Part 1: a-f

Schrick-Noah_CS-6643_Lab1.R

@@ -1,20 +1,47 @@
 # Lab 1 for the University of Tulsa's CS-6643 Bioinformatics Course
-# Introduction to R, Online bioinformatics resources, nucleotide frequency statistics
+# Intro to R, online bioinformatics resources, nucleotide frequency statistics
 # Professor: Dr. McKinney, Fall 2022
 # Noah L. Schrick - 1492657
 
 #### Part A: Seq Function
 ## a
+AAvec <- seq(from = 1, to = 33, by = 2)
 
 ## b
+ABvec <- seq(from = 7, to = 40, length.out = 15)
   
 ## c
+my.dna <- sample(c("A", "C", "G", "T"), size = 20, replace = T)
 
 ## d
+my.dna.A <- length(which(my.dna == "A"))
 
 ## e
+my.dna.table <- table(my.dna)
+
+my.dna.table.df <- as.data.frame(my.dna.table)
+cols <- rainbow(nrow(my.dna.table))
+my.dna.table.df$percent <- 
+  round(100*my.dna.table.df$Freq/sum(my.dna.table.df$Freq), digits = 1)
+my.dna.table.df$label <- paste(my.dna.table.df$my.dna,
+                               " (", my.dna.table.df$percent, "%)", sep = "")
+pie(my.dna.table.df$Freq, labels = my.dna.table.df$label, col = cols, 
+    main = "Pie Chart Representation of Random ACTG Sample") 
+
+
+bp <- barplot(as.matrix(my.dna.table), beside = TRUE, xlab = "Letter", 
+        ylab = "Frequency", ylim = c(-1, max(as.numeric(my.dna.table))+2),
+        main = "Bar Plot Representation of Random ACTG Sample", col = cols,
+        legend = TRUE)
+
+text(x = bp, y = my.dna.table + 0.5, labels = as.numeric(my.dna.table))
+text(x = bp, y = -0.5, labels = names(my.dna.table))
 
 ## f
+my.dna2 <- sample(c("A", "C", "G", "T"), size = 20, replace = T, 
+                  prob = c(0.1, 0.4, 0.4, 0.1))
+my.dna2.table <- table(my.dna2)
+my.dna2.table
 
 
 #### Part B: NCBI (no supporting R code for this part)