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Answers to the First Two Questions #8

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aca7d73
Initial commit.
shdavitt Oct 6, 2017
21dafd5
Question 1, plotting sequences by lengths.
shdavitt Oct 13, 2017
727e4c9
Finished Question 1 (Plotted by GC Content). Note that the code is me…
shdavitt Oct 13, 2017
9b827c0
My answer to the second quetion, using government data on mean househ…
shdavitt Oct 13, 2017
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13 changes: 13 additions & 0 deletions Intro_Biocomp_ND_317_Tutorial7.Rproj
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55 changes: 55 additions & 0 deletions tut7RScript.R
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#Tutorial Exercise 7
#Shane Davitt (No partner as I was the odd one out in our tutorial section)
#
#Question 1
#
#{Plotting by Sequence Lengths}
#
#Putting Lecture11.fasta into an R object
fastaData=read.table("Lecture11.fasta", sep="\t")

@araiho araiho Nov 6, 2017

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you're going to want to use scan() here.

#
#Removing seq labels
conFasta = fastaData[seq(2, nrow(fastaData),2),]
#
#Converting data to a dataframe
data.frame(conFasta)
#
#Counting number of charecters in each sequence
countFasta=apply(conFasta,1,nchar)
#
#Plotting the result as a histogram
seqLenPlot=hist(countFasta, xlab="Sequence Length")
#
#{Plotting by GC Content (proportion of sequence which is GC)}
#
#Counting Gs in each sequence
gCount=str_count(conFasta[,1], pattern="G")
#
#Counting Cs in each sequence
cCount=str_count(conFasta[,1], pattern="C")
#
#Total GC count in each seq
GCCount=gCount+cCount
#
#Getting proportion of seq which is GC
GCfreq=GCCount/countFasta
#
#Plotting GC proportion as histogram
GCfreqPlot=hist(GCfreq, xlab="GC Proportion")
#
#I love it when manipulating matrices is as simple as a plus
#and a minus sign ! :)
#
#{Ouestion 2}
#
#Reading my data
crimeData=read.csv("crimeData.csv")
#
#Turning into R object
crimeTable=data.frame(crimeData)
#
#Plotting Data
plot(crimeTable[,2],crimeTable[,1], type="p", xlab="Median Household Income", ylab="Property Crime Rate", main="Examining Affluence and Crime", sub="(TX Counties, 2015)")
#
#Plotting trendline
abline(lm(crimeTable[,1]~crimeTable[,2],crimeTable))