Week 6 In-class exercise (R base)

Exercise 1: hist and Density Plot

The raw plot

x <- seq(-pi*2, 2*pi, .05)
z <- dnorm(x)
y <- dt(x, df=3)
plot(x, z, type="l", bty="L", xlab="Standard unit", ylab="Density")
polygon(c(x, rev(x)), c(y, rev(z)), col='aliceblue')
lines(x, y, col='cadetblue')

Change the ‘df’ parameter to a slightly larger integer

x <- seq(-pi*2, 2*pi, .05)
z <- dnorm(x)
y <- dt(x, df=5)
plot(x, z, type="l", bty="L", xlab="Standard unit", ylab="Density")
polygon(c(x, rev(x)), c(y, rev(z)), col='aliceblue')
lines(x, y, col='cadetblue')

Again, change the ‘df’ parameter to a slightly larger intege

x <- seq(-pi*2, 2*pi, .05)
z <- dnorm(x)
y <- dt(x, df=10)
plot(x, z, type="l", bty="L", xlab="Standard unit", ylab="Density")
polygon(c(x, rev(x)), c(y, rev(z)), col='aliceblue')
lines(x, y, col='cadetblue')

In my visual inspection of the three plots, I observed that the aliceblue color line progressively close the standard unit densitu function.

The results may related to dt function and when df increases, the normality also increases.

Exercise 2: Lung Cancer and Cigarettes Consumption

Load data file

dta <- read.table("/Users/haolunfu/Documents/資料管理/week6/cigarettes.txt", header = T)
head(dta)

##     Country consumption death
## 1 Australia         480   180
## 2    Canada         500   150
## 3   Denmark         380   170
## 4   Finland        1100   350
## 5        UK        1100   460
## 6   Iceland         230    60

Prepare a plot enviornment, next add regression line between death rate and cigarettes consumption, then add on the label states.

with(dta, plot(consumption, death, type='n', 
               main='Lung Cancer and Cigarettes Consumption',
               xlab='Consumption (per capita per year)',
               ylab='Death rate (per million)'))

# add regression line
abline(lm(death ~ consumption, data=dta))
# label states
with(dta, text(consumption, death, labels=Country, cex=.5))

Exercise 3: National flag of Denmark

par()

## $xlog
## [1] FALSE
## 
## $ylog
## [1] FALSE
## 
## $adj
## [1] 0.5
## 
## $ann
## [1] TRUE
## 
## $ask
## [1] FALSE
## 
## $bg
## [1] "white"
## 
## $bty
## [1] "o"
## 
## $cex
## [1] 1
## 
## $cex.axis
## [1] 1
## 
## $cex.lab
## [1] 1
## 
## $cex.main
## [1] 1.2
## 
## $cex.sub
## [1] 1
## 
## $cin
## [1] 0.15 0.20
## 
## $col
## [1] "black"
## 
## $col.axis
## [1] "black"
## 
## $col.lab
## [1] "black"
## 
## $col.main
## [1] "black"
## 
## $col.sub
## [1] "black"
## 
## $cra
## [1] 10.8 14.4
## 
## $crt
## [1] 0
## 
## $csi
## [1] 0.2
## 
## $cxy
## [1] 0.02604167 0.06329114
## 
## $din
## [1] 7 5
## 
## $err
## [1] 0
## 
## $family
## [1] ""
## 
## $fg
## [1] "black"
## 
## $fig
## [1] 0 1 0 1
## 
## $fin
## [1] 7 5
## 
## $font
## [1] 1
## 
## $font.axis
## [1] 1
## 
## $font.lab
## [1] 1
## 
## $font.main
## [1] 2
## 
## $font.sub
## [1] 1
## 
## $lab
## [1] 5 5 7
## 
## $las
## [1] 0
## 
## $lend
## [1] "round"
## 
## $lheight
## [1] 1
## 
## $ljoin
## [1] "round"
## 
## $lmitre
## [1] 10
## 
## $lty
## [1] "solid"
## 
## $lwd
## [1] 1
## 
## $mai
## [1] 1.02 0.82 0.82 0.42
## 
## $mar
## [1] 5.1 4.1 4.1 2.1
## 
## $mex
## [1] 1
## 
## $mfcol
## [1] 1 1
## 
## $mfg
## [1] 1 1 1 1
## 
## $mfrow
## [1] 1 1
## 
## $mgp
## [1] 3 1 0
## 
## $mkh
## [1] 0.001
## 
## $new
## [1] FALSE
## 
## $oma
## [1] 0 0 0 0
## 
## $omd
## [1] 0 1 0 1
## 
## $omi
## [1] 0 0 0 0
## 
## $page
## [1] TRUE
## 
## $pch
## [1] 1
## 
## $pin
## [1] 5.76 3.16
## 
## $plt
## [1] 0.1171429 0.9400000 0.2040000 0.8360000
## 
## $ps
## [1] 12
## 
## $pty
## [1] "m"
## 
## $smo
## [1] 1
## 
## $srt
## [1] 0
## 
## $tck
## [1] NA
## 
## $tcl
## [1] -0.5
## 
## $usr
## [1] 0 1 0 1
## 
## $xaxp
## [1] 0 1 5
## 
## $xaxs
## [1] "r"
## 
## $xaxt
## [1] "s"
## 
## $xpd
## [1] FALSE
## 
## $yaxp
## [1] 0 1 5
## 
## $yaxs
## [1] "r"
## 
## $yaxt
## [1] "s"
## 
## $ylbias
## [1] 0.2

plot.new()
plot.window(xlim=c(0, 37), ylim=c(0, 28))
rect(xleft = 0, ybottom = 0, xright = 37, ytop = 28, col="red")
rect(12, 0, 16, 37, col="white", border="white")
rect(0, 12, 37, 16, col="white", border = "white")

Exercise 4:

create a sequence from 0 to 2pi, then, set x=sin(t), y=cos(t)

n <- 60
t <- seq(0, 2*pi, length=n)
x <- sin(t)
y <- cos(t)

generates a square plotting

use for-loop to create a animation by making 60 plots

par(pty = "s")
for (i in 1:n) {
  plot.new()
  plot.window(c(-1, 1), c(-1, 1))
  lines(x*y, -y, col="gray")
  points(x[i]*y[i], -y[i], pch=16,
         col=gray((i-1)/(n+1)))
  Sys.sleep(.05)
}

Exercise 5: Draw a pie chart to represent 50 shades of gray.

par(mar=c(1,1,1,1))
pie(rep(1,50), col = grey(seq(0, 1, by = 1/50)))