demo/cfplot_reg2.R

In migest: Methods for the Indirect Estimation of Bilateral Migration

message("Demo file no longer maintained. Please see https://guyabel.github.io/migest/reference/mig_chord.html for easier plotting of chord diagrams for migration data.")
##
## load packages
##
# # install.packages("tidyverse")
# library(tidyverse)
# library(migest)
# 
# # load data sets
# d <- read_csv(system.file("vidwp", "reg_flow.csv", package = "migest"))
# r <- read_csv(system.file("vidwp", "reg_plot.csv", package = "migest"))
# 
# # Estimated global flows for 2010-2015 from 
# #  Abel, G.J. (2016) Estimates of global bilateral migration flows by gender 
# #  between 1960 and 2015. Vienna Institute of Demography Working Papers 2/2016.
# d
# 
# # meta data for plot
# r
# 
# ##
# ## plot using mig_chord adaption of circlize::chordDiagram
# ##
# pdf(file = "chord_reg2.pdf")
# mig_chord(x = d, 
#           order = r$region,
#           grid.col = r %>% 
#             select(region, col1) %>%
#             deframe(), 
#           lab_bend1 = r %>%
#             select(region, reg1) %>%
#             deframe(),
#           lab_bend2 = r %>%
#             select(region, reg2) %>%
#             deframe())
#           # axis_breaks = 1)
# dev.off()
# file.show("chord_reg2.pdf")

# ##
# ## Original code, pre improvements in visualising migration data using
# ## migest::mig_chord. Uncomment to run.
# ##
# ##
# ##install packages if not already done so (uncomment)
# ##
# # install.packages("circlize")
# 
# library("circlize")
# 
# ##
# ##load data (df0: 2010-15 global flows, df1: meta data for plot)
# ##global flow data based on estimates from:
# ##
# #
# # Abel, G.J. (2016) Estimates of global bilateral migration glows by gender between 1960 and 2015. 
# # Vienna Institute of Demography Working Papers 2/2016.
# #
# 
# df0 <- read.csv(system.file("vidwp", "reg_flow.csv", package = "migest"), stringsAsFactors=FALSE)
# df1 <- read.csv(system.file("vidwp", "reg_plot.csv", package = "migest"), stringsAsFactors=FALSE)
# 
# ##
# ##default chord diagram
# ##
# 
# chordDiagram(x = df0)
# 
# ##
# ##plot parameters
# ##
# 
# circos.clear()
# circos.par(start.degree = 90, gap.degree = 4, track.margin = c(-0.1, 0.1), points.overflow.warning = FALSE)
# par(mar = rep(0, 4))
# 
# #increasing the gaps between sectors, start at 12 o'clock, ensure no gap between the chord and the sector at the beginning
# # subdue warning messages and have no margins in the plot
# 
# ##
# ##chord diagram with user selected adjustments for bilateral migration flow data
# ##
# 
# chordDiagram(x = df0 %>% select(1:3), grid.col = df1$col, transparency = 0.25,
#              order = df1$region, directional = 1,
#              direction.type = c("arrows", "diffHeight"), diffHeight  = -0.04,
#              annotationTrack = "grid", annotationTrackHeight = c(0.05, 0.1),
#              link.arr.type = "big.arrow", link.sort = TRUE, link.largest.ontop = TRUE)
# 
# # First line of arguments reads in the data (df0) and sets the colours base on the meta data (df1).
# # Second line provides the order of outer sectors and indicates that chords should be directional.
# # Third line indicates that the direction of the chords will be illustrated by both arrows and a difference in height. The
# #  height difference is negative to make the chord shorter at the end (with the arrow head).
# # Fourth line ensures the annotations outside the sectors are not plotted, but provides a track measures to later add 
# #  annotatinos such as labels and axis (see below).
# # Fifth line indicates the plot should use big arrows, sort the chords left to right in each sector and 
# #  plots the smallest chords first.
# 
# ##
# ##add in labels and axis
# ##
# 
# circos.trackPlotRegion(
#   track.index = 1, 
#   bg.border = NA, 
#   panel.fun = function(x, y) {
#     xlim = get.cell.meta.data("xlim")
#     sector.index = get.cell.meta.data("sector.index")
#     reg1 = df1$reg1[df1$region == sector.index]
#     reg2 = df1$reg2[df1$region == sector.index]
#     
#     circos.text(x = mean(xlim), y = ifelse(test = nchar(reg2) == 0, yes = 5.2, no = 6.0), 
#                 labels = reg1, facing = "bending", cex = 1.4)
#     circos.text(x = mean(xlim), y = 4.4, 
#                 labels = reg2, facing = "bending", cex = 1.4)
#     circos.axis(h = "top", 
#                 major.at = seq(from = 0, to = xlim[2], by = ifelse(test = xlim[2]>10, yes = 2, no = 1)), 
#                 minor.ticks = 1, major.tick.percentage = 0.5,
#                 labels.niceFacing = FALSE)
#   }
# )
# 
# # First line indicates that first track (rather than any other that may have been created) will be used.
# # Second line ensures no borders are plotted on the track.
# # Third line adds a track.
# # Fourth and fifth line collect individual track meta data from plot object.
# # Sixth and seventh line collect matching name information from plot data frame (df1).
# # The first circos.text adds text from (reg1) either at y = 6 (if there is a second part of the name in reg2) or 5.2.
# # The second circost.text adds text (reg2).
# # The circos.axis add axis with major and minor ticks, without flipping the axis labels in the bottom half.
# 
# ##
# ##Printing
# ##
# 
# # text(x = -1.1, y = -1, pos = 4, cex = 0.6, 
# #      labels = "Based on estimates from:")
# # text(x = -1.1, y = -1 - 1*0.03, pos = 4, cex = 0.6, 
# #      labels = expression(paste(
# #        plain(" Abel G.J. (2016) "), italic("Estimates of Global Bilateral Migration Flows by Gender")
# #      )))
# # text(x = -1.1, y = -1 - 2*0.03, pos = 4, cex = 0.6, 
# #      labels = expression(paste(
# #          italic(" between 1960 and 2015. "), plain("Vienna Institute of Demography Working Papers. 2/2016")
# #      )))
# # 
# # dev.copy2pdf(file = "cfplot_reg2.pdf", height=10, width=10)
# # file.show("./cfplot_reg2.pdf")
# #  
# # dev.print(png, file = "cfplot_reg2.png", height=10, width=10, units = "in", res=500)
# # file.show("cfplot_reg2.png")