introduction to r - nus.edu.sgnus.edu.sg/alset/wp-content/uploads/2020/07/...contents...

Introduction to R

Contents – Introduction to R• Introduction to R

• Display datasets• Display First and Last 6 Observations• Display ADL Table Names• Read in Graduate Employment Survey Questionnaire Table• Arithmetic Operators• Function

• Calculate Windsorized Mean• Calculate OLS Estimates and SEs

• Descriptive Statistics• Measures of Central Tendency

• Mean• Median• Trimmed Mean

• Measure of Dispersion• Standard Deviation• Median Absolute Deviation

• Graphics in R• Graphic Settings• Scatter Plot + Line Plot• Line + Bar Chart• Pie Chart• 3 Dimensional Contingency Table

2

…

Display a list of datasets available in JNB

head() – first few observations of the datasettail() – last few observations of the dataset

Display First and Last 6 Observations

…

Display ADL Table Names

Read in Graduate Employment Survey Questionnaireges_2016_questions

Arithmetic Operators

8

FunctionCalculate Windsorised Mean

FunctionCalculate OLS Estimates and SEs

Descriptive Statistics

11

Mean

n

xxx

n

x

x n

n

i

i +++==

= ...211

ǉ𝑥 =1 + 2 + 3 + 5 + 6 + 9 + 10 + 20 + 34

9= 10

Susceptible to the influence of outliers

12

Median

13

1 2 3 4 5 6 7 8 9 10

1 1 2 2 4 5 6 7 8 100000

Trimmed Mean

kn

x

x

kn

ki

i

trimmed2

1

−=−

+=

Trimmed 5%14

Standard Deviation

ix1 x2

1 7 7-7=0 0 12 12-7=5 25

2 8 8-7=1 1 2 2-7=-5 25

3 6 6-7=-1 1 0 0-7=-7 49

4 7 7-7=0 0 14 14-7=7 49

5 7 7-7=0 0 10 10-7=3 9

6 6 6-7=-1 1 9 9-7=2 4

7 8 8-7=1 1 5 5-7=-2 4

8 7 7-7=0 0 4 4-7=-3 9

Total 4 174

( )2xxi −ix xxi −

( )

( )985694.4

7

174

1

755929.07

4

1

1

2

2

1

2

1

==−

−

=

==−

−

=

=

=

n

xx

s

n

xx

s

n

i

i

n

i

i

( )2xxi −ix xxi −

15

Median Absolute Deviation

16

( ) Median Median i xxMAD i −=x1 x1-Median |x1-Median |

2 2 -12 = -10 10

6 6 -12 = -6 6

6 6 -12 = -6 6

12 (Median) 12 -12 = 0 0

17 17 -12 = 5 5

25 25 -12 = 13 13

32 32 -12 = 20 20

Median 6

MAD = 6 σ ≈ 1.4826 * MAD = 1.4826 * 6 = 8.8956

0 5 6 6 10 13 20

16

R Graphics

17

19

…

22

High-Level Function Description

plot() Scatterplothist() Histogramboxplot() Boxplotqqplot(), qqnorm(), qqline() Quantile plotsinteraction.plot() Interaction plotsunflowerplot() Sunflower scatterplotpairs() Scatter plot matrixsymbols() Draw symbols on a plotdotchart(), Dot chartbarplot(), bar chartpie(), pie chartcurve() Draw a curve from a given functionimage() Create a grid of coloured rectangles with colours based

on the values of a third variablecontour(), filled.contour() Contour plotpersp() Plot 3-D surface

High-Level Plot Function

23

Low-Level Plot Function Description

points() Add points to a figurelines() Add lines to a figuretext() Insert text in the plot regionmtext() Insert text in the figure and outer marginstitle() Add figure title or outer titlelegend() Insert legendaxis(), axis.Date() Customize axesabline() Add horizontal and vertical lines or a single linebox() Draw a box around the current plotrug() Add a 1-D plot of the data to the figurepolygon() Draw a polygonrect() Draw a rectanglearrows() Draw arrowssegments() Draw line segmentstrans3d() Add 2-D components to a 3-D plot

low-Level Plot Function

24

Source: Key Household Income Trends, 2012

Year Gini Coefficient

2002 0.454

2003 0.457

2004 0.460

2005 0.465

2006 0.470

2007 0.482

2008 0.474

2009 0.471

2010 0.472

2011 0.473

2012 0.478

Singapore Gini Coefficient from Year 2002 to 2012

25

# Gini Coefficient of Singapore From 2002 to 2012

# Scatter Plot

Year <- c(2002:2012)

Gini <- c(0.454,0.457,0.460,0.465,0.470,0.482,0.474,0.471,0.472,0.473,0.478)

plot(Year,Gini)

plot(x,y)

26

plot(Year,Gini,main="Gini Coefficient\nBased on Household Income from Work per Household Member",

sub="Source: Key Household Income Trends, 2012")

main

sub

27


type = "b",pch=20,


type=“b”

pch=20

28


type = "b",pch=15,

col="red",lwd=2,

ylab="Gini Coefficient",

ylim=c(0.44,0.49),


pch=15

29

plot(Year,Gini,main="Gini Coefficient",

type = "b",pch=15,

col="red",lwd=2,

cex.axis=1.2,cex.lab=1.5,cex.main=1.6,


ylim=c(0.44,0.49))

text(2008,0.44,"Based on Household Income from Work per Household Member",cex=0.7)

mtext("Source: Key Household Income Trends, 2012",side=1,line=4,at=2005)

Plot Region

30

par(bg = "lightblue")

plot(Year,Gini,main="Gini Coefficient",

type = "b",pch=15,

col="red",lwd=2,

cex.axis=1.2,cex.lab=1.5,cex.main=1.6,


ylim=c(0.44,0.49))

text(2008,0.44,"Based on Household Income from Work per Household Member",cex=0.7)

mtext("Source: Key Household Income Trends, 2012",side=1,line=4,at=2005)

31

Source: Zhang (2009) Lifelong education (learning) in China: Present situation and development trend. Convergence, 42(1), 49-63.

YearNumber of Examinees

(in 10,000s)Number of Graduates

(in 10,000s)

1996 858.21 26.02

1997 1014.31 28.88

1998 1180.81 34.54

1999 1305.16 42.20

2000 1327.68 48.94

2001 1330.43 64.10

2002 1285.10 129.42

2003 1155.91 70.45

2004 1234.53 78.81

2005 1058.04 254.26

Number of Examinees and Graduates in China between 1996 and 2005

Examinee <- c(858.21,1014.31,1180.81,1305.16,1327.68,1330.43,1285.10,1155.91,1234.53,1058.04)

Graduate <- c(26.02,28.88,34.54,42.20,48.94,64.10,129.42,70.45,78.81,254.26)

Year <- c(1996:2005)

32

Line + Bar Chart

33

barplot(Examinee)

par(new=TRUE)

plot(Year,Graduate) High-level Plot Function

barplot(Examinee)

plot(Year,Graduate)

plot(Year,Graduate)

barplot(Examinee)

34

barplot(Examinee,

main="No. of Examinees and Graduates\nin China between 1996 and 2005")

par(new=TRUE)

plot(Year,Graduate,type="l")

35

par(mar=c(5,6,4,6))barplot(Examinee,

main="No. of Examinees and Graduates\nin China between 1996 and 2005",

las=1,

names.arg=c(1996:2005))

par(new=TRUE)

plot(Year,Graduate,type="l",col="coral1",

xaxt="n",yaxt="n",xlab="",ylab="",

lwd=3)

axis(4,las=1)

5 Lines

6 Lines

4 Lines

6 L

ine

s

37

par(mar=c(5,6,4,6))

barplot(Examinee,


las=1,

names.arg=c(1996:2005))par(new=TRUE)



lwd=3)

axis(4,las=1)

names.arg a vector of names to be plotted below each bar or group of bars.

38

par(mar=c(5,6,4,6))

barplot(Examinee,


las=1,

names.arg=c(1996:2005))

par(new=TRUE)



lwd=3)

axis(4,las=1)axis(4,las=1)

39

par(mar=c(5,6,4,6))

barplot(Examinee,


las=1,

col="pink",

names.arg=c(1996:2005))

par(new=TRUE)



lwd=3)

axis(4,las=1)

40

Examinee <- c(858.21,1014.31,1180.81,1305.16,1327.68,1330.43,1285.10,1155.91,1234.53,1058.04)

Graduate <- c(26.02,28.88,34.54,42.20,48.94,64.10,129.42,70.45,78.81,254.26)

Year <- c(1996:2005)

par(mar=c(5,6,4,6))

barplot(Examinee,col="pink",


cex.main=1.5,

xlab="Year",

cex.lab=1.2,

ylim=c(0,1400),

las=1,

names.arg=c(1996:2005))

par(new=TRUE)



lwd=3)

axis(4,las=1)

mtext("Number of Graduates",side=4,line=3,cex=1.2,col="coral1")

mtext("Number of Examinees",side=2,line=3,cex=1.2,col="pink")

text(2001,25,"Source:Yearbook of Educational Statistics in China, 2006")

pie.sales <- c(0.12, 0.3, 0.26, 0.16, 0.04, 0.12)

names(pie.sales) <- c("Blueberry", "Cherry",

"Apple", "Boston Cream", "Other", "Vanilla Cream")

pie(pie.sales,col=rainbow(6))

41

heat.colors() terrain.colors() topo.colors() cm.colors()

Table <- matrix(

c(45, 5,16, 2,

1,33, 3, 7,

20,10,56, 4,

2, 3, 5,50),

ncol=4,byrow=T)

rows <- rep(1:4,4)

cols <- c(rep(1,4),rep(2,4),rep(3,4),rep(4,4))

dimnames(Table) = list(

c("Strongly Disagree", "Disagree","Agree","Strongly Agree"),

c("Strongly Disagree", "Disagree","Agree","Strongly Agree"))

library(scatterplot3d)

scatterplot3d(rows, cols, as.vector(Table),

type="h", pch=" ", angle=50,

lab=c(3,3), lwd=5,

main="3 Dimensional Contingency Table",

xlab="Current Satisfaction With Life",

ylab="Last Year Satisfaction with Life",

zlab="Observation",

x.ticklabs=rownames(Table),

y.ticklabs=colnames(Table),

y.margin.add=1.2,

color="red")

42

3 Dimensional Contingency Table

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