R comes with one object called ts() which is useful for regularly spaced time series, such as daily, monthly, or yearly data (see help(ts) for more details). See "Time Series Analysis with R" for an example of how to work with this.

This is frequently insufficient for our purposes. As such, I will primarily use the zoo and xts packages on this blog. The other options are timeSeries (which is part of Rmetrics), its, or fts (from Whit Armstrong). I will touch on some of the differences along the way. You can find more about the time series package on the CRAN view.

zoo was created originally by Achim Zeileis in 2005, and it stands for "Zeileis's ordered observations", with many subsequent contributions from Gabor Grothendieck. One of the nice things about zoo is that it is an S3 class in R, and it works with most of the standard R matrix functions (such as summary, cbind, merge, and aggregate). Hence it has a relatively small learning curve and the authors put a lot of thought into making it just work as expected.

Here's a quick example creating a dummy multivariate time series, getting a summary of the output, and plotting it:

> x1 <- zoo(matrix(rnorm(12), nrow = 6), as.Date("2008-08-01") + 0:10)
> colnames (x1) <- c ("A", "B")
> summary(x1)
Index A B
Min. :2008-08-01 Min. :-1.6231 Min. :-1.3363
1st Qu.:2008-08-03 1st Qu.:-0.9867 1st Qu.:-0.7071
Median :2008-08-06 Median :-0.5078 Median :-0.5753
Mean :2008-08-06 Mean :-0.1310 Mean :-0.1270
3rd Qu.:2008-08-08 3rd Qu.: 0.6633 3rd Qu.: 0.6533
Max. :2008-08-11 Max. : 1.8866 Max. : 1.0704
> plot(x1)

Read the zoo vignette for more details.

It looks to like a multivariate time plot where the area above the lines is filled. My only thought is to use a mosaic chart (as in this post on the Learning R blog), but this was the best I could do with a little bit of effort.  I think that using geom_ribbon would be better but I couldn't get the colors to work.

Here's the code. Is there an easier way to do this? How can I make the axes more like the original? What about the white lines between boxes and the gradual change between years? The sort order is also different.

    library(XML)
    library(plyr)
    library(ggplot2)
    theurl <- "http://en.wikipedia.org/wiki/List_of_Crayola_crayon_colors"
    tables <- readHTMLTable(theurl)
    n.rows <- unlist(lapply(tables, function(t) dim(t)[1]))
    crayola <- tables[[which.max(n.rows)]]
    x <- crayola[,c("Hex Code", "Issued", "Retired")]
    colnames(x) <- c("color", "issued", "retired")
    for (i in 1:ncol(x)) x[, i] <- type.convert(as.character(x[, i]))
    x[is.na(x[,"retired"]), "retired"] <- 2010
    x$color <- as.character(x$color)

    years <- min(x$issued):max(x$retired, na.rm=T)
    x2 <- na.omit(ldply(years, function(yr, x) {
      idx <- x$issued <= yr & x$retired >= yr
      x2 <- data.frame(year=yr, color=x[idx,"color"], size=(1/length(which(idx))))
      x2 <- x2[order(x2$color, decreasing=TRUE),]
      x2[,"xmin"] <- rep(0, nrow(x2))
      x2[,"xmax"] <- rep(1, nrow(x2))
      x2[-1,"xmin"] <- cumsum(x2$size[-1])
      x2[-nrow(x2),"xmax"] <- cumsum(x2$size[-nrow(x2)])
      x2
    }, x=x))

    p <- ggplot(x2, aes(xmin = year, xmax = year+1, ymin = xmin, ymax = xmax, fill=color))
    p <- p + theme_bw() + opts(legend.position = "none", panel.grid.major = theme_line(colour = NA),
                panel.grid.minor = theme_line(colour = NA))
    p.rect <- p + geom_rect() + scale_fill_identity()
    p.rect

Further improvements

Well, the R community never ceases to amaze. I posted this and within hours a vastly improved version was created by the Learning R blog (with some help from Baptiste on the color sorting). All the code is posted on that site. A suggestion was also made by Tobias to smooth the image with Cairo. Great work!

One crucial difference in his version (besides the vastly cleaner code) is his use of geom_area instead of the geom_rect in my version. That also allows you to set a white border above the image.

I would go so far as to say that (with the exception of things like better fonts and other touch ups) this R version is actually better than the original because it is more accurate. As I said previously, there were no color changes early in the timeline, despite that implication in the original chart.