+
+
+
+
+
+
+DAR F24 How do Campaigns Compare?
+DAR Project Name: Mars
+Ashton Compton
+2024-12-10
+ +Note - This notebook is meant as a draft for a page in the Mars +Mission Minder 2D app addressing the topic of campaigns.
+Question - “What is the difference between samples from the two +campaigns Delta Front and Crater Floor?”
+Every sample from the Perseverance Rover is assigned a campaign. For +this analysis, the two accessible campaigns were Delta Front and Crater +Floor. Crater Floor is where Perseverance landed, and is a flat open +plain. Next the rover traveled to a Delta Fan structure and began +climbing it. Samples taken at the beginning of this journey belong to +Delta Front.
+Title: Map of Samples Annotating the Two Campaigns
+
+
+
+
+campaigns Map
+Description: Map showing the first 16 Perserverance Samples with the +Crater Floor and Delta Front regions annotated.
+Title: Counting Mineral Occurrence across Samples separated by +Campaign
+
Description: Sherloc was grouped by campaign
+and the total value of each feature across all samples was summed up and
+displayed in the above plot. If a feature had a value of zero for every
+sample, it was not included in the plot.
Title: Pixl Feature Distribution by Campaign
+
Description: Box plots showing the
+distribution of each feature in Pixl, seperated by Campaign. Note: Sort
+low to high
Title: Libs clustering with Pixl data seperated by campaign on +Ternairy Diagram
+
Description: Libs data was clustered and ternairy
+plotted along with Pixl data separated by campaign. The Pixl data points
+are labelled by name as well. Note-Use the common clusters and use the
+non-aggregate plot
Conclusions:
+In Sherloc, certain minerals are abundant in both campaigns, +especially Crater Floor. -Carbonate is common in both campaigns -Organic +Matter is also common in both campaigns -Sulfate and Olivine are also +common in both
+High in Crater Floor: -Pyroxene and amorphous silicate are abundant +in Crater Floor but sparse in Delta Front
+Fe_Mg_Clay, Hydrated_Mg_Fe_sulfate, Kaolinite, and Mg_sulfates are in +3 samples in Delta Front, but not at all in Crater Floor.
+There are 20 minerals that are exclusively in either Crater Floor or +Delta Front.
+4 minerals have a count of zero, meaning they weren’t detected in any +campaign (Perchlorates, Na_Perchlorate, Hydrated_Carbonates, & +Hydrated_Iron_Oxide). These minerals are present in the atmospheric +sample, which is absent in this analysis.
+The pixl graph reveals some big differences between Crater Floor and +Delta Front. Namely in Al2O3, CaO, Cr2O3, MgO, P2O5, SO3, & SiO2
+CODE: Load libaries Set up dataframes/matrices
+Prepare data
+#Load in data
+###
+# Load the saved lithology data with locations added
+#lithology.df<- readRDS("/academics/MATP-4910-F24/DAR-Mars-F24/Data/mineral_data_static.Rds")
+meta.df<- readRDS("~/DAR-Mars-F24/StudentData/v1_sample_meta.Rds")
+
+#Select 2:16
+meta.df <- meta.df[2:16,]
+
+#Read in v1 lithology
+lithology.df<- readRDS("~/DAR-Mars-F24/StudentData/v1_lithology.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
+#Also i'm getting rid of the atmospheric sample for now
+lithology.df<-lithology.df[2:16,]
+
+lithology.df$campaign <- meta.df$Campaign
+###
+#Used for map
+pixl_pos.df<- meta.df %>% select(Lat, Lon)
+
+# Load the saved PIXL data with locations added
+pixl.df <- readRDS("~/DAR-Mars-F24/StudentData/v1_pixl.Rds")
+
+# Convert to factors
+pixl.df[sapply(pixl.df, is.character)] <- lapply(pixl.df[sapply(pixl.df, is.character)],
+ as.factor)
+
+#Get rid of atmospheric sample
+pixl.df <- pixl.df[2:16,]
+
+#Add campaign
+pixl.df$campaign <- meta.df$Campaign
+# # Make the matrix of just mineral percentage measurements
+# pixl.matrix <- pixl.df[,2:14]
+
+###
+# 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)
+
+#Remove atmospheric sample
+sherloc.matrix <- sherloc.matrix[2:16,]
+
+# Get sample information from PIXL and add to measurements -- assumes order is the same
+
+sherloc.df <- cbind(pixl.df[,c("Sample")],sherloc.matrix)
+
+# Measurements are everything except first column
+sherloc.matrix<-as.matrix(sherloc.matrix[,-1])
+
+###
+#Add in wss plot for elbow method clustering
+wssplot <- function(data, nc = 15, seed =10, title="Quality of k-means by Cluster") {
+ 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(title)
+}
+
+seed <- 14
+set.seed## function (seed, kind = NULL, normal.kind = NULL, sample.kind = NULL)
+## {
+## kinds <- c("Wichmann-Hill", "Marsaglia-Multicarry", "Super-Duper",
+## "Mersenne-Twister", "Knuth-TAOCP", "user-supplied", "Knuth-TAOCP-2002",
+## "L'Ecuyer-CMRG", "default")
+## n.kinds <- c("Buggy Kinderman-Ramage", "Ahrens-Dieter", "Box-Muller",
+## "user-supplied", "Inversion", "Kinderman-Ramage", "default")
+## s.kinds <- c("Rounding", "Rejection", "default")
+## if (length(kind)) {
+## if (!is.character(kind) || length(kind) > 1L)
+## stop("'kind' must be a character string of length 1 (RNG to be used).")
+## if (is.na(i.knd <- pmatch(kind, kinds) - 1L))
+## stop(gettextf("'%s' is not a valid abbreviation of an RNG",
+## kind), domain = NA)
+## if (i.knd == length(kinds) - 1L)
+## i.knd <- -1L
+## }
+## else i.knd <- NULL
+## if (!is.null(normal.kind)) {
+## if (!is.character(normal.kind) || length(normal.kind) !=
+## 1L)
+## stop("'normal.kind' must be a character string of length 1")
+## normal.kind <- pmatch(normal.kind, n.kinds) - 1L
+## if (is.na(normal.kind))
+## stop(gettextf("'%s' is not a valid choice", normal.kind),
+## domain = NA)
+## if (normal.kind == 0L)
+## stop("buggy version of Kinderman-Ramage generator is not allowed",
+## domain = NA)
+## if (normal.kind == length(n.kinds) - 1L)
+## normal.kind <- -1L
+## }
+## if (!is.null(sample.kind)) {
+## if (!is.character(sample.kind) || length(sample.kind) !=
+## 1L)
+## stop("'sample.kind' must be a character string of length 1")
+## sample.kind <- pmatch(sample.kind, s.kinds) - 1L
+## if (is.na(sample.kind))
+## stop(gettextf("'%s' is not a valid choice", sample.kind),
+## domain = NA)
+## if (sample.kind == 0L)
+## warning("non-uniform 'Rounding' sampler used", domain = NA)
+## if (sample.kind == length(s.kinds) - 1L)
+## sample.kind <- -1L
+## }
+## .Internal(set.seed(seed, i.knd, normal.kind, sample.kind))
+## }
+## <bytecode: 0x558e691d4380>
+## <environment: namespace:base>Used as base for map picture
+#Produce sample map with campaign differences
+# pixl_pos.df$Campaign <- meta.df$Campaign
+# ggplot(pixl_pos.df, aes(x= Lat, y= Lon, color=Campaign, size=1)) +
+# geom_point() +
+# theme_classic()Make interactive plotly plot for Lithology
+# Include all data processing code (if necessary), clearly commented
+#Start with lithology
+#Group by campaign & remove metadata
+lithology.df.sorted <- lithology.df %>% group_by(campaign) %>% select(-c(Sample))
+
+#Turn into long form and only keep positive cases
+lithology.df.sorted <- lithology.df.sorted %>% pivot_longer(2:ncol(lithology.df.sorted)-1,names_to = "Feature", values_to="Factor") %>% filter(Factor == 1)
+
+#Count # of identical cases
+lithology.df.sorted <- lithology.df.sorted %>% count(Feature)
+
+#Sort, Crater Floor is High to low & Delta Front is added back in low to high
+lithology.df.sorted <- lithology.df.sorted %>% filter(campaign == "Crater Floor") %>% arrange(desc(n)) %>% ungroup() %>% add_row(lithology.df.sorted %>% filter(campaign == "Delta Front") %>% arrange(n))
+
+
+#Make interactive plot for lithology
+p <- ggplot(lithology.df.sorted, aes(x=factor(Feature, levels = (Feature %>% unique())), y = n, fill = campaign)) +
+ geom_col(position=position_dodge(preserve="total"), width=0.6) +
+ theme(panel.grid.major.x=element_blank(), axis.text.x = element_text(angle = 60, vjust = 1.0, hjust=1, size = 12)) +
+ labs(x="", y="Count") +
+ ggtitle("Sherloc Dataset, Total Mineral Occurance across Samples") +
+ scale_fill_manual(values=c('#d6001c','#54585a'))
+
+ggplotly(p, tooltip = c("campaign",'x', "n"))#Commented out to knit to pdf, picture at top of reportCreate similar plot for Sherloc
+# #Repeat for sherloc
+# #Group by campaign & remove metadata
+# sherloc.df.sorted <- sherloc.df %>% group_by(Campaign) %>% select(-c(Sample))
+#
+# #Turn into long form and only keep positive cases
+# sherloc.df.sorted <- sherloc.df.sorted %>% pivot_longer(2:ncol(sherloc.df.sorted),names_to = "Feature", values_to="Factor") %>% filter(Factor == 1)
+#
+# #Count # of identical cases
+# sherloc.df.sorted <- sherloc.df.sorted %>% count(Feature)
+#
+# #Sort, Crater Floor is High to low & Delta Front is added back in low to high
+# sherloc.df.sorted <- sherloc.df.sorted %>% filter(campaign == "Crater Floor") %>% arrange(desc(n)) %>% ungroup() %>% add_row(sherloc.df.sorted %>% filter(campaign == "Delta Front") %>% arrange(n))
+#
+# p <- ggplot(sherloc.df.sorted, aes(x=factor(Feature, levels = (Feature %>% unique())), y = n, fill = campaign)) +
+# geom_col(position=position_dodge(preserve="total"), width=0.6) +
+# theme(panel.grid.major.x=element_blank(), axis.text.x = element_text(angle = 60, vjust = 1.0, hjust=1, size = 12)) +
+# labs(x="", y="Count") +
+# ggtitle("Sherloc Features Count by Campaign") +
+# scale_fill_paletteer_d(palette = "fishualize::Cephalopholis_argus")
+#
+# ggplotly(p, tooltip = c("campaign",'x', "n"))
+# #Commented out to knit to pdf, picture at top of reportMake box plots for pixl and sherloc
+#Make box plots
+pixl.lf <- pixl.df %>% select(-c(Sample)) %>% pivot_longer(1:13)
+colnames(pixl.lf)<- c("campaign", "feature", "value")
+ggplot(data = pixl.lf, aes(x=factor(feature, levels = (feature %>% unique())), y=value, color = campaign)) +
+ geom_boxplot() +
+ scale_y_log10() +
+ ggtitle("pixl, Compound Distribution by Campaign") +
+ labs(x="", y="log10 scale from percent composition") +
+ scale_colour_manual(values=c('#d6001c','#54585a'))## Warning in scale_y_log10(): log-10 transformation introduced infinite values.## Warning: Removed 5 rows containing non-finite outside the scale range
+## (`stat_boxplot()`).
+Code for ternairy plot
#take the sums of the specific elements
+pixl_ternary <- pixl.df %>%
+ mutate(x=(SiO2+Al2O3)/100,y=(FeOT+MgO)/100,z=(CaO+Na2O+K2O)/100) %>%
+ select(-c(SiO2,Al2O3,FeOT,MgO,CaO,Na2O,K2O)) %>%
+ drop_na()
+
+#This is for the labels on the Ternary Plot below
+pixl_ternary <- cbind(pixl_ternary, Sample_display=
+ c("2","3","4,6,7","5,8,9","","","","",
+ "10,11","","12,13","","14,15","","16"))
+
+# Load the saved LIBS data with locations added
+libs.df <- readRDS("~/DAR-Mars-F24/StudentData/v1_libs.Rds")
+
+libs.df$Point <- as.numeric(libs.df$Point)
+
+#suppressing warnings here because target and point do not get a mean calculated,
+#but thats is fine as we have the target anyways and point is no longer relevant
+suppressWarnings(
+ libs.uniquetar <-
+ aggregate(libs.df, list(Target = libs.df$Target), mean))
+
+#drop target and point from the data frame
+libs.uniquetar <- libs.uniquetar %>% select(!c(Target,Point))
+
+#Sum the elements we are looking at
+libs.df <- libs.df %>%
+ mutate(y = (FeOT + MgO) / 100, z = (CaO+Na2O+K2O) / 100, x = (SiO2 + Al2O3) / 100)
+
+#Same thing but aggregate
+libs.uniquetar <- libs.uniquetar %>%
+ mutate(y = (FeOT + MgO) / 100, z = (CaO+Na2O+K2O) / 100, x = (SiO2 + Al2O3) / 100)
+
+libs_ternplot <- libs.df %>% select(c(x,y,z))
+libs_ternplot2 <- libs.uniquetar %>% select(c(x,y,z))
+
+set.seed(1234)
+
+#kmeans on the original data
+tern.km <- kmeans(libs_ternplot, centers=4)
+
+libs_ternplot <- cbind(libs_ternplot, cluster=as.factor(tern.km$cluster))
+
+#kmeans on the aggregate data
+tern.km2 <- kmeans(as.matrix(libs_ternplot2), centers=4)
+
+libs_ternplot2 <- cbind(libs_ternplot2, cluster=as.factor(tern.km2$cluster))
+
+#ternary plot for LIBS data
+ggtern() +
+ #color by cluster
+ geom_point(data=libs_ternplot, aes(x=x,y=y,z=z, colour = cluster), alpha = 0.5) +
+ scale_colour_manual(values=c('#d6001c','#54585a','#9ea2a2','#000')) +
+ labs(title="Sample Cation Compositions",
+ subtitle="LIBS data Clustered by Cation Group with PIXL samples by Campaign",
+ x="Si+Al2",
+ y="Fe+Mg",
+ z="Ca+Na2+K2") +
+ #Add pixl
+ geom_point(data=pixl_ternary, aes(x=x,y=y,z=z, shape=campaign), colour='green', size=3) +
+ # #Add labels to PIXL data corresponding to sample number
+ geom_text(data=pixl_ternary,
+ ggtern::aes(x=x, y=y, z=z, label=Sample_display,
+ hjust = ifelse(x > 0.43, 1, -0.1), # Horizontal adjust to avoid overlap
+ vjust = ifelse(x == 0.3668, 1.3,
+ ifelse(x == 0.375, 1, ifelse(x > 0.43, 1.5, -0.3))),
+ fontface="bold"),
+ size=3, colour='green') +
+ theme_bw()## Warning in geom_point(data = libs_ternplot, aes(x = x, y = y, z = z, colour =
+## cluster), : Ignoring unknown aesthetics: z## Warning in geom_point(data = pixl_ternary, aes(x = x, y = y, z = z, shape =
+## campaign), : Ignoring unknown aesthetics: z## Warning in geom_text(data = pixl_ternary, ggtern::aes(x = x, y = y, z = z, :
+## Ignoring unknown aesthetics: z