fastR2: Foundations and Applications of Statistics Using R (2nd Edition)

Description

Data sets and utilities to accompany the second edition of "Foundations and Applications of Statistics: an Introduction using R" (R Pruim, published by AMS, 2017), a text covering topics from probability and mathematical statistics at an advanced undergraduate level. R is integrated throughout, and access to all the R code in the book is provided via the snippet() function.

Data sets and utility functions to accompany Foundations and Applications of Statistics: An Introduction Using R by Randall Pruim.

Author(s)

Maintainer: Randall Pruim rpruim@calvin.edu

Randall Pruim

Maintainer: Randall Pruim <rpruim@calvin.edu>

References

R. Pruim, Foundations and Applicaitons of Statistics: An Introduction Using R, AMS, 2011.

See Also

Useful links:

• https://github.com/rpruim/fastR2

• http://rpruim.github.io/fastR2/

• Report bugs at https://github.com/rpruim/fastR2/issues

Examples

require(fastR2)
trellis.par.set(theme=col.fastR())

ACT scores and GPA

Description

ACT scores and college GPA for a small sample of college students.

Format

A data frame with 26 observations on the following 2 variables.

ACT

ACT score

GPA

GPA

Examples

gf_point(GPA ~ ACT, data = ACTgpa)

Air pollution measurements

Description

Air pollution measurements at three locations.

Format

A data frame with 6 observations on the following 2 variables.

pollution

a numeric vector

location

a factor with levels Hill Suburb, Plains Suburb, Urban City

Source

David J. Saville and Graham R. Wood, Statistical methods: A geometric primer, Springer, 1996.

Examples

data(AirPollution)
summary(lm(pollution ~ location, data = AirPollution))

Airline On-Time Arrival Data

Description

Flights categorized by destination city, airline, and whether or not the flight was on time.

Format

A data frame with 11000 observations on the following 3 variables.

airport

a factor with levels LosAngeles, Phoenix, SanDiego, SanFrancisco, Seattle

result

a factor with levels Delayed, OnTime

airline

a factor with levels Alaska, AmericaWest

Source

Barnett, Arnold. 1994. “How numbers can trick you.” Technology Review, vol. 97, no. 7, pp. 38–45.

References

These and similar data appear in many text books under the topic of Simpson's paradox.

Examples

tally(
  airline ~ result, data = AirlineArrival, 
  format = "perc", margins = TRUE)
tally(
  result ~ airline + airport, 
  data = AirlineArrival, format = "perc", margins = TRUE)
AirlineArrival2 <- 
  AirlineArrival %>% 
  group_by(airport, airline, result) %>% 
  summarise(count = n()) %>%
  group_by(airport, airline) %>%
  mutate(total = sum(count), percent = count/total * 100) %>% 
  filter(result == "Delayed") 
AirlineArrival3 <- 
  AirlineArrival %>% 
  group_by(airline, result) %>% 
  summarise(count = n()) %>%
  group_by(airline) %>%
  mutate(total = sum(count), percent = count/total * 100) %>% 
  filter(result == "Delayed") 
  gf_line(percent ~ airport, color = ~ airline, group = ~ airline, 
          data = AirlineArrival2) %>%
    gf_point(percent ~ airport, color = ~ airline, size = ~total, 
             data = AirlineArrival2) %>%
    gf_hline(yintercept = ~ percent, color = ~airline, 
             data = AirlineArrival3, linetype = "dashed") %>%
    gf_labs(y = "percent delayed") 

Ball dropping data

Description

Undergraduate students in a physics lab recorded the height from which a ball was dropped and the time it took to reach the floor.

Format

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

height

height in meters

time

time in seconds

Source

Steve Plath, Calvin College Physics Department

Examples

gf_point(time ~ height, data = BallDrop)

Major League Batting 2000-2005

Description

Major League batting data for the seasons from 2000-2005.

Format

A data frame with 8062 observations on the following 22 variables.

player

unique identifier for each player

year

year

stint

for players who were traded mid-season, indicates which portion of the season the data cover

team

three-letter code for team

league

a factor with levels AA AL NL

G

games

AB

at bats

R

runs

H

hits

H2B

doubles

H3B

triples

HR

home runs

RBI

runs batted in

SB

stolen bases

CS

caught stealing

BB

bases on balls (walks)

SO

strike outs

IBB

intentional base on balls

HBP

hit by pitch

SH

a numeric vector

SF

sacrifice fly

GIDP

grounded into double play

Examples

data(Batting)
gf_histogram( ~ HR, data = Batting)

Buckthorn

Description

Data from an experiment to determine the efficacy of various methods of eradicating buckthorn, an invasive woody shrub. Buckthorn plants were chopped down and the stumps treated with various concentrations of glyphosate. The next season, researchers returned to see whether the plant had regrown.

Format

A data frame with 165 observations on the following 3 variables.

shoots

number of new shoots coming from stump

conc

concentration of glyphosate applied

dead

weather the stump was considered dead

Source

David Dornbos, Calvin College

Examples

data(Buckthorn)

Bugs

Description

This data frame contains data from an experiment to see if insects are more attracted to some colors than to others. The researchers prepared colored cards with a sticky substance so that insects that landed on them could not escape. The cards were placed in a field of oats in July. Later the researchers returned, collected the cards, and counted the number of cereal leaf beetles trapped on each card.

Format

A data frame with 24 observations on the following 2 variables.

color

color of card; one of B(lue) G(reen) W(hite) Y(ellow)

trapped

number of insects trapped on the card

Source

M. C. Wilson and R. E. Shade, Relative attractiveness of various luminescent colors to the cereal leaf beetle and the meadow spittlebug, Journal of Economic Entomology 60 (1967), 578–580.

Examples

data(Bugs)
favstats(trapped ~ color, data = Bugs)

Concrete Compressive Strength Data

Description

These data were collected by I-Cheng Yeh to determine how the compressive strength of concrete is related to its ingredients (cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, and fine aggregate) and age.

Format

Concrete is a data frame with the following variables.

limestone

percentage of limestone

water

water-cement ratio

strength

compressive strength (MPa) after 28 days

References

Appeared in Devore's "Probability and Statsistics for Engineers and the Sciences (6th ed). The variables have been renamed.

#' Concrete Compressive Strength Data

Description

These data were collected by I-Cheng Yeh to determine how the compressive strength of concrete is related to its ingredients (cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, and fine aggregate) and age.

Format

concreteAll is a data frame with the following 9 variables.

cement

amount of cement (kg/m^3)

slag

amount of blast furnace slag (kg/m^3)

ash

amount of fly ash(kg/m^3)

water

amount of water (kg/m^3)

superP

amount of superplasticizer (kg/m^3)

coarseAg

amount of coarse aggregate (kg/m^3)

fineAg

amount of fine aggregate (kg/m^3)

age

age of concrete in days

strength

compressive strength measured in MPa

Concrete is a subset of ConcreteAll.

Source

Data were obtained from the Machine Learning Repository (https://archive.ics.uci.edu/ml/) where they were deposited by I-Cheng Yeh (icyeh@chu.edu.tw) who retains the copyright for these data.

References

I-Cheng Yeh (1998), "Modeling of strength of high performance concrete using artificial neural networks," Cement and Concrete Research, Vol. 28, No. 12, pp. 1797-1808.

Examples

data(Concrete)

Cooling Water

Description

Temperature of a mug of water as it cools.

Usage

data(CoolingWater1)

data(CoolingWater2)

data(CoolingWater3)

data(CoolingWater4)

Format

A data frame with the following variables.

time

time in seconds

temp

temperature in Celsius (CoolingWater1, CoolingWater2) or Fahrenheit (CoolingWater3, CoolingWater4)

Source

These data were collected by Stan Wagon and his students at Macelester College to explore Newton's Law of Cooling and the ways that the law fails to capture all of the physics involved in cooling water. CoolingWater1 and CoolingWater2 appeared in a plot in Wagon (2013) and were (approximatley) extracted from the plot. CoolingWater3 and CoolingWater4 appeared in a plot in Wagon (2005). The data in CoolingWater2 and CoolingWater4 were collected with a film of oil on the surface of the water to minimize evaporation.

References

• R. Portmann and S. Wagon. "How quickly does hot water cool?" Mathematica in Education and Research, 10(3):1-9, July 2005.

• R. Israel, P. Saltzman, and S. Wagon. "Cooling coffee without solving differential equations". Mathematics Magazine, 86(3):204-210, 2013.

Examples

data(CoolingWater1)
data(CoolingWater2)
data(CoolingWater3)
data(CoolingWater4)
if (require(ggformula)) {
  gf_line(
    temp ~ time, color = ~ condition, 
    data = rbind(CoolingWater1, CoolingWater2))
}
if (require(ggformula)) {
  gf_line(
    temp ~ time, color = ~ condition, 
    data = rbind(CoolingWater3, CoolingWater4))
}

Corn Yield

Description

William Gosset analyzed data from an experiment comparing the yield of regular and kiln-dried corn.

Format

A data frame with 11 observations on the following 2 variables.

reg

yield of regular corn (lbs/acre)

kiln

yield of kiln-dried corn (lbs/acre)

Details

Gosset (Student) reported on the results of seeding plots with two different kinds of seed. Each type of seed (regular and kiln-dried) was planted in adjacent plots, accounting for 11 pairs of "split" plots.

Source

These data are also available at DASL, the data and story library (https://dasl.datadescription.com/).

References

W.S. Gosset, "The Probable Error of a Mean," Biometrika, 6 (1908), pp 1-25.

Examples

Corn2 <- stack(Corn)
names(Corn2) <- c('yield','treatment')
lm(yield ~ treatment, data = Corn2)
t.test(yield ~ treatment, data = Corn2)
t.test(Corn$reg, Corn$kiln)

Cuckoo eggs in other birds' nests

Description

Cuckoos are knows to lay their eggs in the nests of other (host) birds. The eggs are then adopted and hatched by the host birds. These data were originally collected by O. M. Latter in 1902 to see how the size of a cuckoo egg is related to the species of the host bird.

Format

A data frame with 120 observations on the following 2 variables.

length

length of egg (mm)

species

a factor with levels hedge sparrow meadow pipet pied wagtail robin tree pipet wren

Source

L.H.C. Tippett, The Methods of Statistics, 4th Edition, John Wiley and Sons, Inc., 1952, p. 176.

References

These data are also available from DASL, the data and story library (https://dasl.datadescription.com/).

Examples

data(Cuckoo)
gf_boxplot(length ~ species, data = Cuckoo)

Death Penalty and Race

Description

A famous example of Simpson's paradox.

Format

A data frame with 326 observations.

id

a subject id

victim

a factor with levels Bl Wh)

defendant

a factor with levels Bl, Wh

death

a factor with levels Yes, No

penalty

a factor with levels death other

Source

Radelet, M. (1981). Racial characteristics and imposition of the death penalty. American Sociological Review, 46:918–927.

Examples

tally(penalty ~ defendant, data = DeathPenalty)
tally(penalty ~ defendant + victim, data = DeathPenalty)

Drag force experiment

Description

The data come from an experiment to determine how terminal velocity depends on the mass of the falling object. A helium balloon was rigged with a small basket and just the ballast to make it neutrally buoyant. Mass was then added and the terminal velocity is calculated by measuring the time it took to fall between two sensors once terminal velocity has been reached. Larger masses were drop from higher heights and used sensors more widely spaced.

Format

A data frame with 42 observations on the following 5 variables.

time

time (in seconds) to travel between two sensors

mass

net mass (in kg) of falling object

height

distance (in meters) between two sensors

velocity

average velocity (in m/s) computed from time and height

force.drag

calculated drag force (in N, force.drag = mass * 9.8) using the fact that at terminal velocity, the drag force is equal to the force of gravity

Source

Calvin College physics students under the supervision of Professor Steve Plath.

Examples

data(Drag)
with(Drag, force.drag / mass)
gf_point(velocity ~ mass, data = Drag)

Endurance and vitamin C

Description

The effect of a single 600 mg dose of ascorbic acid versus a sugar placebo on the muscular endurance (as measured by repetitive grip strength trials) of fifteen male volunteers (19-23 years old).

Format

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

vitamin

number of repetitions until reaching 50 maximal grip after taking viatimin

first

which treatment was done first, a factor with levels Placebo Vitamin

placebo

number of repetitions until reaching 50 strength after taking placebo

Details

Three initial maximal contractions were performed for each subject, with the greatest value indicating maximal grip strength. Muscular endurance was measured by having the subjects squeeze the dynamometer, hold the contraction for three seconds, and repeat continuously until a value of 50 maximum grip strength was achieved for three consecutive contractions. Endurance was defined as the number of repetitions required to go from maximum grip strength to the initial 50 positive verbal encouragement in an effort to have them complete as many repetitions as possible.

The study was conducted in a double-blind manner with crossover.

Source

These data are available from OzDASL, the Australasian data and story library (https://dasl.datadescription.com/).

References

Keith, R. E., and Merrill, E. (1983). The effects of vitamin C on maximum grip strength and muscular endurance. Journal of Sports Medicine and Physical Fitness, 23, 253-256.

Examples

data(Endurance)
t.test(Endurance$vitamin, Endurance$placebo, paired = TRUE)
t.test(log(Endurance$vitamin), log(Endurance$placebo), paired = TRUE)
t.test(1/Endurance$vitamin, 1/Endurance$placebo, paired = TRUE)
gf_qq( ~ vitamin - placebo, data = Endurance)
gf_qq( ~ log(vitamin) - log(placebo), data = Endurance)
gf_qq( ~ 1/vitamin - 1/placebo, data = Endurance)

Family smoking

Description

A cross-tabulation of whether a student smokes and how many of his or her parents smoke from a study conducted in the 1960's.

Format

A data frame with 5375 observations on the following 2 variables.

student

a factor with levels DoesNotSmoke Smokes

parents

a factor with levels NeitherSmokes OneSmokes

BothSmoke

Source

S. V. Zagona (ed.), Studies and issues in smoking behavior, University of Arizona Press, 1967.

References

The data also appear in

Brigitte Baldi and David S. Moore, The Practice of Statistics in the Life Sciences, Freeman, 2009.

Examples

data(FamilySmoking)
xchisq.test( tally(parents ~ student, data = FamilySmoking) )

NCAA football fumbles

Description

This data frame gives the number of fumbles by each NCAA FBS team for the first three weeks in November, 2010.

Format

A data frame with 120 observations on the following 7 variables.

team

NCAA football team

rank

rank based on fumbles per game through games on November 26, 2010

W

number of wins through games on November 26, 2010

L

number of losses through games on November 26, 2010

week1

number of fumbles on November 6, 2010

week2

number of fumbles on November 13, 2010

week3

number of fumbles on November 20, 2010

Details

The fumble counts listed here are total fumbles, not fumbles lost. Some of these fumbles were recovered by the team that fumbled.

Source

https://www.teamrankings.com/college-football/stat/fumbles-per-game

Examples

data(Fumbles)
m <- max(Fumbles$week1)
table(factor(Fumbles$week1,levels = 0:m))
favstats( ~ week1, data = Fumbles)
# compare with Poisson distribution
cbind(
		  fumbles = 0:m,
		  observedCount = table(factor(Fumbles$week1,levels = 0:m)),
		  modelCount= 120* dpois(0:m,mean(Fumbles$week1)),
		  observedPct = table(factor(Fumbles$week1,levels = 0:m))/120,
		  modelPct= dpois(0:m,mean(Fumbles$week1))
	) %>% signif(3)
showFumbles <- function(x, lambda = mean(x),...) {
  result <-
    gf_dhistogram( ~ week1, data = Fumbles, binwidth = 1, alpha = 0.3) %>%
    gf_dist("pois", lambda = mean( ~ week1, data = Fumbles) )
  print(result)
  return(result)
}
showFumbles(Fumbles$week1)
showFumbles(Fumbles$week2)
showFumbles(Fumbles$week3)

GPA, ACT, and SAT scores

Description

GPA, ACT, and SAT scores for a sample of students.

Format

A data frame with 271 observations on the following 4 variables.

act

ACT score

gpa

college grade point average

satm

SAT mathematics score

satv

SAT verbal score

Examples

data(GPA)
splom(GPA)

Goose permits

Description

In a 1979 study by Bishop and Heberlein, 237 hunters were each offered one of 11 cash amounts (bids) ranging from $1 to $200 in return for their hunting permits. The data records how many hunters offered each bid kept or sold their permit.

Format

A data frame with 11 rows and 5 columns. Each row corresponds to a bid (in US dollars) offered for a goose permit. The colums keep and sell indicate how many hunters offered that bid kept or sold their permit, respectively. n is the sum of keep and sell and prop_sell is the proportion that sold.

References

Bishop and Heberlein (Amer. J. Agr. Econ. 61, 1979).

Examples

goose.mod <- glm( cbind(sell, keep) ~ log(bid), data = GoosePermits, family = binomial())
gf_point(0 ~ bid, size = ~keep, color = "gray50", data = GoosePermits) %>%
  gf_point(1 ~ bid, size = ~ sell, color = "navy") %>%
  gf_function(fun = makeFun(goose.mod)) %>%
  gf_refine(guides(size = "none"))
  
ggplot(data = GoosePermits) +
  geom_point( aes(x = bid, y = 0, size = keep), colour = "gray50") +
  geom_point( aes(x = bid, y = 1, size = sell), colour = "navy") +
  stat_function(fun = makeFun(goose.mod)) +
  guides( size = "none")
  
gf_point( (sell / (sell + keep)) ~ bid, data = GoosePermits,
    size = ~ sell + keep, color = "navy") %>%
  gf_function(fun = makeFun(goose.mod))  %>%
  gf_text(label = ~ as.character(sell + keep), colour = "white", size = 3) %>%
  gf_refine(scale_size_area()) %>% 
  gf_labs(y = "probabity of selling")
  
ggplot(data = GoosePermits) +
  stat_function(fun = makeFun(goose.mod)) +
  geom_point( aes(x = bid, y = sell / (sell + keep), size = sell + keep), colour = "navy") +
  geom_text( aes(x = bid, y = sell / (sell + keep), label = as.character(sell + keep)), 
    colour = "white", size = 3) +
  scale_size_area() + 
  labs(y = "probabity of selling")
  

Punting helium- and air-filled footballs

Description

Two identical footballs, one air-filled and one helium-filled, were used outdoors on a windless day at The Ohio State University's athletic complex. Each football was kicked 39 times and the two footballs were alternated with each kick. The experimenter recorded the distance traveled by each ball.

Format

A data frame with 39 observations on the following 3 variables.

trial

trial number

air

distance traveled by air-filled football (yards)

helium

distance traveled by helium-filled football (yards)

Source

These data are available from DASL, the data and story library (https://dasl.datadescription.com/).

References

Lafferty, M. B. (1993), "OSU scientists get a kick out of sports controversy", The Columbus Dispatch (November, 21, 1993), B7.

Examples

data(HeliumFootballs)
gf_point(helium ~ air, data = HeliumFootballs)
gf_dhistogram( 
  ~ (helium - air), data = HeliumFootballs, 
  fill = ~ (helium > air),  bins = 15, boundary = 0 
)

Cooling muscles with ice

Description

This data set contains the results of an experiment comparing the efficacy of different forms of dry ice application in reducing the temperature of the calf muscle.

Details

The 12 subjects in this study came three times, at least four days apart, and received one of three ice treatments (cubed ice, crushed ice, or ice mixed with water). In each case, the ice was prepared in a plastic bag and applied dry to the subjects calf muscle. The temperature measurements were taken on the skin surface and inside the calf muscle (via a 4 cm long probe) every 30 seconds for 20 minutes prior to icing, for 20 minutes during icing, and for 2 hours after the ice had been removed. The temperature measurements are stored in variables that begin with b (baseline), t (treatment), or r (recovery) followed by a numerical code for the elapsed time formed by concatenating the number of minutes and seconds. For example, R1230 contains the temperatures 12 minutes and 30 seconds after the ice had been removed.

Variables include

Subject

identification number

sex

a factor with levels female male

weight

weight of subject (kg)

Height

height of subject (cm)

Skinfold

skinfold thickness

calf

calf diameter (cm)

Age

age of subject

location

a factor with levels intramuscular surface

Treatment

a factor with levels crushed cubed wet

B0

baseline temperature at time 0

b30

baseline temperature 30 seconds after start

b100

baseline temperature 1 minute after start

b1930

baseline temperature 19 minutes 30 seconds start

t0

treatment temperature at beginning of treatment

t30

treatment temperature 30 seconds after start of treatment

t100

treatment temperature 1 minute after start of treatment

t1930

treatment temperature 19 minutes 30 seconds after start of treatment

R0

recovery temperature at start of recovery

r30

recovery temperature 30 seconds after start of recovery

r100

recovery temperature 1 minute after start of recovery

r12000

recovery temperature 120 minutes after start of recovery

Source

Dykstra, J. H., Hill, H. M., Miller, M. G., Michael T. J., Cheatham, C. C., and Baker, R.J., Comparisons of cubed ice, crushed ice, and wetted ice on intramuscular and surface temperature changes, Journal of Athletic Training 44 (2009), no. 2, 136–141.

Examples

data(Ice)
gf_point(weight ~ skinfold, color = ~ sex, data = Ice)
if (require(readr) && require(tidyr)) {
  Ice2 <- Ice %>% 
  gather("key", "temp", b0:r12000) %>% 
  separate(key, c("phase", "time"), sep = 1) %>% 
  mutate(time = parse_number(time), subject = as.character(subject))  
  
  gf_line( temp ~ time, data = Ice2 %>% filter(phase == "t"), 
           color = ~ sex,  group = ~subject, alpha = 0.6) %>%
    gf_facet_grid( treatment ~ location)
}

Inflation data

Description

The article developed four measures of central bank independence and explored their relation to inflation outcomes in developed and developing countries. This datafile deals with two of these measures in 23 nations.

Format

A data frame with 23 observations on the following 5 variables.

country

country where data were collected

ques

questionnaire index of independence

inf

annual inflation rate, 1980-1989 (percent)

legal

legal index of independence

dev

developed (1) or developing (2) nation

Source

These data are available from OzDASL, the Australasian Data and Story Library (https://dasl.datadescription.com/).

References

A. Cukierman, S.B. Webb, and B. Negapi, "Measuring the Independence of Central Banks and Its Effect on Policy Outcomes," World Bank Economic Review, Vol. 6 No. 3 (Sept 1992), 353-398.

Examples

data(Inflation)

Michael Jordan personal scoring

Description

The number of points scored by Michael Jordan in each game of the 1986-87 regular season.

Format

A data frame with 82 observations on the following 2 variables.

game

a numeric vector

points

a numeric vector

Examples

data(Jordan8687)
gf_qq(~ points, data = Jordan8687)

Goals and popularity factors for school kids

Description

Subjects were students in grades 4-6 from three school districts in Michigan. Students were selected from urban, suburban, and rural school districts with approximately 1/3 of their sample coming from each district. Students indicated whether good grades, athletic ability, or popularity was most important to them. They also ranked four factors: grades, sports, looks, and money, in order of their importance for popularity. The questionnaire also asked for gender, grade level, and other demographic information.

Format

A data frame with 478 observations on the following 11 variables.

gender

a factor with levels boy girl

grade

grade in school

age

student age

race

a factor with levels other White

urban.rural

a factor with levels Rural Suburban Urban

school

a factor with levels Brentwood Elementary Brentwood Middle Brown Middle Elm Main Portage Ridge Sand Westdale Middle

goals

a factor with levels Grades Popular Sports

grades

rank of ‘make good grades’ (1 = most important for popularity; 4 = least important)

sports

rank of ‘beging good at sports’ (1 = most important for popularity; 4 = least important)

looks

rank of 'beging handsome or pretty' (1 = most important for popularity; 4 = least important)

money

rank of ‘having lots of money’ (1 = most important for popularity; 4 = least important)

Source

These data are available at DASL, the data and story library (https://dasl.datadescription.com/).

References

Chase, M. A., and Dummer, G. M. (1992), "The Role of Sports as a Social Determinant for Children," Research Quarterly for Exercise and Sport, 63, 418-424.

Examples

data(Kids)
tally(goals ~ urban.rural, data = Kids)
chisq.test(tally(~ goals + urban.rural, data = Kids))

Results from a little survey

Description

These data are from a little survey given to a number of students in introductory statistics courses. Several of the items were prepared in multiple versions and distributed randomly to the students.

Format

A data frame with 279 observations on the following 20 variables.

number

a number between 1 and 30

colorver

which version of the 'favorite color' question was on the survey. A factor with levels v1 v2

color

favorite color if among predefined choices. A factor with levels black green other purple red

othercolor

favorite color if not among choices above.

animalver

which version of the 'favorite color' question was on the survey. A factor with levels v1 v2

animal

favorite animal if among predefined choices. A factor with levels elephant giraffe lion other.

otheranimal

favorite animal if not among the predefined choices.

pulsever

which version of the 'pulse' question was on the survey

pulse

self-reported pulse

tvver

which of three versions of the TV question was on the survey

tvbox

a factor with levels <1 >4 >8 1-2 2-4 4-8 none other

tvhours

a numeric vector

surprisever

which of two versions of the 'surprise' question was on the survey

surprise

a factor with levels no yes

playver

which of two versions of the 'play' question was on the survey

play

a factor with levels no yes

diseasever

which of two versions of the 'play' question was on the survey

disease

a factor with levels A B

homeworkver

which of two versions of the 'homework' question was on the survey

homework

a factor with levels A B

Question Wording

1.1. Write down any number between 1 and 30 (inclusive).

2.1. What is your favorite color? Choices: black red; green; purple; other

2.2. What is your favorite color?

3.1. What is your favorite zoo animal? Choices: giraffe; lion; elephant; other

3.2. What is your favorite zoo animal?

4.1. Measure and record your pulse.

5.1. How much time have you spent watching TV in the last week?

5.2. How much time have you spent watching TV in the last week? Choises: none; under 1; hour 1-2 hours; 2-4 hours; more than 4 hours

5.3. How much time have you spent watching TV in the last week? Choises: under 1 hour; 1-2 hours; 2-4 hours; 4-8 hours; more than 8 hours

6.1. Social science researchers have conducted extensive empirical studies and concluded that the expression "absence makes the heart grow fonder" is generally true. Do you find this result surprising or not surprising?

6.2. Social science researchers have conducted extensive empirical studies and concluded that the expression "out of sight out of mind" is generally true. Do you find this result surprising or not surprising?

7.1. Suppose that you have decided to see a play for which the admission charge is $20 per ticket. As you prepare to purchase the ticket, you discover that you have lost a $20 bill. Would you still pay $20 for a ticket to see the play?

7.2. Suppose that you have decided to see a play for which the admission charge is $20 per ticket. As you prepare to enter the theater, you discover that you have lost your ticket. Would you pay $20 to buy a new ticket to see the play?

8.1. suppose that the United States is preparing for the outbreak of an unusual Asian disease that is expected to kill 600 people. Two alternative programs to combat the disease have been proposed. Assume that the exact scientific estimates of the consequences of the programs are as follows: If program A is adopted, 200 people will be saved. If program B is adopted, there is a 1/3 probability that 600 people will be saved and a 2/3 probability that nobody will be saved. Which of the two programs would you favor?

8.2. Suppose that the United States is preparing for the outbreak of an unusual Asian disease that is expected to kill 600 people. two alternative programs to combat the disease have been proposed. Assume that the exact scientific estimates of the consequences of the programs are as follows:

If program A is adopted, 400 people will die. If program B is adopted, there is a 1/3 probability that no one will die and a 2/3 probability that all 600 people will die. Which of the two programs would you favor? A or B

9.1. A national survey of college students revealed that professors at this college assign "significantly more homework that the nationwide average for an institution of its type." How does this finding compare with your experience? Choises: a. That sounds about right to me; b that doesn't sound right to me.

9.2. A national survey of college students revealed that professors at this college assign an amount of homework that "is fairly typical for institutions of its type." How does this finding compare with your experience? Choices: A that sounds about right to me; b that doesn't sound right to me.

Examples

data(LittleSurvey)
tally(surprise ~ surprisever, data = LittleSurvey)
tally(disease ~ diseasever, data = LittleSurvey)

MIAA basketball 2004-2005 season

Description

Individual player statistics for the 2004-2005 Michigan Intercollegiate Athletic Association basketball season.

Format

A data frame with 134 observations on the following 27 variables.

number

jersey number

player

player's name

GP

games played

GS

games started

Min

minutes played

AvgMin

average minutes played per game

FG

field goals made

FGA

field goals attempted

FGPct

field goal percentage

FG3

3-point field goals made

FG3A

3-point field goals attempted

FG3Pct

3-point field goal percentage

FT

free throws made

FTA

free throws attempted

FTPct

free throw percentage

Off

offensive rebounds

Def

defensive rebounds

Tot

total rebounds

RBG

rebounds per game

PF

personal fouls

FO

games fouled out

A

assists

TO

turn overs

Blk

blocked shots

Stl

steals

Pts

points scored

PTSG

points per game

Source

MIAA sports archives (https://www.miaa.org/)

Examples

data(MIAA05)
gf_histogram(~ FTPct, data = MIAA05)

Major League Baseball 2004 team data

Description

Team batting statistics, runs allowed, and runs scored for the 2004 Major League Baseball season.

Format

A data frame with 30 observations on the following 20 variables.

team

team city, a factor

league

League, a factor with levels AL NL

W

number of wins

L

number of losses

G

number of games

R

number of runs scored

OR

oppnents' runs – number of runs allowed

Rdiff

run difference – R - OR

AB

number of at bats

H

number of hits

DBL

number of doubles

TPL

number of triples

HR

number of home runs

BB

number of walks (bases on balls)

SO

number of strike outs

SB

number of stolen bases

CS

number of times caught stealing

BA

batting average

SLG

slugging percentage

OBA

on base average

Examples

data(MLB2004)
gf_point(W ~ Rdiff, data = MLB2004)

Test performance and noise

Description

In this experiment, hyperactive and control students were given a mathematics test in either a quiet or loud testing environment.

Format

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

score

score on a mathematics test

noise

a factor with levels hi lo

group

a factor with levels control hyper

Source

Sydney S. Zentall and Jandira H. Shaw, Effects of classroom noise on perfor- mance and activity of second-grade hyperactive and control children, Journal of Educational Psychology 72 (1980), no. 6, 830.

Examples

data(MathNoise)
xyplot (score ~ noise, data = MathNoise, group = group, type = 'a', 
		auto.key = list(columns = 2, lines = TRUE, points = FALSE))

gf_jitter(score ~ noise, data = MathNoise, color = ~ group, alpha = 0.4, 
          width = 0.1, height = 0) %>%
  gf_line(score ~ noise, data = MathNoise, color = ~ group, group = ~ group,
        stat = "summary")

NCAA Division I Basketball Results

Description

Results of NCAA basketball games

Format

Nine variables describing NCAA Division I basketball games.

date

date on which game was played

away

visiting team

ascore

visiting team's score

home

home team

hscore

home team's score

notes

code indicting games played at neutral sites (n or N) or in tournaments (T)

location

where game was played

season

a character indicating which season the game belonged to

postseason

a logical indicating whether the game is a postseason game

Source

https://kenpom.com

Examples

data(NCAAbb)
# select one year and add some additional variables to the data frame
NCAA2010 <-
  NCAAbb %>% 
  filter(season == "2009-10") %>%
  mutate(
    dscore = hscore - ascore,
    homeTeamWon = dscore > 0,
    numHomeTeamWon <- -1 + 2 * as.numeric(homeTeamWon),
    winner = ifelse(homeTeamWon, home, away),
    loser  = ifelse(homeTeamWon, away, home),
    wscore = ifelse(homeTeamWon, hscore, ascore),
    lscore = ifelse(homeTeamWon, ascore, hscore)
  )
NCAA2010 %>% select(date, winner, loser, wscore, lscore, dscore, homeTeamWon) %>% head()

NFL 2007 season

Description

Results of National Football League games (2007 season, including playoffs)

Format

A data frame with 267 observations on the following 7 variables.

date

date on which game was played

visitor

visiting team

visitorScore

score for visiting team

home

home team

homeScore

score for home team

line

‘betting line’

totalLine

'over/under' line (for combined score of both teams)

Examples

data(NFL2007) 
NFL <- NFL2007 
NFL$dscore <- NFL$homeScore - NFL$visitorScore 
w <- which(NFL$dscore > 0) 
NFL$winner <- NFL$visitor; NFL$winner[w] <- NFL$home[w] 
NFL$loser <- NFL$home; NFL$loser[w] <- NFL$visitor[w] 
# did the home team win? 
NFL$homeTeamWon <- NFL$dscore > 0 
table(NFL$homeTeamWon)
table(NFL$dscore > NFL$line)

Noise

Description

In order to test the effect of room noise, subjects were given a test under 5 different sets of conditions: 1) no noise, 2) intermittent low volume, 3) intermittent high volume, 4) continuous low volume, and 5) continuous high volume.

Format

A data frame with 50 observations on the following 5 variables.

id

subject identifier

score

score on the test

condition

numeric code for condition

volume

a factor with levels high low none

frequency

a factor with levels continuous intermittent none

Examples

data(Noise)
Noise2 <- Noise %>% filter(volume != 'none')
model <- lm(score ~ volume * frequency, data = Noise2) 
anova(model)
gf_jitter(score ~ volume, data = Noise2, color = ~ frequency, 
          alpha = 0.4, width = 0.1, height = 0) %>%
  gf_line(score ~ volume, data = Noise2, group = ~frequency, color = ~ frequency,
          stat = "summary")
        
gf_jitter(score ~ frequency, data = Noise2, color = ~ volume, 
          alpha = 0.4, width = 0.1, height = 0) %>%
  gf_line(score ~ frequency, data = Noise2, group = ~ volume, color = ~ volume,
          stat = "summary")

Pallet repair data

Description

The paletts data set contains data from a firm that recycles paletts. Paletts from warehouses are bought, repaired, and resold. (Repairing a palette typically involves replacing one or two boards.) The company has four employees who do the repairs. The employer sampled five days for each employee and recorded the number of pallets repaired.

Format

A data frame with 20 observations on the following 3 variables.

pallets

number of pallets repaired

employee

a factor with levels A B C D

day

a factor with levels day1 day2 day3 day4 day5

Source

Michael Stob, Calvin College

Examples

data(Pallets)
# Do the employees differ in the rate at which they repair pallets?
pal.lm1 <- lm(pallets ~ employee, data = Pallets) 
anova(pal.lm1)
# Now using day as a blocking variable
pal.lm2 <- lm(pallets ~ employee + day, data = Pallets) 
anova(pal.lm2)
gf_line(pallets ~ day, data = Pallets,
		group = ~employee,
		color = ~employee) %>%
  gf_point() %>%
  gf_labs(title = "Productivity by day and employee")

Paper airplanes

Description

Student-collected data from an experiment investigating the design of paper airplanes.

Format

A data frame with 16 observations on the following 5 variables.

distance

distance plane traveled (cm)

paper

type of paper used

angle

a numeric vector

design

design of plane (hi performance or simple)

order

order in which planes were thrown

Details

These data were collected by Stewart Fischer and David Tippetts, statistics students at the Queensland University of Technology in a subject taught by Dr. Margaret Mackisack. Here is their description of the data and its collection:

The experiment decided upon was to see if by using two different designs of paper aeroplane, how far the plane would travel. In considering this, the question arose, whether different types of paper and different angles of release would have any effect on the distance travelled. Knowing that paper aeroplanes are greatly influenced by wind, we had to find a way to eliminate this factor. We decided to perform the experiment in a hallway of the University, where the effects of wind can be controlled to some extent by closing doors.

In order to make the experimental units as homogeneous as possible we allocated one person to a task, so person 1 folded and threw all planes, person 2 calculated the random order assignment, measured all the distances, checked that the angles of flight were right, and checked that the plane release was the same each time.

The factors that we considered each had two levels as follows:

Paper: A4 size, 80g and 50g

Design: High Performance Dual Glider, and Incredibly Simple Glider (patterns attached to original report)

Angle of release: Horizontal, or 45 degrees upward.

The random order assignment was calculated using the random number function of a calculator. Each combination of factors was assigned a number from one to eight, the random numbers were generated and accordingly the order of the experiment was found.

Source

These data are also available at OzDASL, the Australasian Data and Story Library (https://dasl.datadescription.com/).

References

Mackisack, M. S. (1994). What is the use of experiments conducted by statistics students? Journal of Statistics Education, 2, no 1.

Examples

data(PaperPlanes)

Pendulum data

Description

Period and pendulum length for a number of string and mass pendulums constructed by physics students. The same mass was used throughout, but the length of the string was varied from 10cm to 16 m.

Format

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

length

length of the pendulum (in meters)

period

average time of period (in seconds) over several swings of the pendulum

delta.length

an estimate of the accuracy of the length measurement

Source

Calvin College physics students under the direction of Professor Steve Plath.

Examples

data(Pendulum)
gf_point(period ~ length, data = Pendulum)

Pets and stress

Description

Does having a pet or a friend cause more stress?

Format

A data frame with 45 observations on the following 2 variables.

group

a factor with levels Control, Friend, or Pet

rate

average heart rate while performing a stressful task

Details

Fourty-five women, all self-proclaimed dog-lovers, were randomly divided into three groups of subjects. Each performed a stressful task either alone, with a friend present, or with their dog present. The average heart rate during the task was used as a measure of stress.

Source

K. M. Allen, J. Blascovich, J. Tomaka, and R. M. Kelsey, Presence of human friends and pet dogs as moderators of autonomic responses to stress in women, Journal of Personality and Social Psychology 61 (1991), no. 4, 582–589.

References

These data also appear in

Brigitte Baldi and David S. Moore, The Practice of Statistics in the Life Sciences, Freeman, 2009.

Examples

data(PetStress)
xyplot(rate ~ group, data = PetStress, jitter.x = TRUE, type = c('p', 'a'))
gf_jitter(rate ~ group, data = PetStress, width = 0.1, height = 0) %>%
  gf_line(group = 1, stat = "summary", color = "red")

FUSION type 2 diabetes study

Description

Phenotype and genotype data from the Finland United States Investigation of NIDDM (type 2) Diabetes (FUSION) study.

Format

Data frames with the following variables.

id

subject ID number for matching between data sets

t2d

a factor with levels case control

bmi

body mass index

sex

a factor with levels F M

age

age of subject at time phenotypes were colelcted

smoker

a factor with levels former never occasional regular

chol

total cholesterol

waist

waist circumference (cm)

weight

weight (kg)

height

height (cm)

whr

waist hip ratio

sbp

systolic blood pressure

dbp

diastolic blood pressure

marker

RS name of SNP

markerID

numeric ID for SNP

allele1

first allele coded as 1 = A, 2 = C, 3 = G, 4 = T

allele2

second allele coded as 1 = A, 2 = C, 3 = G, 4 = T

genotype

both alleles coded as a factor

Adose

number of A alleles

Cdose

number of C alleles

Gdose

number of G alleles

Tdose

number of T alleles

Source

Similar to the data presented in

Laura J. Scott, Karen L. Mohlke, Lori L. Bonnycastle, Cristen J. Willer, Yun Li, William L. Duren, Michael R. Erdos, Heather M. Stringham, Pe- ter S. Chines, Anne U. Jackson, Ludmila Prokunina-Olsson, Chia-Jen J. Ding, Amy J. Swift, Narisu Narisu, Tianle Hu, Randall Pruim, Rui Xiao, Xiao- Yi Y. Li, Karen N. Conneely, Nancy L. Riebow, Andrew G. Sprau, Maurine Tong, Peggy P. White, Kurt N. Hetrick, Michael W. Barnhart, Craig W. Bark, Janet L. Goldstein, Lee Watkins, Fang Xiang, Jouko Saramies, Thomas A. Buchanan, Richard M. Watanabe, Timo T. Valle, Leena Kinnunen, Goncalo R. Abecasis, Elizabeth W. Pugh, Kimberly F. Doheny, Richard N. Bergman, Jaakko Tuomilehto, Francis S. Collins, and Michael Boehnke, A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility vari- ants, Science (2007).

Examples

data(Pheno); data(FUSION1); data(FUSION2)
FUSION1m <- merge(FUSION1, Pheno, by = "id", all.x = FALSE, all.y = FALSE) 
xtabs( ~ t2d + genotype, data = FUSION1m) 
xtabs( ~ t2d + Gdose, data = FUSION1m) 
chisq.test( xtabs( ~ t2d + genotype, data = FUSION1m ) )
f1.glm <- glm( factor(t2d) ~ Gdose, data = FUSION1m, family = binomial) 
summary(f1.glm)

Pass the Pigs

Description

This data set contains information collected from rolling the pair of pigs (found in the game "Pass the Pigs") 6000 times.

Format

A data frame with 6000 observations on the following 6 variables.

roll

roll number (1-6000)

blackScore

numerical code for position of black pig

black

position of black pig coded as a factor

pinkScore

numerical code for position of pink pig

pink

position of pink pig coded as a factor

score

score of the roll

height

height from which pigs were rolled (5 or 8 inches)

start

starting position of the pigs (0 = both pigs backwards, 1 = one bacwards one forwards, 2 = both forwards)

Details

In "Pass the Pigs", players roll two pig-shaped rubber dice and earn or lose points depending on the configuration of the rolled pigs. Players compete individually to earn 100 points. On each turn, a player rolls he or she decides to stop or until "pigging out" or

The pig configurations and their associated scores are

1 = Dot Up (0)

2 = Dot Down (0)

3 = Trotter (5)

4 = Razorback (5)

5 = Snouter (10)

6 = Leaning Jowler (15)

7 = Pigs are touching one another (-1; lose all points)

One pig Dot Up and one Dot Down ends the turn (a "pig out") and results in 0 points for the turn. If the pigs touch, the turn is ended and all points for the game must be forfeited. Two pigs in the Dot Up or Dot Down configuration score 1 point. Otherwise, The scores of the two pigs in different configurations are added together. The score is doubled if both both pigs have the same configuration, so, for example, two Snouters are worth 40 rather than 20.

Source

John C. Kern II, Duquesne University (kern@mathcs.duq.edu)

Examples

data(Pigs)
tally( ~ black, data = Pigs )
if (require(tidyr)) {
  Pigs %>% 
  select(roll, black, pink) %>%
  gather(pig, state, black, pink) %>%
  tally( state ~ pig, data = ., format = "prop", margins = TRUE)
}
  

Major League Baseball 2005 pitching

Description

Major League Baseball pitching statistics for the 2005 season.

Format

A data frame with 653 observations on the following 26 variables.

playerID

unique identifier for each player

yearID

year

stint

for players who played with multiple teams in the same season, stint is increased by one each time the player joins a new team

teamID

three-letter identifier for team

lgID

league team plays in, coded as AL or NL

W

wins

L

losses

G

games played in

GS

games started

CG

complete games

SHO

shut outs

SV

saves recorded

IPouts

outs recorded (innings pitched, measured in outs rather than innings)

H

hits allowed

ER

earned runs allowed

HR

home runs allowed

BB

walks (bases on balls) allowed

SO

strike outs

ERA

earned run average

IBB

intentional walks

WP

wild pitches

HBP

number of batters hit by pitch

BK

balks

BFP

batters faced pitching

GF

ratio of ground balls to fly balls

R

runs allowed

Examples

data(Pitching2005)
gf_point(IPouts/3 ~ W, data = Pitching2005, ylab = "innings pitched", xlab = "wins")

Poison data

Description

The data give the survival times (in hours) in a 3 x 4 factorial experiment, the factors being (a) three poisons and (b) four treatments. Each combination of the two factors is used for four animals. The allocation to animals is completely randomized.

Format

A data frame with 48 observations on the following 3 variables.

poison

type of poison (1, 2, or 3)

treatment

manner of treatment (1, 2, 3, or 4)

time

time until death (hours)

Source

These data are also available from OzDASL, the Australian Data and Story Library (https://dasl.datadescription.com/). (Note: The time measurements of the data at OzDASL are in units of tens of hours.)

References

Box, G. E. P., and Cox, D. R. (1964). An analysis of transformations (with Discussion). J. R. Statist. Soc. B, 26, 211-252.

Aitkin, M. (1987). Modelling variance heterogeneity in normal regression using GLIM. Appl. Statist., 36, 332-339.

Smyth, G. K., and Verbyla, A. P. (1999). Adjusted likelihood methods for modelling dispersion in generalized linear models. Environmetrics 10, 696-709. http://www.statsci.org/smyth/pubs/ties98tr.html.

Examples

data(poison)
poison.lm <- lm(time~factor(poison) * factor(treatment), data = Poison) 
plot(poison.lm,w = c(4,2))
anova(poison.lm)
# improved fit using a transformation
poison.lm2 <- lm(1/time ~ factor(poison) * factor(treatment), data = Poison) 
plot(poison.lm2,w = c(4,2))
anova(poison.lm)

American football punting

Description

Investigators studied physical characteristics and ability in 13 football punters. Each volunteer punted a football ten times. The investigators recorded the average distance for the ten punts, in feet. They also recorded the average hang time (time the ball is in the air before the receiver catches it), and a number of measures of leg strength and flexibility.

Format

A data frame with 13 observations on the following 7 variables.

distance

mean distance for 10 punts (feet)

hang

mean hang time (seconds)

rStrength

right leg strength (pounds)

lStrength

left leg strength (pounds)

rFlexibility

right leg flexibility (degrees)

lFlexibility

left leg flexibility (degrees)

oStrength

overall leg strength (foot-pounds)

Source

These data are also available at OzDASL (https://dasl.datadescription.com/).

References

"The relationship between selected physical performance variables and football punting ability" by the Department of Health, Physical Education and Recreation at the Virginia Polytechnic Institute and State University, 1983.

Examples

data(Punting)
gf_point(hang ~ distance, data = Punting)

Rat poison – unfinished documentation

Description

Data from an experiment to see whether flavor and location of rat poison influence the consumption by rats.

Format

A data frame with 20 observations on the following 3 variables.

consumption

a numeric vector

flavor

a factor with levels bread butter-vanilla plain roast beef

location

a factor with levels A B C D E

Examples

data(RatPoison)
gf_line(consumption ~ flavor, group = ~ location, color = ~ location, data = RatPoison) %>%
  gf_point()

Rubber band launching – unfinished documentation

Description

Results of an experiment comparing a rubber band travels to the amount it was stretched prior to launch.

Format

A data frame with 16 observations on the following 2 variables.

stretch

amount rubber band was stretched before launch

distance

distance rubber band traveled

Examples

data(RubberBand)
gf_point(distance ~ stretch, data = RubberBand) %>%
  gf_lm(interval = "confidence")

Sum of Squares Plots

Description

This function creates plots showing the "consumption" of residual sum of squares resulting from adding predictors to a model.

Usage

SSplot(
  model1,
  model2,
  n = 1,
  col1 = "gray50",
  size1 = 0.6,
  col2 = "navy",
  size2 = 1,
  col3 = "red",
  size3 = 1,
  ...,
  env = parent.frame()
)

Arguments

Examples

  
SSplot(
  lm(strength ~ limestone + water, data = Concrete),
  lm(strength ~ limestone + rand(7), data = Concrete),
  n = 50) 
## Not run: 
SSplot(
  lm(strength ~ water + limestone, data = Concrete),
  lm(strength ~ water + rand(7), data = Concrete),
  n = 1000) 

## End(Not run)

Maze tracing and scents

Description

Subjects were asked to to complete a pencil and paper maze when they were smelling a floral scent and when they were not.

Format

A data frame with 21 observations on the following 12 variables.

id

ID number

sex

a factor with levels F andM

smoker

a factor with levels N, Y

opinion

opinion of the odor (indiff, neg, or pos

)

age

age of subject (in years)

first

which treatment was first, scented or unscented

u1

time (in seconds) in first unscented trial

u2

time (in seconds) in second unscented trial

u3

time (in seconds) in third unscented trial

s1

time (in seconds) in first scented trial

s2

time (in seconds) in second scented trial

s3

time (in seconds) in third scented trial

Source

These data are also available at DASL, the data and story library (https://dasl.datadescription.com/).

References

Hirsch, A. R., and Johnston, L. H. "Odors and Learning," Smell & Taste Treatment and Research Foundation, Chicago.

Examples

data(Scent)
summary(Scent)

Dwindling soap

Description

A bar of soap was weighed after showering to see how much soap was used each shower.

Format

A data frame with 15 observations on the following 3 variables.

date

day

days since start of soap usage and data collection

weight

weight of bar of soap (in grams)

Details

According to Rex Boggs:

I had a hypothesis that the daily weight of my bar of soap [in grams] in my shower wasn't a linear function, the reason being that the tiny little bar of soap at the end of its life seemed to hang around for just about ever. I wanted to throw it out, but I felt I shouldn't do so until it became unusable. And that seemed to take weeks.

Also I had recently bought some digital kitchen scales and felt I needed to use them to justify the cost. I hypothesized that the daily weight of a bar of soap might be dependent upon surface area, and hence would be a quadratic function ... .

The data ends at day 22. On day 23 the soap broke into two pieces and one piece went down the plughole.

Source

Data collected by Rex Boggs and available from OzDASL (https://dasl.datadescription.com/).

Examples

data(Soap)
gf_point(weight ~ day, data = Soap)

Measuring spheres

Description

Measurements of the diameter (in meters) and mass (in kilograms) of a set of steel ball bearings.

Format

A data frame with 12 observations on the following 2 variables.

diameter

diameter of bearing (m)

mass

mass of the bearing (kg)

Source

These data were collected by Calvin College physics students under the direction of Steve Plath.

Examples

data(Spheres)
gf_point(mass ~ diameter, data = Spheres)
gf_point(mass ~ diameter, data = Spheres) %>%
  gf_refine(scale_x_log10(), scale_y_log10())

Stepping experiment

Description

An experiment was conducted by students at The Ohio State University in the fall of 1993 to explore the nature of the relationship between a person's heart rate and the frequency at which that person stepped up and down on steps of various heights.

Format

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

order

performance order

block

number of experimenter block

restHR

resting heart rate (beats per minute)

HR

final heart rate

height

height of step (hi or lo)

freq

whether subject stepped fast, medium, or slow

Details

An experiment was conducted by students at The Ohio State University in the fall of 1993 to explore the nature of the relationship between a person's heart rate and the frequency at which that person stepped up and down on steps of various heights. The response variable, heart rate, was measured in beats per minute. There were two different step heights: 5.75 inches (coded as lo), and 11.5 inches (coded as hi). There were three rates of stepping: 14 steps/min. (coded as slow), 21 steps/min. (coded as medium), and 28 steps/min. (coded as fast). This resulted in six possible height/frequency combinations. Each subject performed the activity for three minutes. Subjects were kept on pace by the beat of an electric metronome. One experimenter counted the subject's pulse for 20 seconds before and after each trial. The subject always rested between trials until her or his heart rate returned to close to the beginning rate. Another experimenter kept track of the time spent stepping. Each subject was always measured and timed by the same pair of experimenters to reduce variability in the experiment. Each pair of experimenters was treated as a block.

Source

These data are available at DASL, the data and story library (https://dasl.datadescription.com/).

Examples

data(Step)
gf_jitter(HR-restHR ~ freq, color = ~height, data = Step, group = ~height,
          height = 0, width = 0.1) %>%
  gf_line(stat = "summary", group = ~height)
gf_jitter(HR-restHR ~ height, color = ~freq, data = Step, group = ~freq,
          height = 0, width = 0.1) %>%
  gf_line(stat = "summary", group = ~freq)

Stereogram fusion

Description

Results of an experiment on the effect of prior information on the time to fuse random dot steregrams. One group (NV) was given either no information or just verbal information about the shape of the embedded object. A second group (group VV) received both verbal information and visual information (e.g., a drawing of the object).

Format

A data frame with 78 observations on the following 2 variables.

time

time until subject was able to fuse a random dot stereogram

group

treatment group: NV(no visual instructions) VV (visual instructions)

Source

These data are available at DASL, the data and story library (https://dasl.datadescription.com/).

References

Frisby, J. P. and Clatworthy, J. L., "Learning to see complex random-dot stereograms," Perception, 4, (1975), pp. 173-178.

Cleveland, W. S. Visualizing Data. 1993.

Examples

data(Stereogram)
favstats(time ~ group, data = Stereogram)
gf_violin(time ~ group, data = Stereogram, alpha = 0.2, fill = "skyblue") %>%
gf_jitter(time ~ group, data = Stereogram, height = 0, width = 0.25)

Standardized test scores and GPAs

Description

Standardized test scores and GPAs for 1000 students.

Format

A data frame with 1000 observations on the following 6 variables.

ACT

ACT score

SAT

SAT score

grad

has the student graduated from college?

gradGPA

college GPA at graduation

hsGPA

high school GPA

cohort

year of graduation or expected graduation

Examples

data(Students)
gf_point(ACT ~ SAT, data = Students)
gf_point(gradGPA ~ hsGPA, data = Students)

Taste test data

Description

The results from a study comparing different preparation methods for taste test samples.

Format

A data frame with 16 observations on 2 (taste1) or 4 (tastetest) variables.

score

taste score from a group of 50 testers

scr

a factor with levels coarse fine

liq

a factor with levels hi lo

type

a factor with levels A B C D

Details

The samples were prepared for tasting using either a coarse screen or a fine screen, and with either a high or low liquid content. A total taste score is recorded for each of 16 groups of 50 testers each. Each group had 25 men and 25 women, each of whom scored the samples on a scale from -3 (terrible) to 3 (excellent). The sum of these individual scores is the overall taste score for the group.

Source

E. Street and M. G. Carroll, Preliminary evaluation of a food product, Statistics: A Guide to the Unknown (Judith M. Tanur et al., eds.), Holden-Day, 1972, pp. 220-238.

Examples

data(TasteTest)
data(Taste1)
gf_jitter(score ~ scr, data = TasteTest, color = ~liq, width = 0.2, height =0) %>%
  gf_line(stat = "summary", group = ~liq)
df_stats(score ~ scr | liq, data = TasteTest)

Estimating tire wear

Description

Tread wear is estimated by two methods: weight loss and groove wear.

Format

A data frame with 16 observations on the following 2 variables.

weight

estimated wear (1000's of miles) base on weight loss

groove

estimated wear (1000's of miles) based on groove wear

Source

These data are available at DASL, the Data and Story Library (https://dasl.datadescription.com/).

References

R. D. Stichler, G. G. Richey, and J. Mandel, "Measurement of Treadware of Commercial Tires", Rubber Age, 73:2 (May 1953).

Examples

data(TireWear)
gf_point(weight ~ groove, data = TireWear)

New England traffic fatalities (1951-1959)

Description

Used by Tufte as an example of the importance of context, these data show the traffic fatality rates in New England in the 1950s. Connecticut increased enforcement of speed limits in 1956. In their full context, it is difficult to say if the decline in Connecticut traffic fatalities from 1955 to 1956 can be attributed to the stricter enforcement.

Format

A data frame with 9 observations on the following 6 variables.

year

a year from 1951 to 1959

cn.deaths

number of traffic deaths in Connecticut

ny

deaths per 100,000 in New York

cn

deaths per 100,000 in Connecticut

ma

deaths per 100,000 in Massachusetts

ri

deaths per 100,000 in in Rhode Island

Source

Tufte, E. R. The Visual Display of Quantitative Information, 2nd ed. Graphics Press, 2001.

References

Donald T. Campbell and H. Laurence Ross. "The Connecticut Crackdown on Speeding: Time-Series Data in Quasi-Experimental Analysis", Law & Society Review Vol. 3, No. 1 (Aug., 1968), pp. 33-54.

Gene V. Glass. "Analysis of Data on the Connecticut Speeding Crackdown as a Time-Series Quasi-Experiment" Law & Society Review, Vol. 3, No. 1 (Aug., 1968), pp. 55-76.

Examples

data(Traffic)
gf_line(cn.deaths ~ year, data = Traffic)
if (require(tidyr)) {
  TrafficLong <- 
    Traffic %>% 
    select(-2) %>%
    gather(state, fatality.rate, ny:ri)
   gf_line(fatality.rate ~ year, group = ~state, color = ~state, data = TrafficLong) %>%
     gf_point(fatality.rate ~ year, group = ~state, color = ~state, data = TrafficLong) %>%
		  gf_lims(y = c(0, NA))
}

Trebuchet data

Description

Measurements from an experiment that involved firing projectiles with a small trebuchet under different conditions.

Format

Data frames with the following variables.

object

the object serving as projectilebean big washerb bigWash BWB foose golf MWB SWB tennis ball wood

projectileWt

weight of projectile (in grams)

counterWt

weight of counter weight (in kg)

distance

distance projectile traveled (in cm)

form

a factor with levels a b B c describing the configuration of the trebuchet.

Details

Trebuchet1 and Trebuchet2 are subsets of Trebuchet restricted to a single value of counterWt

Source

Data collected by Andrew Pruim as part of a Science Olympiad competition.

Examples

data(Trebuchet); data(Trebuchet1); data(Trebuchet2)
gf_point(distance ~ projectileWt, data = Trebuchet1)
gf_point(distance ~ projectileWt, data = Trebuchet2)
gf_point(distance ~ projectileWt, color = ~ factor(counterWt), data = Trebuchet) %>%
  gf_smooth(alpha = 0.2, fill = ~factor(counterWt))

Unemployment data

Description

Unemployment data

Usage

data(Unemployment)

Format

A data.frame with 10 observations on the following 4 variables.

unemp

Millions of unemployed people

production

Federal Reserve Board index of industrial production

year

iyear

indexed year

Source

Paul F. Velleman and Roy E. Welsch. "Efficient Computing of Regression Diagnostics", The American Statistician, Vol. 35, No. 4 (Nov., 1981), pp. 234-242. (https://www.jstor.org/stable/2683296)

Examples

data(Unemployment)

Women in the workforce

Description

The labor force participation rate of women in each of 19 U.S. cities in each of two years. # Reference: United States Department of Labor Statistics # # Authorization: free use # # Description: # # Variable Names: # # 1. City: City in the United States # 2. labor72: Labor Force Participation rate of women in 1972 # 3. labor68: Labor Force Participation rate of women in 1968 # # The Data: #

Format

A data frame with 19 observations on the following 3 variables.

city

name of a U.S. city (coded as a factor with 19 levels)

labor72

percent of women in labor force in 1972

labor68

percent of women in labor force in 1968

Source

These data are from the United States Department of Labor Statistics and are also available at DASL, the Data and Story Library (https://dasl.datadescription.com/).

Examples

data(WorkingWomen)
gf_point(labor72 ~ labor68, data = WorkingWomen)

Lattice Theme

Description

A theme for use with lattice graphics.

Usage

col.fastR(bw = FALSE, lty = 1:7)

Arguments

Value

Returns a list that can be supplied as the theme to trellis.par.set().

Note

This theme was used in the production of the book Foundations and Applications of Statistics

Author(s)

Randall Pruim

See Also

trellis.par.set, show.settings

Examples

trellis.par.set(theme=col.fastR(bw=TRUE))
show.settings()
trellis.par.set(theme=col.fastR())
show.settings()

Row and Column Percentages

Description

Convenience wrappers around apply() to compute row and column percentages of matrix-like structures, including output of xtabs.

Usage

col.perc(x)

row.perc(x)

Arguments

Author(s)

Randall Pruim

Examples

row.perc(tally(~ airline + result, data = AirlineArrival))
col.perc(tally(~ airline + result, data = AirlineArrival))

Geometric representation of linear model

Description

geolm create a graphical representation of the fit of a linear model.

Usage

geolm(formula, data = parent.env(), type = "xz", version = 1, plot = TRUE, ...)

to2d(x, y, z, type = NULL, xas = c(0.4, -0.3), yas = c(1, 0), zas = c(0, 1))

Arguments

Author(s)

Randall Pruim

See Also

lm.

Examples

geolm(pollution ~ location, data = AirPollution)
geolm(distance ~ projectileWt, data = Trebuchet2)

Create ordered factor with order inferred from order given

Description

The order of the resulting factor is determined by the order in which unique labels first appear in the vector or factor x.

Usage

givenOrder(x)

Arguments

Examples

givenOrder(c("First", "Second", "Third", "Fourth", "Fifth", "Sixth"))

Golf ball numbers

Description

Allan Rossman used to live on a golf course in a spot where dozens of balls would come into his yard every week. He collected the balls and eventually tallied up the numbers on the first 5000 golf balls he collected. Of these 486 bore the number 1, 2, 3, or 4. The remaining 14 golf balls were omitted from the data.

Format

The format is: num [1:4] 137 138 107 104

Source

Data collected by Allan Rossman in Carlisle, PA.

Examples

data(golfballs)
golfballs/sum(golfballs)
chisq.test(golfballs, p = rep(.25,4))

Information

Description

Extract information from a maxLik object

Usage

information(object, ...)

Arguments

Augmented version of maxLik

Description

This version of maxLik stores additional information in the returned object enabling a plot method.

Usage

maxLik2(loglik, ..., env = parent.frame())

Arguments

Nonlinear maximization and minimization

Description

nlmin and nlmax are thin wrappers around nlm, a non-linear minimizer. nlmax avoids the necessity of modifying the function to construct a minimization problem from a problem that is naturally a maximization problem. The summary method for the resulting objects provides output that is easier for humnans to read.

Usage

nlmax(f, ...)

nlmin(f, ...)

## S3 method for class 'nlmax'
summary(object, nsmall = 4, ...)

## S3 method for class 'nlmin'
summary(object, nsmall = 4, ...)

Arguments

Examples

summary( nlmax( function(x) 5 - 3*x - 5*x^2, p=0 ) )

plot method for augment maxLik objects

Description

See maxLik2 and maxLik for how to create the objects this method prints.

Usage

## S3 method for class 'maxLik2'
plot(x, y, ci = "Wald", hline = FALSE, ...)

Arguments

Simulated golf ball data

Description

A matrix of random golf ball numbers simulated using rmultinom(n = 10000,size = 486,prob = rep(0.25,4)).

Examples

data(rgolfballs)

Display or execute a snippet of R code

Description

This command will display and/or execute small snippets of R code from the book Foundations and Applications of Statistics: An Introduction Using R.

Usage

snippet(
  name,
  eval = TRUE,
  execute = eval,
  view = !execute,
  echo = TRUE,
  ask = getOption("demo.ask"),
  verbose = getOption("verbose"),
  lib.loc = NULL,
  character.only = FALSE,
  regex = NULL,
  max.files = 10L
)

Arguments

Details

snippet works much like demo, but the interface is simplified. Partial matching is used to select snippets, so any unique prefix is sufficient to specify a snippet. Sequenced snippets (identified by trailing 2-digit numbers) will be executed in sequence if a unique prefix to the non-numeric portion is given. To run just one of a sequence of snippets, provide the full snippet name. See the examples.

Author(s)

Randall Pruim

See Also

demo, source.

Examples

snippet("normal01")
# prefix works
snippet("normal")
# this prefix is ambiguous
snippet("norm")
# sequence of "histogram" snippets
snippet("hist", eval = FALSE, echo = TRUE, view = FALSE)
# just one of the "histogram" snippets
snippet("histogram04", eval = FALSE, echo = TRUE, view = FALSE)
# Prefix too short, but a helpful message is displayed
snippet("h", eval = FALSE, echo = TRUE, view = FALSE)

Compute degrees of freedom for a 2-sample t-test

Description

This function computes degrees of freedom for a 2-sample t-test from the standard deviations and sample sizes of the two samples.

Usage

tdf(sd1, sd2, n1, n2)

Arguments

Value

estimated degrees of freedom for 2-sample t-test

Examples

data(KidsFeet, package="mosaicData")
fs <- favstats( length ~ sex, data=KidsFeet ); fs
t.test( length ~ sex, data=KidsFeet )
tdf( fs[1,'sd'], fs[2,'sd'], fs[1,'n'], fs[2,'n'])

Undocumented functions

Description

These objects are undocumented.

Details

Some are left-overs from a previous version of the book and package. In other cases, the functions are of limited suitability for general use.

Author(s)

Randall Pruim

ANOVA vectors

Description

Compute vectors associated with 1-way ANOVA

Usage

vaov(x, ...)

## S3 method for class 'formula'
vaov(x, data = parent.frame(), ...)

Arguments

Details

This is primarily designed for demonstration purposes to show how 1-way ANOVA models partition variance. It may not work properly for more complicated models.

Value

A data frame with variables including grandMean, groupMean, ObsVsGrand, STotal, ObsVsGroup, SError, GroupVsGrand, and STreatment. The usual SS terms can be computed from these by summing.

Examples

aov(pollution ~ location, data = AirPollution)
vaov(pollution ~ location, data = AirPollution)

Confidence Intervals for Proportions

Description

Alternatives to prop.test and binom.test.

Usage

wilson.ci(x, n = 100, conf.level = 0.95)

Arguments

Details

wald.ci produces Wald confidence intervals. wilson.ci produces Wilson confidence intervals (also called “plus-4” confidence intervals) which are Wald intervals computed from data formed by adding 2 successes and 2 failures. The Wilson confidence intervals have better coverage rates for small samples.

Value

Lower and upper bounds of a two-sided confidence interval.

Author(s)

Randall Pruim

References

A. Agresti and B. A. Coull, Approximate is better then ‘exact’ for interval estimation of binomial proportions, American Statistician 52 (1998), 119–126.

Examples

prop.test(12,30)
prop.test(12,30, correct=FALSE)
wald.ci(12,30)
wilson.ci(12,30)
wald.ci(12+2,30+4)