Allometry

Description

This dataset contains measurements of tree dimensions and biomass. Data kindly provided by John Marshall, University of Idaho.

Usage

allometry

Format

A data frame with 63 rows and 5 variables:

species

factor The tree species (PSME = Douglas fir, PIMO = Western white pine, PIPO = Ponderosa pine).

diameter

double Tree diameter at 1.3m above ground (cm).

height

double Tree height (m).

leafarea

double Total leaf area (m2)

branchmass

double Total (oven-dry) mass of branches (kg).

Examples

data(allometry)
with(allometry, plot(diameter, height, pch=19, col=species))

Child anthropometry

Description

Data include measurements of age, foot length, and height for 3898 children. These data are a small subset of many dozens of measurements on the same children, described in detail by Snyder (1977).

Usage

anthropometry

Format

A data frame with 3898 rows and 4 variables:

age

double Age in years

gender

integer "female" or "male"

foot_length

integer Total foot length (mm)

height

double Total height (cm)

Source

<http://mreed.umtri.umich.edu/mreed/downloads.html>.

Examples

data(anthropometry)
with(anthropometry, plot(age, foot_length, pch=16, cex=0.5, col=gender))

Cars data

Description

Fuel efficiency, weight, acceleration, and other measurements on 398 cars. The majority of the data come from American cars (n = 249), and some European (n = 70) and Japanese (n = 79). Not to be confused with cars data provided by base R, see cars and mtcars.

Usage

automobiles

Format

A data frame with 398 rows and 9 variables:

car_name

character Make and model

origin

factor 'American', 'European' or 'Japanese'

build_year

double Year car was built

fuel_efficiency

double Liters / 100km

cylinders

integer Nr. of cylinders

engine_volume

double Engine volume ('displacement') in liters.

horsepower

integer Engine power (hp)

weight

double Car weight in kg

acceleration

double Time to accelerate to 60mph

Source

Data originally hosted on <http://lib.stat.cmu.edu/datasets/>, also used in ISLR (as the 'Auto' dataset). Converted to metric units for use in this package.

Berkeley admissions data, 1973

Description

A well-known example dataset, used as an excellent example for Simpson's Paradox. The Wikipedia page (see source), describes: "The admission figures for the fall of 1973 showed that men applying were more likely than women to be admitted, and the difference was so large that it was unlikely to be due to chance. But when examining the individual departments, it appeared that six out of 85 departments were significantly biased against men, whereas only four were significantly biased against women. In fact, the pooled and corrected data showed a 'small but statistically significant bias in favor of women.'"

Usage

berkeley

Format

A data frame with 6 rows and 5 variables:

Department

integer University Department, A-F

Admitted_Male

integer Nr. Admitted male applicants

Denied_Male

integer Nr. Denied male applicants

Admitted_Female

integer Nr. Addmitted female applicants

Denied_Female

integer Nr. Denied female applicants.

Source

<https://en.wikipedia.org/wiki/Simpson

A Baboon Named Brunhilda

Description

The observed responses are Geiger counter counts (times 10-4) used to measure the amount of radioactively tagged sulfate drug in the blood of a baboon named Brunhilda after an injection of the drug.

Usage

brunhild

Format

A data frame with 21 rows and 2 variables:

Hours

integer Hours after drug injection

Sulfate

double Tagged sulfate concentration in blood

Source

<http://www.statsci.org/data/general/brunhild.html>

Cavitation resistance for Callitris branches

Description

Measurements of so-called 'percent loss conductivity' (PLC) curves on terminal twigs of Callitris trees (a member of the Cupressaceae in Australia). Twigs are subjected to increasingly negative xylem pressure (Psi, included as a positive pressure in MPa), and the loss in conductivity (i.e. the conductivity of water transport in the xylem) is measured.

Usage

callitrishydraulic

Format

A data frame with 31 rows and 3 variables:

Rep

integer Replicate - four branches are included.

Psi

double Positive-valued negative xylem water pressure (MPa)

PLC

double Percent loss conductivity (sometimes < 0)

Examples

data(callitrishydraulic)
with(callitrishydraulic, plot(Psi, PLC, pch=Rep))

Cereal nutrition data - small subset nr1

Description

Small subset nr1 of the Cereals data to practice merging, see cereals (available are cereal1, cereal2 and cereal3).

Usage

cereal1

Format

An object of class data.frame with 10 rows and 2 columns.

Cereal nutrition data - small subset nr2

Description

Small subset nr1 of the Cereals data to practice merging, see cereals (available are cereal1, cereal2 and cereal3).

Usage

cereal2

Format

An object of class data.frame with 8 rows and 2 columns.

Cereal nutrition data - small subset nr3

Description

Small subset nr1 of the Cereals data to practice merging, see cereals (available are cereal1, cereal2 and cereal3).

Usage

cereal3

Format

An object of class data.frame with 6 rows and 2 columns.

Cereal nutrition data

Description

This dataset summarizes 77 different brands of breakfast cereals, including calories, proteins, fats, and so on, and gives a 'rating' that indicates the overall nutritional value of the cereal.

Usage

cereals

Format

A data frame with 77 rows and 13 variables:

Cereal.name

character Cereal name

Manufacturer

factor Cereal manufacturer (letter code)

Cold.or.Hot

factor 'C' or 'H'

calories

integer

protein

integer

fat

integer

sodium

integer

fiber

double

carbo

double

sugars

integer

potass

integer

vitamins

integer

rating

double Health rating of the cereal (unknown calculation method).

Source

<https://dasl.datadescription.com/datafile/cereals/> (Originally at Statlib CMU).

Choat's Plant Drought Tolerance

Description

Data include a measure of plant drought tolerance (P50, more negative values indicate plant stems can tolerate lower water contents), and mean annual precipitation of the location where the sample was taken. Data are for 115 individual species (species name not included). Data are from original source were simplified for the purpose of this book.

Usage

choat_precipp50

Format

A data frame with 115 rows and 2 variables:

annualprecip

integer Annual rainfall (mm) where the plant was sampled.

P50

double The negative water pressure in the xylem at which 50% of stem conductivity is lost. More negative indicates higher tolerance to drought.

Source

Choat B. et al., 2012, Global convergence in the vulnerability of forests to drought, Nature 491, pages 752–755 <https://www.nature.com/articles/nature11688>.

Coweeta tree data

Description

Tree measurements in the Coweeta LTER.

Usage

coweeta

Format

A data frame with 87 rows and 9 variables:

species

integer One of 10 tree species

site

integer Site abbreviation

elev

integer Elevation (m asl)

age

integer Tree age (yr)

DBH

double Diameter at breast height (cm)

height

double Tree height (m)

folmass

double Foliage mass (kg)

SLA

double Specific leaf area (index of leaf thinness) (cm2 g-1)

biomass

double Total tree biomass

Details

DETAILS

Source

Martin J.G., et al., 1998, Aboveground biomass and nitrogen allocation of ten deciduous southern Appalachian tree species, Canadian Journal of Forest Research 28, 1648-1659.

Dutch election data

Description

Polls for the 12 leading political parties in the Netherlands, leading up to the general election on 12 Sept. 2012. Data are in 'wide' format, with a column for each party. Values are in percentages.

Usage

dutchelection

Format

A data frame with 22 rows and 12 variables:

Date

factor Date of poll (NOTE: has not been converted to Date class)

VVD

double Vote for this part in percentage.

PvdA

double Vote for this part in percentage.

PVV

double Vote for this part in percentage.

CDA

double Vote for this part in percentage.

SP

double Vote for this part in percentage.

D66

double Vote for this part in percentage.

GL

double Vote for this part in percentage.

CU

double Vote for this part in percentage.

SGP

double Vote for this part in percentage.

PvdD

double Vote for this part in percentage.

FiftyPlus

double Vote for this part in percentage.

Source

<http://en.wikipedia.org/wiki/Dutch_general_election,_2012>

Leaf gas exchange at the EucFACE

Description

Measurements of leaf net photosynthesis at the EucFACE experiment, on leaves of different trees growing in ambient and elevated CO$_2$ concentrations. Measurements were repeated four times during 2013 (labelled as Date=A,B,C,D).

Usage

eucface_gasexchange

Format

A data frame with 84 rows and 7 variables:

Date

factor Date label (A-D)

CO2

integer CO2 treatment, Amb=ambient, Ele=elevated

Ring

integer One of six plots ('rings') where treatment was applied

Tree

integer Tree number

Photo

double Rate of leaf photosynthesis (mu mol m-2 s-1)

Trmmol

double Rate of leaf transpiration (mmol m-2 s-1)

VpdL

double Vapour pressure deficit (kPa)

Source

Gimeno T.E., 2015, Conserved stomatal behaviour under elevated CO2 and varying water availability in a mature woodland. Functional Ecology <https://doi.org/10.1111/1365-2435.12532>

EucFACE ground cover data

Description

This file contains estimates of plant and litter cover within the rings of the EucFACE experiment, evaluating forest ecosystem responses to elevated CO$_2$, on two dates. Within each ring are four plots and within each plot are four 1m by 1m subplots. Values represent counts along a grid of 16 points within each subplot.

Usage

eucfacegc

Format

A data frame with 192 rows and 8 variables:

Date

integer Date of measurement (d/m/y, not yet converted to Date class)

Ring

integer The identity of the EucFACE Ring, the level at which the experimental treatment is applied.

Plot

integer A total of four plots, nested within each level of Ring.

Sub

integer A total of four subplots, nested within each level of Plot.

Forbes

integer Number of points where dicot plants are observed.

Grass

integer Number of points where grass is observed.

Litter

integer Number of points where leaf litter is observed.

Trt

integer The experimental treatment: ctrl for ambient levels of atmospheric carbon dioxide, elev for ambient plus 150ppm.

Source

Jeff Powell

Fluxtower data

Description

This dataset contains measurements of CO$_2$ and H$_2$O fluxes (and related variables) over a pine forest in Quintos de Mora, Spain. The site is a mixture of Pinus pinaster and Pinus pinea, and was planted in the 1960's.

Data need to be cleaned to some extent (the purpose of this example dataset).

Usage

fluxtower

Format

A data frame with 244 rows and 8 variables:

TIMESTAMP

factor Date and time

FCO2

double Canopy CO2 flux (mu mol m$^-2$ s$^-1$)

FH2O

double Canopy H2O flux (mmol m$^-2$ s$^-1$)

ustar

double Roughness length (m s$^-1$)

Tair

double Air temperature (degrees C)

RH

double Relative humidity (%)

Tsoil

double Soil temperature (degrees C)

Rain

integer Rainfall (mm half hour$^-1$)

Source

Data kindly provided by Victor Resco de Dios (in 2011), and simplified somewhat.

Seed germination as affected by fire

Description

Two datasets on the germination success of seeds of four Melaleuca species, when subjected to temperature, fire cue, and dehydration treatments. Seeds were collected from a number of sites and subjected to 6 temperature treatments and fire cues (in the fire germination data), or two a range of dehydration levels (in the water germination data).

This dataset contains the fire treatment data.

Usage

germination_fire

Format

A data frame with 576 rows and 7 variables:

species

factor One of four Melaleuca species

temp

integer Temperature treatment (C)

fire.cues

integer Fire cue treatment (yes or no)

site

integer Coding for the site where the seed was collected

cabinet

integer ID for the cabinet where seeds were treated

germ

integer Number of germinated seeds

n

integer Number of seeds tested (20 for all rows)

Source

Data are from Hewitt et al. 2015 (Austral Ecology 40(6):661-671), shared by Charles Morris, and simplified for the purpose of this book.

See Also

germination_water

Seed germination as affected by water

Description

Two datasets on the germination success of seeds of four Melaleuca species, when subjected to temperature, fire cue, and dehydration treatments. Seeds were collected from a number of sites and subjected to 6 temperature treatments and fire cues (in the fire germination data), or two a range of dehydration levels (in the water germination data).

This dataset contains the water treatment data.

Usage

germination_water

Format

A data frame with 352 rows and 5 variables:

species

factor One of four Melaleuca species

site

integer Coding for the site where the seed was collected

water.potential

double Water potential of the seed (Mpa) after incubation (low values is drier)

germ

integer Number of germinated seeds

n

integer Number of seeds tested (25 for all rows)

Source

Data are from Hewitt et al. 2015 (Austral Ecology 40(6):661-671), shared by Charles Morris, and simplified for the purpose of this package.

See Also

germination_fire

Examples

data(germination_water)
with(germination_water,
  plot(jitter(water.potential), germ/n,
    pch=21, bg=terrain.colors(4)[species])
)

I x F at the HFE - tree observations

Description

Heights and stem diameters of trees growing in a fertilization x irrigation experiment in Richmond, New South Wales, Australia, as part pf the Hawkesbury Forest Experiment (HFE). A total of 16 plots, each with 72 Eucalyptus saligna trees, was remeasured 17 times between 2008 and 2012. Treatments to the plots were either control (C), applied with fertilizer (F), irrigation (I), or irrigation+fertilization (IF).

This dataset contains the tree-level observations, see hfeifplotmeans for averaged data.

Usage

hfeifbytree

Format

A data frame with 9592 rows and 6 variables:

ID

integer A unique identifier for each tree.

plotnr

integer A total of sixteen plots (four treatments).

treat

integer One of four treatments (I - irrigated, F - dry fertilized, IL - Liquid fertilizer plus irrigation, C - control)

Date

factor The date of measurement (YYYY-MM-DD)

height

double Mean height for the sample trees ($m$).

diameter

double Mean diameter for the sample trees ($cm$).

Source

Data courtesy of Craig Barton and Burhan Amiji, from Western Sydney University.

Examples

# Variable sample sizes over time. On many occassions, subsamples were measured.
data(hfeifbytree)
ftable(xtabs(~Date+treat, data=hfeifbytree))

I x F at the HFE - plot-level observations

Description

Heights and stem diameters of trees growing in a fertilization x irrigation experiment in Richmond, New South Wales, Australia, as part pf the Hawkesbury Forest Experiment (HFE). A total of 16 plots, each with 72 Eucalyptus saligna trees, was remeasured 17 times between 2008 and 2012. Treatments to the plots were either control (C), applied with fertilizer (F), irrigation (I), or irrigation+fertilization (IF).

This dataset contains the plot-level means, see hfeifbytree for tree-level measurements.

Usage

hfeifplotmeans

Format

A data frame with 320 rows and 5 variables:

plotnr

integer A total of sixteen plots (four treatments).

Date

factor The date of measurement (YYYY-MM-DD)

diameter

double Mean diameter for the sample trees ($cm$).

height

double Mean height for the sample trees ($m$).

treat

integer One of four treatments (I - irrigated, F - dry fertilized, IL - Liquid fertilizer plus irrigation, C - control)

Weather data at the Hawkesbury Forest Experiment

Description

Data for the weather station at the Hawkesbury Forest Experiment (HFE) for the year 2008. The HFE is in Richmond, New South Wales (in western Sydney), Australia.

Data are in 30min timestep.

Usage

hfemet2008

Format

A data frame with 17568 rows and 9 variables:

DateTime

integer Date Time (half-hourly steps)

Tair

double Air temperature (degrees C)

AirPress

double Air pressure (kPa)

RH

double Relative humidity (%)

VPD

double Vapour pressure deficit (kPa)

PAR

double Photosynthetically active radiation (mu mol m$^-2$ s$^-1$)

Rain

double Precipitation (mm)

wind

double Wind speed (m s$^-1$)

winddirection

double Wind direction (degrees)

Source

Data courtesy of Craig Barton at Western Sydney University.

Howell height, age and weight data

Description

These data were also used by McElreath (2016, "Statistical Rethinking", CRC Press). Data include measurements of height, age and weight on Khosan people.

Usage

howell

Format

A data frame with 783 rows and 4 variables:

sex

factor male or female

age

double Age (years)

weight

double Body weight (kg)

height

double Total height (cm)

Source

<https://tspace.library.utoronto.ca/handle/1807/17996>, subsetted for non-missing data and one outlier removed.

Examples

data(howell)
with(howell, plot(age, height, pch=19, col=sex))

Hydro dam storage data

Description

This dataset describes the storage of the hydrodam on the Derwent river in Tasmania (Lake King William \& Lake St. Clair), in equivalent of energy stored.

Usage

hydro

Format

A data frame with 314 rows and 2 variables:

Date

factor The date of the bi-weekly reading (d/m/yyyy)

storage

integer Total water stored, in energy equivalent ($GWh$).

Details

DETAILS

Icecream sales and temperature

Description

A synthetic dataset on weekly ice cream sales in two locations in Amsterdam, along with air temperature. The idea is that the ice cream salesman first sold icecream in 'Oosterpark', and decided to move shop to the 'Dappermarkt' the year after. Did sales improve? This dataset can be used to show that naive conclusions from simple linear model fits can be misleading, and that the use of covariates (here, air temperature) can change conclusions about effects.

Usage

icecream

Format

A data frame with 40 rows and 3 variables:

temperature

double Air temperature (C)

sales

double Icecream sales per week (in local currency)

location

factor Either 'Dappermarkt' or 'Oosterpark'

Examples

data(icecream)

# Linear model, temperature as covariate
fit_ice <- lm(sales ~ temperature*location, data=icecream)

# Try to guess from coefficients where the sales were higher:
summary(fit_ice)

# What about now?
with(icecream, plot(temperature, sales, pch=19, col=location))
legend("topleft", levels(icecream$location), fill=palette())

Genetically modified soybean litter decomposition

Description

Soybean litter decomposition as a function of time (date), type of litter (variety), herbicides applied (herbicide), and where in the soil profile it is placed (profile). masslost refers to the proportion of the litter that was lost from the bag (decomposed) relative to the start of the experiment. Herbicide treatments were applied at the level of whole plots, with both treatments represented within each of four blocks. Both levels of variety and profile were each represented within each plot, with six replicates of each treatment added to each plot.

Usage

masslost

Format

A data frame with 246 rows and 8 variables:

plot

integer A total of eight plots.

block

integer A total of four blocks.

variety

integer Soybean variety is genetically modified ('gm') or not ('nongm'); manipulated at the subplot level.

herbicide

integer Herbicide applied is glyphosate ('gly') or conventional program ('conv'); manipulated at plot level.

profile

integer Whether litter was 'buried' in the soil or placed at the soil 'surface'; manipulated at the subplot level.

date

integer Date at which litter bags were recovered.

sample

integer Factor representing timing of sampling ('incrop1', 'incrop2', 'postharvest').

masslost

double The proportion of the initial mass that was lost from each litter bag during field incubation. Some values are lower than zero due to insufficient washing of dirt and biota from litter prior to weighing.

Source

Jeff Powell

Memory of words dataset

Description

A dataset on the number of words remembered from list, for various learning techniques, and in two age groups.

Usage

memory

Format

A data frame with 100 rows and 3 variables:

Age

integer Age of person tested (yr)

Process

factor One of five methods used to memorize the words.

Words

double Number of words recalled.

Details

Description taken from source: "Why do older people often seem not to remember things as well as younger people? Do they not pay attention? Do they just not process the material as thoroughly? One theory regarding memory is that verbal material is remembered as a function of the degree to which is was processed when it was initially presented. Eysenck (1974) randomly assigned 50 younger subjects and 50 older (between 55 and 65 years old) to one of five learning groups. The Counting group was asked to read through a list of words and count the number of letters in each word. This involved the lowest level of processing. The Rhyming group was asked to read each word and think of a word that rhymed with it. The Adjective group was asked to give an adjective that could reasonably be used to modify each word in the list. The Imagery group was instructed to form vivid images of each word, and this was assumed to require the deepest level of processing. None of these four groups was told they would later be asked to recall the items. Finally, the Intentional group was asked to memorize the words for later recall. After the subjects had gone through the list of 27 items three times they were asked to write down all the words they could remember."

Source

<http://www.statsci.org/data/general/eysenck.html>.

Crude oil production

Description

Crude oil production for the top 8 oil-producing countries (minus Russia, for which understandably no data were available pre-1990), for the period 1971-2017.

Usage

oil

Format

A data frame with 376 rows and 3 variables:

country

factor Country code

year

integer 1971 - 2017

production

double Annual crude oil production in TOE.

Pulse Rates before and after Exercise

Description

Pulse rates measured on 110 participating students. Half of the students ran in place for one minute, before their pulse rate was measured again.

Usage

pulse

Format

A data frame with 110 rows and 11 variables:

Height

integer Height (cm)

Weight

double Weight (kg)

Age

integer Age (years)

Gender

integer Sex (1 = male, 2 = female)

Smokes

integer Regular smoker? (1 = yes, 2 = no)

Alcohol

integer Regular drinker? (1 = yes, 2 = no)

Exercise

integer Frequency of exercise (1 = high, 2 = moderate, 3 = low)

Ran

integer Whether the student ran or sat between the first and second pulse measurements (1 = ran, 2 = sat)

Pulse1

integer First pulse measurement (rate per minute)

Pulse2

integer Second pulse measurement (rate per minute)

Year

integer Year of class (93 - 98)

Details

Description taken from source: "Students in an introductory statistics class (MS212 taught by Professor John Eccleston and Dr Richard Wilson at The University of Queensland) participated in a simple experiment. The students took their own pulse rate. They were then asked to flip a coin. If the coin came up heads, they were to run in place for one minute. Otherwise they sat for one minute. Then everyone took their pulse again. The pulse rates and other physiological and lifestyle data are given in the data. Five class groups between 1993 and 1998 participated in the experiment. The lecturer, Richard Wilson, was concerned that some students would choose the less strenuous option of sitting rather than running even if their coin came up heads, so in the years 1995-1998 a different method of random assignment was used. In these years, data forms were handed out to the class before the experiment. The forms were pre-assigned to either running or non-running and there were an equal number of each. In 1995 and 1998 not all of the forms were returned so the numbers running and sitting was still not entirely controlled."

Source

<http://www.statsci.org/data/oz/ms212.html>

Examples

data(pulse)
with(pulse, plot(Weight, Pulse2-Pulse1,
  pch=19, col=c("red2", "dimgrey")[Ran]))
abline(h=0, lty=5)

Pupae data

Description

This dataset is from an experiment where larvae were left to feed on Eucalyptus leaves, in a glasshouse that was controlled at two different levels of temperature and CO$_2$ concentration. After the larvae pupated (that is, turned into pupae), the body weight was measured, as well as the cumulative 'frass' (larvae excrement) over the entire time it took to pupate.

Usage

pupae

Format

A data frame with 84 rows and 5 variables:

T_treatment

integer Temperature treatments ('ambient' and 'elevated')

CO2_treatment

integer CO$_2$ treatment (280 or 400 ppm).

Gender

integer The gender of the pupae : 0 (male), 1 (female)

PupalWeight

double Weight of the pupae ($g$)

Frass

double Frass produced ($g$)

Source

Data courtesy of Tara Murray, and simplified for the purpose of this package.

Rain data

Description

This dataset contains ten years (1995-2006) of daily rainfall amounts as measured at the Richmond RAAF base.

Usage

rain

Format

A data frame with 3653 rows and 3 variables:

Year

integer Year

DOY

integer Day of year (1-366)

Rain

double Daily rainfall amount (mm)

Source

<http://www.bom.gov.au/climate/data/>, simplified and adjusted for this package.

Sydney to Hobart winning times

Description

Winning times for the Sydney to Hobart Yacht Race. An annual sail yacht race over 1170km, from Sydney's harbour, to Hobart in Tasmania. The race is infamous for the rough conditions, long distance, and large number of dropouts in some years. The data include the winning time, and the number of starting yachts, and the number of yachts reaching the finish.

Usage

sydney_hobart_times

Format

A data frame with 72 rows and 5 variables:

Year

integer Year race was held

Time

double Total time (days)

fleet_start

integer Number yachts at start

fleet_finish

integer Number yachts at finish

Time_record

double Record race up to this year

Source

<https://en.wikipedia.org/wiki/Sydney_to_Hobart_Yacht_Race>

Examples

data(sydney_hobart_times)
with(sydney_hobart_times, {
    plot(Year, Time)
    lines(Year, Time_record, type='s', col="red")
})

Passengers on the Titanic

Description

Survival status of passengers on the Titanic, together with their names, age, sex and passenger class. Not to be confused with the dataset Titanic, provided with R, which lists only tables of passengers. This dataset on the other hand provides one row per passenger.

Usage

titanic

Format

A data frame with 1313 rows and 5 variables:

Name

integer Recorded name of passenger

PClass

integer Passenger class: 1st, 2nd or 3rd

Age

double Age in years (many missing)

Sex

integer male or female

Survived

integer 1 = Yes, 0 = No

Details

DETAILS

Source

<http://www.statsci.org/data/general/titanic.html>

Tree canopy gradients in the Priest River Experimental Forest (PREF)

Description

Leaves of two pine species (35 trees in total) were sampled throughout their canopy, usually 8 samples were taken at various heights. The height is expressed as the 'distance from top', i.e. the distance to the apex of the tree. Leaves (conifer needles) were analysed for nitrogen content (narea), and an index of leaf thickness, the 'leaf mass per area'. The data show the usual pattern of higher leaf thickness (higher LMA) toward the top of the trees, but individual trees show a lot of variation in LMA.

Usage

treecanopy

Format

A data frame with 249 rows and 7 variables:

ID

integer ID of the individual tree

species

integer Pinus ponderosa or Pinus monticola

dfromtop

double Distance from top of tree (where leaf sample was taken) (m)

totheight

double Total height of the tree (m)

height

double Height from the ground (where sample was taken) (m)

LMA

double Leaf mass per area (g m$^-2$)

narea

double Nitrogen per area (gN m$^-2$)

Source

Marshall, J.D., Monserud, R.A. 2003. Foliage height influences specific leaf area of three conifer species. Can J For Res 33:164-170

Examples

data(treecanopy)
if(require(ggplot2)){
 ggplot(treecanopy, aes(dfromtop,LMA,group=ID,col=species)) +
   geom_point() +
   stat_smooth(method="lm",se=FALSE) +
   theme_minimal()
}

Xylem vessel diameters

Description

Measurements of diameters of xylem (wood) vessels on a single Eucalyptus saligna tree grown at the Hawkesbury Forest Experiment.

Usage

vessel

Format

A data frame with 550 rows and 3 variables:

position

integer Either 'base' or 'apex' : the tree was sampled at stem base and near the top of the tree.

imagenr

integer At the stem base, six images were analyzed (and all vessels measured in that image). At apex, three images.

vesseldiam

double Diameter of individual water-conducting vessels (mu m).

Source

Sebastian Pfautsch

Weight loss data

Description

This dataset contains measurements of a Jeremy Zawodny over a period of about 3 months while he was trying to lose weight. This is an example of an irregular timeseries dataset (intervals between measurements vary).

Usage

weightloss

Format

A data frame with 67 rows and 2 variables:

Date

factor Date, d/m/yy

Weight

double Weight, in pounds

Source

<http://jeremy.zawodny.com/blog/archives/006851.html>

Mouse metabolism

Description

Wild mice were placed in a device where the metabolic rate (energy used by the animal) can be measured directly, and continuously. Measurements were made at varying temperature (15, 20 and 31C), mice were provided with food or not, and were able to exercise (with a treadmill) or not.

Usage

wildmousemetabolism

Format

A data frame with 864 rows and 9 variables:

id

integer Individual number

run

integer The experiment was repeated three times (run = 1,2,3)

day

integer Day of experiment (1-6)

temp

integer Temperature (deg C)

food

integer Whether food was provided ('Yes') or not ('No')

bm

double Body mass (g)

wheel

integer Whether the mouse could use an exercise wheel ('Yes') or not ('No')

rmr

double Resting metabolic rate (minimum rate of a running average over 12min) (kC hour-1)

sex

integer Male or Female

Source

Christopher Turbill