politicalKnowledge | R Documentation |
Data from McChesney and Nichols (2010) on domestic and international knowledge in Denmark, Finland, the UK and the US among college graduates, people with some college, and roughly 12th grade only.
data(politicalKnowledge)
A data.frame
containing 12 columns and 4 rows.
a character vector of Denmark, Finland, UK, and US, being the four countries compared in this data set.
percent correct answers to calibrated questions
regarding knowledge of prominent items in domestic
news in a survey of residents of the four
countries among college graduates (ending
".c
"), some college (".sc
") and
high school (".hs
"). Source: McChesney
and Nichols (2010, chapter 1, chart 8).
percent correct answers to calibrated
questions regarding knowledge of
prominent items in international news
in a survey of residents of the four
countries by education level as for
DomesticKnowledge
. Source:
McChesney and Nichols (2010, chapter 1,
chart 7).
average of domestic and international knowledge
Per capital spending on public media in 2007 in US dollars from McChesney and Nichols (2010, chapter 4, chart 1)
Spending on public media relative to the US, being
PublicMediaPerCapita / PublicMediaPerCapita[4]
.
Spencer Graves
Robert W. McChesney and John Nichols (2010) The Death and Life of American Journalism (Nation Books)
## ## 1. Combine first 2 rows ## data(politicalKnowledge) pk <- politicalKnowledge[-1,] pk[1, -1] <- ((politicalKnowledge[1, -1] + politicalKnowledge[2, -1])/2) pk[1, 'country'] <- 'DK-FI' ## ## 2. plot ## xlim <- range(pk[, 'PublicMediaPerCapita']) ylim <- 100*range(pk[2:7]) text.cex <- 2 # to label the lines (US.UK <- (pk[2, -1]+pk[3, -1])/2) #png('Knowledge v. public media.png') op <- par(mar=c(5, 7, 4, 2)+.1) plot(c(0, 110), 100*ylim, type='n', axes=FALSE, xlab='public media $ per capita', ylab='Political Knowledge\n(% of standard questions)', cex.lab=2) axis(1, cex.axis=2) axis(2, las=2, cex.axis=2) with(pk, text(PublicMediaPerCapita, 100*PoliticalKnowledge.hs, country, cex=text.cex, xpd=NA, col=c('forestgreen', 'orange', 'red'))) with(pk, text(PublicMediaPerCapita, 100*PoliticalKnowledge.sc, country, cex=text.cex, xpd=NA, col=c('forestgreen', 'orange', 'red'))) with(pk, text(PublicMediaPerCapita, 100*PoliticalKnowledge.c, country, cex=text.cex, xpd=NA, col=c('forestgreen', 'orange', 'red'))) with(pk, lines(PublicMediaPerCapita, 100*PoliticalKnowledge.hs, type='b', pch=' ')) with(pk, lines(PublicMediaPerCapita, 100*PoliticalKnowledge.sc, type='b', pch=' ')) with(pk, lines(PublicMediaPerCapita, 100*PoliticalKnowledge.c, type='b', pch=' ')) with(US.UK, text(PublicMediaPerCapita, 100*PoliticalKnowledge.hs, 'High School\nor less', srt=37, cex=1.5)) with(US.UK, text(PublicMediaPerCapita, 100*PoliticalKnowledge.sc, 'some\ncollege', srt=10.5, cex=1.5)) with(US.UK, text(PublicMediaPerCapita, 100*PoliticalKnowledge.c, "Bachelor's\nor more", srt=-1, cex=1.5)) par(op) #dev.off() ## ## redo for Wikimedia commons ## without English axis labels ## to facilitate multilingual use ## #svg('Knowledge v. public media.svg') op <- par(mar=c(3,3,2,2)+.1) plot(c(0, 110), 100*ylim, type='n', axes=FALSE, xlab='', ylab='', cex.lab=2) axis(1, cex.axis=2) axis(2, las=2, cex.axis=2) with(pk, text(PublicMediaPerCapita, 100*PoliticalKnowledge.hs, country, cex=text.cex, xpd=NA, col=c('forestgreen', 'orange', 'red'))) with(pk, text(PublicMediaPerCapita, 100*PoliticalKnowledge.sc, country, cex=text.cex, xpd=NA, col=c('forestgreen', 'orange', 'red'))) with(pk, text(PublicMediaPerCapita, 100*PoliticalKnowledge.c, country, cex=text.cex, xpd=NA, col=c('forestgreen', 'orange', 'red'))) with(pk, lines(PublicMediaPerCapita, 100*PoliticalKnowledge.hs, type='b', pch=' ')) with(pk, lines(PublicMediaPerCapita, 100*PoliticalKnowledge.sc, type='b', pch=' ')) with(pk, lines(PublicMediaPerCapita, 100*PoliticalKnowledge.c, type='b', pch=' ')) par(op) #dev.off()
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