chunk$set(echo = TRUE)
+```
+
+
+### Data Preparation
+
+Load in LIBS data
+```{r, data01}
+#load in LIBS data
+libs.df <- readRDS("/academics/MATP-4910-F24/DAR-Mars-F24/Data/supercam_libs_moc_loc.Rds")
+
+#Drop the standard deviation features, the sum of the percentages, 
+#the distance, and the total frequencies
+libs.df <- libs.df %>% 
+  select(!(c(distance_mm,Tot.Em.,SiO2_stdev,TiO2_stdev,Al2O3_stdev,FeOT_stdev,
+             MgO_stdev,Na2O_stdev,CaO_stdev,K2O_stdev,Total)))
+
+# Convert the points to numeric
+libs.df$point <- as.numeric(libs.df$point)
+libs.df[,6:13] <- sapply(libs.df[,6:13],as.numeric)
+
+#remove the scct/reference samples
+libs.df<-libs.df%>%
+  filter(!(grepl("scct", target)))
+
+#add a column to indicate the nearest pixl
+libs.df<-cbind(nearestpixl=0,libs.df)
+
+#make a dataframe of just the LIBS Lat/Long and target name and remove duplicates
+libstargets.df<-libs.df[,c(1,3,4,5)]
+libstargets.df<-distinct(libstargets.df)
+```
+
+Load in PIXL data
+
+```{r, data02}
+#read in pixl data with lat/long
+pixl.df<-readRDS("/academics/MATP-4910-F24/DAR-Mars-F24/StudentData/pixl_sol_coordinates.Rds")
+
+#include only pixl metadata
+pixl.df<-pixl.df %>%
+  select(c(1,2,19,20,22))
+
+#convert Lat/Long to numeric
+pixl.df$Lat <- as.numeric(pixl.df$Lat)
+pixl.df$Long <- as.numeric(pixl.df$Long)
+
+#remove rows so we only have one sample per abrasion and remove atmospheric sample
+pixl.df<-pixl.df[c(2,4,6,8,10,12,14,16),]
+```
+
+
+### Combining datasets
+
+```{r}
+distance=100
+distCosine(pixl.df[1,c(1,2)],libstargets.df[1,c(2,3)], r=3393169)
+for(i in 1:nrow(pixl.df)) {
+    libstargets.df<-libstargets.df%>%mutate(nearestpixl = ifelse(distHaversine(pixl.df[i,c(1,2)],c(lat,lon),r=3393169),pixl.df[i,5], nearestpixl))
+
+}
+```
+
+```{r}
+libstargets.df<-cbind(libstargets.df,"Distance"=0,"Bellegrade"=0,"Dourbes"=0,"Quartier"=0,"Alfalfa"=0,"ThorntonGap"=0,"BerryHollow"=0,"Novarupta"=0,"UganikIsland"=0)
+```
+
+```{r}
+#DistCosine(pixl.df[1,c(1,2)],libstargets.df[1,c(2,3)], r=3393169)
+
+for(i in 1:nrow(libstargets.df)) {
+    libstargets.df[i,c(6:13)]<-c(distHaversine(pixl.df[1,c(1,2)],libstargets.df[i,c(2,3)],r=3393169),
+                                 distHaversine(pixl.df[2,c(1,2)],libstargets.df[i,c(2,3)],r=3393169),
+                                 distHaversine(pixl.df[3,c(1,2)],libstargets.df[i,c(2,3)],r=3393169),
+                                 distHaversine(pixl.df[4,c(1,2)],libstargets.df[i,c(2,3)],r=3393169),
+                                 distHaversine(pixl.df[5,c(1,2)],libstargets.df[i,c(2,3)],r=3393169),
+                                 distHaversine(pixl.df[6,c(1,2)],libstargets.df[i,c(2,3)],r=3393169),
+                                 distHaversine(pixl.df[7,c(1,2)],libstargets.df[i,c(2,3)],r=3393169),
+                                 distHaversine(pixl.df[8,c(1,2)],libstargets.df[i,c(2,3)],r=3393169))
+    
+    libstargets.df[i,1]<-which.min(libstargets.df[i,c(6:13)])
+    libstargets.df[i,5]<-min(libstargets.df[i,c(6:13)])
+}
+
+libstargets.df$nearestpixl<-as.factor(libstargets.df$nearestpixl)
+
+levels(libstargets.df$nearestpixl)<-(c("Bellegrade","Dourbes","Quartier","Alfalfa","ThorntonGap","BerryHollow","Novarupta","UganikIsland"))
+```
+
+
+```{r}
+Bellegrade<-libstargets.df[libstargets.df$nearestpixl=="Bellegrade",]$target
+Dourbes<-libstargets.df[libstargets.df$nearestpixl=="Dourbes",]$target
+Quartier<-libstargets.df[libstargets.df$nearestpixl=="Quartier",]$target
+Alfalfa<-libstargets.df[libstargets.df$nearestpixl=="Alfalfa",]$target
+ThorntonGap<-libstargets.df[libstargets.df$nearestpixl=="ThorntonGap",]$target
+BerryHollow<-libstargets.df[libstargets.df$nearestpixl=="BerryHollow",]$target
+Novarupta<-libstargets.df[libstargets.df$nearestpixl=="Novarupta",]$target
+UganikIsland<-libstargets.df[libstargets.df$nearestpixl=="UganikIsland",]$target
+```
+
+```{r}
+meters=100
+
+included.libs<-(libstargets.df%>%
+  filter(Distance<meters))$target
+
+libs.matrix <-libs.df %>%
+  filter(target %in% included.libs)
+
+libs.matrix <- libs.matrix[,c(5,7:14)]
+
+libs.tern <- as.data.frame(libs.matrix) %>%
+  mutate(x=(SiO2+Al2O3)/100,y=(FeOT+MgO)/100,z=(CaO+Na2O+K2O)/100) %>%
+  select(-c(SiO2,Al2O3,FeOT,MgO,CaO,Na2O,K2O,TiO2))
+
+libs.tern<-cbind("Abrasion"=0,libs.tern)
+
+libs.tern<-libs.tern%>%
+  mutate(Abrasion = ifelse(target%in%Alfalfa,"Alfalfa", 
+                    ifelse(target %in% Bellegrade, "Belegrade",
+                    ifelse(target %in% BerryHollow, "BerryHollow",
+                    ifelse(target %in% Dourbes, "Dourbes",
+                    ifelse(target %in% Novarupta, "Novarupta",
+                    ifelse(target %in% Quartier, "Quartier",
+                    ifelse(target %in% ThorntonGap, "ThorntonGap",
+                    ifelse(target %in% UganikIsland, "UganikIsland",Abrasion)))))))))
+
+kabledf<-rbind("Distance (m)"=meters,"Targets"=length(included.libs),"Points"=nrow(libs.tern))
+
+kable(kabledf)
+```
+
+
+```{r}
+ggtern(libs.tern, ggtern::aes(x=x,y=y,z=z)) +
+  geom_point(data=libs.tern,aes(color=Abrasion,alpha=0.5)) + 
+  theme_rgbw() + 
+  labs(title=paste("Mars LIBS Data Within",distance,"meters of PIXL",sep=" "),
+       x="Si+Al",
+       y="Fe+Mg",
+       z="Ca+Na+K")+theme(legend.position="right") + 
+  guides(alpha="none")
+```
+