R Dataset / Package pscl / AustralianElections

How To Create a Barplot

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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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How To Create a Plot

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How to Compute the Median

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Boxplot

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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

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Pie Chart

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Plot

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Ratio

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Regression

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Stem and Leaf Plots

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Visual Summaries

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<iframe src="https://embed.picostat.com/r-dataset-package-pscl-australianelections.html" frameBorder="0" width="100%" height="307px" />
Attachment Size
dataset-10270.csv 1.98 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 AustralianElections data set which pertains to elections to Australian House ofRepresentatives, 1949-2007. The AustralianElections data set is found in the pscl R package. Try to load the AustralianElections data set in R by issuing the following command at the console data("AustralianElections"). This may load the data into a variable called AustralianElections. If R says the AustralianElections data set is not found, you can try installing the package by issuing this command install.packages("pscl") and then attempt to reload the data with library("pscl") followed by data("AustralianElections"). Perhaps strangley, if R gives you no output after entering a command, it means the command succeeded. If it succeeded you can see the data by typing AustralianElections at the command-line which should display the entire dataset.

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 AustralianElections R data set. The size of this file is about 2,030 bytes.


elections to Australian House of Representatives, 1949-2007

Description

Aggregate data on the 24 elections to Australia's House of Representatives, 1949 to 2007.

Usage

data(AustralianElections)

Format

A data frame with the following variables:

date

date of election, stored using the Date class

Seats

numeric, number of seats in the House of Representatives

Uncontested

numeric, number of uncontested seats

ALPSeats

numeric, number of seats won by the Australian Labor Party

LPSeats

numeric, number of seats won by the Liberal Party

NPSeats

numeric, number of seats won by the National Party (previously known as the Country Party)

OtherSeats

numeric, number of seats won by other parties and/or independent candidates

ALP

numeric, percentage of first preference votes cast for Australian Labor Party candidates

ALP2PP

numeric, percentage of the two-party preferred vote won by Australian Labor Party candidates

LP

numeric, percent of first preference votes cast for Liberal Party candidates

NP

numeric, percent of first preference votes cast for National Party (Country Party) candidates

DLP

numeric, percent of first preference votes cast for Democratic Labor Party candidates

Dem

numeric, percent of first preference votes cast for Australian Democrat candidates

Green

numeric, percent of first preference votes cast for Green Party candidates

Hanson

numeric, percent of first preference votes cast for candidates from Pauline Hanson's One Nation party

Com

numeric, percent of first preference votes cast for Communist Party candidates

AP

numeric, percent of first preference votes cast for Australia Party candidates

Informal

numeric, percent of ballots cast that are spoiled, blank, or otherwise uncountable (usually because of errors in enumerating preferences)

Turnout

numeric, percent of enrolled voters recorded as having turned out to vote (Australia has compulsory voting)

Source

Australian Electoral Commission. http://www.aec.gov.au.

References

Jackman, Simon. 2009. Bayesian Analysis for the Social Sciences. Wiley: Hoboken, New Jersey. Example 3.5.

Examples

data(AustralianElections)
attach(AustralianElections)
alpSeatShare <- ALPSeats/Seats
alpVoteShare <- ALP2PP/100## log-odds transforms
x <- log(alpVoteShare/(1-alpVoteShare))
y <- log(alpSeatShare/(1-alpSeatShare))ols <- lm(y~x)   ## Tufte-style seats-votes regressionxseq <- seq(-4.5,4.5,length=500)
yhat <- coef(ols)[1] + coef(ols)[2]*xseq
yhat <- exp(yhat)/(1+exp(yhat))
xseq <- exp(xseq)/(1+exp(xseq))## seats vote curve
plot(x=alpVoteShare,
     y=alpSeatShare,
     xlab="ALP Vote Share",
     ylab="ALP Seat Share")
lines(xseq,yhat,lwd=2)
abline(h=.5,lty=2)
abline(v=.5,lty=2)

--

Dataset imported from https://www.r-project.org.
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