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

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. The original dataset has 397 observations, of which 5 have missing values for the variable "horsepower". These rows are removed here. The original dataset is avaliable as a CSV file in the docs directory, as well as at https://www.statlearning.com.

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)

Bike sharing data

Description

This data set contains the hourly and daily count of rental bikes between years 2011 and 2012 in Capital bikeshare system, along with weather and seasonal information.

Usage

Bikeshare

Format

A data frame with 8645 observations on a number of variables.

season

Season of the year, coded as Winter=1, Spring=2, Summer=3, Fall=4.

mnth

Month of the year, coded as a factor.

day

Day of the year, from 1 to 365

hr

Hour of the day, coded as a factor from 0 to 23.

holiday

Is it a holiday? Yes=1, No=0.

weekday

Day of the week, coded from 0 to 6, where Sunday=0, Monday=1, Tuesday=2, etc.

workingday

Is it a work day? Yes=1, No=0.

weathersit

Weather, coded as a factor.

temp

Normalized temperature in Celsius. The values are derived via (t-t_min)/(t_max-t_min), t_min=-8, t_max=+39.

atemp

Normalized feeling temperature in Celsius. The values are derived via (t-t_min)/(t_max-t_min), t_min=-16, t_max=+50.

hum

Normalized humidity. The values are divided to 100 (max).

windspeed

Normalized wind speed. The values are divided by 67 (max).

casual

Number of casual bikers.

registered

Number of registered bikers.

bikers

Total number of bikers.

Source

The UCI Machine Learning Repository https://archive.ics.uci.edu/ml/datasets/bike+sharing+dataset

References

James, G., Witten, D., Hastie, T., and Tibshirani, R. (2021) An Introduction to Statistical Learning with applications in R, Second Edition, https://www.statlearning.com, Springer-Verlag, New York

Examples

lm(bikers~hr, data=Bikeshare)

Boston Data

Description

A data set containing housing values in 506 suburbs of Boston.

Usage

Boston

Format

A data frame with 506 rows and 13 variables.

crim

per capita crime rate by town.

zn

proportion of residential land zoned for lots over 25,000 sq.ft.

indus

proportion of non-retail business acres per town.

chas

Charles River dummy variable (= 1 if tract bounds river; 0 otherwise).

nox

nitrogen oxides concentration (parts per 10 million).

rm

average number of rooms per dwelling.

age

proportion of owner-occupied units built prior to 1940.

dis

weighted mean of distances to five Boston employment centres.

rad

index of accessibility to radial highways.

tax

full-value property-tax rate per $10,000.

ptratio

pupil-teacher ratio by town.

lstat

lower status of the population (percent).

medv

median value of owner-occupied homes in $1000s.

Source

This dataset was obtained from, and is slightly modified from, the Boston dataset that is part of the MASS library. References are available in the MASS library.

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

lm(medv ~ crim + rm, data=Boston)

Brain Cancer Data

Description

A data set consisting of survival times for patients diagnosed with brain cancer.

Usage

BrainCancer

Format

A data frame with 88 observations and 8 variables:

sex

Factor with levels "Female" and "Male"

diagnosis

Factor with levels "Meningioma", "LG glioma", "HG glioma", and "Other".

loc

Location factor with levels "Infratentorial" and "Supratentorial".

ki

Karnofsky index.

gtv

Gross tumor volume, in cubic centimeters.

stereo

Stereotactic method factor with levels "SRS" and "SRT".

status

Whether the patient is still alive at the end of the study: 0=Yes, 1=No.

time

Survival time, in months.

Source

I. Selingerova, H. Dolezelova, I. Horova, S. Katina, and J. Zelinka. Survival of patients with primary brain tumors: Comparison of two statistical approaches. PLoS One, 11(2):e0148733, 2016. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4749663/

References

James, G., Witten, D., Hastie, T., and Tibshirani, R. (2021) An Introduction to Statistical Learning with applications in R, Second Edition, https://www.statlearning.com, Springer-Verlag, New York

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 400 customers.

Usage

Credit

Format

A data frame with 400 observations on a number of variables.

Income

Income in $1,000's

Limit

Credit limit

Rating

Credit rating

Cards

Number of credit cards

Age

Age in years

Education

Education in years

Own

A factor with levels No and Yes indicating whether the individual owns a home

Student

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

Married

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

Region

A factor with levels East, South, and West indicating the individual's geographical location

Balance

Average credit card balance in $.

Source

Simulated data. Many thanks to Albert Kim for helpful suggestions, and for supplying a draft of the man documentation page on Oct 19, 2017.

References

James, G., Witten, D., Hastie, T., and Tibshirani, R. (2021) An Introduction to Statistical Learning with applications in R, Second Edition, 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)

Fund Manager Data

Description

A simulated data set containing the returns for 2,000 hedge fund managers.

Usage

Fund

Format

A data frame containing the returns of 2,000 hedge fund managers over 50 months.

Source

Simulated data.

References

James, G., Witten, D., Hastie, T., and Tibshirani, R. (2021) An Introduction to Statistical Learning with applications in R, Second Edition, https://www.statlearning.com, Springer-Verlag, New York

Examples

t.test(Fund$Manager1, mu=0)

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)

New York Stock Exchange Data

Description

Data consisting of the Dow Jones returns, log trading volume, and log volatility for the New York Stock Exchange over a 20 year period

Usage

Portfolio

Format

A data frame with 6,051 observations and 6 variables:

date

Date

day_of_week

Day of the week

DJ_return

Return for Dow Jones Industrial Average

log_volume

Log of trading volume

log_volatility

Log of volatility

train

For the first 4,281 observations, this is set to TRUE

Source

B. LeBaron and A. Weigend (1998), IEEE Transactions on Neural Networks 9(1): 213-220.

References

James, G., Witten, D., Hastie, T., and Tibshirani, R. (2021) An Introduction to Statistical Learning with applications in R, Second Edition, https://www.statlearning.com, Springer-Verlag, New York

Examples

attach(NYSE)
plot(log_volatility)

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)

Time-to-Publication Data

Description

Publication times for 244 clinical trials funded by the National Heart, Lung, and Blood Institute.

Usage

Publication

Format

A data frame with 244 observations, each representing a clinical trial, and 9 variables:

posres

Did the trial produce a positive (significant) result? 1=Yes, 0=No.

multi

Did the trial involve multiple centers? 1=Yes, 0=No.

clinend

Did the trial focus on a clinical endpoint? 1=Yes, 0=No.

mech

Funding mechanism within National Institute of Health: a qualitative variable.

sampsize

Sample size for the trial.

budget

Budget of the trial, in millions of dollars.

impact

Impact of the trial; this is related to the number of publications.

time

Time to publication, in months.

status

Whether or not the trial was published at time: 1=Published, 0=Not yet published.

Source

Gordon, Taddei-Peters, Mascette, Antman, Kaufmann, and Lauer. Publication of trials funded by the National Heart, Lung, and Blood Institute. New England Journal of Medicine, 369(20):1926-1934, 2013.

References

James, G., Witten, D., Hastie, T., and Tibshirani, R. (2021) An Introduction to Statistical Learning with applications in R, Second Edition, https://www.statlearning.com, Springer-Verlag, New York

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)