For a long time I’ve been wondering why we are not able to use Enter in the LyX Scrap environment which was set up by Gregor Gorjanc for Sweave. Two weeks ago, I (finally!) could not help asking Gregor about this issue, as I’m using “LyX + Sweave” more and more in my daily work. He explained it here: LyX-Sweave: mandatory use of control+enter in code chunks

After digging into the LyX customization manual for a while, I found a solution which allows us to press the Enter key just as we normally do when typing in a LyX document. The key is to use Environment instead of paragraph as LatexType for the style definition of Scrap. Besides, I used the LatexName as wrapsweave, as a LatexName is required by LyX. The definition for wrapsweave is simple: just two empty lines by \par. (If you define it as \newenvironment{wrapsweave}{}{}, you will run into troubles sometimes; especially when you use indent for paragraphs.)

As we know, LaTeX environment cannot be centered in LyX (only paragraphs can), so I defined a special environment ScrapCenter when I want to insert graphics via Sweave and make them center-aligned.

I have to admit that the previous post on Christmas is actually not much fun. Today I received another pResent from Yixuan which is more interesting:

Life should be fun. I saw a post in R-help list saying Merry Christmas to other useRs, and I followed up by some R code which can produce a naive animation like this:

Here is the code to generate the above Flash animation with shining Christmas:

library(animation)
saveSWF({
    n = length(speed <- runif(angle <- runif(x <- strsplit("MERRY CHRISTMAS",
        "")[[1]], 0, 360), 0, 15))
    for (j in 1:300) {
        angle = angle + speed
        plot.new()
        plot.window(c(1, n), c(0, 1))
        for (i in 1:n) text(i, 0.5, x[i], srt = angle[i], cex = runif(1,
            1, 4), col = sample(colors(), 1))
        text(n, 0, "Yihui @ 2009-12-24 (http://yihui.name)",
            adj = c(1, 0), col = "white", cex = 0.8)
    }
}, interval = 0.04, dev = "pdf", outdir = getwd(), para = list(mar = rep(0,
    4), bg = "black"), width = 8, height = 1)
## in animation package (>=1.1-0), see demo('Xmas')

There are other animation formats in the R package animation:

1. use saveMovie() to get a GIF animation (need ImageMagick)
2. ani.start() and ani.stop() can produce an HTML page with the animation in it
3. saveLatex() can embed an animation into a PDF document

Since animation 1.0-9, we will be able to create a PDF document with an animation embedded in it; the function is saveLatex(), and its usage is similar to saveMovie() and saveSWF(): you pass an R expression for creating animations to this function, and this expression will be evaluated in the function; the image frames get recorded by a graphics device. In the end, a LaTeX document is written in a directory, and we can get a PDF document by running pdflatex on the document.

In fact, the key point is the LaTeX package named animate, which can be used to insert image frames into a PDF document to generate an animation. The interface of animations created by this package is quite similar to the HTML animation page by the R package animation, moreover, it also uses JavaScript (in PDF) to animate the image frames.

Today Romain Francois posted an interesting topic in the R-help list, and you can read his blog post for more details: celebrating R commit #50000. 50000 is certainly not a small number; we do owe R core members a big “thank you” for their great efforts in this fantastic statistical language in the 13 years. When I saw Romain’s data, I suddenly remembered a question I asked to one of Prof Ripley’s student a couple of years ago: does Prof Ripley ever sleep? And he answered “No!”. No wonder we can see Prof Ripley so frequently in the R-help/devel mailing list. If you have stayed on R-help list for enough long time, you’ll surely know several facts, e.g. Martin Maechler will arrive in less than 3 minutes if you dare call an R package “library”, and you will get “Ripleyed” if you are not careful enough in posting your R code.

> library(fortunes)
> fortune("Ripleyed")

And the fear of getting Ripleyed on the mailing list also makes me think, read,
and improve before submitting half baked questions to the list.
 -- Eric Kort
 R-help (January 2006)

As Sir Francis Bacon said, “Histories make men wise; poets witty; the mathematics subtile; natural philosophy deep; moral grave; logic and rhetoric able to contend.” And Windows stupid.

He should have added the last sentence if he were a Windows user in this age.

1. Avoid Using M$ Excel

A lot of R users often ask this question: “How to import MS Excel data into R?” Well, my suggestion is, avoid using M$ Excel if you are a statistician (or going to be a statistician) because you just cannot imagine how messy Excel data can be: some cells might be merged, some are colored, some texts are bold, several data tables can be put everywhere (e.g. cell(1,1) to (10,4), and (17,3) to (25,9)), stupid bar plots and pie charts are inserted in the sheets, silly statistical procedures that are wrong forever… If you don’t trust my words (yes, I’m a nobody), just read the examples here: Problems with Excel (collected by Prof Harrell).

I know there are reasons for you to continue using Excel. Your boss required you to do so; you don’t have time to learn more about various data formats; everybody is using Excel, and you don’t want to be so cool to use R; or if you finish your tasks too quickly and accurately, your boss will doubt whether you have really spent time on working, hence you will get less money paid (this is a REAL story for me – though I didn’t get less payment, I was indeed doubted when I used R); …

Linlin Yan posted a cool (hot?) simulation of burning fire with R in the COS forum yesterday, which was indeed a warm welcome. I’m not sure whether our forum members will be scared by the “fire” under the title “Welcome to COS Forum”. The fire was mainly created by the function image() with carefully designed rows and columns in heated colors heat.colors(). Here is one of the pictures generated from his code:

Tag cloud is a bunch of words drawn in a graph with their sizes proportional to their frequency; it’s widely used in blogs to visualize tags. We can observe important words quickly from a tag cloud, as they often appear in large fontsize. Tony N. Brown asked how to “graphically represent frequency of words in a speech” the other day in R-help list, which is actually a problem about the tag cloud:

I recently saw a graph on television that displayed selected words/phrases in a speech scaled in size according to their frequency. So words/phrases that were often used appeared large and words that were rarely used appeared small. [...]

Marc Schwartz mentioned that Gorjanc Gregor has done some work years ago using R (in grid graphics). The obstacle of creating tag cloud in R, as Gorjanc wrote, lies in deciding the placement of words, and it would be much easier for other applications such as browsers to arrange the texts. That’s true — there have already been a lot of mature programs to deal with tag cloud. One of them is the wp-cumulus plugin for WordPress, which makes use of a Flash object to generate the tag cloud, and it has fantastic 3D rotation effect of the cloud.

1. Arranging text labels with pointLabel()

Before introducing how to port the plugin into R, I’d like to introduce an R function pointLabel() in maptools package and it can partially solve the problem of arranging text labels in a plot (using simulated annealing or genetic algorithm). Here is a simulated example:

After a few hours’ work, I modified the function tidy.source() in the animation package so that it can preserve complete comment lines. See the tidy.source() wiki page for example.

Downdload the R code here

tidy.source <- function(source = "clipboard", keep.comment = TRUE,
  keep.blank.line = FALSE, begin.comment, end.comment, ...) {
  # parse and deparse the code
  tidy.block = function(block.text) {
      exprs = parse(text = block.text)
      n = length(exprs)
      res = character(n)
      for (i in 1:n) {
        dep = paste(deparse(exprs[i]), collapse = "\n")
        res[i] = substring(dep, 12, nchar(dep) - 1)
      }
      return(res)
  }
  text.lines = readLines(source, warn = FALSE)
  if (keep.comment) {
      # identifier for comments
      identifier = function() paste(sample(LETTERS), collapse = "")
      if (missing(begin.comment))
        begin.comment = identifier()
      if (missing(end.comment))
        end.comment = identifier()
      # remove leading and trailing white spaces
      text.lines = gsub("^[[:space:]]+|[[:space:]]+$", "",
        text.lines)
      # make sure the identifiers are not in the code
      # or the original code might be modified
      while (length(grep(sprintf("%s|%s", begin.comment, end.comment),
        text.lines))) {
        begin.comment = identifier()
        end.comment = identifier()
      }
      head.comment = substring(text.lines, 1, 1) == "#"
      # add identifiers to comment lines to cheat R parser
      if (any(head.comment)) {
        text.lines[head.comment] = gsub("\"", "\'", text.lines[head.comment])
        text.lines[head.comment] = sprintf("%s=\"%s%s\"",
          begin.comment, text.lines[head.comment], end.comment)
      }
      # keep blank lines?
      blank.line = text.lines == ""
      if (any(blank.line) & keep.blank.line)
        text.lines[blank.line] = sprintf("%s=\"%s\"", begin.comment,
          end.comment)
      text.tidy = tidy.block(text.lines)
      # remove the identifiers
      text.tidy = gsub(sprintf("%s = \"|%s\"", begin.comment,
        end.comment), "", text.tidy)
  }
  else {
      text.tidy = tidy.block(text.lines)
  }
  cat(paste(text.tidy, collapse = "\n"), "\n", ...)
  invisible(text.tidy)
}

Note that inline comments will still be removed. I don’t want to spend more time on dealing with inline comments any more.

I was surprised to find the density estimation of a constant was also “bell-shaped” by default when a friend passed some R code to me to illustrate CLT, but I realized the reason soon.

Downdload the R code here

# png(width = 500, height = 300)
x = rep(0, 1000)
par(mfrow = c(1, 2), mar = c(4, 4, 0.1, 0.1))
plot(density(x), main = "")
plot(density(x), main = "")
rug(jitter(x))
# dev.off()

Note that I added a rug (jittered) to the right plot to tell you the true locations of the data points.