**R Programming Demo**

This is a
short demonstration of various things in R, some from an online class I
recently completed and some from learning about R in its manual.

**Matrix Multiplication**

> "Matrix
Multiplication"

[1] "Matrix
Multiplication"

>
a<-matrix(c(1,3,4,-8),nrow=2,byrow=TRUE)

> a

[,1] [,2]

[1,] 1
3

[2,] 4
-8

>
b<-matrix(c(-3,2,0,1),nrow=2,byrow=TRUE)

> b

[,1] [,2]

[1,] -3
2

[2,] 0
1

>
"Element by Element"

[1] "Element
by Element"

> a*b

[,1] [,2]

[1,] -3
6

[2,] 0
-8

> "Linear
Algebra Multiplication: Use %*%"

[1] "Linear
Algebra Multiplication: Use %*%"

> a%*%b

[,1] [,2]

[1,] -3
5

[2,] -12
0

**Solving a Linear Equation**

>
"Solving a Linear System"

[1] "Solving
a Linear System"

> A <-
matrix(c(3,5,-5,-1,0,3,13,4,-3),nrow=3,byrow=TRUE)

> A

[,1] [,2] [,3]

[1,] 3
5 -5

[2,] -1
0 3

[3,] 13
4 -3

> x <-
matrix(c(11,-2,10),nrow=3)

> x

[,1]

[1,] 11

[2,] -2

[3,] 10

> solve(A,x)

[,1]

[1,] 0.1707317

[2,] 1.4878049

[3,] -0.6097561

**Selecting Elements from a Vector**

>
"Selecting Things from a List"

[1]
"Selecting Things from a List"

> randomvect
<-
c(-1.597,3.638,0.055,2.606,-2.053,-4.565,-2.470,-2.990,3.002,0.747,-2.813,2.524,0.096,-4.409,0.637)

> randomvect

[1] -1.597
3.638 0.055 2.606 -2.053 -4.565 -2.470 -2.990 3.002
0.747 -2.813 2.524 0.096 -4.409
0.637

> "Select
Every Third Element"

[1] "Select
Every Third Element"

>
randomvect[c(FALSE,FALSE,TRUE)]

[1] 0.055 -4.565
3.002 2.524 0.637

> "Select
Elements Greater Than Zero"

[1] "Select
Elements Greater Than Zero"

>
randomvect[randomvect>0]

[1] 3.638 0.055
2.606 3.002 0.747 2.524 0.096 0.637

> "Select
Elements In Between -1 and 1"

[1] "Select
Elements In Between -1 and 1"

>
randomvect[randomvect>-1 & randomvect<1]

[1] 0.055 0.747
0.096 0.637

**Basic Linear Fit**

> xdata <-
c(-2,-1,-0,1,2,3,4)

> ydata <-
c(-0.56,-0.36,0.15,0.33,0.52,0.77,0.93)

> df <-
data.frame(x = xdata, y = ydata)

> df

x
y

1 -2 -0.56

2 -1 -0.36

3 0 0.15

4 1 0.33

5 2 0.52

6 3 0.77

7 4 0.93

>
fit<-lm(df$y~df$x)

> fit

Call:

lm(formula = df$y
~ df$x)

Coefficients:

(Intercept) df$x

0.0007143
0.2535714

> "Clear
the Plot Screen"

[1] "Clear
the Plot Screen"

> plot.new()

>
"Scatterplot"

[1]
"Scatterplot"

>
plot(df$x,df$y,xlab="x",ylab="y",main="Demo
Scatterplot")

> "Linear
Plot"

[1] "Linear
Plot"

>
abline(lm(df$y~df$x),col="blue",lw=2)

**Plot the Trigonometric Numbers**

> "Plot
The Trig Functions"

[1] "Plot
The Trig Functions"

> xvect <-
seq(-2*pi,2*pi,by=pi/64)

> ysin <-
sin(xvect)

> plot.new()

>
plot(xvect,ysin,main="y = sin x",col="blue")

> xtan <-
seq(-pi/4, pi/4, by = pi/64)

> ytan <-
tan(xtan)

>
par(mfrow=c(2,2))

> plot(xvect,
ysin, main = "y = sin x", xlab="x", ylab="y",
col="blue",pch=8)

> plot(xvect,
ycos, main = "y = cos x", xlab="x", ylab="y",
col="red", pch=16)

> plot(xtan,
ytan, main = "y = tan x", xlab="x", ylab="y",
col="forestgreen", pch=20)

**Quadratic Fit**

>
"Quadratic Fit"

[1]
"Quadratic Fit"

> x <-
c(-3.5,-2.25,-1.15,0,1.15,2.25,3.5)

> y <-
c(10.6,6.3,3.1,2,3.3,6.4,10.4)

>
plot(x,y,main="Quadratic Fit",col="chocolate",pch=20)

> fit <-
lm(y~x+I(x^2))

> cvect <-
coef(fit)

> xrng <-
seq(-3.5,3.5,by=.01)

> yrng <-
cvect[1] + cvect[2]*xrng + cvect[3]*xrng^2

>
lines(xrng,yrng)

**Histogram and One Variable Statistics**

> scores <-
c(99,97,96,95,94,29,39,90,92,92,91,86,84,54,65,72,76,77,81,82,90,33,93,92,89,88,90,98,68,71,73,75,97)

>
"Minimum Score"

[1] "Minimum
Score"

> min(scores)

[1] 29

>
"Maximum Score"

[1] "Maximum
Score"

> max(scores)

[1] 99

>
"Average Score"

[1] "Average
Score"

> mean(scores)

[1] 80.24242

> "Number
of Students"

[1] "Number
of Students"

>
length(scores)

[1] 33

[1] "Basic
Histogram"

> hist(scores)

> "10
Categories and Labels"

[1] "10
Categories and Labels"

> hist(scores,
breaks=10, main="Student Score Breakdown", col="yellow")

> "Fiting
a Distribution"

[1] "Fiting
a Distribution"

> hist(scores,
breaks=10, main="Student Score Distribution",
col="gray85",prob=TRUE)

>
lines(density(scores), lwd=2, col="navy")

**Citation:**

R Core Team (2015). R: A language and
environment for statistical computing. R Foundation for Statistical Computing,
Vienna, Austria. URL

A personal note,
thank you for your support. My mom and I
are still in recovering/coping mode.

See you next time,

Eddie

This blog is
property of Edward Shore. 2015

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