81 lines
3.2 KiB
R
81 lines
3.2 KiB
R
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require(rworldmap)
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require(ggplot2)
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theCountries <- c("DE", "US", "BR")
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# These are the ISO3 names of the countries you'd like to plot in red
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malDF <- data.frame(country = c("DE", "US", "BR", "ZA"), malaria = c(2000, 2001, 2002, 2002), news = c(2, 3, 0, 1))
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# malDF is a data.frame with the ISO3 country names plus a variable to
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# merge to the map data
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malMap <- joinCountryData2Map(malDF, joinCode = "ISO2", nameJoinColumn = "country")
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# This will join your malDF data.frame to the country map data
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mapCountryData(malMap, nameColumnToPlot="malaria", catMethod = "categorical", missingCountryCol = gray(.8))
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# And this will plot it, with the trick that the color palette's first
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# color is red
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# Absolute Frequ Newsfocus Map --------------------------------------------
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# # Absolute Häufigkeiten der Highlights mit Bubbles
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# malMap <- joinCountryData2Map(cl_supfoc_total, joinCode = "ISO2", nameJoinColumn = "code")
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# mapBubbles( dF=malMap, nameZSize="total",nameZColour="GEO3major",
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# colourPalette=c("black", "yellow", "blue", "orange", "red", "white", "green"),
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# oceanCol="lightblue",
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# landCol="wheat",
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# fill=TRUE,
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# symbolSize=0.5,
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# pch=21)
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# Absolute Newfokus Häufigkeiten Welt:
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absMap <- joinCountryData2Map(cl_supfoc_total, joinCode = "ISO2", nameJoinColumn = "code", verbose=TRUE)
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mapCountryData(absMap, nameColumnToPlot="total", catMethod="fixedWidth",
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numCats=5,
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mapTitle="Anzahl Überraschungsfokusse weltweit",
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oceanCol="lightblue",
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missingCountryCol=gray(.9)
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)
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# Absolute Newfokus Häufigkeiten EU-Asien-Nordafrika:
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mapCountryData(absMap, nameColumnToPlot="total", catMethod="fixedWidth",
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numCats=5,
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mapTitle="Anzahl Überraschungsfokusse Nordafrika und Asien",
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oceanCol="lightblue",
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missingCountryCol=gray(.9),
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xlim=c(10,140),
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ylim=c(30,70)
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)
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# Development Newsfocus over time -----------------------------------------
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# Entwicklung der Surprising Focuses über die Jahre
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cc <- ggplot(cl_supfoc_turn_mon, aes(month,highs))
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cc <- cc + geom_histogram(fill="steelblue", stat="identity")
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cc <- cc + stat_smooth(size=1,colour="red",method="loess", se=FALSE)
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cc <- cc + ggtitle("Zeitliche Entwicklung von plötzlichen Medienfokussen") + xlab("Einzelne Monate") + ylab("Plötzliche Medienfokusse")
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cc
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# Beispiel1: Jährliche Durchschnittsanzahl 2000-2014 der Nachrichten über Syrien
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yearspan <- 2000:2014
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avergdf <- getAverages(df = cl_stats, codecol = "code", code = "SY", yearspan = yearspan)
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averg <- ggplot(data = avergdf, aes(x = year, y=averg))
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averg + geom_line() + ggtitle("Durchschnittliche Nachrichten pro Jahr über Syrien") + xlab("Jahre") + ylab("Durchschnittliche Nachrichten")
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# Beispiel2: Jährliche Durchschnittsanzahl 2000-2014 der Nachrichten über Israel
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yearspan <- 2000:2014
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avergdf <- getAverages(df = cl_stats, codecol = "code", code = "IL", yearspan = yearspan)
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averg <- ggplot(data = avergdf, aes(x = year, y=averg))
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averg + geom_line() + ggtitle("Durchschnittliche Nachrichten pro Jahr über Israel") + xlab("Jahre") + ylab("Durchschnittliche Nachrichten")
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