Mirror.its.dal.ca

Maintainer Peter Dalgaard <pd.mes@cbs.dk> Description Data sets and scripts for text examples and exercises in P. Dalgaard (2008), ‘Introductory Statistics with R’, 2nd ed.,Springer Verlag, ISBN 978-0387790534.
alkfos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
ashina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
bcmort . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
bp.obese . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
caesarean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
coking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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eba1977 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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fake.trypsin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . graft.vs.host . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . heart.rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IgM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . intake . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . juul . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . juul2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . kfm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . lung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . malaria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . nickel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . nickel.expand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . philion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . react . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . red.cell.folate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . rmr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . secher . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . secretin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . stroke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . tb.dilute . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . thuesen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . tlc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vitcap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vitcap2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . wright . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . zelazo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Repeated measurements of alkaline phosphatase in a randomized trial of Tamoxifen treatment ofbreast cancer patients.
A data frame with 43 observations on the following 8 variables.
grp a numeric vector, group code (1=placebo, 2=Tamoxifen).
c0 a numeric vector, concentration at baseline.
c3 a numeric vector, concentration after 3 months.
c6 a numeric vector, concentration after 6 months.
c9 a numeric vector, concentration after 9 months.
c12 a numeric vector, concentration after 12 months.
c18 a numeric vector, concentration after 18 months.
c24 a numeric vector, concentration after 24 months.
B. Kristensen et al. (1994), Tamoxifen and bone metabolism in postmenopausal low-risk breastcancer patients: a randomized study. Journal of Clinical Oncology, 12(2):992–997.
The ashina data frame has 16 rows and 3 columns. It contains data from a crossover trial forthe effect of an NO synthase inhibitor on headaches. Visual analog scale recordings of pain levelswere made at baseline and at five time points after infusion of the drug or placebo. A score wascalculated as the sum of the differences from baseline. Data were recorded during two sessionsfor each patient. Six patients were given treatment on the first occasion and the placebo on thesecond. Ten patients had placebo first and then treatment. The order of treatment and the placebowas randomized.
This data frame contains the following columns: vas.active a numeric vector, summary score when given active substance.
vas.plac a numeric vector, summary score when given placebo treatment.
grp a numeric vector code, 1: placebo first, 2: active first.
M.Ashina et al. (1999), Effect of inhibition of nitric oxide synthase on chronic tension-type headache:a randomised crossover trial. Lancet 353, 287–289 plot(vas.active~vas.plac,pch=grp,data=ashina)abline(0,1) Danish study on the effect of screening for breast cancer.
A data frame with 24 observations on the following 4 variables.
age a factor with levels 50-54, 55-59, 60-64, 65-69, 70-74, and 75-79.
cohort a factor with levels Study gr., Nat.ctr., Hist.ctr., and Hist.nat.ctr.
bc.deaths a numeric vector, number of breast cancer deaths.
p.yr a numeric vector, person-years under study.
Four cohorts were collected. The “study group” consists of the population of women in the appro-priate age range in Copenhagen and Frederiksberg after the introduction of routine mammographyscreening. The “national control group” consisted of the population in the parts of Denmark inwhich routine mammography screening was not available. These two groups were both collected inthe years 1991–2001. The “historical control group” and the “historical national control group” aresimilar cohorts from 10 years earlier (1981–1991), before the introduction of screening in Copen-hagen and Frederiksberg. The study group comprises the entire population, not just those acceptingthe invitation to be screened.
A.H. Olsen et al. (2005), Breast cancer mortality in Copenhagen after introduction of mammogra-phy screening. British Medical Journal, 330: 220–222.
The bp.obese data frame has 102 rows and 3 columns. It contains data from a random sample ofMexican-American adults in a small California town.
This data frame contains the following columns: sex a numeric vector code, 0: male, 1: female.
obese a numeric vector, ratio of actual weight to ideal weight from New York Metropolitan Life bp a numeric vector,systolic blood pressure (mm Hg).
B.W. Brown and M. Hollander (1977), Statistics: A Biomedical Introduction, Wiley.
plot(bp~obese,pch = ifelse(sex==1, "F", "M"), data = bp.obese) The table caesar.shoe contains the relation between caesarean section and maternal shoe size (UKsizes!).
A matrix with two rows and six columns.
D.G. Altman (1991), Practical Statistics for Medical Research, Table 10.1, Chapman & Hall.
prop.trend.test(caesar.shoe["Yes",],margin.table(caesar.shoe,2)) The coking data frame has 18 rows and 3 columns. It contains the time to coking in an experimentwhere the oven width and temperature were varied.
This data frame contains the following columns: width a factor with levels 4, 8, and 12, giving the oven width in inches.
temp a factor with levels 1600 and 1900, giving the temperature in Fahrenheit.
time a numeric vector, time to coking.
R.A. Johnson (1994), Miller and Freund’s Probability and Statistics for Engineers, 5th ed., Prentice-Hall.
attach(coking)matplot(tapply(time,list(width,temp),mean))detach(coking) The cystfibr data frame has 25 rows and 10 columns. It contains lung function data for cysticfibrosis patients (7–23 years old).
This data frame contains the following columns: sex a numeric vector code, 0: male, 1:female.
height a numeric vector, height (cm).
weight a numeric vector, weight (kg).
bmp a numeric vector, body mass (% of normal).
fev1 a numeric vector, forced expiratory volume.
rv a numeric vector, residual volume.
frc a numeric vector, functional residual capacity.
tlc a numeric vector, total lung capacity.
pemax a numeric vector, maximum expiratory pressure.
D.G. Altman (1991), Practical Statistics for Medical Research, Table 12.11, Chapman & Hall.
O’Neill et al. (1983), The effects of chronic hyperinflation, nutritional status, and posture on respi-ratory muscle strength in cystic fibrosis, Am. Rev. Respir. Dis., 128:1051–1054.
Lung cancer incidence in four Danish cities 1968–1971 This data set contains counts of incident lung cancer cases and population size in four neighbouringDanish cities by age group.
A data frame with 24 observations on the following 4 variables: city a factor with levels Fredericia, Horsens, Kolding, and Vejle.
age a factor with levels 40-54, 55-59, 60-64, 65-69, 70-74, and 75+.
pop a numeric vector, number of inhabitants.
cases a numeric vector, number of lung cancer cases.
These data were “at the center of public interest in Denmark in 1974”, according to Erling Ander-sen’s paper. The city of Fredericia has a substantial petrochemical industry in the harbour area.
E.B. Andersen (1977), Multiplicative Poisson models with unequal cell rates, Scandinavian Journalof Statistics, 4:153–158.
J. Clemmensen et al. (1974), Ugeskrift for Læger, pp. 2260–2268.
The energy data frame has 22 rows and 2 columns. It contains data on the energy expenditure ingroups of lean and obese women.
This data frame contains the following columns: expend a numeric vector, 24 hour energy expenditure (MJ).
stature a factor with levels lean and obese.
D.G. Altman (1991), Practical Statistics for Medical Research, Table 9.4, Chapman & Hall.
Rates of lung and nasal cancer mortality, and total mortality.
England and Wales mortality rates from lung cancer, nasal cancer, and all causes, 1936–1980. The1936 rates are repeated as 1931 rates in order to accommodate follow-up for the study.
A data frame with 150 observations on the following 5 variables: year calendar period, 1931: 1931–35, 1936: 1936–40, . . . .
age age class, 10: 10–14, 15:15–19, . . . .
lung lung cancer mortality rate per 1 million person-years nasal nasal cancer mortality rate per 1 million person-years other all cause mortality rate per 1 million person-years Taken from the “Epi” package by Bendix Carstensen et al.
N.E. Breslow, and N. Day (1987). Statistical Methods in Cancer Research. Volume II: The Designand Analysis of Cohort Studies, Appendix IX. IARC Scientific Publications, Lyon.
The trypsin data frame has 271 rows and 3 columns. Serum levels of immunoreactive trypsin inhealthy volunteers (faked!).
This data frame contains the following columns: trypsin a numeric vector, serum-trypsin in ng/ml.
grp a numeric vector, age coding. See below.
grpf a factor with levels 1: age 10–19, 2: age 20–29, 3: age 30–39, 4: age 40–49, 5: age 50–59, Data have been simulated to match given group means and SD.
D.G. Altman (1991), Practical Statistics for Medical Research, Table 9.12, Chapman & Hall.
The gvhd data frame has 37 rows and 7 columns. It contains data from patients receiving a nonde-pleted allogenic bone marrow transplant with the purpose of finding variables associated with thedevelopment of acute graft-versus-host disease.
This data frame contains the following columns: pnr a numeric vector patient number.
rcpage a numeric vector, age of recipient (years).
donage a numeric vector, age of donor (years).
type a numeric vector, type of leukaemia coded 1: AML, 2: ALL, 3: CML for acute myeloid, acute lymphatic, and chronic myeloid leukaemia.
preg a numeric vector code indicating whether donor has been pregnant. 0: no, 1: yes.
index a numeric vector giving an index of mixed epidermal cell-lymphocyte reactions.
gvhd a numeric vector code, graft-versus-host disease, 0: no, 1: yes.
dead a numeric vector code, 0: no (censored), 1: yes D.G. Altman (1991), Practical Statistics for Medical Research, Exercise 12.3, Chapman & Hall.
plot(jitter(gvhd,0.2)~index,data=graft.vs.host) The heart.rate data frame has 36 rows and 3 columns. It contains data for nine patients withcongestive heart failure before and shortly after administration of enalaprilat, in a balanced two-way layout.
This data frame contains the following columns: hr a numeric vector, heart rate in beats per minute.
time a factor with levels 0 (before), 30, 60, and 120 (minutes after administration).
D.G. Altman (1991), Practical Statistics for Medical Research, Table 12.2, Chapman & Hall.
evalq(interaction.plot(time,subj,hr), heart.rate) The hellung data frame has 51 rows and 3 columns. diameter and concentration of Tetrahymenacells with and without glucose added to growth medium.
This data frame contains the following columns: glucose a numeric vector code, 1: yes, 2: no.
conc a numeric vector, cell concentration (counts/ml).
diameter a numeric vector, cell diameter (µm).
D. Kronborg and L.T. Skovgaard (1990), Regressionsanalyse, Table 1.1, FADLs Forlag (in Danish).
plot(diameter~conc,pch=glucose,log="xy",data=hellung) Serum IgM in 298 children aged 6 months to 6 years.
D.G. Altman (1991), Practical Statistics for Medical Research, Table 3.2, Chapman & Hall.
The intake data frame has 11 rows and 2 columns. It contains paired values of energy intake for11 women.
This data frame contains the following columns: pre a numeric vector, premenstrual intake (kJ).
post a numeric vector, postmenstrual intake (kJ).
D.G. Altman (1991), Practical Statistics for Medical Research, Table 9.3, Chapman & Hall.
The juul data frame has 1339 rows and 6 columns. It contains a reference sample of the distributionof insulin-like growth factor (IGF-I), one observation per subject in various ages, with the bulk ofthe data collected in connection with school physical examinations.
This data frame contains the following columns: menarche a numeric vector. Has menarche occurred (code 1: no, 2: yes)? sex a numeric vector (1: boy, 2: girl).
igf1 a numeric vector, insulin-like growth factor (µg/l).
tanner a numeric vector, codes 1–5: Stages of puberty ad modum Tanner.
testvol a numeric vector, testicular volume (ml).
The juul2 data frame has 1339 rows and 8 columns; extended version of |juul|.
This data frame contains the following columns: menarche a numeric vector. Has menarche occurred (code 1: no, 2: yes)? sex a numeric vector (1: boy, 2: girl).
igf1 a numeric vector, insulin-like growth factor (µg/l).
tanner a numeric vector, codes 1–5: Stages of puberty ad modum Tanner.
testvol a numeric vector, testicular volume (ml).
weight a numeric vector, weight (kg).
The kfm data frame has 50 rows and 7 columns. It was collected by Kim Fleischer Michaelsenand contains data for 50 infants of age approximately 2 months. They were weighed immediatelybefore and after each breast feeding. and the measured intake of breast milk was registered alongwith various other data.
This data frame contains the following columns: no a numeric vector, identification number.
dl.milk a numeric vector, breast-milk intake (dl/24h).
sex a factor with levels boy and girl.
weight a numeric vector, weight of child (kg).
ml.suppl a numeric vector, supplementary milk substitute (ml/24h).
mat.weight a numeric vector, weight of mother (kg).
mat.height a numeric vector, height of mother (cm).
The amount of supplementary milk substitute refers to a period before the data collection.
plot(dl.milk~mat.height,pch=c(1,2)[sex],data=kfm) The lung data frame has 18 rows and 3 columns. It contains data on three different methods ofdetermining human lung volume.
This data frame contains the following columns: volume a numeric vector, measured lung volume.
method a factor with levels A, B, and C.
Anon. (1977), Exercises in Applied Statistics, Exercise 4.15, Dept.\ of Theoretical Statistics, AarhusUniversity.
The malaria data frame has 100 rows and 4 columns.
This data frame contains the following columns: mal a numeric vector code, Malaria: 0: no, 1: yes.
A random sample of 100 children aged 3–15 years from a village in Ghana. The children werefollowed for a period of 8 months. At the beginning of the study, values of a particular antibodywere assessed. Based on observations during the study period, the children were categorized intotwo groups: individuals with and without symptoms of malaria.
The melanom data frame has 205 rows and 7 columns. It contains data relating to the survival ofpatients after an operation for malignant melanoma, collected at Odense University Hospital byK.T. Drzewiecki.
This data frame contains the following columns: status a numeric vector code, survival status; 1: dead from melanoma, 2: alive, 3: dead from days a numeric vector, observation time.
ulc a numeric vector code, ulceration; 1: present, 2: absent.
thick a numeric vector, tumor thickness (1/100 mm).
sex a numeric vector code; 1: female, 2: male.
P.K. Andersen, Ø. Borgan, R.D. Gill, and N. Keiding (1991), Statistical Models Based on CountingProcesses, Appendix 1, Springer-Verlag.
require(survival)plot(survfit(Surv(days,status==1)~1,data=melanom)) The data concern a cohort of nickel smelting workers in South Wales, with information on exposure,follow-up period, and cause of death.
A data frame containing 679 observations of the following 7 variables: icd ICD cause of death if dead, 0 otherwise (numeric).
exposure exposure index for workplace (numeric) age1st age at first exposure (numeric).
agein age at start of follow-up (numeric).
ageout age at end of follow-up (numeric).
Taken from the “Epi” package by Bendix Carstensen et al. For comparison purposes, England andWales mortality rates (per 1,000,000 per annum) from lung cancer (ICDs 162 and 163), nasal cancer(ICD 160), and all causes, by age group and calendar period, are supplied in the data set N.E. Breslow and N. Day (1987). Statistical Methods in Cancer Research. Volume II: The Designand Analysis of Cohort Studies, IARC Scientific Publications, Lyon.
The data concern a cohort of nickel smelting workers in South Wales, with information on exposure,follow-up period, and cause of death, as in the data. This version has follow-up times splitaccording to age groups and is merged with the mortality rates in A data frame with 3724 observations on the following 12 variables: agr age class: 10: 10–14, 15: 15–19, . . . .
ygr calendar period, 1931: 1931–35, 1936: 1936–40, . . . .
icd ICD cause of death if dead, 0 otherwise (numeric).
exposure exposure index for workplace (numeric).
age1st age at first exposure (numeric).
agein age at start of follow-up (numeric).
ageout age at end of follow-up (numeric).
lung lung cancer mortality rate per 1 million person-years.
nasal nasal cancer mortality rate per 1 million person-years.
other all cause mortality rate per 1 million person-years.
Four small experiments with the purpose of estimating the EC50 of a biological dose-responserelation.
A data frame with 30 observations on the following 3 variables: experiment a numeric vector; codes 1 through 4 denote the experiment number.
response a numeric vector, the response (counts).
These data were discussed on the R mailing lists, initially suggesting a log-linear Poisson regres-sion, but actually a relation like y = ymax/(1 + (x/β)α) is more suitable.
Original data from Vincent Philion, IRDA, Qu\’ebec.
The numeric vector react contains differences between two nurses’ determinations of 334 tuber-culin reaction sizes.
A single vector, differences between reaction sizes in mm.
Anon. (1977), Exercises in Applied Statistics, Exercise 2.9, Dept.\ of Theoretical Statistics, AarhusUniversity.
hist(react) # not good because of discretization effects.
plot(density(react)) The folate data frame has 22 rows and 2 columns. It contains data on red cell folate levels inpatients receiving three different methods of ventilation during anesthesia.
This data frame contains the following columns: folate a numeric vector, folate concentration (µg/l).
ventilation a factor with levels N2O+O2,24h: 50% nitrous oxide and 50% oxygen, continuously for 24 hours; N2O+O2,op: 50% nitrous oxide and 50% oxygen, only during operation; O2,24h:no nitrous oxide but 35%–50% oxygen for 24 hours.
D.G. Altman (1991), Practical Statistics for Medical Research, Table 9.10, Chapman & Hall.
plot(folate~ventilation,data=red.cell.folate) The rmr data frame has 44 rows and 2 columns. It contains the resting metabolic rate and bodyweight data for 44 women.
This data frame contains the following columns: body.weight a numeric vector, body weight (kg).
metabolic.rate a numeric vector, metabolic rate (kcal/24hr).
D.G. Altman (1991), Practical Statistics for Medical Research, Exercise 11.2, Chapman & Hall.
plot(metabolic.rate~body.weight,data=rmr) The secher data frame has 107 rows and 4 columns. It contains ultrasonographic measurements offetuses immediately before birth and their subsequent birth weight.
This data frame contains the following columns: bwt a numeric vector, birth weight (g).
bpd a numeric vector, biparietal diameter (mm).
ad a numeric vector, abdominal diameter (mm).
no a numeric vector, observation number.
D. Kronborg and L.T. Skovgaard (1990), Regressionsanalyse, Table 3.1, FADLs Forlag (in Danish).
Secher et al. (1987), European Journal of Obstetrics, Gynecology, and Reproductive Biology, 24:1–11.
plot(bwt~ad, data=secher, log="xy") The secretin data frame has 50 rows and 6 columns. It contains data from a glucose responseexperiment.
This data frame contains the following columns: gluc a numeric vector, blood glucose level.
time a factor with levels 20, 30, 60, 90 (minutes since injection), and pre (before injection).
repl a factor with levels a: 1st sample; b: 2nd sample.
time20plus a factor with levels 20+: 20 minutes or longer since injection; pre: before injection.
time.comb a factor with levels 20: 20 minutes since injection; 30+: 30 minutes or longer since Secretin is a hormone of the duodenal mucous membrane. An extract was administered to fivepatients with arterial hypertension. Primary registrations (double determination) of blood glucosewere on graph paper and later quantified with the smallest of the two measurements recorded first.
Anon. (1977), Exercises in Applied Statistics, Exercise 5.8, Dept.\ of Theoretical Statistics, AarhusUniversity.
All cases of stroke in Tartu, Estonia, during the period 1991–1993, with follow-up until January 1,1996.
A data frame with 829 observations on the following 10 variables.
sex a factor with levels Female and Male.
age a numeric vector, age at stroke.
dgn a factor, diagnosis, with levels ICH (intracranial haemorrhage), ID (unidentified). INF (infarc- tion, ischaemic), SAH (subarchnoid haemorrhage).
coma a factor with levels No and Yes, indicating whether patient was in coma after the stroke.
diab a factor with levels No and Yes, history of diabetes.
minf a factor with levels No and Yes, history of myocardial infarction.
han a factor with levels No and Yes, history of hypertension.
obsmonths a numeric vector, observation times in months (set to 0.1 for patients dying on the same dead a logical vector, whether patient died during the study.
J. Korv, M. Roose, and A.E. Kaasik (1997). Stroke Registry of Tartu, Estonia, from 1991 through1993. Cerebrovascular Disorders 7:154–162.
The tb.dilute data frame has 18 rows and 3 columns. It contains data from a drug test involvingdilutions of tuberculin.
This data frame contains the following columns: reaction a numeric vector, reaction sizes (average of diameters) for tuberculin skin pricks.
logdose a factor with levels 0.5, 0, and -0.5.
The actual dilutions were 1:100, 1 : 100 10, 1:1000. Setting the middle one to 1 and using base-10logarithms gives the logdose values.
Anon. (1977), Exercises in Applied Statistics, part of Exercise 4.15, Dept.\ of Theoretical Statistics,Aarhus University.
The thuesen data frame has 24 rows and 2 columns. It contains ventricular shortening velocity andblood glucose for type 1 diabetic patients.
This data frame contains the following columns: blood.glucose a numeric vector, fasting blood glucose (mmol/l).
short.velocity a numeric vector, mean circumferential shortening velocity (%/s).
D.G. Altman (1991), Practical Statistics for Medical Research, Table 11.6, Chapman & Hall.
plot(short.velocity~blood.glucose, data=thuesen) The tlc data frame has 32 rows and 4 columns. It contains data on pretransplant total lung capacity(TLC) for recipients of heart-lung transplants by whole-body plethysmography.
This data frame contains the following columns: age a numeric vector, age of recipient (years).
sex a numeric vector code, female: 1, male: 2.
height a numeric vector, height of recipient (cm).
tlc a numeric vector, total lung capacity (l).
D.G. Altman (1991), Practical Statistics for Medical Research, Exercise 12.5, 10.1, Chapman &Hall.
The vitcap data frame has 24 rows and 3 columns. It contains data on vital capacity for workers inthe cadmium industry. It is a subset of the vitcap2 data set.
This data frame contains the following columns: group a numeric vector; group codes are 1: exposed > 10 years, 3: not exposed.
vital.capacity a numeric vector, vital capacity (a measure of lung volume) in liters.
P. Armitage and G. Berry (1987), Statistical Methods in Medical Research, 2nd ed., Blackwell,p.286.
plot(vital.capacity~age, pch=group, data=vitcap) The vitcap2 data frame has 84 rows and 3 columns. Age and vital capacity for workers in thecadmium industry.
This data frame contains the following columns: group a numeric vector; group codes are 1: exposed > 10 years, 2: exposed < 10 years, 3: not vital.capacity a numeric vector, vital capacity (a measure of lung volume) (l).
P. Armitage and G. Berry (1987), Statistical Methods in Medical Research, 2nd ed., Blackwell,p.286.
plot(vital.capacity~age, pch=group, data=vitcap2) The wright data frame has 17 rows and 2 columns. It contains data on peak expiratory flow ratewith two different flow meters on each of 17 subjects.
This data frame contains the following columns: std.wright a numeric vector, data from large flow meter (l/min).
mini.wright a numeric vector, data from mini flow meter (l/min).
J.M. Bland and D.G. Altman (1986), Statistical methods for assessing agreement between twomethods of clinical measurement, Lancet, 1:307–310.
The zelazo object is a list with four components.
This is a list containing data on age at walking (in months) for four groups of infants: active test group receiving active training; these children had their walking and placing reflexes trained during four three-minute sessions that took place every day from their second to theireighth week of life.
passive passive training group; these children received the same types of social and gross motor stimulation, but did not have their specific walking and placing reflexes trained.
none no training; these children had no special training, but were tested along with the children who underwent active or passive training.
ctr.8w eighth-week controls; these children had no training and were only tested at the age of 8 When asked to enter these data from a text source, many students will use one vector per group andwill need to reformat data into a data frame for some uses. The rather unusual format of this dataset mimics that situation.
P.R. Zelazo, N.A. Zelazo, and S. Kolb (1972), “Walking” in the newborn, Science, 176: 314–315.

Source: http://mirror.its.dal.ca/cran/web/packages/ISwR/ISwR.pdf