Package 'RSADBE'

Data Sets for the "R Statistical Application Development by Example" Book

Description

The RSADBE package contains all the data sets used in the book "R Statistical Application Development by Example". Data sets have been collected from various sources and an attempt has been made to ensure that all the right credits are given. If some omissions are there, kindly accept the current work as a compliment for your work.

Details

Package:	RSADBE
Type:	Package
Version:	1.0
Date:	2013-05-13
License:	GPL-2

This package is aimed to complement the book. Any data set required in the book may simply loaded using data(GC) as an example.

Author(s)

Prabhanjan

Maintainer: Prabhanjan Tattar <[email protected]>

References

Tattar, P.N. (2013). R Statistical Application Development by Example. Packt Publication.

Examples

data(GC)

---

Bug Metrics Data

Description

A data set which reports the 5 different type of bugs for 5 software. The count frequencies are available for pre- and post- release of the data.

Usage

data(Bug_Metrics_Software)

Format

A three dimensional array on the bug counts of 5 software at 5 severity levels.

Source

http://www.eclipse.org/jdt/core/index.php

Examples

data(Bug_Metrics_Software)

---

A cooked-data set for illustration of the partitions of CART concept

Description

Partitions play a very important aspect of CART methodology. This data set has been cooked to translate the intuitions into partitions!

Usage

data(CART_Dummy)

Format

A data frame with 54 observations on the following 3 variables.

X1

Input variable 1

X2

Input variable 2

Y

category of the output

References

Berk, R. A. (2008). Statistical Learning from a Regression Perspective. Springer.

Examples

data(CART_Dummy)
CART_Dummy$Y = as.factor(CART_Dummy$Y)
par(mfrow=c(1,2))
plot(c(0,12),c(0,10),type="n",xlab="X1",ylab="X2")
points(CART_Dummy$X1[CART_Dummy$Y==0],CART_Dummy$X2[CART_Dummy$Y==0],pch=15,col="red")
points(CART_Dummy$X1[CART_Dummy$Y==1],CART_Dummy$X2[CART_Dummy$Y==1],pch=19,col="green")
title(main="A Difficult Classification Problem")
plot(c(0,12),c(0,10),type="n",xlab="X1",ylab="X2")
points(CART_Dummy$X1[CART_Dummy$Y==0],CART_Dummy$X2[CART_Dummy$Y==0],pch=15,col="red")
points(CART_Dummy$X1[CART_Dummy$Y==1],CART_Dummy$X2[CART_Dummy$Y==1],pch=19,col="green")
segments(x0=c(0,0,6,6),y0=c(3.75,6.25,2.25,5),x1=c(6,6,12,12),y1=c(3.75,6.25,2.25,5),lwd=2)
abline(v=6,lwd=2)
title(main="Looks a Solvable Problem Under Partitions")

---

The Cow Temperature Data

Description

The data set is adapted from Velleman and Hoaglin (1984). The body temperature of a cow is measured at 6:30am on 75 consecutive days. We use this data set with the intent of explaining the concept of "data smooting". The data appears on page 165 where we have 30 days body temperature.

Usage

data(CT)

Format

A data frame with 30 observations on the following 2 variables.

Day

day number

Temperature

temperature at 6:30am

Source

The entire classic book of Velleman and Hoaglin is available at http://dspace.library.cornell.edu/bitstream/1813/78/2/A-B-C_of_EDA_040127.pdf

References

Velleman, P.F., and Hoaglin, D. (1984). Applications, Basics, and Computing of Exploratory Data Analysis.

Examples

data(CT)
plot.ts(CT$Temperature,col="red",pch=1)

---

Understanding Drain Current Vs Ground-to-Source Voltage

Description

The data pertains to an experiment where the drain current is measured against the ground-to-source voltage. We use this data set for understanding of a simple scatterplot.

Usage

data(DCD)

Format

A data frame with 10 observations on the following 2 variables.

GTS_Voltage

The voltage

Drain_Current

Drain in the current

References

Montgomery, D. C., and Runger, G. C. (2007). Applied Statistics and Probability for Engineers, (With CD). J.Wiley.

Examples

data(DCD)
plot(DCD)

---

A data set used for understanding the very basic steps in R

Description

The data set is used to simply understand the working of read.table, View, class and sapply R functions

Usage

data(employ)

Format

A data frame with 60 observations on the following 3 variables.

Trade

a numeric vector

Food

a numeric vector

Metals

a numeric vector

Examples

data(employ)

---

The famous Galton data set

Description

Sir Francis Galton used this data set for understanding the (linear) relationship between the height of parent and its effect on the height of child.

Usage

data(galton)

Format

A data frame with 928 observations on the following 2 variables.

child

children's height

parent

parent's height

Details

A scatter plot may be used for preliminary investigation of the kind of relationship between parent's height and their children. A simple linear regression model may also be built for quantifying the relationship.

References

http://en.wikipedia.org/wiki/Francis_Galton

Examples

data(galton)
plot(galton)

---

Car Mileage Dataset

Description

This data set has been used primarily for understanding a multivariate data set. Multiple regression model is also introduced and discussed completely through this example.

Usage

data(Gasoline)

Format

A data frame with 25 observations on the following 12 variables.

y

Miles per gallon

x1

Displacement (cubic inches)

x2

Horsepower (foot-pounds)

x3

Torque (foot-pounds)

x4

Compression ratio

x5

Rear axle ratio

x6

Carburetor (barrels)

x7

Number of transmission speeds

x8

Overall length (inches)

x9

Width (inches)

x10

Weight (pounds)

x11

Type of transmission (A-automatic, M-manual)

References

Montgomery, D. C., Peck, E.A., and Vining, G.G. (2012). Introduction to linear regression analysis. Wiley.

Examples

data(Gasoline)

---

German Credit Screening Data

Description

Loans are an assest for the banks! However, not all the loans are promptly returned and it is thus important for a bank to build a classification model which can identify the loan defaulters from those who complete the loan tenure.

Usage

data(GC)

Format

A data frame with 1000 observations on the following 21 variables.

checking

Status of existing checking account

duration

Duration in month

history

Credit history

purpose

Purpose of loan

amount

Credit amount

savings

Savings account or bonds

employed

Present employment since

installp

Installment rate in percentage of disposable income

marital

Personal status and sex

coapp

Other debtors or guarantors

resident

Present residence since

property

Property

age

Age in years

other

Other installment plans

housing

Housing

existcr

Number of existing credits at this bank

job

Job

depends

Number of people being liable to provide maintenance for

telephon

Telephone

foreign

foreign worker

good_bad

Loan Defaulter

Source

http://www.stat.auckland.ac.nz/~reilly/credit-g.arff and http://archive.ics.uci.edu/ml/datasets/Statlog+(German+Credit+Data)

References

cran.r-project.org/doc/contrib/Sharma-CreditScoring.pdf

Examples

data(GC)

---

CPU Time and IO Processes Relationship

Description

The CPU is known to depend on the number of active IO processes. This data set will be used for the purposes of understanding scatterplots, resistant lines, and simple linear regression model.

Usage

data(IO_Time)

Format

A data frame with 10 observations on the following 2 variables.

No_of_IO

Number of IO Processes

CPU_Time

The CPU time

Source

http://www.cs.gmu.edu/~menasce/cs700/files/SimpleRegression.pdf

Examples

data(IO_Time)
plot(IO_Time)

---

Low Birth Weight

Description

A consolidation of the concepts learnt the later half of the book is worked trough using this example.

Usage

data(lowbwt)

Format

A data frame with 189 observations on the following 10 variables.

LOW

indicator of birth weight less than 2.5kg

AGE

mother's age in years

LWT

mother's weight in pounds at last menstrual period

RACE

mothers race ("white", "black", "other")

SMOKE

smoking status during pregnancy

PTL

number of previous premature labours

HT

history of hypertension

UI

presence of uterine irritability

FTV

number of physician visits during the first trimester

BWT

birth weight in grams

Source

http://www.statlab.uni-heidelberg.de/data/linmod/birthweight.html

References

Hosmer, D.W. and Lemeshow, S. (2001). Applied Logistic Regression. New York: Wiley.

Examples

data(lowbwt)
plot(lowbwt)

---

Male Death Rates

Description

The problem is to understand the effect of the average amount of tobacco smoked and the cause of death on the male death rates per 1000.

Usage

data(MDR)

Format

A data frame with 15 observations on the following 5 variables.

X

Death Causes

G0

No smoking

G14

Between 1-14 grams

G24

Between 15-24 grams

G25

More than 25 grams

Source

http://dspace.library.cornell.edu/bitstream/1813/78/2/A-B-C_of_EDA_040127.pdf

References

Velleman, Paul F., and David C. Hoaglin. Applications, basics, and computing of exploratory data analysis. Vol. 142. Boston: Duxbury Press, 1981.

Examples

data(MDR)
boxplot(MDR)

---

Octane Rating Data set

Description

An experiment is conducted where the octane rating of gasoline blends can be obtained using two methods. Two samples are available for testing each type of blend, and Snee (1981) obtains 32 different blends over an appropriate spectrum of the target octane ratings.

Usage

data(octane)

Format

A data frame with 32 observations on the following 2 variables.

Method_1

Ratings under Method 1

Method_2

Ratings under Method 2

References

Vining, G.G., and Kowalski, S.M. (2011). Statistical Methods for Engineers, 3e. Brooks/Cole.

Examples

data(octane)
par(mfrow=c(1,2))
hist(octane$Method_1)
hist(octane$Method_2)
## maybe str(octane) ; plot(octane) ...

---

Understanding the Overfitting Problem

Description

This is a data set cooked up by the author to highlight the problem of overfitting. The variables have no physical meaning.

Usage

data(OF)

Format

A data frame with 10 observations on the following 2 variables.

X

Just another covariate

Y

Just another output

Examples

data(OF)
plot(OF)

---

A data set for illustrating "Piecewise Linear Regression Model"

Description

As with the "OF" data set, this data set has been created by the author to build up the ideas leading up to piecewise linear regression model.

Usage

data(PW_Illus)

Format

A data frame with 100 observations on the following 2 variables.

X

an input vector

Y

an output vector

Examples

data(PW_Illus)
plot(PW_Illus)

---

Resistivity of wires

Description

The resistivity of wires is known to depend on its manufacturing process. The data set is used primarily to understand the boxplot.

Usage

data(resistivity)

Format

A data frame with 8 observations on the following 2 variables.

Process.1

Resistivity of wires under process 1

Process.2

Resistivity of wires under process 2

References

Gunst, R. F. (2002). Finding confidence in statistical significance. Quality Progress, 35 (10), 107-108.

Examples

data(resistivity)
boxplot(resistivity)

---

A hypothetical data set

Description

This data set shows that data may also have skewness inherent in them!

Usage

data(Samplez)

Format

A data frame with 2000 observations on the following 2 variables.

Sample_1

a numeric vector

Sample_2

a numeric vector

Examples

data(Samplez)
hist(Samplez$Sample_1)
hist(Samplez$Sample_2)

---

SAT-M marks and its impact on the final exams of a course

Description

The final completion of a stat course is believed to depend on the marks scored by the student during his qualifying SAT-M marks. This data set is used to setup the motivation for binary regression models such as probit and logistic regressino models.

Usage

data(sat)

Format

A data frame with 30 observations on the following 5 variables.

Student.No

Student number

Grade

Grade of the student

Pass

Pass-Fail indicator in the final exam

Sat

The SAT-M marks

GPP

The GPP group

References

Johnson, Valen E., and James H. Albert. Ordinal data modeling. Springer, 1999.

Examples

data(sat)

---

An illustrative data set where the "Response" depends on four variables A-D and a fifth categorical variable

Description

This data set is primarily used to illustrate some basic R functions.

Usage

data(SCV)

Format

A data frame with 16 observations on the following 6 variables.

Response

an output vector

A

variable A

B

variable B

C

Variable C

D

variable D

E

a factor with two levels Modified Usual

Examples

data(SCV)

---

SCV data set by category "Modified"

Description

This data set is a part of the SCV dataset.

Usage

data(SCV_Modified)

Format

A data frame with 8 observations on the following 6 variables.

Response

an output vector

A

variable A

B

variable B

C

Variable C

D

variable D

E

a factor with two levels Modified

Examples

data(SCV_Modified)

---

SCV data set with caterogy "Usual"

Description

This data set is part of the SCV data set.

Usage

data(SCV_Usual)

Format

A data frame with 8 observations on the following 6 variables.

Response

an output vector

A

variable A

B

variable B

C

Variable C

D

variable D

E

a factor with two levels Usual

Examples

data(SCV_Usual)

---

Severity counts for the JDT software

Description

The software system Eclipse JDT Core has 997 different class environments related to the development. The bug identified on each occasion is classified by its severity as Bugs, NonTrivial, Major, Critical, and High. We need to understand the bug counts before- and after- software release.

Usage

data(Severity_Counts)

Format

Before and after release bug counts at five severity levels for the JDT software.

Source

http://www.eclipse.org/jdt/core/index.php

Examples

data(Severity_Counts)
barplot(Severity_Counts,xlab="Bug Count",xlim=c(0,12000), col=rep(c(2,3),5))

---

A simulated data set for illustrating the ROC concept

Description

ROC is an important tool for comparing different models for the same classification problem. This data set comes with barebones infrastructure and is simply complementary in nature towards setting up a clear understanding the ROC construction.

Usage

data(simpledata)

Format

A data frame with 200 observations on the following 2 variables.

Predictions

Predicted probabilities

Label

True class of the observations

Examples

data(simpledata)

---

The supervisor performance data

Description

This data is used to check your understanding of the multiple linear regression model.

Usage

data(SPD)

Format

A data frame with 30 observations on the following 7 variables.

Y

Supervisors performance

X1

Aspect 1

X2

Aspect 2

X3

Aspect 3

X4

Aspect 4

X5

Aspect 5

X6

Aspect 6

References

"Regression analysis by example" by Samprit Chatterjee and Ali S. Hadi, Wiley

Examples

data(SPD)
pairs(SPD)

---

Sample Questionnaire Data

Description

The sample questionnaire data is simply used to familiarize the reader with data and statistical terminologies.

Usage

data(SQ)

Format

A data frame with 20 observations on the following 12 variables.

Customer_ID

Customer ID

Questionnaire_ID

Questionnaire ID

Name

Customers Name

Gender

Customers gender Female Male

Age

Age of the customer

Car_Model

Car Model's name

Car_Manufacture_Year

Month and year of car's manufacturing

Minor_Problems

Minor problems were fixed by the workshop center indicator No Yes

Major_Problems

Major problems were fixed by the workshop center indicator No Yes Yes

Mileage

The overall mileage of the car (kms/litre)

Odometer

The overall kilometers travelled by the car

Satisfaction_Rating

How satisfied was the customer Very Poor < Poor < Average < Good < Very Good

Examples

data(SQ)

---

Test centuries of Rahul Dravid

Description

Rahul Dravid has been a modern arthictet of Indian test cricket team. His resilent centuries and holding the wicket at one end of the cricket pitch has earned him the name "The Wall". We analyze his centuries at "Home" and "Away" test matches.

Usage

data(TheWALL)

Format

A data frame with 36 observations on the following 11 variables.

Sl_No

An indicator

Score

The century scores

Not_Out_Indicator

Indicates whether Dravid remained unbeaten at the end of the team innings

Against

The teams against whom Dravid scored the century

Position

Dravid's batting position, out of 11

Innings

An indicator of the first to fourth innings

Test

Test number

Venue

Venue of the test match

HA_Ind

Match was in home country or away

Date

Date on the which the test began

Result

Did India won the match?

Examples

data(TheWALL)

---

Voltage Drop Dataset

Description

The voltage is known to drop in a guided missile after a certain time. The data has been to illustrate certain cubic spline models.

Usage

data(VD)

Format

A data frame with 41 observations on the following 2 variables.

Time

Time of missile

Voltage_Drop

Drop in the voltage

References

Montgomery, Douglas C., Elizabeth A. Peck, and G. Geoffrey Vining. Introduction to linear regression analysis. Wiley, 2012.

Examples

data(VD)

---