Package 'ISLR'

Title: Data for an Introduction to Statistical Learning with Applications in R
Description: We provide the collection of data-sets used in the book 'An Introduction to Statistical Learning with Applications in R'.
Authors: Gareth James, Daniela Witten, Trevor Hastie and Rob Tibshirani
Maintainer: Trevor Hastie <[email protected]>
License: GPL-2
Version: 1.4
Built: 2024-12-16 06:33:02 UTC
Source: CRAN

Help Index

Auto Data Set

Description

Gas mileage, horsepower, and other information for 392 vehicles.

Usage

Auto

Format

A data frame with 392 observations on the following 9 variables.

mpg

miles per gallon

cylinders

Number of cylinders between 4 and 8

displacement

Engine displacement (cu. inches)

horsepower

Engine horsepower

weight

Vehicle weight (lbs.)

acceleration

Time to accelerate from 0 to 60 mph (sec.)

year

Model year (modulo 100)

origin

Origin of car (1. American, 2. European, 3. Japanese)

name

Vehicle name

The orginal data contained 408 observations but 16 observations with missing values were removed.

Source

This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University. The dataset was used in the 1983 American Statistical Association Exposition.

References

James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013) An Introduction to Statistical Learning with applications in R, https://www.statlearning.com, Springer-Verlag, New York

Examples

pairs(Auto)
attach(Auto)
hist(mpg)

The Insurance Company (TIC) Benchmark

Description

The data contains 5822 real customer records. Each record consists of 86 variables, containing sociodemographic data (variables 1-43) and product ownership (variables 44-86). The sociodemographic data is derived from zip codes. All customers living in areas with the same zip code have the same sociodemographic attributes. Variable 86 (Purchase) indicates whether the customer purchased a caravan insurance policy. Further information on the individual variables can be obtained at http://www.liacs.nl/~putten/library/cc2000/data.html

Usage

Caravan

Format

A data frame with 5822 observations on 86 variables.

Source

The data was originally supplied by Sentient Machine Research and was used in the CoIL Challenge 2000.

References

P. van der Putten and M. van Someren (eds) . CoIL Challenge 2000: The Insurance Company Case. Published by Sentient Machine Research, Amsterdam. Also a Leiden Institute of Advanced Computer Science Technical Report 2000-09. June 22, 2000. See http://www.liacs.nl/~putten/library/cc2000/
P. van der Putten and M. van Someren. A Bias-Variance Analysis of a Real World Learning Problem: The CoIL Challenge 2000. Machine Learning, October 2004, vol. 57, iss. 1-2, pp. 177-195, Kluwer Academic Publishers
James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013) An Introduction to Statistical Learning with applications in R, https://www.statlearning.com, Springer-Verlag, New York

Examples

summary(Caravan)
plot(Caravan$Purchase)

Sales of Child Car Seats

Description

A simulated data set containing sales of child car seats at 400 different stores.

Usage

Carseats

Format

A data frame with 400 observations on the following 11 variables.

Sales

Unit sales (in thousands) at each location

CompPrice

Price charged by competitor at each location

Income

Community income level (in thousands of dollars)

Advertising

Local advertising budget for company at each location (in thousands of dollars)

Population

Population size in region (in thousands)

Price

Price company charges for car seats at each site

ShelveLoc

A factor with levels Bad, Good and Medium indicating the quality of the shelving location for the car seats at each site

Age

Average age of the local population

Education

Education level at each location

Urban

A factor with levels No and Yes to indicate whether the store is in an urban or rural location

US

A factor with levels No and Yes to indicate whether the store is in the US or not

Source

Simulated data

References

James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013) An Introduction to Statistical Learning with applications in R, https://www.statlearning.com, Springer-Verlag, New York

Examples

summary(Carseats)
lm.fit=lm(Sales~Advertising+Price,data=Carseats)

U.S. News and World Report's College Data

Description

Statistics for a large number of US Colleges from the 1995 issue of US News and World Report.

Usage

College

Format

A data frame with 777 observations on the following 18 variables.

Private

A factor with levels No and Yes indicating private or public university

Apps

Number of applications received

Accept

Number of applications accepted

Enroll

Number of new students enrolled

Top10perc

Pct. new students from top 10% of H.S. class

Top25perc

Pct. new students from top 25% of H.S. class

F.Undergrad

Number of fulltime undergraduates

P.Undergrad

Number of parttime undergraduates

Outstate

Out-of-state tuition

Room.Board

Room and board costs

Books

Estimated book costs

Personal

Estimated personal spending

PhD

Pct. of faculty with Ph.D.'s

Terminal

Pct. of faculty with terminal degree

S.F.Ratio

Student/faculty ratio

perc.alumni

Pct. alumni who donate

Expend

Instructional expenditure per student

Grad.Rate

Graduation rate

Source

This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University. The dataset was used in the ASA Statistical Graphics Section's 1995 Data Analysis Exposition.

References

James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013) An Introduction to Statistical Learning with applications in R, https://www.statlearning.com, Springer-Verlag, New York

Examples

summary(College)
lm(Apps~Private+Accept,data=College)

Credit Card Balance Data

Description

A simulated data set containing information on ten thousand customers. The aim here is to predict which customers will default on their credit card debt.

Usage

Credit

Format

A data frame with 10000 observations on the following 4 variables.

ID

Identification

Income

Income in $1,000's

Limit

Credit limit

Rating

Credit rating

Cards

Number of credit cards

Age

Age in years

Education

Number of years of education

Gender

A factor with levels Male and Female

Student

A factor with levels No and Yes indicating whether the individual was a student

Married

A factor with levels No and Yes indicating whether the individual was married

Ethnicity

A factor with levels African American, Asian, and Caucasian indicating the individual's ethnicity

Balance

Average credit card balance in $.

Source

Simulated data, with thanks to Albert Kim for pointing out that this was omitted, and supplying the data and man documentation page on Oct 19, 2017

References

James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013) An Introduction to Statistical Learning with applications in R, https://www.statlearning.com, Springer-Verlag, New York

Examples

summary(Credit)
lm(Balance ~ Student + Limit, data=Credit)

Credit Card Default Data

Description

A simulated data set containing information on ten thousand customers. The aim here is to predict which customers will default on their credit card debt.

Usage

Default

Format

A data frame with 10000 observations on the following 4 variables.

default

A factor with levels No and Yes indicating whether the customer defaulted on their debt

student

A factor with levels No and Yes indicating whether the customer is a student

balance

The average balance that the customer has remaining on their credit card after making their monthly payment

income

Income of customer

Source

Simulated data

References

James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013) An Introduction to Statistical Learning with applications in R, https://www.statlearning.com, Springer-Verlag, New York

Examples

summary(Default)
glm(default~student+balance+income,family="binomial",data=Default)

Baseball Data

Description

Major League Baseball Data from the 1986 and 1987 seasons.

Usage

Hitters

Format

A data frame with 322 observations of major league players on the following 20 variables.

AtBat

Number of times at bat in 1986

Hits

Number of hits in 1986

HmRun

Number of home runs in 1986

Runs

Number of runs in 1986

RBI

Number of runs batted in in 1986

Walks

Number of walks in 1986

Years

Number of years in the major leagues

CAtBat

Number of times at bat during his career

CHits

Number of hits during his career

CHmRun

Number of home runs during his career

CRuns

Number of runs during his career

CRBI

Number of runs batted in during his career

CWalks

Number of walks during his career

League

A factor with levels A and N indicating player's league at the end of 1986

Division

A factor with levels E and W indicating player's division at the end of 1986

PutOuts

Number of put outs in 1986

Assists

Number of assists in 1986

Errors

Number of errors in 1986

Salary

1987 annual salary on opening day in thousands of dollars

NewLeague

A factor with levels A and N indicating player's league at the beginning of 1987

Source

This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University. This is part of the data that was used in the 1988 ASA Graphics Section Poster Session. The salary data were originally from Sports Illustrated, April 20, 1987. The 1986 and career statistics were obtained from The 1987 Baseball Encyclopedia Update published by Collier Books, Macmillan Publishing Company, New York.

References

James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013) An Introduction to Statistical Learning with applications in R, https://www.statlearning.com, Springer-Verlag, New York

Examples

summary(Hitters)
lm(Salary~AtBat+Hits,data=Hitters)

Khan Gene Data

Description

The data consists of a number of tissue samples corresponding to four distinct types of small round blue cell tumors. For each tissue sample, 2308 gene expression measurements are available.

Usage

Khan

Format

The format is a list containing four components: xtrain, xtest, ytrain, and ytest. xtrain contains the 2308 gene expression values for 63 subjects and ytrain records the corresponding tumor type. ytrain and ytest contain the corresponding testing sample information for a further 20 subjects.

Source

This data were originally reported in:

Khan J, Wei J, Ringner M, Saal L, Ladanyi M, Westermann F, Berthold F, Schwab M, Antonescu C, Peterson C, and Meltzer P. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nature Medicine, v.7, pp.673-679, 2001.

The data were also used in:

Tibshirani RJ, Hastie T, Narasimhan B, and G. Chu. Diagnosis of Multiple Cancer Types by Shrunken Centroids of Gene Expression. Proceedings of the National Academy of Sciences of the United States of America, v.99(10), pp.6567-6572, May 14, 2002.

References

James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013) An Introduction to Statistical Learning with applications in R, https://www.statlearning.com, Springer-Verlag, New York

Examples

table(Khan$ytrain)
table(Khan$ytest)

NCI 60 Data

Description

NCI microarray data. The data contains expression levels on 6830 genes from 64 cancer cell lines. Cancer type is also recorded.

Usage

NCI60

Format

The format is a list containing two elements: data and labs.

data is a 64 by 6830 matrix of the expression values while labs is a vector listing the cancer types for the 64 cell lines.

Source

The data come from Ross et al. (Nat Genet., 2000). More information can be obtained at http://genome-www.stanford.edu/nci60/

References

James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013) An Introduction to Statistical Learning with applications in R, https://www.statlearning.com, Springer-Verlag, New York

Examples

table(NCI60$labs)

Orange Juice Data

Description

The data contains 1070 purchases where the customer either purchased Citrus Hill or Minute Maid Orange Juice. A number of characteristics of the customer and product are recorded.

Usage

OJ

Format

A data frame with 1070 observations on the following 18 variables.

Purchase

A factor with levels CH and MM indicating whether the customer purchased Citrus Hill or Minute Maid Orange Juice

WeekofPurchase

Week of purchase

StoreID

Store ID

PriceCH

Price charged for CH

PriceMM

Price charged for MM

DiscCH

Discount offered for CH

DiscMM

Discount offered for MM

SpecialCH

Indicator of special on CH

SpecialMM

Indicator of special on MM

LoyalCH

Customer brand loyalty for CH

SalePriceMM

Sale price for MM

SalePriceCH

Sale price for CH

PriceDiff

Sale price of MM less sale price of CH

Store7

A factor with levels No and Yes indicating whether the sale is at Store 7

PctDiscMM

Percentage discount for MM

PctDiscCH

Percentage discount for CH

ListPriceDiff

List price of MM less list price of CH

STORE

Which of 5 possible stores the sale occured at

Source

Stine, Robert A., Foster, Dean P., Waterman, Richard P. Business Analysis Using Regression (1998). Published by Springer.

References

James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013) An Introduction to Statistical Learning with applications in R, https://www.statlearning.com, Springer-Verlag, New York

Examples

summary(OJ)
plot(OJ$Purchase,OJ$PriceCH)

Portfolio Data

Description

A simple simulated data set containing 100 returns for each of two assets, X and Y. The data is used to estimate the optimal fraction to invest in each asset to minimize investment risk of the combined portfolio. One can then use the Bootstrap to estimate the standard error of this estimate.

Usage

Portfolio

Format

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

X

Returns for Asset X

Y

Returns for Asset Y

Source

Simulated data

References

James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013) An Introduction to Statistical Learning with applications in R, https://www.statlearning.com, Springer-Verlag, New York

Examples

summary(Portfolio)
attach(Portfolio)
plot(X,Y)

S&P Stock Market Data

Description

Daily percentage returns for the S&P 500 stock index between 2001 and 2005.

Usage

Smarket

Format

A data frame with 1250 observations on the following 9 variables.

Year

The year that the observation was recorded

Lag1

Percentage return for previous day

Lag2

Percentage return for 2 days previous

Lag3

Percentage return for 3 days previous

Lag4

Percentage return for 4 days previous

Lag5

Percentage return for 5 days previous

Volume

Volume of shares traded (number of daily shares traded in billions)

Today

Percentage return for today

Direction

A factor with levels Down and Up indicating whether the market had a positive or negative return on a given day

Source

Raw values of the S&P 500 were obtained from Yahoo Finance and then converted to percentages and lagged.

References

James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013) An Introduction to Statistical Learning with applications in R, https://www.statlearning.com, Springer-Verlag, New York

Examples

summary(Smarket)
lm(Today~Lag1+Lag2,data=Smarket)

Mid-Atlantic Wage Data

Description

Wage and other data for a group of 3000 male workers in the Mid-Atlantic region.

Usage

Wage

Format

A data frame with 3000 observations on the following 11 variables.

year

Year that wage information was recorded

age

Age of worker

maritl

A factor with levels 1. Never Married 2. Married 3. Widowed 4. Divorced and 5. Separated indicating marital status

race

A factor with levels 1. White 2. Black 3. Asian and 4. Other indicating race

education

A factor with levels 1. < HS Grad 2. HS Grad 3. Some College 4. College Grad and 5. Advanced Degree indicating education level

region

Region of the country (mid-atlantic only)

jobclass

A factor with levels 1. Industrial and 2. Information indicating type of job

health

A factor with levels 1. <=Good and 2. >=Very Good indicating health level of worker

health_ins

A factor with levels 1. Yes and 2. No indicating whether worker has health insurance

logwage

Log of workers wage

wage

Workers raw wage

Source

Data was manually assembled by Steve Miller, of Inquidia Consulting (formerly Open BI). From the March 2011 Supplement to Current Population Survey data.

https://www.re3data.org/repository/r3d100011860

References

James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013) An Introduction to Statistical Learning with applications in R, https://www.statlearning.com, Springer-Verlag, New York

Examples

summary(Wage)
lm(wage~year+age,data=Wage)
## maybe str(Wage) ; plot(Wage) ...

Weekly S&P Stock Market Data

Description

Weekly percentage returns for the S&P 500 stock index between 1990 and 2010.

Usage

Weekly

Format

A data frame with 1089 observations on the following 9 variables.

Year

The year that the observation was recorded

Lag1

Percentage return for previous week

Lag2

Percentage return for 2 weeks previous

Lag3

Percentage return for 3 weeks previous

Lag4

Percentage return for 4 weeks previous

Lag5

Percentage return for 5 weeks previous

Volume

Volume of shares traded (average number of daily shares traded in billions)

Today

Percentage return for this week

Direction

A factor with levels Down and Up indicating whether the market had a positive or negative return on a given week

Source

Raw values of the S&P 500 were obtained from Yahoo Finance and then converted to percentages and lagged.

References

James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013) An Introduction to Statistical Learning with applications in R, https://www.statlearning.com, Springer-Verlag, New York

Examples

summary(Weekly)
lm(Today~Lag1+Lag2,data=Weekly)