# Lab 1 for the University of Tulsa's CS-6643 Bioinformatics Course
# 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)

#### Part C: Reading fasta files, nucelotide and dinucleotide frequencies

## Pre-cursor: Load associated supportive libraries

## 1

## 2

## 3

#### Part D: GC Content

## 1

#### Part E: Coronavirus

## 1