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+# Question 1
+
+#Uploading data
+setwd("~/Desktop/Biocomputing/Week6")
+fasta=scan(file = "Lecture11.fasta.txt",what=character())
+
+# Using Stuart's code to get my matrix values becuase my code 
+# is still kind of weird
+
+# pre-allocate matrix to store sequence information
+summ=matrix(NA,length(fasta)/2,4)
+
+# loop through lines of fasta and process each record
+for(i in 1:length(fasta)){
+  # if-else to figure out if we have a recordID line or sequence line
+  if(grepl(">",fasta[i])){
+    summ[((i+1)/2),1]=substr(fasta[i],2,nchar(fasta[i])) # could use counter instead of complicated row calculation
+  }else{
+    seqLength=nchar(fasta[i])
+    
+    numG=nchar(fasta[i])-nchar(gsub("G","",fasta[i]))
+    numC=nchar(fasta[i])-nchar(gsub("C","",fasta[i]))
+    if(seqLength<=14){
+      Tm=2*(numG+numC)+2*seqLength
+    }else{
+      Tm=-9999
+    }
+    
+    summ[i/2,2]=seqLength
+    summ[i/2,3]=round((numG+numC)/seqLength*100,1)
+    summ[i/2,4]=Tm
+  }
+}
+
+seqSumm=data.frame(sequenceID=summ[,1],sequenceLength=as.numeric(summ[,2]),percentGC=as.numeric(summ[,3]),meltingTemp=as.numeric(summ[,4]))
+
+#### Graph 1 - Histogram of sequence lengths
+
+a = ggplot(data = seqSumm, aes(x = sequenceLength))
+a + geom_histogram() + theme_classic() + stat_bin(binwidth = 1) + 
+  xlab("Sequence Length") + ylab("Count") + 
+  ggtitle("Histogram of Sequence Length")
+
+#### Graph 2 - Histogram of %GC Content
+
+b  = ggplot(data = seqSumm, aes(x = percentGC))
+b + geom_histogram() + theme_classic() + xlab("% GC Content") + 
+  ylab("Count") + ggtitle("Histogram of %GC Content")
+
+
+
+#Question 2
+#Point-framing data and LAI data for a site in Alaska. 
+#Point-framing is just a measure of species richness from the number of times a pin dropped from the top of the canopy hits a certain spp.
+#LAI measurements are from a leaf area index meter.
+
+pfdata <- read.csv("/Users/elizabethfortin12/Documents/ND First Year/Meetings with Adrian/Fert Project/WorkingData/Point_Framing_Data_2016.csv", header = TRUE)
+laidata <- read.csv("/Users/elizabethfortin12/Documents/ND First Year/Meetings with Adrian/Fert Project/WorkingData/LAI.output.csv", header = FALSE)
+colnames(laidata) <- c("Site","Block","Treatment","LAI2016","LAI2017")
+
+library(plyr)
+pftot <- ddply(pfdata, c("SITE","Treatment"), summarize, Betula=sum(Betula), Tussock=sum(Tussock), Forb=sum(Forb), Total=sum(Total))
+colnames(pftot) <- c("Site","Treatment", "Betula","Tussock","Forb","Total")
+perc.cov <- ddply(pftot, c("Site", "Treatment"), summarize, Betula = (Betula/Total)*100, Tussock = (Tussock/Total)*100, Forb = (Forb/Total)*100)
+
+laitot <- ddply(laidata, c("Site", "Treatment"), summarize, LAI2016=mean(LAI2016), LAI2017=mean(LAI2017))
+
+pc.lai <- merge(perc.cov, laitot, by = c("Site","Treatment"))
+write.table(pc.lai, file = "pc.lai.txt")
+
+pclai <- read.table("/Users/elizabethfortin12/Documents/ND First Year/Biocomputing/R_Programming/pc.lai.txt")
+
+library(ggplot2)
+library(grid)
+library(gridExtra)
+library(reshape2)
+
+pclai.melt <- melt(pclai, id.vars=c('Site','Treatment','LAI2016','LAI2017'))
+colnames(pclai.melt) <- c("Site","Treatment","LAI2016","LAI2017","GrowthForm","PercentCover")
+
+a <- ggplot(data = pclai.melt, aes(x=LAI2016, y=PercentCover)) + theme_classic() + 
+  geom_point(aes(colour=as.factor(GrowthForm))) +
+  stat_smooth(method = 'lm') +
+  theme(legend.position = "none") +
+  scale_color_manual(values = c("red", "green", "blue")) +
+  labs(x="LAI 2016", y="Percent Cover %")
+a
+
+b <- ggplot(data = pclai.melt, aes(x=LAI2017, y=PercentCover)) + theme_classic() + 
+  geom_point(aes(colour=as.factor(GrowthForm))) +
+  stat_smooth(method = 'lm') +
+  theme(axis.title.y=element_blank(), legend.title = element_blank(), legend.position = c(0.2,0.9)) +
+  scale_color_manual(values = c("red", "green", "blue")) +
+  xlab("LAI 2017") 
+
+b
+
+grid.arrange(a, b, ncol = 2)
+
+
+#Question 3
+data <- read.csv("/Users/elizabethfortin12/Documents/ND First Year/Biocomputing/R_Programming/Intro_Biocomp_ND_317_Tutorial7/data.txt")
+
+ddply(data, c("region"), summarize, mean=mean(observations))
+
+d <- ggplot(data = data)
+d + geom_bar(aes(x = as.factor(region), y = observations), stat = "summary",
+            fun.y = "mean", fill = "black", color = "black") + theme_classic() +
+            xlab("Region") + ylab("Mean Observation") + ggtitle("Mean Observations in Each Region")
+
+a <- ggplot(data = data)
+a + theme_classic() + geom_jitter(aes(x=as.factor(region),y=observations), alpha=0.1) + labs(x='Region', y='Observations') + ggtitle("Observations in Each Region")
+#Yes because the bar plot compares the averages of each region, 
+#while the geom_jitter plot gives you an idea of the average and 
+#the spread because it shows each individual data point