Best estimate of the age of the universe

Description

For years people have tried to estimate the age of the universe. This data set collects a few estimates starting with lower bounds using estimates for the earth's age.

Usage

data(age.universe)

Format

A data frame with 16 observations on the following 4 variables.

lower

a numeric vector

upper

a numeric vector

year

a numeric vector

source

Short description of source

Details

In the last two decades estimates for the age of the universe have been greatly improved. As of 2013, the best guess is 13.7 billion years with a margin of error of 1 percent. This last estimate is found by WMAP using microwave background radiation. Previous estimates were also based on estimates of Hubble's constant, and dating of old stars.

Source

This data was collected from the following web sites: https://arxiv.org/abs/1212.5225, https://case.edu/pubaff/univcomm/2003/1-03/kraussuniverse.html (now off-line), https://www.astro.ucla.edu/~wright/age.html, http://www.lhup.edu/~dsimanek/cutting/ageuniv.htm (now off-line), and https://map.gsfc.nasa.gov/m_uni/uni_101age.html.

Examples

data(age.universe)
n <- nrow(age.universe)
x <- 1:n
names(x) = age.universe$year
plot(x,age.universe$upper,ylim=c(0,20))
points(x,age.universe$lower)
with(age.universe,sapply(x,function(i) lines(c(i,i),c(lower[i],upper[i]))))

monthly payment for federal program

Description

monthly payment for federal program

Usage

data(aid)

Format

The format is: Named num [1:51] 57.2 253.5 114.2 68.2 199.6 ... - attr(*, "names")= chr [1:51] "Alabama" "Alaska" "Arizona" "Arkansas" ...

Source

From Kitchen's Exploring Statistics

Examples

data(aid)
hist(aid)

Comparison of in-field and laboratory measurement of defects

Description

The Alaska pipeline data consists of in-field ultrasonic measurements of the depths of defects in the Alaska pipeline. The depth of the defects were then re-measured in the laboratory. These measurements were performed in six different batches.

Usage

data(alaska.pipeline)

Format

A data frame with 107 observations on the following 3 variables.

field.defect

Depth of defect as measured in field

lab.defect

Depth of defect as measured in lab

batch

One of 6 batches

Source

From an example in Engineering Statistics Handbook from http://www.itl.nist.gov/div898/handbook/

Examples

data(alaska.pipeline)
res = lm(lab.defect ~ field.defect, alaska.pipeline)
plot(lab.defect ~ field.defect, alaska.pipeline)
abline(res)
plot(fitted(res),resid(res))

Top movies of all time

Description

The top 79 all-time movies as of 2003 by domestic (US) gross receipts.

Usage

data(alltime.movies)

Format

A data frame with 79 observations on the following 2 variables.

Gross

a numeric vector

Release.Year

a numeric vector

The row names are the titles of the movies.

Source

This data was found on http://movieweb.com/movie/alltime.html on June 17, 2003. The source of the data is attributed to (partially) Exhibitor Relations Co. .

Examples

data(alltime.movies)
hist(alltime.movies$Gross)

Answers to selected problems

Description

Opens pdf file containing answers to selected problems

Usage

answers()

Value

Called for its side-effect of opening a pdf

Examples

## answers()

Artic Oscillation data based on SAT data

Description

A time series of January, February, and March measurements of the annular modes from January 1851 to March 1997.

Usage

data(aosat)

Format

The format is: first column is date in years with fraction to indicate month. The second column is the measurement.

Details

This site http://jisao.washington.edu/ao/ had more details on the importance of this time series.

Source

This data came from the file AO\_SATindex\_JFM\_Jan1851March1997.ascii at http://www.atmos.colostate.edu/ao/Data/ao\_index.html

Examples

data(aosat)
## Not run: 
library(zoo)
z = zoo(aosat[,2], order.by=aosat[,1])
plot(z)
## yearly
plot(aggregate(z, floor(index(z)), mean))
## decade-long means
plot(aggregate(z, 10*floor(index(z)/10), mean))

## End(Not run)

Measurement of sea-level pressure at the arctic

Description

A monthly time series from January 1899 to June 2002 of sea-level pressure measurements relative to some baseline.

Usage

data(arctic.oscillations)

Format

The format is: chr "arctic.oscillations"

Details

See https://toptotop.org/ for more details on the importance of climate studies.

Source

The data came from the file AO\_TREN\_NCEP\_Jan1899Current.ascii found many years ago at http://www.atmos.colostate.edu/ao/Data/ao\_index.html.

Examples

data(arctic.oscillations)
x = ts(arctic.oscillations, start=c(1899,1), frequency=12)
plot(x)

Mothers and their babies data

Description

A collection of variables taken for each new mother in a Child and Health Development Study.

Usage

data(babies)

Format

A data frame with 1,236 observations on the following 23 variables.

Variables in data file

id

identification number

pluralty

5= single fetus

outcome

1= live birth that survived at least 28 days

date

birth date where 1096=January1,1961

gestation

length of gestation in days

sex

infant's sex 1=male 2=female 9=unknown

wt

birth weight in ounces (999 unknown)

parity

total number of previous pregnancies including fetal deaths and still births, 99=unknown

race

mother's race 0-5=white 6=mex 7=black 8=asian 9=mixed 99=unknown

age

mother's age in years at termination of pregnancy, 99=unknown

ed

mother's education 0= less than 8th grade, 1 = 8th -12th grade - did not graduate, 2= HS graduate–no other schooling , 3= HS+trade, 4=HS+some college 5= College graduate, 6\&7 Trade school HS unclear, 9=unknown

ht

mother's height in inches to the last completed inch 99=unknown

wt1

mother prepregnancy wt in pounds, 999=unknown

drace

father's race, coding same as mother's race.

dage

father's age, coding same as mother's age.

ded

father's education, coding same as mother's education.

dht

father's height, coding same as for mother's height

dwt

father's weight coding same as for mother's weight

marital

1=married, 2= legally separated, 3= divorced, 4=widowed, 5=never married

inc

family yearly income in \$2500 increments 0 = under 2500, 1=2500-4999, ..., 8= 12,500-14,999, 9=15000+, 98=unknown, 99=not asked

smoke

does mother smoke? 0=never, 1= smokes now, 2=until current pregnancy, 3=once did, not now, 9=unknown

time

If mother quit, how long ago? 0=never smoked, 1=still smokes, 2=during current preg, 3=within 1 yr, 4= 1 to 2 years ago, 5= 2 to 3 yr ago, 6= 3 to 4 yrs ago, 7=5 to 9yrs ago, 8=10+yrs ago, 9=quit and don't know, 98=unknown, 99=not asked

number

number of cigs smoked per day for past and current smokers 0=never, 1=1-4,2=5-9, 3=10-14, 4=15-19, 5=20-29, 6=30-39, 7=40-60, 8=60+, 9=smoke but don't know,98=unknown, 99=not asked

Source

This dataset is found from https://www.stat.berkeley.edu/users/statlabs/labs.html. It accompanies the excellent text Stat Labs: Mathematical Statistics through Applications Springer-Verlag (2001) by Deborah Nolan and Terry Speed.

Examples

data(babies)
plot(wt ~ factor(smoke), data=babies)
plot(wt1 ~ dwt, data=babies, subset=wt1 < 800 & dwt < 800)

Babyboom: data for 44 babies born in one 24-hour period.

Description

The babyboom dataset contains the time of birth, sex, and birth weight for 44 babies born in one 24-hour period at a hospital in Brisbane, Australia.

Usage

data(babyboom)

Format

A data frame with 44 observations on the following 4 variables.

clock.time

Time on clock

gender

a factor with levels girl boy

wt

weight in grams of child

running.time

minutes after midnight of birth

Source

This data set was submitted to the Journal of Statistical Education, https://www.amstat.org/publications/jse/secure/v7n3/datasets.dunn.cfm (now off-line), by Peter K. Dunn.

Examples

data(babyboom)
hist(babyboom$wt)
hist(diff(babyboom$running.time))

Batting statistics for 2002 baseball season

Description

This dataset contains batting statistics for the 2002 baseball season. The data allows you to compute batting averages, on base percentages, and other statistics of interest to baseball fans. The data only contains players with more than 100 atbats for a team in the year. The data is excerpted with permission from the Lahman baseball database at http://www.seanlahman.com/.

Usage

data(batting)

Format

A data frame with 438 observations on the following 22 variables.

playerID

This is coded, but those familiar with the players should be able to find their favorites.

yearID

a numeric vector. Always 2002 in this dataset.

stintID

a numeric vector. Player's stint (order of appearances within a season)

teamID

a factor with Team

lgID

a factor with levels AL NL

G

number of games played

AB

number of at bats

R

number of runs

H

number of hits

DOUBLE

number of doubles. "2B" in original dat a base.

TRIPLE

number of triples. "3B" in original data base

HR

number of home runs

RBI

number of runs batted in

SB

number of stolen bases

CS

number of times caught stealing

BB

number of base on balls (walks)

SO

number of strikeouts

IBB

number of intentional walks

HBP

number of hit by pitches

SH

number of sacrifice hits

SF

number of sacrifice flies

GIDP

number of grounded into double plays

Details

Baseball fans are “statistics” crazy. They love to talk about things like RBIs, BAs and OBPs. In order to do so, they need the numbers. This data comes from the Lahman baseball database at http://www.seanlahman.com/. The complete dataset includes data for all of baseball not just the year 2002 presented here.

Source

Lahman baseball database, http://www.seanlahman.com/)

References

In addition to the data set above, the book Curve Ball, by Albert, J. and Bennett, J., Copernicus Books, gives an extensive statistical analysis of baseball.

See https://www.baseball-almanac.com/stats.shtml for definitions of common baseball statistics.

Examples

data(batting)
attach(batting)
BA = H/AB			# batting average
OBP = (H + BB + HBP) / (AB + BB + HBP + SF) # On base "percentage"

Population estimate of type of Bay Checkerspot butterfly

Description

Estimates of the population of a type of Bay Checkerspot butterfly near San Francisco.

Usage

data(baycheck)

Format

A data frame with 27 observations on the following 2 variables.

year

a numeric vector

Nt

estimated number

Source

From chapter 4 of Morris and Doak, Quantitative Conservation Biology: Theory and Practice of Population Viability Analysis, Sinauer Associates, 2003.

Examples

data(baycheck)
plot(Nt ~ year,baycheck)
## fit Ricker model N_{t+1} = N_t e^{-rt}W_t
n = length(baycheck$year)
yt = with(baycheck,log(Nt[-1]/Nt[-n]))
nt = with(baycheck,Nt[-n])
lm(yt ~ nt,baycheck)

Best track and field times by age and distance

Description

A dataset giving world records in track and field running events for various distances and different age groups.

Usage

data(best.times)

Format

A data frame with 113 observations on the following 6 variables.

Dist

Distance in meters (42195 is a marathon)

Name

Name of record holder

Date

Date of record

Time

Time in seconds

Time.1

Time as character

age

Age at time of record

Details

Age-graded race results allow competitors of different ages to compare their race performances. This data set allows one to see what the relationship is based on peak performances.

Source

The data came from http://www.personal.rdg.ac.uk/~snsgrubb/athletics/agegroups.html which included a calculator to compare results.

Examples

data(best.times)
attach(best.times)
by.dist = split(best.times,as.factor(Dist))
lm(scale(Time) ~ age, by.dist[['400']])
dists = names(by.dist)
lapply(dists, function(n) print(lm(scale(Time) ~ age, by.dist[[n]])))

blood pressure readings

Description

blood pressure of 15 males taken by machine and expert

Usage

data(blood)

Format

This data frame contains the following columns:

Machine

a numeric vector

Expert

a numeric vector

Source

Taken from Kitchen's Exploring Statistics.

References

~~ possibly secondary sources and usages ~~

Examples

data(blood)
attach(blood)
t.test(Machine,Expert)
detach(blood)

Time of insulating fluid to breakdown

Description

The time in minutes for an insulating fluid to break down under varying voltage loads

Usage

data(breakdown)

Format

A data frame with 75 observations on the following 2 variables.

voltage

Number of kV

time

time in minutes

Details

An example from industry where a linear model is used with replication and transformation of variables.

Source

Data is from Display 8.3 of Ramsay and Shafer, The Statistical Sleuth Duxbury Press, 1997.

Examples

data(breakdown)
plot(log(time) ~ voltage, data = breakdown)

List of bright stars with Hipparcos catalog number

Description

List of bright stars with Hipparcos catalog number.

Usage

data(bright.stars)

Format

A data frame with 96 observations on the following 2 variables.

name

Common name of star

hip

HIP number for identification

Details

The source of star names goes back to the Greeks and Arabs. Few are modern. This is a list of 96 common stars.

Source

Form the Hipparcos website http://astro.estec.esa.nl/Hipparcos/ident6.html.

Examples

data(bright.stars)
all.names  = paste(bright.stars$name, sep="", collapse="")
x = unlist(strsplit(tolower(all.names), ""))
letter.dist = sapply(letters, function(i) sum(x == i))
data(scrabble)			#  for frequency info
p = scrabble$frequency[1:26];p=p/sum(p)
chisq.test(letter.dist, p=p)	# compare with English

Brightness of 966 stars

Description

The Hipparcos Catalogue has information on over 100,000 stars. Listed in this dataset are brightness measurements for 966 stars from a given sector of the sky.

Usage

data(brightness)

Format

A univariate dataset of 966 numbers.

Details

This is field H5 in the catalog measuring the magnitude, V , in the Johnson UBV photometric system. The smaller numbers are for brighter stars.

Source

http://astro.estec.esa.nl/hipparcos

Examples

data(brightness)
hist(brightness)

Bumper repair costs for various automobiles

Description

bumper repair costs

Usage

data(bumpers)

Format

Price in dollars to repair a bumper.

Source

From Exploring Statistics, Duxbury Press, 1998, L. Kitchens.

Examples

data(bumpers)
stem(bumpers)

U.S. President George Bush approval ratings

Description

Approval ratings as reported by six different polls.

Usage

data(BushApproval)

Format

A data frame with 323 observations on the following 3 variables.

date

The date poll was begun (some take a few days)

approval

a numeric number between 0 and 100

who

a factor with levels fox gallup newsweek time.cnn upenn zogby

Details

A data set of approval ratings of George Bush over the time of his presidency, as reported by several agencies. Most polls were of size approximately 1,000 so the margin of error is about 3 percentage points.

Source

This data was found at http://www.pollingreport.com/BushJob.htm. The idea came from an article in Salon http://salon.com/opinion/feature/2004/02/09/bush_approval/index.html by James K. Galbraith.

Examples

data(BushApproval)
attach(BushApproval)

## Plot data with confidence intervals. Each poll gets different line type
## no points at first
plot(strptime(date,"%m/%d/%y"),approval,type="n",
     ylab = "Approval Rating",xlab="Date",
     ylim=c(30,100)
     )

## plot line for CI. Margin or error about 3
## matlines has trouble with dates from strptime()
colors = rainbow(6)

for(i in 1:nrow(BushApproval)) {
  lines(rep(strptime(date[i],"%m/%d/%y"),2),
        c(approval[i]-3,approval[i]+3),
        lty=as.numeric(who[i]),
        col=colors[as.numeric(who[i])]
        )
  
}

## plot points
points(strptime(date,"%m/%d/%y"),approval,pch=as.numeric(who))

## add legend
legend((2003-1970)*365*24*60*60,90,legend=as.character(levels(who)),lty=1:6,col=1:6)
detach(BushApproval)

Number of Albatrosses accidentaly caught during a fishing haul

Description

This data set from Hillborn and Mangel contains data on the number of Albatrosses accidentally caught while fishing by commercial fisheries.

Usage

data(bycatch)

Format

A data frame with 18 observations on the following 2 variables.

no.albatross

The number of albatross caught

no.hauls

Number of hauls with this many albatross caught

Details

During fishing operations non-target species are often captured. These are called “incidental catch”. In some cases, large-scale observer programs are used to monitor this incidental catch.

When fishing for squid, albatrosses are caught while feeding on the squid at the time of fising. This feeding is encouraged while the net is being hauled in, as the squid are clustered making it an opportunistic time for the albatross to eat.

Source

This is from Hilborn and Mangel, The Ecological Detective, Princeton University Press, 1997. Original source of data is Bartle.

Examples

data(bycatch)
hauls = with(bycatch,rep(no.albatross,no.hauls))

Estimated tax savings for US President Bush's cabinet

Description

Estimated savings from a repeal of the tax on capital gains and dividends for Bush's cabinet members.

Usage

data(cabinet)

Format

A data frame with 19 observations on the following 4 variables.

name

Name of individual

position

Position of individual

est.dividend.cg

Estimated amount of dividend and capital gain income

est.tax.savings

Estimated tax savings

Details

Quoting from the data source http://www.house.gov/reform/min/pdfs_108/pdf_inves/pdf_admin_tax_law_cabinet_june_3_rep.pdf (From Henry Waxman, congressional watchdog.)

“On May 22, 2003, the House of Representatives and the Senate passed tax legislation that included \$320 billion in tax cuts. The final tax cut bill was signed into law by President Bush on May 28, 2003. The largest component of the new tax law is the reduction of tax rates on both capital gains and dividend income. The law also includes the acceleration of future tax cuts, as well as new tax reductions for businesses.

This capital gains and dividend tax cut will have virtually no impact on the average American. The vast majority of Americans (88 no capital gains on their tax returns. These taxpayers will receive no tax savings at all from the reduction in taxes on capital gains. Similarly, most Americans (75 from the reduction of taxes on dividends.

While the average American will derive little, if any, benefit from the cuts in dividend and capital gains taxes, the law offers significant benefits to the wealthy. For example, the top 1 receive an average tax cut of almost \$21,000 each. In particular, some of the major beneficiaries of this plan will be Vice President Cheney, President Bush, and other members of the cabinet. Based on 2001 and 2002 dividends and capital gains income, Vice President Cheney, President Bush, and the cabinet are estimated to receive an average tax cut of at least \$42,000 per year. Their average tax savings equals the median household income in the United States.”

Source

From http://www.house.gov/reform/min/pdfs_108/pdf_inves/pdf_admin_tax_law_cabinet_june_3_rep.pdfx

Examples

data(cabinet)
attach(cabinet)
median(est.dividend.cg)
mean(est.dividend.cg)
detach(cabinet)

Mount Campito Yearly Treering Data, -3435-1969.

Description

Contains annual tree-ring measurements from Mount Campito from 3426 BC through 1969 AD.

Usage

data(camp)

Format

A univariate time series with 5405 observations. The object is of class '"ts"'.

Details

This series is a standard example for the concept of long memory time series.

The data was produced and assembled at the Tree Ring Laboratory at the University of Arizona, Tuscon.

Source

Time Series Data Library:https://robjhyndman.com/TSDL/

References

This data set is in the tseries package. It is repackaged here for convenience only.

Examples

data(camp)
acf(camp)

cancer survival times

Description

cancer survival times

Usage

data(cancer)

Format

The format is: The format is: List of 5 numeric components stomach, bronchus, colon, ovary and breast

Source

Taken from L. Kitchens, Exploring Statistics, Duxbury Press, 1997.

Examples

data(cancer)
boxplot(cancer)

Carbon Monoxide levels at different sites

Description

Carbon Monoxide levels at different sites

Usage

data(carbon)

Format

This data frame contains the following columns:

Monoxide

a numeric vector

Site

a numeric vector

Source

Borrowed from Kitchen's Exploring Statistics

Examples

data(carbon)
boxplot(Monoxide ~ Site,data=carbon)

Fatality information in U.S. for several popular cars

Description

Safety statistics appearing in a January 12th, 2004 issue of the New Yorker showing fatality rates per million vehicles both for drivers of a car, and drivers of other cars that are hit.

Usage

data(carsafety)

Format

A data frame with 33 observations on the following 4 variables.

Make.model

The make and model of the car

type

Type of car

Driver.deaths

Number of drivers deaths per year if 1,000,000 cars were on the road

Other.deaths

Number of deaths in other vehicle caused by accidents involving these cars per year if 1,000,000 cars were on the road

Details

The article this data came from wishes to make the case that SUVs are not safer despite a perception among the U.S. public that they are.

Source

From "Big and Bad" by Malcolm Gladwell. New Yorker, Jan. 12 2004 pp28-33. Data attributed to Tom Wenzel and Marc Ross who have written https://www2.lbl.gov/Science-Articles/Archive/assets/images/2002/Aug-26-2002/SUV-report.pdf.

Examples

data(carsafety)
plot(Driver.deaths + Other.deaths ~ type, data = carsafety)
plot(Driver.deaths + Other.deaths ~ type, data = carsafety)

Weather in Central Park NY in May 2003

Description

A listing of various weather measurements made at Central Park in New York City during the month of May 2003.

Usage

data(central.park)

Format

A data frame with 31 observations on the following 19 variables.

DY

the day

MAX

maximum temperature (temperatures in Farenheit)

MIN

minimum temperature

AVG

average temperature

DEP

departure from normal

HDD

heating degree days

CDD

cooling degree days

WTR

Water fall. A factor as "T" is a trace.

SNW

Amount of snowfall

DPTH

Depth of snow

SPD

Average wind speed

SPD1

Max wind speed

DIR

2 minimum direction

MIN2

Sunshine measurement a factor with two levels 0 M

PSBL

Sunshine measurement a factor with levels 0 M

S.S

Sunshine measurement. 0-3 = Clear, 4-7 partly cloudy, 8-10 is cloudy

WX

(This is not as documented in the data source. Ignore this variable. It should be: 1 = FOG, 2 = FOG REDUCING VISIBILITY TO 1/4 MILE OR LESS, 3 = THUNDER, 4 = ICE PELLETS, 5 = HAIL, 6 = GLAZE OR RIME, 7 = BLOWING DUST OR SAND: VSBY 1/2 MILE OR LESS, 8 = SMOKE OR HAZE, 9 = BLOWING SNOW, X = TORNADO)

SPD3

peak wind speed

DR

direction of peak wind

Details

This datasets summarizes the weather in New York City during the merry month of May 2003. This data set comes from the daily climate report issued by the National Weather Service Office.

Source

This data was published on http://www.noah.gov

Examples

data(central.park)
attach(central.park)
barplot(rbind(MIN,MAX-MIN),ylim=c(0,80))

Type of day in Central Park, NY May 2003

Description

The type of day in May 2003 in Central Park, NY

Usage

data(central.park.cloud)

Format

A factor with levels clear,partly.cloudy and cloudy.

Source

This type of data, and much more, is available from https://www.noaa.gov.

Examples

data(central.park.cloud)
table(central.park.cloud)

CEO compensation in 2013

Description

Data on top 200 CEO compensations in the year 2013

Usage

data(ceo2013)

Format

a data frame.

Source

Scraped from https://archive.nytimes.com/www.nytimes.com/interactive/2013/06/30/business/executive-compensation-tables.html?ref=business

Examples

data(ceo2013)

Bootstrap sample from the Survey of Consumer Finances

Description

A bootstrap sample from the “Survey of Consumer Finances”.

Usage

data(cfb)

Format

A data frame with 1000 observations on the following 14 variables.

WGT

Weights to comensate for undersampling. Not applicable

AGE

Age of participants

EDUC

Education level (number of years) of participant

INCOME

Income in year 2001 of participant

CHECKING

Amount in checking account for participant

SAVING

Amount in savings accounts

NMMF

Total directly-held mutual funds

STOCKS

Amount held in stocks

FIN

Total financial assets

VEHIC

Value of all vehicles (includes autos, motor homes, RVs, airplanes, boats)

HOMEEQ

Total home equity

OTHNFIN

Other financial assets

DEBT

Total debt

NETWORTH

Total net worth

Details

The SCF dataset is a comprehensive survey of consumer finances sponsored by the United States Federal Reserve, https://www.federalreserve.gov/pubs/oss/oss2/2001/scf2001home.html.

The data is oversampled to compensate for low response in the upper brackets. To compensate, weights are assigned. By bootstrapping the data with the weights, we get a “better” version of a random sample from the population.

Source

https://www.federalreserve.gov/pubs/oss/oss2/2001/scf2001home.html

Examples

data(cfb)
attach(cfb)
mean(INCOME)

weight gain of chickens fed 3 different rations

Description

weight gain of chickens fed 3 different rations

Usage

data(chicken)

Format

This data frame contains the following columns:

Ration1

a numeric vector

Ration2

a numeric vector

Ration3

a numeric vector

Source

From Kitchens' Exploring Statistics.

Examples

data(chicken)
boxplot(chicken)

Measurements of chip wafers

Description

The chips data frame has 30 rows and 8 columns.

Usage

data(chips)

Format

This data frame contains the following columns:

wafer11

a numeric vector

wafer12

a numeric vector

wafer13

a numeric vector

wafer14

a numeric vector

wafer21

a numeric vector

wafer22

a numeric vector

wafer23

a numeric vector

wafer24

a numeric vector

Source

From Kitchens' Exploring Statistics

Examples

data(chips)
boxplot(chips)

Carbon Dioxide Emissions from the U.S.A. from fossil fuel

Description

Carbon Dioxide Emissions from the U.S.A. from fossil fuel

Usage

data(co2emiss)

Format

The format is: Time-Series [1:276] from 1981 to 2004: -30.5 -30.4 -30.3 -29.8 -29.6 ...

Details

Monthly estimates of 13C/12C in fossil-fuel CO2 emissions. Originally at http://cdiac.esd.ornl.gov/trends/emis_mon/emis_mon_co2.html; now off-line.

At one time: "An annual cycle, peaking during the winter months and reflecting natural gas consumption, and a semi-annual cycle of lesser amplitude, peaking in summer and winter and reflecting coal consumption, comprise the dominant features of the annual pattern. The relatively constant emissions until 1987, followed by an increase from 1987-1989, a decrease in 1990-1991 and record highs during the late 1990s, are also evident in the annual data of Marland et al. However, emissions have declined somewhat since 2000."

Source

http://cdiac.esd.ornl.gov/ftp/trends/emis_mon/emis_mon_c13.dat (off-line)

Examples

data(co2emiss)
monthplot(co2emiss)
stl(co2emiss, s.window="periodic")

The coins in my change bin

Description

The coins in author's change bin with year and value.

Usage

data(coins)

Format

A data frame with 371 observations on the following 2 variables.

year

Year of coin

value

Value of coin: quarter, dime, nickel, or penny

Examples

data(coins)
years = cut(coins$year,seq(1920,2010,by=10),include.lowest=TRUE,
  labels = paste(192:200,"*",sep=""))
table(years)

Daily minimum temperature in Woodstock Vermont

Description

Recordings of daily minimum temperature in Woodstock Vermont from January 1 1980 through 1985.

Usage

data(coldvermont)

Format

A ts object with daily frequency

Source

Extracted from http://www.ce.washington.edu/pub/HYDRO/edm/met_thru_97/vttmin.dly.gz. Errors were possibly introduced.

Examples

data(coldvermont)
plot(coldvermont)

Produce confidence interval for objects of class htest

Description

Simple means to output a confidence interval for an htest object.

Usage

  ## S3 method for class 'htest'
confint(object, parm, level, ...)

Arguments

Value

No return value, outputs interval through cat.

Examples

confint(t.test(rnorm(10)))	

Comparison of corn for new and standard variety

Description

Comparison of corn for new and standard variety

Usage

data(corn)

Format

This data frame contains the following columns:

New

a numeric vector

Standard

a numeric vector

Source

From Kitchens' Exploring Statitistcs

Examples

data(corn)
t.test(corn)

violent crime rates in 50 states of US

Description

crime rates for 50 states in 1983 and 1993

Usage

data(crime)

Format

This data frame contains the following columns:

y1983

a numeric vector

y1993

a numeric vector

Source

from Kitchens' Exploring Statistics

Examples

data(crime)
boxplot(crime)
t.test(crime[,1],crime[,2],paired=TRUE)

Deflection under load

Description

The data collected in a calibration experiment consisting of a known load, applied to the load cell, and the corresponding deflection of the cell from its nominal position.

Usage

data(deflection)

Format

A data frame with 40 observations on the following 2 variables.

Deflection

a numeric vector

Load

a numeric vector

Source

From an example in Engineering Statistics Handbook from http://www.itl.nist.gov/div898/handbook/

Examples

data(deflection)
res = lm(Deflection ~ Load, data = deflection)
plot(Deflection ~ Load, data = deflection)
abline(res)			# looks good?
plot(res)

Provide menu for possible shiny demonstrations

Description

Provides a menu to open one of the provided demonstrations which use shiny for animation.

Usage

demos()

Details

User must have installed shiny prior to usage. As shiny has some dependencies that don't always work, this package is not a dependency of UsingR.

Value

No return value, when called a web page opens. Use Ctrl-C (or equivalent) in terminal to return to an interactive session.

Examples

## demos()

Plots densities of data

Description

Allows one to compare empirical densities of different distributions in a simple manner. The density is used as graphs with multiple histograms are too crowded. The usage is similar to side-by-side boxplots.

Usage

DensityPlot(x, ...)

Arguments

Value

Makes a plot

Author(s)

John Verzani

References

Basically a modified boxplot function. As well it should be as it serves the same utility: comparing distributions.

See Also

boxplot,violinplot,density

Examples

## taken from boxplot
## using a formula
data(InsectSprays)
DensityPlot(count ~ spray, data = InsectSprays)
## on a matrix (data frame)
mat <- cbind(Uni05 = (1:100)/21, Norm = rnorm(100),
             T5 = rt(100, df = 5), Gam2 = rgamma(100, shape = 2))
DensityPlot(data.frame(mat))

Price by size for diamond rings

Description

A data set on 48 diamond rings containing price in Singapore dollars and size of diamond in carats.

Usage

data(diamond)

Format

A data frame with 48 observations on the following 2 variables.

carat

A measurement of a diamond's size

price

Price in Singapore dollars

Details

This data comes from a collection of the Journal of Statistics Education. The accompanying documentation says:

“Data presented in a newspaper advertisement suggest the use of simple linear regression to relate the prices of diamond rings to the weights of their diamond stones. The intercept of the resulting regression line is negative and significantly different from zero. This finding raises questions about an assumed pricing mechanism and motivates consideration of remedial actions.”

Source

This comes from http://jse.amstat.org/datasets/diamond.txt. Data set is contributed by Singfat Chu.

Examples

data(diamond)
plot(price ~ carat, diamond, pch=5)

Time until divorce for divorced women (by age)

Description

The divorce data frame has 25 rows and 6 columns.

Usage

data(divorce)

Format

This data frame contains the following columns:

time of divorce

a factor

all ages

a numeric vector

0-17

a numeric vector

18-19

a numeric vector

20-24

a numeric vector

25-100

a numeric vector

Source

Forgot source

Examples

data(divorce)
apply(divorce[,2:6],2,sum)	# percent divorced by age of marriage

Make big DOT plot likestripchart

Description

A variant of the stripchart using big dots as the default.

Usage

DOTplot(x, ...)

Arguments

Value

Returns the graphic only.

Author(s)

John Verzani

See Also

See also as stripchart, dotplot

Examples

x = c(1,1,2,3,5,8)
DOTplot(x,main="Fibonacci",cex=2)

Dot-to-dot puzzle

Description

A set of points to make a dot-to-dot puzzle

Usage

data(dottodot)

Format

A data frame with 49 observations on the following 4 variables.

x

x position

y

y position

pos

where to put label

ind

number for label

Details

Points to make a dot to dot puzzle to illustrate, text, points, and the argument pos.

Source

Illustration by Noah Verzani.

Examples

data(dottodot)
# make a blank graph
plot(y~x,data=dottodot,type="n",bty="n",xaxt="n",xlab="",yaxt="n",ylab="")
# add the points
points(y~x,data=dottodot)
# add the labels using pos argument
with(dottodot, text(x,y,labels=ind,pos=pos))
# solve the puzzle
lines(y~x, data=dottodot)

The Dow Jones average from Jan 1999 to October 2000

Description

The dowdata data frame has 443 rows and 5 columns.

Usage

data(dowdata)

Format

This data frame contains the following columns:

Open

a numeric vector

High

a numeric vector

Date

a numeric vector

Low

a numeric vector

Close

a numeric vector

Source

this data comes from the site http://www.forecasts.org/

Examples

data(dowdata)
the.close <- dowdata$Close
n <- length(the.close)
plot(log(the.close[2:n]/the.close[1:(n-1)]))

Monthly DVD player sales since introduction to May 2004

Description

Monthly DVD player sales since introduction of DVD format to May 2004

Usage

data(dvdsales)

Format

Matrix with rows recording the year, and columns the month.

Source

Original data retrieved from http://www.thedigitalbits.com/articles/cemadvdsales.html

Examples

data(dvdsales)
barplot(t(dvdsales[7:1,]),beside=TRUE)

CO2 emissions data and gross domestic product for 26 countries

Description

The emissions data frame has 26 rows and 3 columns.

A data set listing GDP, GDP per capita, and CO2 emissions for 1999.

Usage

data(emissions)

Format

This data frame contains the following columns:

GDP

a numeric vector

perCapita

a numeric vector

CO2

a numeric vector

Source

http://www.grida.no for CO2 data and http://www.mrdowling.com for GDP data.

Prompted by a plot appearing in a June 2001 issue of the New York Times.

Examples

data(emissions)
plot(emissions)

Show errata

Description

Show errata

Usage

errata()

Value

opens browse to errata page

Taxi in and taxi out times at EWR (Newark) airport for 1999-2001

Description

The ewr data frame has 46 rows and 11 columns.

Gives taxi in and taxi out times for 8 different airlines and several months at EWR airport.

Airline codes are AA (American Airlines), AQ (Aloha Airlines), AS (Alaska Airlines), CO (Continental Airlines), DL (Delta Airlines), HP (America West Airlines), NW (Northwest Airlines), TW (Trans World Airlines), UA (United Airlines), US (US Airways), and WN (Southwest Airlines)

Usage

data(ewr)

Format

This data frame contains the following columns:

Year

a numeric vector

Month

a factor for months

AA

a numeric vector

CO

a numeric vector

DL

a numeric vector

HP

a numeric vector

NW

a numeric vector

TW

a numeric vector

UA

a numeric vector

US

a numeric vector

inorout

a factor with levels in or out

Source

Retrieved from http://www.bts.gov/oai/taxitime/html/ewrtaxi.html

Examples

data(ewr)
boxplot(ewr[3:10])

Direct compensation for 199 United States CEOs in the year 2000

Description

Direct compensation for 199 United States CEOs in the year 2000 in units of \$10,000.

Usage

data(exec.pay)

Format

A numeric vector with 199 entries each measuring compensation in 10,000s of dollars.

Source

New York Times Business section 04/01/2001. See also https://aflcio.org.

Examples

data(exec.pay)
hist(exec.pay)

Body measurements to predict percentage of body fat in males

Description

A data set containing many physical measurements of 252 males. Most of the variables can be measured with a scale or tape measure. Can they be used to predict the percentage of body fat? If so, this offers an easy alternative to an underwater weighing technique.

Usage

data(fat)

Format

A data frame with 252 observations on the following 19 variables.

case

Case Number

body.fat

Percent body fat using Brozek's equation, 457/Density - 414.2

body.fat.siri

Percent body fat using Siri's equation, 495/Density - 450

density

Density (gm/cm\mbox{\textasciicircum}2)

age

Age (yrs)

weight

Weight (lbs)

height

Height (inches)

BMI

Adiposity index = Weight/Height\mbox{\textasciicircum}2 (kg/m\mbox{\textasciicircum}2)

ffweight

Fat Free Weight = (1 - fraction of body fat) * Weight, using Brozek's formula (lbs)

neck

Neck circumference (cm)

chest

Chest circumference (cm)

abdomen

Abdomen circumference (cm) "at the umbilicus and level with the iliac crest"

hip

Hip circumference (cm)

thigh

Thigh circumference (cm)

knee

Knee circumference (cm)

ankle

Ankle circumference (cm)

bicep

Extended biceps circumference (cm)

forearm

Forearm circumference (cm)

wrist

Wrist circumference (cm) "distal to the styloid processes"

Details

From the source:

“The data are as received from Dr. Fisher. Note, however, that there are a few errors. The body densities for cases 48, 76, and 96, for instance, each seem to have one digit in error as can be seen from the two body fat percentage values. Also note the presence of a man (case 42) over 200 pounds in weight who is less than 3 feet tall (the height should presumably be 69.5 inches, not 29.5 inches)! The percent body fat estimates are truncated to zero when negative (case 182).”

Source

This data set comes from the collection of the Journal of Statistics Education at http://jse.amstat.org/datasets/fat.txt. The data set was contributed by Roger W. Johnson.

References

The source of the data is attributed to Dr. A. Garth Fisher, Human Performance Research Center, Brigham Young University, Provo, Utah 84602,

Examples

data(fat)
f = body.fat ~ age + weight + height + BMI + neck + chest + abdomen +
hip + thigh + knee + ankle + bicep + forearm + wrist
res = lm(f, data=fat)
summary(res)

Pearson's data set on heights of fathers and their sons

Description

1078 measurements of a father's height and his son's height.

Usage

data(father.son)

Format

A data frame with 1078 observations on the following 2 variables.

fheight

Father's height in inches

sheight

Son's height in inches

Details

Data set used by Pearson to investigate regression. See data set galton for data set used by Galton.

Source

Read into R by the command

read.table("http://stat-www.berkeley.edu/users/juliab/141C/pearson.dat",sep=" ")[,-1],

as mentioned by Chuck Cleland on the r-help mailing list.

Examples

data(father.son)
## like cover of Freedman, Pisani, and Purves
plot(sheight ~ fheight, data=father.son,bty="l",pch=20)
abline(a=0,b=1,lty=2,lwd=2)
abline(lm(sheight ~ fheight, data=father.son),lty=1,lwd=2)

Income distribution for females in 2001

Description

A data set containing incomes for 1,000 females along with race information. The data is sampled from data provided by the United States Census Bureau.

Usage

data(female.inc)

Format

A data frame with 1,000 observations on the following 2 variables.

income

Income for 2001 in dollars

race

a factor with levels black, hispanic or white

Details

The United States Census Bureau provides alot of data on income distributions. This data comes from the Current Population Survey (CPS) for the year 2001. The raw data appears in table format. This data is sampled from the data in that table.

Source

The original table was found at http://ferret.bls.census.gov/macro/032002/perinc/new11_002.htm

Examples

data(female.inc)
boxplot(income ~ race, female.inc)
boxplot(log(income,10) ~ race, female.inc)
sapply(with(female.inc,split(income,race)),median)

Age of mother at birth of first child

Description

Age of mother at birth of first child

Usage

data(firstchi)

Format

The format is: num [1:87] 30 18 35 22 23 22 36 24 23 28 ...

Source

From Exploring Statistics, L. Kitchens, Duxbury Press, 1998.

Examples

data(firstchi)
hist(firstchi)

Five years of weather in New York City

Description

Five years of maximum temperatures in New York City

Usage

data(five.yr.temperature)

Format

A data frame with 2,439 observations on the following 3 variables.

days

Which day of the year

years

The year

temps

Maximum temperature

Source

Dataset found on the internet, but original source is lost.

Examples

data(five.yr.temperature)
attach(five.yr.temperature)
scatter.smooth(temps ~ days,col=gray(.75))
lines(smooth.spline(temps ~ days), lty=2)
lines(supsmu(days, temps), lty=3)

County-by-county results of year 2000 US presidential election in Florida

Description

The florida data frame has 67 rows and 13 columns.

Gives a county by county accounting of the US elections in the state of Florida.

Usage

data(florida)

Format

This data frame contains the following columns:

County

Name of county

GORE

Votes for Gore

BUSH

Votes for Bush

BUCHANAN

Votes for Buchanan

NADER

Votes for Nader

BROWN

a numeric vector

HAGELIN

a numeric vector

HARRIS

a numeric vector

MCREYNOLDS

a numeric vector

MOOREHEAD

a numeric vector

PHILLIPS

a numeric vector

Total

a numeric vector

Source

Found in the excellent notes Using R for Data Analysis and Graphics by John Maindonald. (As of 2003 a book published by Cambridge University Press.)

Examples

data(florida)
attach(florida)
result.lm <- lm(BUCHANAN ~ BUSH)
plot(BUSH,BUCHANAN)
abline(result.lm) ## can you find Palm Beach and Miami Dade counties?

Galileo data on falling bodies

Description

Data recorded by Galileo in 1609 during his investigations of the trajectory of a falling body.

Usage

data(galileo)

Format

A data frame with 7 observations on the following 2 variables.

init.h

Initial height of ball

h.d

Horizontal distance traveled

Details

A simple ramp 500 punti above the ground was constructed. A ball was placed on the ramp at an indicated height from the ground and released. The horizontal distance traveled is recorded (in punti). (One punto is 169/180 millimeter, not a car by FIAT.)

Source

This data and example come from the Statistical Sleuth by Ramsay and Schafer, Duxbury (2001), section 10.1.1. They attribute an article in Scientific American by Drake and MacLachlan.

Examples

data(galileo)
polynomial = function(x,coefs) {
  sum = 0
  for(i in 0:(length(coefs)-1)) {
    sum = sum + coefs[i+1]*x^i
  }
  sum
}
res.lm = lm(h.d ~ init.h, data = galileo)
res.lm2 = update(res.lm, . ~ . + I(init.h^2), data=galileo)
res.lm3 = update(res.lm2, . ~ . + I(init.h^3), data=galileo)
plot(h.d ~ init.h, data = galileo)
curve(polynomial(x,coef(res.lm)),add=TRUE)
curve(polynomial(x,coef(res.lm2)),add=TRUE)
curve(polynomial(x,coef(res.lm3)),add=TRUE)

Galton's height data for parents and children

Description

Data set from tabulated data set used by Galton in 1885 to study the relationship between a parent's height and their childrens.

Usage

data(galton)

Format

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

child

The child's height

parent

The “midparent” height

Details

The midparent's height is an average of the fathers height and 1.08 times the mother's. In the data there are 205 different parents and 928 children. The data here is truncated at the ends for both parents and children so that it can be treated as numeric data. The data were tabulated and consequently made discrete. The father.son data set is similar data used by Galton and is continuous.

Source

This data was found at http://www.bun.kyoto-u.ac.jp/~suchii/galton86.html.

See also the data.set father.son which was found from http://stat-www.berkeley.edu/users/juliab/141C/pearson.dat.

Examples

data(galton)
plot(galton)
## or with some jitter.
plot(jitter(child,5) ~ jitter(parent,5),galton)
## sunflowerplot shows flowers for multiple plots (Thanks MM)
sunflowerplot(galton)

Sales data for the Gap

Description

Sales data for the Gap from Jan

Usage

data(gap)

Format

The format is a ts object storing data from June 2002 through June 2005.

Source

http://home.businesswire.com

Examples

data(gap)
monthplot(gap)

Monthly average gasoline prices in the United States

Description

Average retail gasoline prices per month in the United States from January 2000 through February 2006. The hurricane Katrina caused a percentage loss of refinery capability leading to rapidly increasing prices.

Usage

data(gasprices)

Format

The format is: Time-Series [1:74] from 2000 to 2006: 129 138 152 146 148 ...

Source

Oringally from the Department of Energy web site: https://www.eia.gov/petroleum/gasdiesel/

Examples

data(gasprices)
plot(gasprices)

function to get answer to problem

Description

Returns answers for the first edition.

Usage

getAnswer(chapter = NULL, problem = NULL)

Arguments

Value

opens web page to answer

Goals per game in NHL

Description

Goals per game in NHL

Usage

data(goalspergame)

Format

The format is: mts [1:53, 1:4] 6 6 6 6 6 6 6 6 6 6 ... - attr(*, "dimnames")=List of 2 ..$ : NULL ..$ : chr [1:4] "n.teams" "n.games" "n.goals" "gpg" - attr(*, "tsp")= num [1:3] 1946 1998 1 - attr(*, "class")= chr [1:2] "mts" "ts"

Source

Off internet site. Forgot which.

Examples

data(goalspergame)

Google stock values during 2005-02-07 to 2005-07-07

Description

Closing stock price of a share of Google stock during 2005-02-07 to 2005-07-07

Usage

data(google)

Format

A data vector of numeric values with names attribute giving the dates.

Source

finance.yahoo.com

Examples

data(google)
plot(google,type="l")

Current and previous grades

Description

A dataframe of a students grade and their grade in their previous class. Graded on American A-F scale.

Usage

data(grades)

Format

A dataframe of 122 rows with 2 columns

prev

The grade in the previous class in the subject matter

grade

The grade in the current class

Examples

data(grades)
table(grades)

Effects of cross-country ski-pole grip

Description

Simulated data set investigating effects of cross-country ski-pole grip.

Usage

data(grip)

Format

A data frame with 36 observations on the following 4 variables.

UBP

Measurement of upper-body power

person

One of four skiers

grip.type

Either classic, modern, or integrated.

replicate

a numeric vector

Details

Based on a study originally described at http://www.montana.edu/wwwhhd/movementscilab/ and mentioned on http://www.xcskiworld.com/. The study investigated the effect of grip type on upper body power. As this influences performance in races, presumably a skier would prefer the grip that provides the best power output.

Examples

data(grip)
ftable(xtabs(UBP ~ person + replicate + grip.type,grip))

Data frame containing baseball statistics including Hall of Fame membership

Description

A data frame containing baseball statistics for several players.

Usage

data(hall.fame)

Format

A data frame with 1340 observations on the following 28 variables.

first

first name

last

last name

seasons

Seasons played

games

Games played

AB

Official At Bats

runs

Runs scored

hits

hits

doubles

doubles

triples

triples numeric vector

HR

Home runs

RBI

Runs batted in

BB

Base on balls

SO

Strike outs

BA

Batting Average

OBP

On Base percentage

SP

Slugging Percentage

AP

Adjusted productions

BR

batting runs

ABRuns

adjusted batting runs

Runs.Created

Runs created

SB

Stolen Bases

CS

Caught stealing

Stolen.Base.Runs

Runs scored by stealing

Fielding.Average

Fielding average

Fielding.Runs

Fielding runs

Primary.Position.Played

C = Catcher, 1 = First Base, 2 = Second Base, 3 = Third Base, S = Shortstop, O = Outfield, and D = Designated hitter

Total.Player.Rating

a numeric vector

Hall.Fame.Membership

Not a member, Elected by the BBWAA, or Chosen by the Old Timers Committee or Veterans Committee

Details

The sport of baseball lends itself to the collection of data. This data set contains many variables used to assess a players career. The Hall of Fame is reserved for outstanding players as judged initially by the Baseball Writers Association and subsequently by the Veterans Committee.

Source

This data set was submitted to the Journal of Statistical Education, https://www.amstat.org/publications/jse/secure/v8n2/datasets.cochran.new.cfm (now off-line), by James J. Cochran.

Examples

data(hall.fame)
hist(hall.fame$OBP)
with(hall.fame,last[Hall.Fame.Membership != "not a member"])

Show head and tail

Description

helper function to shorten display of a data frame

Usage

headtail(x, k = 3)

Arguments

Value

No return value. Uses cat to show data

Examples

headtail(mtcars)

Healthy or not?

Description

Data on whether a patient is healthy with two covariates.

Usage

data(healthy)

Format

A data frame with 32 observations on the following 3 variables.

p

One covariate

g

Another covariate

healthy

0 is healthy, 1 is not

Details

Data on health with information from two unspecified covariates.

Examples

data(healthy)
library(MASS)
stepAIC(glm(healthy ~ p + g, healthy, family=binomial))

Simulated data of age vs. max heart rate

Description

Simulated data of age vs. max heart rate

Usage

data(heartrate)

Format

This data frame contains the following columns:

age

a numeric vector

maxrate

a numeric vector

Details

Does this fit the workout room value of 220 - age?

Source

Simulated based on “Age-predicted maximal heart rate revisited” Hirofumi Tanaka, Kevin D. Monahan, Douglas R. Seals Journal of the American College of Cardiology, 37:1:153-156.

Examples

data(heartrate)
plot(heartrate)
abline(lm(maxrate ~ age,data=heartrate))

Maplewood NJ homedata

Description

The home data frame has 15 rows and 2 columns.

Usage

data(home)

Format

This data frame contains the following columns:

old

a numeric vector

new

a numeric vector

Details

See full dataset homedata

Source

See full dataset homedata

Examples

data(home)
## compare on the same scale
boxplot(data.frame(scale(home))) 

Maplewood NJ assessed values for years 1970 and 2000

Description

The homedata data frame has 6841 rows and 2 columns.

Data set containing assessed values of homes in Maplewood NJ for the years 1970 and 2000. The properties were not officially assessed during that time and it is interesting to see the change in percentage appreciation.

Usage

data(homedata)

Format

This data frame contains the following columns:

y1970

a numeric vector

y2000

a numeric vector

Source

Maplewood Reval

Examples

data(homedata)
plot(homedata)

Sale price of homes in New Jersey in the year 2001

Description

The homeprice data frame has 29 rows and 7 columns.

Usage

data(homeprice)

Format

This data frame contains the following columns:

list

list price of home (in thousands)

sale

actual sale price

full

Number of full bathrooms

half

number of half bathrooms

bedrooms

number of bedrooms

rooms

total number of rooms

neighborhood

Subjective assessment of neighborhood on scale of 1-5

Details

This dataset is a random sampling of the homes sold in Maplewood, NJ during the year 2001. Of course the prices will either seem incredibly high or fantastically cheap depending on where you live, and if you have recently purchased a home.

Source

Source Burgdorff Realty.

Examples

data(homeprice)
plot(homeprice$sale,homeprice$list)
abline(lm(homeprice$list~homeprice$sale))

Homework averages for Private and Public schools

Description

Homework averages for Private and Public schools

Usage

data(homework)

Format

This data frame contains the following columns:

Private

a numeric vector

Public

a numeric vector

Source

This is from Kitchens Exploring Statistics

Examples

data(homework)
boxplot(homework)

Deliveries of new HUMMER vehicles

Description

Gives monthly delivery numbers for new HUMMER vehicles from June 2003 through February 2006. During July, August, and September 2005 there was an Employee Pricing Incentive.

Usage

data(HUMMER)

Format

The format is: Time-Series [1:33] from 2003 to 2006: 2493 2654 2987 2837 3157 2837 3157 1927 2141 2334 ...

Source

Compiled from delivery data avalailble at http://www.gm.com/company/investor_information/sales_prod/hist_sales.html

Examples

data(HUMMER)
plot(HUMMER)

Top percentiles of U.S. income

Description

Top percentiles of U.S. income

Usage

data(income_percentiles)

Format

A data frame with Year and various percentile (90th, 95th, ...)

Source

Not available

Examples

data(income_percentiles)

IQ scores

Description

simulated IQ scores

Usage

data(iq)

Format

The format is: num [1:100] 72 75 77 77 81 82 83 84 84 86 ...

Source

From Kitchens Exploring Statistics

Examples

data(iq)
qqnorm(iq)

Weight and height measurement for a sample of U.S. children

Description

A sample from the data presented in the NHANES III survey (https://www.cdc.gov/nchs/nhanes.htm). This survey is used to form the CDC Growth Charts (https://www.cdc.gov/growthcharts/) for children.

Usage

data(kid.weights)

Format

A data frame with 250 observations on the following 4 variables.

age

Age in months

weight

weight in pounds

height

height in inches

gender

Male of Female

Source

This data is extracted from the NHANES III survey: https://www.cdc.gov/nchs/nhanes.htm.

Examples

data(kid.weights)
attach(kid.weights)
plot(weight,height,pch=as.character(gender))
## find the BMI -- body mass index
m.ht = height*2.54/100        # 2.54 cm per inch
m.wt = weight / 2.2046        # 2.2046 lbs. per kg
bmi = m.wt/m.ht^2
hist(bmi)

Data set on automobile deaths and injuries in Great Britain

Description

Data on car drivers killed, car drivers killed or seriously injured (KSI), and light goods drivers killed during the years 1969 to 1984 in Great Britain. In February 1982 a compulsory seat belt law was introduced.

Usage

data(KSI)

Format

The data is stored as a multi-variate zoo object.

Source

Data copied from Appendix 2 "Forecasting, structural time series, models and the Kalman Filter" by Andrew Harvey. The lg.k data is also found in the vandrivers dataset contained in the sspir package.

References

Source: HMSO: Road Accidents in Great Britain 1984.

Examples

data(KSI)
plot(KSI)
seatbelt = time(KSI) < 1983 + (2-1)/12

Last tie in 100 coin tosses

Description

Toss a coin 100 times and keep a running count of the number of heads and the number of tails. Record the times when the number is tied and report the last one. The distribution will have an approximate “arc-sine” law or well-shaped distribution.

Usage

data(last.tie)

Format

200 numbers between 0 and 100 indicating when the last tie was.

Details

This data comes from simulating the commands: x = cumsum(sample(c(-1,1),100,replace=T))

and then finding the last tie with

last.tie[i]<-max(0,max(which(!sign(x) == sign(x[length(x)])))).

Examples

data(last.tie)
hist(last.tie)

Law suit settlements

Description

A simulated dataset on the settlement amount of 250 lawsuits based on values reported by Class Action Reports.

Usage

data(lawsuits)

Format

The format is: num [1:250] 16763 10489 17693 14268 442 ...

Details

Class Action Reports completed an extensive survey of attorney fee awards from 1,120 common fund class actions (Volume 24, No. 2, March/April 2003). The full data set is available for a fee. This data is simulated from the values published in an excerpt.

Source

Original data from http://www.classactionreports.com/classactionreports/attorneyfee.htm

References

See also "Study Disputes View of Costly Surge in Class-Action Suits" by Jonathan D. Glater in the January 14, 2004 New York Times which cites a Jan. 2004 paper in the Journal of Empirical Legal Studies by Eisenberg and Miller.

Examples

data(lawsuits)
mean(lawsuits)
median(lawsuits)

Placeholder text

Description

Lorem Ipsum is simply dummy text of the printing and typesetting industry.

Usage

lorem

Format

a character string

Source

https://www.lipsum.com/

Examples

table(unlist(strsplit(lorem, "")))

malpractice settlements

Description

malpractice settlements

Usage

data(malpract)

Format

The format is: num [1:17] 760 380 125 250 2800 450 100 150 2000 180 ...

Source

From Kitchens Exploring Statistics

Examples

data(malpract)
boxplot(malpract)

Proportions of colors in various M and M's varieties

Description

A bag of the candy M and M's has many different colors. Each large production batch is blended to the ratios given in this data set. The batches are thoroughly mixed and then the individual packages are filled by weight using high-speed equipment, not by count.

Usage

data(mandms)

Format

A data frame with 5 observations on the following 6 variables.

blue

percentage of blue

brown

percentage of brown

green

percentage of green

orange

percentage of orange

red

percentage of red

yellow

percentage of yellow

Source

This data is attributed to an email sent by Masterfoods USA, A Mars, Incoporated Company. This email was archived at the Math Forum, http://www.mathforum.org (now off-line).

Examples

data(mandms)
bagfull = c(15,34,7,19,29,24)
names(bagfull) = c("blue","brown","green","orange","red","yellow")
prop = function(x) x/sum(x)
chisq.test(bagfull,p = prop(mandms["milk chocolate",]))
chisq.test(bagfull,p = prop(mandms["Peanut",]))

Standardized math scores

Description

Standardized math scores

Usage

data(math)

Format

The format is: num [1:30] 44 49 62 45 51 59 57 55 70 64 ...

Source

From Larry Kitchens, Exploring Statistics, Duxbury Press.

Examples

data(math)
hist(math)

Dow Jones industrial average and May maximum temperature

Description

A data set of both the Dow Jones industrial average and the maximum daily temperature in New York City for May 2003.

Usage

data(maydow)

Format

A data frame with 21 observations on the following 3 variables.

Day

Day of the month

DJA

The daily close of the DJIQ

max.temp

Daily maximum temperature in Central Park

Details

Are stock traders influenced by the weather? This dataset looks briefly at this question by comparing the daily close of the Dow Jones industrial average with the maximum daily temperature for the month of May 2003. This month was rainy and unseasonably cool weather wise, yet the DJIA did well.

Source

The DJIA data was taken from https://finance.yahoo.com the temperature data from https://www.noaa.gov.

Examples

data(maydow)
attach(maydow)
plot(max.temp,DJA)
plot(max.temp[-1],diff(DJA))

Sample from "Medicare Provider Charge Data"

Description

Sample from "Medicare Provider Charge Data"

Usage

data(Medicare)

Format

A data frame with 10000 observations and data for on billings for procedures at many different hospitals.

Source

http://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/Medicare-Provider-Charge-Data/index.html

References

This data came from http://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/Medicare-Provider-Charge-Data/index and was referenced in the article https://www.nytimes.com/2013/05/08/business/hospital-billing-varies-wildly-us-data-shows.html, as retrieved on 5/8/2013.

Examples

data(Medicare)

Price of new and used of three mid-sized cars

Description

New and used prices of three popular mid-sized cars.

Usage

data(midsize)

Format

A data frame with 15 observations on the following 4 variables.

Year

2004 is new car price, others are for used car

Accord

Honda Accord

Camry

Toyota Camry

Taurus

Ford Taurus

Details

The value of a car depreciates over time. This data gives the price of a new car and values of similar models for previous years as reported by https://www.edmunds.com.

Examples

data(midsize)
plot(Accord ~ I(2004-Year), data = midsize)

Major league baseball attendance data

Description

Data on home-game attendance in Major League Baseball for the years 1969-2000.

Usage

data(MLBattend)

Format

A data frame with 838 observations on the following 10 variables.

franchise

Which team

league

American or National league

division

Which division

year

The year (the year 2000 is recorded as 0)

attendance

Actual attendance

runs.scored

Runs scored by the team during year

runs.allowed

Runs allows by the team during year

wins

Number of wins for season

losses

Number of losses for season

games.behind

A measure of how far from division winner the team was. Higher numbers are worse.

Source

This data was submitted to The Journal of Statistical Education by James J. Cochran, http://jse.amstat.org/v10n2/datasets.cochran.html.

Examples

data(MLBattend)
boxplot(attendance ~ franchise, MLBattend)
with(MLBattend, cor(attendance,wins))

Movie data for 2011 by weekend

Description

Movie data for 2011 by weekend

Usage

data(movie_data_2011)

Format

A data frame with variables Previous (previous weekend rank), Movie (title), Distributor, Genre, Gross (per current weekend), Change (change from previous week), Theaters (number of theaters), TotalGross (total gross to date), Days (days out), weekend (weekend of report)

Source

Scraped from pages such as https://www.the-numbers.com/box-office-chart/weekend/2011/04/29

Examples

data(movie_data_2011)

Data frome on top 25 movies for some week, many weeks ago

Description

Data on 25 top movies

Usage

data(movies)

Format

A data frame with 26 observations on the following 5 variables.

title

Titles

current

Current week

previous

Previous weel

gross

Total

Source

Some movie website, sorry lost the url.

Examples

data(movies)
boxplot(movies$previous)

Age distribution in year 2000 in Maplewood New Jersey

Description

Age distribution in Maplewood New Jersey, a suburb of New York City. Data is broken down by Male and Female.

Usage

data(mw.ages)

Format

A data frame with 103 observations on the following 2 variables.

Male

Counts per age group. Most groups are 1 year, except for 100-104, 105-110, 110+

Female

Same

Source

US Census 2000 data from http://factfinder.census.gov/

Examples

data(mw.ages)
barplot(mw.ages$Male + mw.ages$Female)

NBA draft lottery odds for 2002

Description

The NBA draft in 2002 has a lottery

Usage

data(nba.draft)

Format

A data frame with 13 observations on the following 2 variables.

Team

Team name

Record

The team won-loss record

Balls

The number of balls (of 1000) that this team has in the lottery selection

Details

The NBA draft has a lottery to determing the top 13 placings. The odds in the lottery are determined by the won-loss record of the team, with poorer records having better odds of winning.

Source

Data is taken from https://www.nba.com/news/draft_ties_020424.html.

Examples

data(nba.draft)
top.pick = sample(row.names(nba.draft),1,prob = nba.draft$Balls)

NISCD

Description

A data frame measuring daily sea-ice extent from 1978 until 2013.

Usage

data(nisdc)

Format

A data frame measuring daily sea-ice extent from 1978 until 2013

Source

ftp://sidads.colorado.edu/DATASETS/NOAA/G02135/north/daily/data/NH_seaice_extent_final.csv and ftp://sidads.colorado.edu/DATASETS/NOAA/G02135/north/daily/data/NH_seaice_extent_nrt.csv (now offline).

References

See the blog post https://www.r-bloggers.com/2012/08/arctic-sea-ice-at-lowest-levels-since-observations-began/ for a description and nice script to play with.

Body temperature and heart rate of 130 health individuals

Description

A data set used to investigate the claim that “normal” temperature is 98.6 degrees.

Usage

data(normtemp)

Format

A data frame with 130 observations on the following 3 variables.

temperature

normal body temperature

gender

Gender 1 = male, 2 = female

hr

Resting heart rate

Details

Is normal body temperature 98.6 degrees Fahrenheit? This dataset was constructed to match data presented in an are article intending to establish the true value of “normal” body temperature.

Source

This data set was contributed by Allen L. Shoemaker to the Journal of Statistics Education, http://jse.amstat.org/datasets/normtemp.txt.

References

Data set is simulated from values contained in Mackowiak, P. A., Wasserman, S. S., and Levine, M. M. (1992), "A Critical Appraisal of 98.6 Degrees F, the Upper Limit of the Normal Body Temperature, and Other Legacies of Carl Reinhold August Wunderlich," Journal of the American Medical Association, 268, 1578-1580.

Examples

data(normtemp)
hist(normtemp$temperature)
t.test(normtemp$temperature,mu=98.2)
summary(lm(temperature ~ factor(gender), normtemp))

National Practioner Data Bank

Description

Selected variables from the publicly available data from the National Practioner Data Bank (NPDB).

Usage

data(npdb)

Format

A data frame with 6797 observations on the following 6 variables.

state

2 digit abbreviation of state

field

Field of practice

age

Age of practictioner (rounded down to 10s digit)

year

Year of claim

amount

Dollar amount of reward

ID

a practioner ID, masked for anonymity

The variable names do not match the original. The codings for field come from a document on http://63.240.212.200/publicdata.html.

Details

This dataset excerpts some interesting variables from the NPDB for the years 2000-2003. The question of capping medical malpractice awards to lower insurance costs is currently being debated nationwide (U.S.). This data is a primary source for determining this debate.

A quotation from https://npdb-hipdb.com/:

“The legislation that led to the creation of the NPDB was enacted the U.S. Congress believed that the increasing occurrence of medical malpractice litigation and the need to improve the quality of medical care had become nationwide problems that warranted greater efforts than any individual State could undertake. The intent is to improve the quality of health care by encouraging State licensing boards, hospitals and other health care entities, and professional societies to identify and discipline those who engage in unprofessional behavior; and to restrict the ability of incompetent physicians, dentists, and other health care practitioners to move from State to State without disclosure or discovery of previous medical malpractice payment and adverse action history. Adverse actions can involve licensure, clinical privileges, professional society membership, and exclusions from Medicare and Medicaid.”

Source

This data came from https://npdb-hipdb.com/

Examples

data(npdb)
table(table(npdb$ID))		# big offenders
hist(log(npdb$amount))		# log normal?

Random sample of 2002 New York City Marathon finishers

Description

A random sample of finishers from the New York City Marathon.

Usage

data(nym.2002)

Format

A data frame with 1000 observations on the following 5 variables.

place

Place in the race

gender

What gender

age

Age on day of race

home

Indicator of hometown or nation

time

Time in minutes to finish

Details

Each year thousands of particpants line up to run the New York City Marathon. This list is a random sample from the finishers.

Source

From the New York City Road Runners web site http://www.nyrc.org

Examples

data(nym.2002)
with(nym.2002, cor(time,age))

Approval ratings for President Obama

Description

A collection of approval ratings for President Obama spanning a duration from early 2010 to the summer of 2013.

Usage

data(ObamaApproval)

Format

A data frame 7 variables.

Source

Scraped on 7-5-13 from https://www.realclearpolitics.com/epolls/other/president_obama_job_approval-1044.html.

Examples

data(ObamaApproval)

On base percentage for 2002 major league baseball season

Description

The on base percentage, OBP, is a measure of how often a players gets on base. It differs from the more familiar batting average, as it include bases on balls (BB) and hit by pitches (HBP). The exact formula is OBP = (H + BB + HBP) / (AB + BB + HBP + SF).

Usage

data(OBP)

Format

438 numbers between 0 and 1 corresponding the on base “percentage” for the 438 players who had 100 or more at bats in the 2002 baseball season. The "outlier" is Barry Bonds.

Source

This data came from the interesting Lahman baseball data base http://www.seanlahman.com/. The names attribute uses the playerID from this database. Unfortunately there were some errors in the extraction from the original data set. Consult the original for accurate numbers.

Examples

data(OBP)
hist(OBP)
OBP[OBP>.5]			# who is better than 50%? (only Barry Bonds)

Oral lesion location by town

Description

A data set on oral lesion location for three Indian towns.

Usage

data(oral.lesion)

Format

A data frame with 9 observations on the following 3 variables.

Kerala

a numeric vector

Gujarat

a numeric vector

Andhra

a numeric vector

Source

"Exact Inference for Categorical Data", by Cyrus R. Mehta and Nitin R. Patel. Found at http://www.cytel.com/papers/sxpaper.pdf.

Examples

data(oral.lesion)
chisq.test(oral.lesion)$p.value
chisq.test(oral.lesion,simulate.p.value=TRUE)$p.value ## exact is.0269

Monthly mean ozone values at Halley Bay Antartica

Description

A time series showing ozone values at Halley Bay Antartica

Usage

data(ozonemonthly)

Format

The format is: Time-Series [1:590] from 1957 to 2006: 313 311 370 359 334 296 288 274 NA NA ... - attr(*, "names")= chr [1:590] "V5" "V6" "V7" "V8" ...

Details

Provisional monthly mean ozone values for Halley Bay Antartica between 1956 and 2005. Data comes from https://legacy.bas.ac.uk/met/jds/ozone/.

Source

Found at https://legacy.bas.ac.uk/met/jds/ozone/data/ZNOZ.DAT, now off-line.

References

See https://www.meteohistory.org/2004proceedings1.1/pdfs/11christie.pdf for a discussion of data collection and the Ozone hole.

Examples

data(ozonemonthly)
## notice decay in the 80s
plot(ozonemonthly)
## October plot shows dramatic swing
monthplot(ozonemonthly)

Annual snowfall at Paradise Ranger Station, Mount Ranier

Description

Annual snowfall (from July 1 to June 30th) measured at Paradise ranger station at Mount Ranier Washington.

Usage

data(paradise)

Format

The data is stored as a zoo class object. The time index refers to the year the snowfall begins.

Details

Due to its rapid elevation gain, and proximity to the warm moist air of the Pacific Northwest record amounts of snow can fall on Mount Ranier. This data set shows the fluctuations.

Source

Original data from http://www.nps.gov/mora/current/weather.htm

Examples

require(zoo)
data(paradise)
range(paradise, na.rm=TRUE)
plot(paradise)

first 2000 digits of pi

Description

first 2000 digits of pi

Usage

data(pi2000)

Format

The format is: num [1:2000] 3 1 4 1 5 9 2 6 5 3 ...

Source

Generated by Mathematica, http://www.wolfram.com.

Examples

data(pi2000)
chisq.test(table(pi2000))

Primes numbers less than 2003

Description

Prime numbers between 1 and 2003.

Usage

data(primes)

Format

The format is: num [1:304] 2 3 5 7 11 13 17 19 23 29 ...

Source

Generated using http://www.rsok.com/~jrm/printprimes.html.

Examples

data(primes)
diff(primes)

Incomes for Puerto Rican immigrants to Miami

Description

Incomes for Puerto Rican immigrants to Miami

Usage

data(puerto)

Format

The format is: num [1:50] 150 280 175 190 305 380 290 300 170 315 ...

Source

From Kitchens Exploring Statistics

Examples

data(puerto)
hist(puerto)

Creates a qqplot with shaded density estimate

Description

Creates a qqplot of two variables along with graphs of their densities, shaded so that the corresponding percentiles are clearly matched up.

Usage

QQplot(x, y, n = 20, xsf = 4, ysf = 4, main = "qqplot", xlab = deparse(substitute(x)),
        ylab = deparse(substitute(y)), pch = 16, pcol = "black", shade = "gray", ...)

Arguments

Details

Shows density estimates for the two samples in a qqplot. Meant to make this useful plot more transparent to first-time users of quantile-quantile plots.

This function has some limitations: the scale factor may need to be adjusted; the code to shade only shaded trapezoids, and does not completely follow the density.

Value

Produces a graphic

Author(s)

John Verzani

See Also

qqplot, qqnorm

Examples

x = rnorm(100)
y = rt(100, df=3)
QQplot(x,y)

Survival times of 20 rats exposed to radiation

Description

Survival times of 20 rats exposed to radiation

Usage

data(rat)

Format

The format is: num [1:20] 152 152 115 109 137 88 94 77 160 165 ...

Source

From Kitchents Exploring Statistics

Examples

data(rat)
hist(rat)

Reaction time with cell phone usage

Description

A simulated dataset on reaction time to an external event for subject using cell phones.

Usage

data(reaction.time)

Format

A data frame with 60 observations on the following 4 variables.

age

Age of participant coded as 16-24 or 25+

gender

Male of Female

control

Code to indicate if subject is using a cell phone "T" or is in the control group "C"

time

Time in seconds to react to external event

Details

Several studies indicate that cell phone usage while driving can effect reaction times to external events. This dataset uses simulated data based on values from the NHTSA study "The Influence of the Use of Mobile Phones on Driver Situation Awareness".

Source

The NHTSA study was found at http://www-nrd.nhtsa.dot.gov/departments/nrd-13/driver-distraction/PDF/2.PDF

References

This study and others were linked from the web page http://www.accidentreconstruction.com/research/cellphones/ (now off-line).

Examples

data(reaction.time)
boxplot(time ~ control, data = reaction.time)

Growth of red drum

Description

Simulated length-at-age data for the red drum.

Usage

data(reddrum)

Format

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

age

age

length

a numeric vector

Details

This data is simulated from values reported in a paper by Porch, Wilson and Nieland titled "A new growth model for red drum (Sciaenops ocellaus) that accommodates seasonal and ontogenic changes in growth rates" which appeard in Fishery Bulletin 100(1) (was at http://fishbull.noaa.gov/1001/por.pdf, now off-line). They attribute the data to Beckman et. al and say it comes from measurements in the Northern Gulf of Mexico, between September 1985 and October 1998.

Examples

data(reddrum)
plot(length ~ age, reddrum)

Simulated Data on Rate of Recruitment for Salmon

Description

The Ricker model is used to model the relationship of recruitment of a salmon species versus the number of spawners. The model has two parameters, a rate of growth at small numbers and a decay rate at large numbers. This data set is simulated data for 83 different recordings using parameters found in a paper by Chen and Holtby.

Usage

data(salmon.rate)

Format

The format is: 83 numbers on decay rates.

Details

The Ricker model for recruitment modeled by spawner count

R_t = S_t e^{a - bS_t}

The paramter b is a decay rate for large values of S. In the paper by Chen and Holtby, they studied 83 datasets and found that b is log-normally distributed. The data is simulated from their values to illustrate a log normal distribution.

Source

These values are from D.G. Chen and L. Blair Holtby, “A regional meta-model for stock recruitment analysis using an empirical Bayesian approach”, found at https://iphc.int/.

Examples

data(salmon.rate)
hist(log(salmon.rate))

Salmon harvest in Alaska from 1980 to 1998

Description

A data set of unofficial tallies of salmon harvested in Alaska between the years 1980 and 1998. The units are in thousands of fish.

Usage

data(salmonharvest)

Format

A multiple time series object with yearly sampling for the five species Chinook, Sockeye, Coho, Pink, and Chum.

Source

This data was found at http://seamarkets.alaska.edu/ak_harv_fish.htm

Examples

data(salmonharvest)
acf(salmonharvest)

Substance Abuse and Mental Health Data for teens

Description

A data frame containing data on health behaviour for school-aged children.

Usage

data(samhda)

Format

A data frame with 600 observations on the following 9 variables.

wt

A numeric weight used in sampling

gender

1=Male, 2=Female, 7=not recorded

grade

1 = 6th, 2 = 8th, 3 = 10th

live.with.father

1 = Y, 2 = N

amt.smoke

Amount of days you smoked cigarettes in last 30. 1 = all 30, 2= 20-29, 3 = 10-19, 4 = 6-9, 5= 3-5, 6 = 1-2, 7=0

alcohol

Have you ever drank alcohol, 1 = Y, 2 = N

amt.alcohol

Number of days in last 30 in which you drank alcohol

marijuana

Ever smoke marijuana. 1 = Y, 2= N

amt.marijuana

Number of days in lst 30 that marijuana was used. 1 = Never used, 2 = all 30, 3 = 20-29, 4 = 10-19, 5 = 6-9, 6 = 3-5, 7 = 1-2, 8 =Used, but not in last 30 days

Details

A data frame containing data on health behaviour for school-aged children.

Source

This data is sampled from the data set "Health Behavior in School-Aged Children, 1996: [United States]" collected by the World Health Organization, https://www.icpsr.umich.edu/. It is available at the Substance Abuse and Mental Health Data Archive (SAMHDA). Only complete cases are given.

Examples

data(samhda)
attach(samhda)
table(amt.smoke)

SAT data with expenditures

Description

This dataset contains variables that address the relationship between public school expenditures and academic performance, as measured by the SAT.

Usage

data(SAT)

Format

A data frame with variables state, expend (expenditure per pupil), ratio (pupil/teacher ratio); salary (average teacher salary; percentage of SAT takers; verbal (verbal score); math (math score); total (average total).

Source

The data came from http://www.amstat.org/publications/jse/datasets/sat.txt

References

This data comes from http://www.amstat.org/publications/jse/secure/v7n2/datasets.guber.cfm. It is also included in the mosaic package and commented on at http://sas-and-r.blogspot.com/2012/02/example-920-visualizing-simpsons.html. The variables are described at http://www.amstat.org/publications/jse/datasets/sat.txt.

The author references the original source: The variables in this dataset, all aggregated to the state level, were extracted from the 1997 Digest of Education Statistics, an annual publication of the U.S. Department of Education. Data from a number of different tables were downloaded from the National Center for Education Statistics (NCES) website (Available at: http://nces01.ed.gov/pubs/digest97/index.html) and merged into a single data file.

Examples

data(SAT)

Scatterplot with histograms

Description

Draws a scatterplot of the data, and histogram in the margins. A trend line can be added, if desired.

Usage

scatter.with.hist(x, y,
  hist.col = gray(0.95),
  trend.line = "lm",
   ...)

Arguments

Value

Draws the graphic. No return value.

Author(s)

John Verzani

References

This example comes from the help page for layout.

See Also

layout

Examples

data(emissions)
attach(emissions)
scatter.with.hist(perCapita,CO2)

Distribution of Scrabble pieces

Description

Distribution and point values of letters in Scrabble.

Usage

data(scrabble)

Format

A data frame with 27 observations on the following 3 variables.

piece

Which piece

points

point value

frequency

Number of pieces

Details

Scrabble is a popular board game based on forming words from the players' pieces. These consist of letters drawn from a pile at random. The game has a certain frequency of letters given by this data. These match fairly well with the letter distribution of the English language.

Examples

data(scrabble)
## perform chi-squared analysis on long string. Is it in English?
quote = " R is a language and environment for statistical computing  \
and graphics. It is a GNU project which is similar to the S language \
and environment which was developed at Bell Laboratories (formerly   \
AT&T, now Lucent Technologies) by John Chambers and colleagues. R    \
can be considered as a different implementation of S. There are      \
some important differences, but much code written for S runs         \
unaltered under R."
quote.lc = tolower(quote)
quote = unlist(strsplit(quote.lc,""))
ltr.dist = sapply(c(letters," "),function(x) sum(quote == x))
chisq.test(ltr.dist,,scrabble$freq)

simulate a chutes and ladder game

Description

This function will simulate a chutes and ladder game. It returns a trajectory for a single player. Optionally it can return the transition matrix which can be used to speed up the simulation.

Usage

simple.chutes(sim=FALSE, return.cl=FALSE, cl=make.cl())

Arguments

Details

To make a chutes and ladders trajectory

simple.chutes(sim=TRUE)

To return the game board

simple.chutes(return.cl=TRUE)

when doing a lot of simulations, it may be best to pass in the game board

cl <- simple.chutes(return.cl=TRUE) simple.chutes(sim=TRUE,cl)

Value

returns a trajectory as a vector, or a matrix if asked to return the transition matrix

Author(s)

John Verzani

References

board was from http://www.ahs.uwaterloo.ca/~musuem/vexhibit/Whitehill/snakes/snakes.gif

Examples

plot(simple.chutes(sim=TRUE))

Plots densities of data

Description

Allows one to compare empirical densities of different distributions in a simple manner. The density is used as graphs with multiple histograms are too crowded. The usage is similar to side-by-side boxplots.

Usage

simple.densityplot(x, ...)

Arguments

Value

Makes a plot

Author(s)

John Verzani

References

Basically a modified boxplot function. As well it should be as it serves the same utility: comparing distributions.

See Also

boxplot,simple.violinplot,density

Examples

## taken from boxplot
## using a formula
data(InsectSprays)
simple.densityplot(count ~ spray, data = InsectSprays)
## on a matrix (data frame)
mat <- cbind(Uni05 = (1:100)/21, Norm = rnorm(100),
             T5 = rt(100, df = 5), Gam2 = rgamma(100, shape = 2))
simple.densityplot(data.frame(mat))

Simple function to plot histogram, boxplot and normal plot

Description

Simply plots histogram, boxplot and normal plot for experimental data analysis.

Usage

simple.eda(x)

Arguments

Value

Just does the plots. No return value

Author(s)

John Verzani

References

Inspired by S-Plus documentation

See Also

hist,boxplot,qnorm

Examples

  x<- rnorm(100,5,10)
  simple.eda(x)

Makes 3 useful graphs for eda of times series

Description

This makes 3 graphs to check for serial correlation in data. The graphs are a sequential plot (i vs X_i), a lag plot (plotting X_i vs X_i where k=1 by default) and an autocorrelation plot from the times series ("ts") package.

Usage

simple.eda.ts(x, lag=1)

Arguments

Value

Makes the graph with 1 row, 3 columns

Author(s)

John Verzani

References

Downloaded from http://www.itl.nist.gov/div898/handbook/eda/section3/eda34.htm.

Examples

## The function is currently defined as

## look for no correlation
x <- rnorm(100);simple.eda.ts(x)
## you will find correlation here
simple.eda.ts(cumsum(x))

Makes a fancier strip chart: plots means and a line

Description

Not much, just hides some ugly code

Usage

simple.fancy.stripchart(l)

Arguments

Value

Creates the plot

Author(s)

John Verzani

See Also

stripchart

Examples

x = rnorm(10);y=rnorm(10,1)
simple.fancy.stripchart(list(x=x,y=y))

Simply plot histogram and frequency polygon

Description

Simply plot histogram and frequency polygon. Students do not need to know how to add lines to a histogram, and how to extract values.

Usage

simple.freqpoly(x, ...)

Arguments

Value

returns just the plot

Author(s)

John Verzani

See Also

hist,density

Examples

x <- rt(100,4)
simple.freqpoly(x)

A function to plot both a histogram and a boxplot

Description

Simple function to plot both histogram and boxplot to compare

Usage

simple.hist.and.boxplot(x, ...)

Arguments

Value

Just prints the two graphs

Author(s)

John Verzani

See Also

hist,boxplot,layout

Examples

x<-rnorm(100)
simple.hist.and.boxplot(x)

applies function to moving subsets of a data vector

Description

Used to apply a function to subsets of a data vector. In particular, it is used to find moving averages over a certain "lag" period.

Usage

simple.lag(x, lag, FUN = mean)

Arguments

Details

The function FUN is applied to the data x[(i-lag):i] and assigned to the (i-lag)th component of the return vector. Useful for finding moving averages.

Value

returns a vector.

Author(s)

Provided to R help list by Martyn Plummer

See Also

filter

Examples

## find a moving average of the dow daily High
data(dowdata)
lag = 50; n = length(dowdata$High)
plot(simple.lag(dowdata$High,lag),type="l")
lines(dowdata$High[lag:n])

Simplify usage of lm

Description

Simplify usage of lm by avoiding model notation, drawing plot, drawing regression line, drawing confidence intervals.

Usage

simple.lm(x, y, show.residuals=FALSE, show.ci=FALSE, conf.level=0.95,pred=)

Arguments

Value

returns plots and an instance of lm, as though it were called lm(y ~ x)

Author(s)

John Verzani

See Also

lm

Examples

## on simulated data
x<-1:10
y<-5*x + rnorm(10,0,1)
tmp<-simple.lm(x,y)
summary(tmp)

## predict values
simple.lm(x,y,pred=c(5,6,7))

Do simple sign test for median – no ranks

Description

Do simple sign test like wilcox.test without ranking. Just computes two-sided p-value, no confidence interval is given.

Usage

simple.median.test(x, median=NA)

Arguments

Details

Unlike wilcox.test, this tests the null hypothesis that the median is specified agains the two-sided alternative. For illustration purposes only.

Value

Returns the p value.

Author(s)

John Verzani

See Also

wilcox.test

Examples

x<-c(12,2,17,25,52,8,1,12)
simple.median.test(x,20)

Simple scatter plot of x versus y with histograms of each

Description

Shows scatterplot of x vs y with histograms of each on sides of graph. As in the example from layout.

Usage

simple.scatterplot(x, y, ...)

Arguments

Value

Returns the plot

Author(s)

John Verzani

See Also

layout

Examples

  x<-sort(rnorm(100))
  y<-sort(rt(100,3))
  simple.scatterplot(x,y)

Simplify the process of simulation

Description

'simple.sim' is intended to make it a little easier to do simulations with R. Instead of writing a for loop, or dealing with column or row sums, a student can use this "simpler" interface.

Usage

simple.sim(no.samples, f, ...)

Arguments

Details

This is simply a wrapper for a for loop that uses the function f to create random numbers from some distribution.

Value

returns a vector of size no.samples

Note

There must be a 1000 better ways to do this. See replicate or sapply for example.

Author(s)

John Verzani

Examples

## First shows trivial (and very unnecessary usage)
## define a function f and then simulate
f<-function() rnorm(1)     # create a single random real number
sim <- simple.sim(100,f)   # create 100 random normal numbers
hist(sim)

## what does range look like?
f<- function (n,mu=0,sigma=1) {
  tmp <- rnorm(n,mu,sigma)
  max(tmp) - min(tmp)
}
sim <- simple.sim(100,f,5)
hist(sim)

Plots violinplots instead of boxplots

Description

This function serves the same utility as side-by-side boxplots, only it provides more detail about the different distribution. It plots violinplots instead of boxplots. That is, instead of a box, it uses the density function to plot the density. For skewed distributions, the results look like "violins". Hence the name.

Usage

simple.violinplot(x, ...)

Arguments

Value

Returns a plot.

Author(s)

John Verzani

References

This is really the boxplot function from R/base with some minor adjustments

See Also

boxplot, simple.densityplot

Examples

## make a "violin"
x <- rnorm(100) ;x[101:150] <- rnorm(50,5)
simple.violinplot(x,col="brown")
f<-factor(rep(1:5,30))
## make a quintet. Note also choice of bandwidth
simple.violinplot(x~f,col="brown",bw="SJ")

Implement basic z-test for illustrative purposes

Description

Imlements a z-test similar to the t.test function

Usage

simple.z.test(x, sigma, conf.level=0.95)

Arguments

Value

Returns a confidence interval for the mean

Author(s)

Joh Verzani

See Also

t.test, prop.test

Examples

  x<-rnorm(10,0,5)
  simple.z.test(x,5)

Judges scores for disputed ice skating competition

Description

Judges scores from the disputed ice skating competition at the 2002 Winter olympics

Usage

data(skateranks)

Format

A data frame with 20 observations on the following 11 variables.

Name

a factor with levels Berankova/Diabola Berezhnaya/Sikharulidze Bestnadigova/Bestandif Chuvaeva/Palamarchuk Cobisi/DePra Ina/Zimmerman Kautz/Jeschke Krasitseva/Znachkov Langlois/Archetto Lariviere/Faustino Pang/Tong Petrova/Tikhonov Ponomareva/SWviridov Savchenko/Morozov Scott/Dulebohn Sele/Pelletier Shen/Zhao Totmianina/Marinin Zagorska/Siudek Zhang/Zhang

Country

a factor with levels Armenia Canada China Czech Germany Italy Poland Russia Slovakia US Ukraine Uzbekistan

Russia

a numeric vector

China

a numeric vector

US

a numeric vector

France

a numeric vector

Poland

a numeric vector

Canada

a numeric vector

Ukraine

a numeric vector

Germany

a numeric vector

Japan

a numeric vector

Examples

data(skateranks)

Sodium-Lithium countertransport

Description

Sodium-Lithium countertransport

Usage

data(slc)

Format

The format is: num [1:190] 0.467 0.430 0.192 0.192 0.293 ...

Source

From Kitchens' Exploring Statistics

Examples

data(slc)
hist(slc)

Water pH levels at 75 water samples in the Great Smoky Mountains

Description

Water pH levels at 75 water samples in the Great Smoky Mountains

Usage

data(smokyph)

Format

This data frame contains the following columns:

waterph

a numeric vector

elev

a numeric vector

code

a numeric vector

Source

From Kitchens' Exploring Statistics

Examples

data(smokyph)
plot(smokyph$elev,smokyph$waterph)

Snack data from the USDA

Description

subset of SR26 data on nutrients compiled by the USDA.

Usage

data(snacks)

Format

A data frame with some nutrition variables

Source

This data came from the SR26 data set found at http://www.ars.usda.gov/Services/docs.htm?docid=8964.

Examples

data(snacks)

Murder rates for 30 Southern US cities

Description

Murder rates for 30 Southern US cities

Usage

data(south)

Format

The format is: num [1:30] 12 10 10 13 12 12 14 7 16 18 ...

Source

From Kitchens' Exploring Statistics

Examples

data(south)
hist(south)

Southern Oscillations

Description

The southern oscillation is defined as the barametric pressure difference between Tahiti and the Darwin Islands at sea level. The southern oscillation is a predictor of el nino which in turn is thought to be a driver of world-wide weather. Specifically, repeated southern oscillation values less than -1 typically defines an el nino.

Usage

data(southernosc)

Format

The format is: Time-Series [1:456] from 1952 to 1990: -0.7 1.3 0.1 -0.9 0.8 1.6 1.7 1.4 1.4 1.5 ...

Source

Originally downloaded from http://www.itl.nist.gov/div898/handbook/pmc/section4/pmc4412.htm

References

A description was available at http://www.itl.nist.gov/div898/handbook/pmc/section4/pmc4461.htm

Examples

data(southernosc)
plot(southernosc)

Excess returns of S\&P 500

Description

Excess returns of S\&P 500. These are defined as the difference between the series and some riskless asset.

Usage

data(sp500.excess)

Format

The format is: Time-Series [1:792] from 1929 to 1995: 0.0225 -0.044 -0.0591 0.0227 0.0077 0.0432 0.0455 0.0171 0.0229 -0.0313 ...

Source

This data set is used in Tsay, Analysis of Financial Time Series. At the time, it was downloaded from www.gsb.uchicago.edu/fac/ruey.tsay/teaching/fts (now off-line). The fSeries package may also contain this data set.

Examples

data(sp500.excess)
plot(sp500.excess)

Add split method for zoo objects

Description

Splits zoo objects by a grouping variable ala split(). Each univariate series is turned into a multivariate zoo object. If the original series is multivariate, the output is a list of multivariate zoo objects.

Usage

Split.zoo(x, f)

Arguments

Value

Returns a multivariate zoo object, or list of such.

Author(s)

John Verzani

See Also

split

Examples

if(require(zoo)) {
split.zoo = Split.zoo ## make generic
x = zoo(1:30,1:30)
f = sample(letters[1:5],30, replace=TRUE)
split(x,f)
}

Create a squareplot alternative to a segmented barplot

Description

Create a squareplot as an alternative to a segmented barplot. Useful when the viewer is interested in exact counts in the categories. A squareplot is often used by the New York Times. A grid of squares is presented with each color representing a different category. The colors appear contiguously reading top to bottom, left to right. The colors segment the graph as a segmented bargraph, but the squares allow an interested reader to easily tally the counts.

Usage

squareplot(x, col = gray(seq(0.5, 1, length = length(x))),
border =NULL, nrows = ceiling(sqrt(sum(x))), ncols =
ceiling(sum(x)/nrows),
...)

Arguments

Value

Creates the graph, but has no return value.

Author(s)

John Verzani

References

The New York Times, https://www.nytimes.com. In particular, Sports page 6, June 15, 2003.

Examples

## A Roger Clemens Cy Young year -- roids?
squareplot(c(21,7,6),col=c("blue","green","white"))

Student records

Description

A simulation of student records used for placement purposes

Usage

data(stud.recs)

Format

A data frame with 160 observations on the following 6 variables.

seq.1

Score on sequential 1 test

seq.2

Score on sequential 2 test

seq.3

Score on sequential 3 test

sat.v

SAT verbal score

sat.m

SAT math score

num.grade

grade on first math class

letter.grade

grade on first math class

Details

Some simulated student records for placement purpores

Examples

data(stud.recs)
hist(stud.recs$sat.v)
with(stud.recs,cor(sat.v,sat.m))

Some simulated data on student expenses

Description

Some data for possible student expenses

Usage

data(student.expenses)

Format

A data frame of 5 variables for 10 students. All answers are coded "Y" for yes, "N" for no.

cell.phone

Does student have cell phone.

cable.tv

Does student have cable TV.

dial.up

Does student pay for dial-up internet access.

cable.modem

Does student pay for high-speed or cable modem access to internet.

car

Does student own a car.

Details

Sample dataset of students expenses.

Examples

data(student.expenses)
attach(student.expenses)
table(dial.up,cable.modem)

super segmented barplot

Description

Plot a barplot, with bars nested and ranging from a max to a minimum value. A similar graphic is used on the weather page of the New York Times.

Usage

superbarplot(x, names = 1:dim(x)[2], names_height = NULL,
  col = gray(seq(0.8, 0.5, length = dim(x)[1]/2)), ...
)

Arguments

Details

A similar graphic on the weather page of the New York Times shows bars for record highs and lows, normal highs and lows and actual (or predicted) highs or lows for 10 days of weather. This graphic succintly and elegantly displays a wealth of information. Intended as an illustration of the polygon function.

Value

Returns a plot, but no other values.

Author(s)

John Verzani

References

The weather page of the New York Times

See Also

squareplot

Examples

record.high=c(95,95,93,96,98,96,97,96,95,97)
record.low= c(49,47,48,51,49,48,52,51,49,52)
normal.high=c(78,78,78,79,79,79,79,80,80,80)
normal.low= c(62,62,62,63,63,63,64,64,64,64)
actual.high=c(80,78,80,68,83,83,73,75,77,81)
actual.low =c(62,65,66,58,69,63,59,58,59,60)
x=rbind(record.low,record.high,normal.low,normal.high,actual.low,actual.high)
the.names=c("S","M","T","W","T","F","S")[c(3:7,1:5)]
superbarplot(x,names=the.names)

Does new goo taste great?

Description

Fictitious data on taste test for new goo

Usage

data(tastesgreat)

Format

A data frame with 40 observations on the following 3 variables.

gender

a factor with levels Female Male

age

a numeric vector

enjoyed

1 if enjoyed, 0 otherwise

Details

Fictitious data on a taste test with gender and age as covariates.

Examples

data(tastesgreat)
summary(glm(enjoyed ~ gender + age, data=tastesgreat, family=binomial))

One-year treasury security values

Description

The yields at constant fixed maturity have been constructed by the Treasury Department, based on the most actively traded marketable treasury securities.

Usage

data(tcm1y)

Format

The format is: Time-Series [1:558] from 1953 to 2000: 2.36 2.48 2.45 2.38 2.28 2.2 1.79 1.67 1.66 1.41 ...

Source

From the tcm data set in the tseries package. Given here for convenience only. They reference https://www.federalreserve.gov/Releases/H15/data.htm.

Examples

data(tcm1y)
ar(diff(log(tcm1y)))

Temperature/Salinity measurements along a moving Eddy

Description

Simulated measurements of temperature and salinity in the center of 'Eddy Juggernaut', a huge anti-cyclone (clockwise rotating) Loop Current Ring in the Gulf of Mexico. The start date is October 18, 1999.

Usage

data(tempsalinity)

Format

The data is stored as multivariate zooreg object with variables longitude, latitude, temperature (Celsius), and salinity (psu - practical salinity units, originally from https://toptotop.org/2014/10/21/climate_solutio/).

Details

The temperature salinity profile of body of water can be characteristic. This data shows a change in the profile in time as the eddy accumulates new water.

Source

Data from simulation by Andrew Poje.

Examples

data(tempsalinity)
if(require(zoo)) {
  plot(tempsalinity[,3:4])
  ## overide plot.zoo method
  plot.default(tempsalinity[,3:4])
  abline(lm(salinity ~ temperature, tempsalinity, subset = 1:67))
  abline(lm(salinity ~ temperature, tempsalinity, subset = -(1:67)))
  }

What age is too young for a male to data a female?

Description

In U.S. culture, an older man dating a younger woman is not uncommon, but when the age difference becomes too great is may seem to some to be unacceptable. This data set is a survey of 10 people with their minimum age for an acceptable partner for a range of ages for the male. A surprising rule of thumb (in the sense that someone took the time to figure this out) for the minimum is half the age plus seven. Does this rule hold for this data set?

Usage

data(too.young)

Format

A data frame with 80 observations on the following 2 variables.

Male

a numeric vector

Female

a numeric vector

Examples

data(too.young)
lm(Female ~ Male, data=too.young)

Burt's IQ data for twins

Description

IQ data of Burt on identical twins that were separated near birth.

Usage

data(twins)

Format

A data frame with 27 observations on the following 3 variables.

Foster

IQ for twin raised with foster parents

Biological

IQ for twin raised with biological parents

Social

Social status of biological parents

Source

This data comes from the R package that accompanies Julian Faraway's notes Practical Regression and Anova in R (now a book).

Examples

data(twins)
plot(Foster ~ Biological, twins)

Song and lengths for U2 albums

Description

Song titles and lengths of U2 albums from 1980 to 1997.

Usage

data(u2)

Format

The data is stored as a list with names. Each list entry correspond to an album stored as a vector. The values of the vector are the song lengths in seconds and the names are the track titles.

Source

Original data retrieved from http://www.u2station.com/u2ography.html

Examples

data(u2)
sapply(u2,mean)			# average track length
max(sapply(u2,max))		# longest track length
sort(unlist(u2))		# lengths in sorted order

Data on growth of sea urchins

Description

Data on growth of sea urchins.

Usage

data(urchin.growth)

Format

A data frame with 250 observations on the following 2 variables.

age

Estimated age of sea urchin

size

Measurement of size

Details

Data is sampled from a data set that accompanies the thesis of P. Grosjean.

Source

Thesis was found at http://www.sciviews.org/_pgrosjean

Examples

data(urchin.growth)
plot(jitter(size) ~ jitter(age), data=urchin.growth)

vacation days

Description

vacation days

Usage

data(vacation)

Format

The format is: num [1:35] 23 12 10 34 25 16 27 18 28 13 ...

Source

From Kitchens' Exploring Statistics

Examples

data(vacation)
hist(vacation)

Plots violinplots instead of boxplots

Description

This function serves the same utility as side-by-side boxplots, only it provides more detail about the different distribution. It plots violinplots instead of boxplots. That is, instead of a box, it uses the density function to plot the density. For skewed distributions, the results look like "violins". Hence the name.

Usage

violinplot(x, ...)

Arguments

Value

Returns a plot.

Author(s)

John Verzani

References

This is really the boxplot function from R/base with some minor adjustments

See Also

boxplot, densityplot

Examples

## make a "violin"
x <- rnorm(100) ;x[101:150] <- rnorm(50,5)
violinplot(x,col="brown")
f<-factor(rep(1:5,30))
## make a quintet. Note also choice of bandwidth
violinplot(x~f,col="brown",bw="SJ")

Temperature measurement of water at 85m depth

Description

Water temperature measurements at 10 minute intervals at a site off the East coast of the United States in the summer of 1974.

Usage

data(watertemp)

Format

A zoo class object with index stored as POSIXct elements. The measurements are in Celsius.

Source

NODC Coastal Ocean Time Series Database Search Page which was at http://www.nodc.noaa.gov/dsdt/tsdb/search.html

Examples

if(require(zoo)) {
data(watertemp)	 
plot(watertemp)
acf(watertemp)
acf(diff(watertemp))
}

A random sample of Wake County, North Carolina residential real estate plots

Description

This data set comes from a JSE article http://jse.amstat.org/v20n3/woodard.pdf by Roger Woodard. The data is described by: The information for this data set was taken from a Wake County, North Carolina real estate database. Wake County is home to the capital of North Carolina, Raleigh, and to Cary. These cities are the fifteenth and eighth fastest growing cities in the USA respectively, helping Wake County become the ninth fastest growing county in the country. Wake County boasts a 31.18 of approximately 823,345 residents. This data includes 100 randomly selected residential properties in the Wake County registry denoted by their real estate ID number. For each selected property, 11 variables are recorded. These variables include year built, square feet, adjusted land value, address, et al.

Usage

data(wchomes)

Format

a data frame

Source

https://www.amstat.org/publications/jse/v16n3/woodard.xls (now off-line)

References

http://jse.amstat.org/v20n3/woodard.pdf

Examples

data(wchomes)

What makes us happy?

Description

Correlated data on what makes us happy

Usage

data(wellbeing)

Format

A data frame with data about what makes people happy (well being) along with several other covariates

Source

Found from https://www.prcweb.co.uk/lab/what-makes-us-happy/.

References

https://www.prcweb.co.uk/lab/what-makes-us-happy/ and https://www.nationalaccountsofwellbeing.org/

Examples

data(wellbeing)

Download stock data from Yahoo!

Description

Downloads stock data from Yahoo!

Usage

yahoo.get.hist.quote(instrument = "^gspc", 
destfile = paste(instrument, ".csv", sep = ""), 
start, end, quote = c("Open", "High", "Low", "Close"), 
adjusted = TRUE, download = TRUE, 
origin = "1970-01-01", compression = "d")

Arguments

Details

Goes to chart.yahoo.com and downloads the stock data. By default returns a multiple time series of class mts with missing days padded by NAs.

Value

A multiple time series with time measureing the number of days since the value specified to origin.

Author(s)

Daniel Herlemont <dherlemont@yats.com>

References

This function was found on the mailling list for R-SIG finance

See Also

yahoo.get.hist.quote in the tseries package

Yellow fin tuna catch rate in Tropical Indian Ocean

Description

Mean catch rate of yellow fin tuna in Tropical Indian Ocean for the given years.

Usage

data(yellowfin)

Format

A data frame with 49 observations on the following 2 variables.

year

The year

count

Mean number of fish per 100 hooks cast

Details

Estimates for the mean number of fish caught per 100 hooks are given for a number of years. This can be used to give an estimate for the size, or biomass, of the species during these years assuming the more abundant the fish, the larger the mean. In practice this assumption is viewed with a wide range of attitudes.

Source

This data is read from a graph that accompanies Myers RA, Worm B (2003) “Rapid worldwide depletion of predatory fish communities”. Nature 423:280-283.

References

See also http://www.soest.hawaii.edu/PFRP/large_pelagic_predators.html for rebuttals to the Myers and Worm article.

Examples

data(yellowfin)
plot(yellowfin)