Refugees data

Description

Top 10 countries/territories of origin (excluding "Various") for period 2003-13 of UNHCR statistics on "Persons recognized as refugees under the 1951 UN Convention/1967 Protocol, the 1969 OAU Convention, in accordance with the UNHCR Statute, persons granted a complementary form of protection and those granted temporary protection."

Format

Data frame with the following columns:

country

Country or territory of origin

year

Year (2003-13)

refugees

Persons recognized as refugees under the 1951 UN Convention, etc..

Source

http://data.un.org/Data.aspx?d=UNHCR&f=indID%3aType-Ref

Alluvial diagram

Description

Drawing alluvial diagrams, also known as parallel set plots.

Usage

alluvial(..., freq, col = "gray", border = 0, layer, hide = FALSE,
  alpha = 0.5, gap.width = 0.05, xw = 0.1, cw = 0.1, blocks = TRUE,
  ordering = NULL, axis_labels = NULL, cex = par("cex"),
  cex.axis = par("cex.axis"))

Arguments

Value

Invisibly a list with elements:

Note

Please mind that the API is planned to change to be more compatible with dplyr verbs.

Examples

# Titanic data
tit <- as.data.frame(Titanic)

# 2d
tit2d <- aggregate( Freq ~ Class + Survived, data=tit, sum)
alluvial( tit2d[,1:2], freq=tit2d$Freq, xw=0.0, alpha=0.8,
         gap.width=0.1, col= "steelblue", border="white",
         layer = tit2d$Survived != "Yes" )

alluvial( tit2d[,1:2], freq=tit2d$Freq, 
         hide=tit2d$Freq < 150,
         xw=0.0, alpha=0.8,
         gap.width=0.1, col= "steelblue", border="white",
         layer = tit2d$Survived != "Yes" )

# 3d
tit3d <- aggregate( Freq ~ Class + Sex + Survived, data=tit, sum)

alluvial(tit3d[,1:3], freq=tit3d$Freq, alpha=1, xw=0.2,
         col=ifelse( tit3d$Survived == "No", "red", "gray"),
         layer = tit3d$Sex != "Female",
         border="white")


# 4d
alluvial( tit[,1:4], freq=tit$Freq, border=NA,
         hide = tit$Freq < quantile(tit$Freq, .50),
         col=ifelse( tit$Class == "3rd" & tit$Sex == "Male", "red", "gray") )

# 3d example with custom ordering
# Reorder "Sex" axis according to survival status
ord <- list(NULL, with(tit3d, order(Sex, Survived)), NULL)
alluvial(tit3d[,1:3], freq=tit3d$Freq, alpha=1, xw=0.2,
         col=ifelse( tit3d$Survived == "No", "red", "gray"),
         layer = tit3d$Sex != "Female",
         border="white", ordering=ord)

# Possible blocks options
for (blocks in c(TRUE, FALSE, "bookends")) {
    
    # Elaborate alluvial diagram from main examples file
    alluvial( tit[, 1:4], freq = tit$Freq, border = NA,
              hide = tit$Freq < quantile(tit$Freq, .50),
              col = ifelse( tit$Class == "3rd" & tit$Sex == "Male",
                            "red", "gray" ),
              blocks = blocks )
}


# Data returned
x <- alluvial( tit2d[,1:2], freq=tit2d$Freq, xw=0.0, alpha=0.8,
          gap.width=0.1, col= "steelblue", border="white",
          layer = tit2d$Survived != "Yes" )
points( rep(1, 16), x$endpoints[[1]], col="green")
points( rep(2, 16), x$endpoints[[2]], col="blue")

Alluvial diagram for multiple time series data

Description

This is a variant of alluvial diagram suitable for multiple (cross-sectional) time series. It also works with continuous variables equivalent to time

Usage

alluvial_ts(dat, wave = NA, ygap = 1, col = NA, alpha = NA,
  plotdir = "up", rankup = FALSE, lab.cex = 1, lab.col = "black",
  xmargin = 0.1, axis.col = "black", title = NA, title.cex = 1,
  axis.cex = 1, grid = FALSE, grid.col = "grey80", grid.lwd = 1,
  leg.mode = TRUE, leg.x = 0.1, leg.y = 0.9, leg.cex = 1,
  leg.col = "black", leg.lty = NA, leg.lwd = NA, leg.max = NA,
  xlab = NA, ylab = NA, xlab.pos = 2, ylab.pos = 1, lwd = 1, ...)

Arguments

Examples

if( require(reshape2) )
{
data(Refugees)
reshape2::dcast(Refugees, country ~ year, value.var = 'refugees')
d <- Refugees

set.seed(39) # for nice colours
cols <- hsv(h = sample(1:10/10), s = sample(3:12)/15, v = sample(3:12)/15)

alluvial_ts(d)
alluvial_ts(d, wave = .2, ygap = 5, lwd = 3)
alluvial_ts(d, wave = .3, ygap = 5, col = cols)
alluvial_ts(d, wave = .3, ygap = 5, col = cols, rankup = TRUE)
alluvial_ts(d, wave = .3, ygap = 5, col = cols, plotdir = 'down')
alluvial_ts(d, wave = .3, ygap = 5, col = cols, plotdir = 'centred', grid=TRUE,
            grid.lwd = 5)
alluvial_ts(d, wave =  0, ygap = 0, col = cols, alpha = .9, border = 'white',
            grid = TRUE, grid.lwd = 5)
alluvial_ts(d, wave = .3, ygap = 5, col = cols, xmargin = 0.4)
alluvial_ts(d, wave = .3, ygap = 5, col = cols, xmargin = 0.3, lab.cex = .7)
alluvial_ts(d, wave = .3, ygap = 5, col = cols, xmargin = 0.3, lab.cex=.7,
            leg.cex=.7, leg.col = 'white')
alluvial_ts(d, wave = .3, ygap = 5, col = cols, leg.mode = FALSE, leg.x = .1,
            leg.y = .7, leg.max = 3e6)
alluvial_ts(d, wave = .3, ygap = 5, col = cols, plotdir = 'centred', alpha=.9,
            grid = TRUE, grid.lwd = 5, xmargin = 0.2, lab.cex = .7, xlab = '',
            ylab = '', border = NA, axis.cex = .8, leg.cex = .7,
            leg.col='white', 
         title = "UNHCR-recognised refugees\nTop 10 countries (2003-13)\n")

# non time-series example - Virginia deaths dataset
d <- reshape2::melt(data.frame(age=row.names(VADeaths), VADeaths), id.vars='age')[,c(2,1,3)]
names(d) = c('pop_group','age_group','deaths')
alluvial_ts(d)
}