fixing error with dataframe size

StudentNotebooks/Assignment05/MatchingLIBSandPIXL.Rmd

@@ -90,11 +90,8 @@ libs.df[,6:13] <- sapply(libs.df[,6:13],as.numeric)
 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<-cbind("nearestpixl"=0,libs.df[,1:4])
 libstargets.df<-distinct(libstargets.df)
 ```
 
@@ -120,33 +117,24 @@ 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}
+# Create a new dataframe with the LIBS metatdata as well as features corresponding to each PIXL Abrasion
 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)
-
+#Calculate the distance between each LIBS target and each PIXL abrasion, then fill in the minimum distances
 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,c(7:14)]<-c(distHaversine(pixl.df[1,c(1,2)],libstargets.df[i,c(3,4)],r=3393169),
+                                 distHaversine(pixl.df[2,c(1,2)],libstargets.df[i,c(3,4)],r=3393169),
+                                 distHaversine(pixl.df[3,c(1,2)],libstargets.df[i,c(3,4)],r=3393169),
+                                 distHaversine(pixl.df[4,c(1,2)],libstargets.df[i,c(3,4)],r=3393169),
+                                 distHaversine(pixl.df[5,c(1,2)],libstargets.df[i,c(3,4)],r=3393169),
+                                 distHaversine(pixl.df[6,c(1,2)],libstargets.df[i,c(3,4)],r=3393169),
+                                 distHaversine(pixl.df[7,c(1,2)],libstargets.df[i,c(3,4)],r=3393169),
+                                 distHaversine(pixl.df[8,c(1,2)],libstargets.df[i,c(3,4)],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[i,1]<-which.min(libstargets.df[i,c(7:14)])
+    libstargets.df[i,6]<-min(libstargets.df[i,c(7:14)])
 }
 
 libstargets.df$nearestpixl<-as.factor(libstargets.df$nearestpixl)
@@ -156,6 +144,7 @@ levels(libstargets.df$nearestpixl)<-(c("Bellegrade","Dourbes","Quartier","Alfalf
 
 
 ```{r}
+#Create vectors of LIBS targets corresponding to each PIXL Abrasion
 Bellegrade<-libstargets.df[libstargets.df$nearestpixl=="Bellegrade",]$target
 Dourbes<-libstargets.df[libstargets.df$nearestpixl=="Dourbes",]$target
 Quartier<-libstargets.df[libstargets.df$nearestpixl=="Quartier",]$target
@@ -166,82 +155,89 @@ Novarupta<-libstargets.df[libstargets.df$nearestpixl=="Novarupta",]$target
 UganikIsland<-libstargets.df[libstargets.df$nearestpixl=="UganikIsland",]$target
 ```
 
-
-### Ternary Diagram
 ```{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))
+#Add the nearest Abrasion to the LIBS data
+libs.pixl.merged <- cbind("Abrasion"=0,libs.df)
 
-libs.tern<-cbind("Abrasion"=0,libs.tern)
-
-libs.tern<-libs.tern%>%
+libs.pixl.merged<-libs.pixl.merged%>%
   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)))))))))
+                    ifelse(target %in% UganikIsland, "Uganik Island",Abrasion)))))))))
+
+targetdistance<-libstargets.df[,2:6]
+libs.pixl.merged<-merge(libs.pixl.merged,targetdistance,by=c("target","sol","lat","lon"),all.x=T)
+```
+
+```{r}
+#Add back some pixl features
+pix<-pixl.df[,c(1,2,4,5)]
+libs.pixl <- merge(libs.pixl.merged, pix, by.x="Abrasion",by.y="abrasion",all.x=TRUE)
+
+#rename and reorder columns
+libs.pixl<-cbind("LIBS.Target"=libs.pixl$target,libs.pixl[,4:5],"LIBS.Sol"=libs.pixl$sol,"LIBS.Point"=libs.pixl$point,"Distance"=libs.pixl$Distance,"PIXL.Abrasion"=libs.pixl$Abrasion,libs.pixl[,16:18],libs.pixl[,7:14])
+colnames(libs.pixl)<-c("LIBS.Target","LIBS.Lat","LIBS.Lon","LIBS.Sol","LIBS.Point",   "Distance","PIXL.Abrasion","PIXL.Lat","PIXL.Lon","PIXL.Campaign","LIBS.SiO2","LIBS.TiO2","LIBS.Al2O3","LIBS.FeOT","LIBS.MgO","LIBS.CaO","LIBS.Na2O","LIBS.K2O")
+```
 
-kabledf<-rbind("Distance (m)"=meters,"Targets"=length(included.libs),"Points"=nrow(libs.tern))
+```{r}
+#setwd("~/DAR-Mars-F24/StudentData")
+#saveRDS(libs.pixl,"PIXL_LIBS_Combined.Rds")
+```
+
+The libs.pixl dataframe is now saved to PIXL_LIBS_Combined.Rds, which can be found in the StudentData folder. 
+
+### Ternary Diagram
+
+Example of creating a ternary plot filtering the combined LIBS and PIXL data
+```{r}
+libs.tern <- libs.pixl %>%
+  mutate(x=(LIBS.SiO2+LIBS.Al2O3)/100,y=(LIBS.FeOT+LIBS.MgO)/100,z=(LIBS.CaO+LIBS.Na2O+LIBS.K2O)/100)
 
-kable(kabledf)
+libs.tern<-libs.tern[,c(6,7,19:21)]
 ```
 
 
 ```{r}
+meters=7
+
 ggtern(libs.tern, ggtern::aes(x=x,y=y,z=z)) +
-  geom_point(data=libs.tern,aes(color=Abrasion,alpha=0.5)) + 
+  geom_point(data=subset(libs.tern,Distance<=meters),aes(color=PIXL.Abrasion,alpha=0.5)) + 
   theme_rgbw() + 
-  labs(title=paste("Mars LIBS Data Within",distance,"meters of PIXL",sep=" "),
+  labs(title=paste("Mars LIBS Data Within",meters,"meters of PIXL",sep=" "),
        x="Si+Al",
        y="Fe+Mg",
        z="Ca+Na+K")+theme(legend.position="right") + 
   guides(alpha="none")
-```
 
-
-### Line plot
-
-```{r}
 meters=100
 
-included.libs<-(libstargets.df%>%
-  filter(Distance<meters))$target
-
-
-libs.matrix <-libs.df %>%
-  filter(target %in% included.libs)
+ggtern(libs.tern, ggtern::aes(x=x,y=y,z=z)) +
+  geom_point(data=subset(libs.tern,Distance<=meters),aes(color=PIXL.Abrasion,alpha=0.5)) + 
+  theme_rgbw() + 
+  labs(title=paste("Mars LIBS Data Within",meters,"meters of PIXL",sep=" "),
+       x="Si+Al",
+       y="Fe+Mg",
+       z="Ca+Na+K")+theme(legend.position="right") + 
+  guides(alpha="none")
+```
 
-libs.matrix <- libs.matrix[,c(5,7:14)]
 
-libs.matrix<-libs.matrix[,c(1:2,4:9,3)]
+### Line plot
 
-libs.matrix<-cbind("Abrasion"=0,libs.matrix)
+Example of how to add the pixl data as extra rows to the LIBS data, so that it can be graphed on the same plot
 
-libs.matrix<-libs.matrix%>%
-  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)))))))))
+```{r}
+#remove certain features from the combined pixl libs dataset
+libs.matrix <- libs.pixl[,c(7,1,11,13:18,12)]
 
+#rename columns
 libs.matrix<-cbind(libsorpixl=1,libs.matrix)
+
+colnames(libs.matrix)<-c("libsorpixl","Abrasion","targets+names","SiO2","Al2O3","FeOT","MgO","CaO","Na2O","K2O","TiO2")
 ```
 
 
@@ -262,9 +258,7 @@ pixl.df<-cbind(libsorpixl=0,pixl.df)
 ```
 
 ```{r}
+#rename pixl columns and combine dataframes
 colnames(pixl.df)<-colnames(libs.matrix)
 pixllibs.df<-rbind(pixl.df,libs.matrix)
 ```
-
-
-