Penultimate post, I'm going to take a quick look at the Gini indicator for wealth inequality. Data comes from the World Bank.

I've downloaded the zipped file, decompressed it and given it a different name. I'm going to

dfGini = read.csv("./Data/Gini.csv", stringsAsFactors = FALSE, skip = 2)
colnames(dfGini) = gsub("X", "", colnames(dfGini))
library(reshape2)
mdf = melt(dfGini, id.vars = colnames(dfGini)[1:4])
mdf = mdf[!is.na(mdf$value), ]
colnames(mdf)[1:2] = c("Name", "Code")
library(plyr)
dfCount = ddply(mdf, "Code", .fun = nrow)
atLeast5 = dfCount$Code[dfCount$V1 >= 5]
mdf = mdf[mdf$Code %in% atLeast5, ]

This will give us a decent set of data. How does this look when we plot it?

library(ggplot2)
ggplot(mdf, aes(x = variable, y = value, group = Code)) + geom_line()

Ugh. That looks like nothing. It's a bit late and I'm not all that keen for insight. What countries have had the biggest reduction in income inequality?

dfMove = ddply(mdf, "Code", summarize, Diff = max(value) - min(value))
dfMove = dfMove[order(dfMove$Diff, decreasing = TRUE), ]
bigMoves = dfMove$Code[1:5]

ggplot(mdf[mdf$Code %in% bigMoves, ], aes(x = variable, y = value, group = Code)) + 
    geom_line()

That's better, but still not very informative. I'm sleepy and going to bed.

sessionInfo

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##     z$locale <- Sys.getlocale()
##     if (is.null(package)) {
##         package <- grep("^package:", search(), value = TRUE)
##         keep <- sapply(package, function(x) x == "package:base" || 
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##         package <- sub("^package:", "", package[keep])
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##     pkgDesc <- lapply(package, packageDescription, encoding = NA)
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##     loadedOnly <- loadedNamespaces()
##     loadedOnly <- loadedOnly[!(loadedOnly %in% package)]
##     if (length(loadedOnly)) {
##         names(loadedOnly) <- loadedOnly
##         pkgDesc <- c(pkgDesc, lapply(loadedOnly, packageDescription))
##         z$loadedOnly <- pkgDesc[loadedOnly]
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##     class(z) <- "sessionInfo"
##     z
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## <bytecode: 0x0000000012974858>
## <environment: namespace:utils>