Compute the growth rate of Unicode.

unicode-growth-rate.R

#! /usr/bin/Rscript

suppressPackageStartupMessages({
    library(ggplot2)
    library(scales)
    library(grid)
})


# Data from https://en.wikipedia.org/wiki/Unicode#Versions
# as of 29 Oct 2015
ugrowth.d <- data.frame(
    date=as.Date(c(
        "1991-10-01", "1992-06-01", "1993-06-01", "1996-07-01", "1998-05-01",
        "1999-09-01", "2001-03-01", "2002-03-01", "2003-04-01", "2005-03-01",
        "2006-07-01", "2008-04-01", "2009-10-01", "2010-10-01", "2012-01-01",
        "2012-09-01", "2013-09-01", "2014-06-01", "2015-06-01")),
    nchars=c(
          7161,        28359,       34233,         38950,        38952,
         49259,        94205,       95221,         96447,        97720,
         99089,       100713,      107361,        109449,       110181,
        110182,       110187,      113021,        120737),
    what=factor(c("Actual"))
)
# The last large jump in the number of assigned code points:
mat.cutoff <- as.Date("2000-01-01")
# The total number of *publicly assignable* code points.
# There are 1,114,112 code points reachable by UTF-16, of which 66 are
# permanently unassigned, 2048 are surrogates, and 137,468 are
# reserved for private use.
max.codepoint <- 974530
# Similarly, for the BMP, there are 65,536 code points, of which
# 34 are permanently unassigned, 2048 are surrogates, and 6400 are
# reserved for private use.
max.bmp <- 57054
# Supplemental planes all consist of 65,536 code points of which
# 2 are permanently unassigned.  At present there are two all-PUA
# planes; this calculation is included in the above.
max.sup <- 65534

m.all <- lm(nchars ~ date, ugrowth.d)
m.mat <- lm(nchars ~ date, subset(ugrowth.d, date > mat.cutoff))

p.dates <- data.frame(date=seq.Date(as.Date("1991-10-01"),
                                    as.Date("2991-10-01"), by="10 years"))
p.mdate <- subset(p.dates, date > mat.cutoff)

p.all <- as.data.frame(predict(m.all, new=p.dates, interval="prediction"))
p.mat <- as.data.frame(predict(m.mat, new=p.mdate, interval="prediction"))

colnames(p.all) <- c("nchars", "lb", "ub")
colnames(p.mat) <- c("nchars", "lb", "ub")

p.all$what <- factor(c("Predicted (all)"))
p.mat$what <- factor(c("Predicted (since 2000)"))

ugrowth.p <- rbind(cbind(p.dates, p.all), cbind(p.mdate, p.mat))

baseplot <-
    ggplot() +
    geom_point(aes(x=date, y=nchars, colour=what, fill=what), data=ugrowth.d) +
    geom_smooth(aes(x=date, y=nchars, ymin=lb, ymax=ub, colour=what, fill=what),
                data=ugrowth.p, stat="identity") +
    scale_colour_manual(values=c("#000000", "#66c2a5", "#8da0cb")) +
    scale_fill_manual(values=c("#000000", "#66c2a5", "#8da0cb")) +
    theme(legend.title=element_blank(),
          legend.background=element_rect(fill="#f8f8f8"),
          legend.position=c(0, 1),
          legend.justification=c("left", "top"))

fullplot <-
    baseplot +
    scale_y_continuous("Assigned codepoints", expand=c(0,0),
                       breaks=c(0, max.bmp, max.bmp + (1:14)*max.sup,
                                max.codepoint)) +
    scale_x_date("Year", labels=date_format("%Y"), expand=c(0.01,0),
                 limits=c(as.Date("1991-10-01"), as.Date("2541-10-01")),
                 breaks=seq.Date(as.Date("1991-10-01"),
                                 as.Date("2541-10-01"),
                                 length.out=6)) +
    coord_cartesian(ylim=c(-max.sup/4, max.codepoint + max.sup/4))

insetplot <-
    baseplot +
    scale_y_continuous("Assigned codepoints", expand=c(0,0),
                       breaks=c(0, max.bmp/2, max.bmp,
                                max.bmp + max.sup/2, max.bmp+max.sup)) +
    scale_x_date("Year", labels=date_format("%Y"), expand=c(0.01,0),
                 limits=c(as.Date("1991-10-01"), as.Date("2021-10-01")),
                 breaks=seq.Date(as.Date("1991-10-01"),
                                 as.Date("2021-10-01"),
                                 length.out=6)) +
    coord_cartesian(ylim=c(-max.sup/12, max.bmp + max.sup + max.sup/12)) +
    theme(legend.position="none",
          axis.title=element_blank())

# annotation_custom ... just doesn't work, and I don't understand why.
# We do it the hard way instead.

png("unicode-growth-rate.png", width=1900, height=1100, res=96)
grid.newpage()
grid.draw(ggplotGrob(fullplot))
pushViewport(viewport(x=0.98, y=0.06, w=0.38, h=0.36,
                      just=c("right", "bottom")))
grid.draw(ggplotGrob(insetplot))
popViewport()
invisible(dev.off())

😁 Nice!