R Dataset / Package DAAG / possum

How To Create a Barplot

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Visual
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How To Create a Stacked Barplot

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How To Create a Pie Chart

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How To Compute the Mean

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Numerical
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How To Create a Plot

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How to Compute the Median

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Numerical

Boxplot

Submitted by pmagunia on April 22, 2018 - 3:07 PM
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Correlation Coefficient

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Cumulative Frequency Histogram

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Dotplot

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Hollow Histogram

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Numerical Summaries

Submitted by pmagunia on April 22, 2018 - 2:51 PM
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Pie Chart

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Plot

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Regression

Submitted by pmagunia on April 22, 2018 - 3:12 PM

Stem and Leaf Plots

Submitted by pmagunia on April 22, 2018 - 3:12 PM
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Visual Summaries

Submitted by pmagunia on April 22, 2018 - 3:13 PM
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Submitted by pmagunia on March 9, 2018 - 1:06 PM
<iframe src="https://embed.picostat.com/r-dataset-package-daag-possum.html" frameBorder="0" width="100%" height="307px" />
Attachment Size
dataset-24907.csv 6.25 KB
Dataset License
GNU General Public License v2.0
Documentation License
GNU General Public License v2.0
Documentation

On this Picostat.com statistics page, you will find information about the possum data set which pertains to Possum Measurements. The possum data set is found in the DAAG R package. You can load the possum data set in R by issuing the following command at the console data("possum"). This will load the data into a variable called possum. If R says the possum data set is not found, you can try installing the package by issuing this command install.packages("DAAG") and then attempt to reload the data. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the possum R data set. The size of this file is about 6,399 bytes.


Possum Measurements

Description

The possum data frame consists of nine morphometric measurements on each of 104 mountain brushtail possums, trapped at seven sites from Southern Victoria to central Queensland.

Usage

possum

Format

This data frame contains the following columns:

case

observation number

site

one of seven locations where possums were trapped

Pop

a factor which classifies the sites as Vic Victoria, other New South Wales or Queensland

sex

a factor with levels f female, m male

age

age

hdlngth

head length

skullw

skull width

totlngth

total length

taill

tail length

footlgth

foot length

earconch

ear conch length

eye

distance from medial canthus to lateral canthus of right eye

chest

chest girth (in cm)

belly

belly girth (in cm)

Source

Lindenmayer, D. B., Viggers, K. L., Cunningham, R. B., and Donnelly, C. F. 1995. Morphological variation among columns of the mountain brushtail possum, Trichosurus caninus Ogilby (Phalangeridae: Marsupiala). Australian Journal of Zoology 43: 449-458.

Examples

boxplot(earconch~sex, data=possum)
pause()sex <- as.integer(possum$sex)
oldpar <- par(oma=c(2,4,5,4))
pairs(possum[, c(9:11)], pch=c(0,2:7), col=c("red","blue"),
  labels=c("tail\nlength","foot\nlength","ear conch\nlength"))
chh <- par()$cxy[2]; xleg <- 0.05; yleg <- 1.04
oldpar <- par(xpd=TRUE)  
legend(xleg, yleg, c("Cambarville", "Bellbird", "Whian Whian  ",
  "Byrangery", "Conondale  ","Allyn River", "Bulburin"), pch=c(0,2:7),
  x.intersp=1, y.intersp=0.75, cex=0.8, xjust=0, bty="n", ncol=4)
text(x=0.2, y=yleg - 2.25*chh, "female", col="red", cex=0.8, bty="n")
text(x=0.75, y=yleg - 2.25*chh, "male", col="blue", cex=0.8, bty="n")
par(oldpar)
pause()sapply(possum[,6:14], function(x)max(x,na.rm=TRUE)/min(x,na.rm=TRUE))
pause()here <- na.omit(possum$footlgth)
possum.prc <- princomp(possum[here, 6:14])
pause()plot(possum.prc$scores[,1] ~ possum.prc$scores[,2],
  col=c("red","blue")[as.numeric(possum$sex[here])],
  pch=c(0,2:7)[possum$site[here]], xlab = "PC1", ylab = "PC2")
  # NB: We have abbreviated the axis titles
chh <- par()$cxy[2]; xleg <- -15; yleg <- 20.5
oldpar <- par(xpd=TRUE)
legend(xleg, yleg, c("Cambarville", "Bellbird", "Whian Whian  ",
  "Byrangery", "Conondale  ","Allyn River", "Bulburin"), pch=c(0,2:7),
  x.intersp=1, y.intersp=0.75, cex=0.8, xjust=0, bty="n", ncol=4)
text(x=-9, y=yleg - 2.25*chh, "female", col="red", cex=0.8, bty="n")
summary(possum.prc, loadings=TRUE, digits=2)
par(oldpar)
pause()require(MASS)
here <- !is.na(possum$footlgth)
possum.lda <- lda(site ~ hdlngth+skullw+totlngth+ taill+footlgth+
  earconch+eye+chest+belly, data=possum, subset=here)
options(digits=4)
possum.lda$svd   # Examine the singular values   
plot(possum.lda, dimen=3)
  # Scatterplot matrix - scores on 1st 3 canonical variates (Figure 11.4)
possum.lda

--

Dataset imported from https://www.r-project.org.
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R Output Date
Picostat Output - Simple Linear Regression Apr 9, 2020
Picostat Output - Simple Linear Regression Apr 9, 2020
Picostat Output - Simple Linear Regression Apr 9, 2020
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