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.

To put all efforts together, this is the new literate-scrap.inc (compare it with http://cran.r-project.org/contrib/extra/lyx/literate-scrap.inc):

Format 2

Preamble
\usepackage{Sweave}
\newenvironment{wrapsweave}{\par}{\par}
EndPreamble

OutputType              literate

Style Scrap
  LatexType             Environment
  LatexName             wrapsweave
  Margin                static
  TopSep                0.4
  BottomSep             0.4
  ParSep                0.0
  LabelType             static
  Align                 left
  AlignPossible         block, left, right, center
  NewLine               0
  PassThru              1
  FreeSpacing           1
  TextFont
    Color               latex
    Family              typewriter
  EndFont
End

Style ScrapCenter
  LatexType             Environment
  LatexName             center
  Margin                static
  TopSep                0.4
  BottomSep             0.4
  ParSep                0.0
  LabelType             static
  Align                 center
  NewLine               0
  PassThru              1
  FreeSpacing           1
  TextFont
    Color               latex
    Family              typewriter
  EndFont
End

Enjoy pressing a single Enter key in LyX now!

P.S. the side effect of this modification is, your R code will be separated by empty lines. But Sweave will remove blank lines by default, so that is not really a big trouble.

P.P.S. Next time I will write my solution of using pgfSweave in LyX — if you have never tried the pgfSweave package in R, I’d strongly recommend you do it right now!! My comment on this package is: amazingly beautiful! See a preview lyx-pgfsweave-demo.

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Basically the code deals with letter polygons (i.e. glyphs) and plot them with proper projections from 3D to 2D space:

## original code by Yixuan <yixuan.qiu@cos.name>, with my slight modification
h.x = c(0.193, 0.295, 0.295, 0.703, 0.703, 0.804, 0.804, 0.703, 0.703,
    0.295, 0.295, 0.193, 0.193)
h.y = c(0.935, 0.935, 0.575, 0.575, 0.935, 0.935, 0.063, 0.063, 0.475,
    0.475, 0.063, 0.063, 0.935)
a1.x = c(0.433, 0.546, 0.865, 0.746, 0.656, 0.328, 0.242, 0.136)
a1.y = c(0.935, 0.935, 0.063, 0.063, 0.326, 0.326, 0.063, 0.063)
a2.x = c(0.488, 0.629, 0.355)
a2.y = c(0.841, 0.418, 0.418)
y.x = c(0.136, 0.259, 0.496, 0.734, 0.853, 0.543, 0.543, 0.441, 0.441)
y.y = c(0.935, 0.935, 0.523, 0.935, 0.935, 0.431, 0.063, 0.063, 0.427)
n.x = c(0.189, 0.295, 0.707, 0.707, 0.804, 0.804, 0.701, 0.287, 0.287, 0.189)
n.y = c(0.935, 0.935, 0.248, 0.935, 0.935, 0.063, 0.063, 0.747, 0.063, 0.063)
e.x = c(0.222, 0.787, 0.787, 0.324, 0.324, 0.757, 0.757, 0.324, 0.324,
    0.804, 0.804, 0.222)
e.y = c(0.935, 0.935, 0.834, 0.834, 0.564, 0.564, 0.464, 0.464, 0.163,
    0.163, 0.063, 0.063)
w.x = c(0, 0.099, 0.254, 0.439, 0.564, 0.738, 0.902, 1, 0.789, 0.687,
    0.5, 0.31, 0.205)
w.y = c(0.935, 0.935, 0.187, 0.935, 0.935, 0.189, 0.935, 0.935, 0.063,
    0.063, 0.826, 0.063, 0.063)
p.x = c(0.218, 0.56, 0.56, 0.56, 0.56, 0.32, 0.32, 0.218, 0.32, 0.32)
p.y = c(0.935, 0.933, 0.832, 0.518, 0.418, 0.418, 0.063, 0.063, 0.518, 0.834)
p.cir.th = seq(pi/2, -pi/2, length.out = 50)
p1.cir.x = 0.56 + 0.2575 * cos(p.cir.th)
p1.cir.y = 0.6755 + 0.2575 * sin(p.cir.th)
p2.cir.x = 0.56 + 0.157 * cos(p.cir.th)
p2.cir.y = 0.6755 + 0.157 * sin(p.cir.th)
p1.x = c(0.218, p1.cir.x, 0.32, 0.32, 0.218)
p1.y = c(0.935, p1.cir.y, 0.418, 0.063, 0.063)
p2.x = c(0.32, p2.cir.x, 0.32)
p2.y = c(0.832, p2.cir.y, 0.518)
r1.cir.th = seq(pi/2, -7/18 * pi, length.out = 50)
r2.cir.th = seq(pi/2, -pi/2, length.out = 50)
r1.cir.x = 0.578 + 0.243 * cos(r1.cir.th)
r1.cir.y = 0.6955 + 0.243 * sin(r1.cir.th)
r2.cir.x = 0.578 + 0.145 * cos(r2.cir.th)
r2.cir.y = 0.6955 + 0.145 * sin(r2.cir.th)
r1.x = c(0.191, r1.cir.x, 0.877, 0.75, 0.54, 0.297, 0.297, 0.191)
r1.y = c(0.937, r1.cir.y, 0.059, 0.059, 0.448, 0.448, 0.059, 0.059)
r2.x = c(0.297, r2.cir.x, 0.297)
r2.y = c(0.841, r2.cir.y, 0.55)
draw.ch = function(x1, y1, x2 = NULL, y2 = NULL, center.x,
    color, alpha, xscale) {
    rgb.col = col2rgb(color)/255
    x1 = x1 * xscale + center.x - 0.5 * xscale
    x2 = x2 * xscale + center.x - 0.5 * xscale
    polygon(x1, y1, col = rgb(rgb.col[1], rgb.col[2], rgb.col[3],
        alpha = alpha), border = NA)
    polygon(x2, y2, col = "black", border = NA)
}
x1 = list(h.x, a1.x, p1.x, p1.x, y.x, n.x, e.x, w.x,
    y.x, e.x, a1.x, r1.x)
y1 = list(h.y, a1.y, p1.y, p1.y, y.y, n.y, e.y, w.y,
    y.y, e.y, a1.y, r1.y)
x2 = list(NULL, a2.x, p2.x, p2.x, NULL, NULL, NULL,
    NULL, NULL, NULL, a2.x, r2.x)
y2 = list(NULL, a2.y, p2.y, p2.y, NULL, NULL, NULL,
    NULL, NULL, NULL, a2.y, r2.y)
th = seq(pi/6, 2 * pi, length.out = 12)
cols = rainbow(200)
## if you don't want to save the animation into SWF,
## just comment the function saveSWF()
saveSWF({
    for (j in 1:238) {
        th = th - pi/120
        center.x = 3 + 5 * cos(th)
        cols = c(cols[-1], cols[1])
        alpha = 0.1 + (50 * (1 - sin(th)))/100
        alpha = ifelse(alpha > 1, 1, alpha)
        xscale = -sin(th) * 1.2
        plot(1, xlim = c(-2, 8), ylim = c(-2.5, 3.5), type = "n")
        plot.order = (1:12)[order(xscale > 0)]
        for (k in 1:12) {
            i = plot.order[k]
            draw.ch(x1[[i]], y1[[i]], x2[[i]], y2[[i]], center.x[i],
                color = cols[k + 60 * ( i >= 6 & i <= 8 ) + 120 *
                    (i > 8)], alpha[i], xscale[i])
        }
        text(8, -2.5, "Yixuan @ 2009-12-31 (http://yixuan.cos.name)",
            adj = c(1, 0), col = "white", cex = 0.8)

    }
}, interval = 0.05, dev = "pdf", para = list(bg = "black", mar = rep(0,
    4)), width = 7, height = 4)
## in the 'fun' package (currently on R-Forge),
## see demo('HappyNewYear2010Yixuan')

Thanks, Yixuan!

By the way, there are even more interesting demos in the fun package, just install the package by install.packages('fun', repos = 'http://r-forge.r-project.org') to see demo(package = 'fun')$results[, 'Item'].

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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

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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.

Here is an example:

library(animation)
oopt = ani.options(interval = 0.1, nmax = 100)
## brownian motion: note the 'loop' option and how to set graphics
#    parameters with 'ani.first'
saveLatex({
    brownian.motion(pch = 21, cex = 5, col = "red", bg = "yellow",
        main = "Demonstration of Brownian Motion")
}, ani.basename = "BM", ani.opts = "controls,loop,width=0.8\\textwidth",
    ani.first = par(mar = c(3, 3, 1, 0.5), mgp = c(2, 0.5, 0),
        tcl = -0.3, cex.axis = 0.8, cex.lab = 0.8, cex.main = 1),
    latex.filename = "brownian.motion.tex")
ani.options(oopt)

Download the demo: Brownian Motion in PDF (205K)

The PDF document will be automatically opened if there is nothing wrong with LaTeX and your PDF viewer; if nothing happened, you can find the PDF document brownian.motion.pdf in the directory ani.options("outdir").

The animation will work in Acrobat Reader 9.0, and I do not know if other PDF viewers can deal with JavaScript correctly (AFAIK, the default PDF viewer in Mac OS will not).

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1. swfDevice: R graphics device for SWF output (by Cameron Bracken)

This package is still at a pre-alpha stage but you can see a sketch now in R-Forge: https://r-forge.r-project.org/projects/swfdevice/

Its author, Cameron, certainly knows well that I will be excited to see it, because I’ve been waiting for a long long time for the REAL Flash animation output in R. What I’ve done in my animation package is simply using SWF Tools to combine several “static” pictures (PNG or PDF, …) into a naive Flash animation — by “naive” I mean there is no interaction or real dynamic stuff in the Flash animation. Hopefully Cameron will provide a useful tool to create genuine Flash animations directly from R (with the help of the library libming).

By the way, I have to mention that the tikzDevice package by Cameron and another author is also fantastic for generating high-quality graphics LaTeX.

2. RGoogleTrends: Query and download Google Trends data in R

Ever heard of Google Trends? Duncan Temple Lang released an R package named “RGoogleTrends” that allows you to download Google Trends data directly from R. Basically this package uses RCurl to log in your Google account and send queries to get Google Trends data. Well, you may ask “why bother using a package since I can manually download the data by myself?”; just imagine R can automatically and dynamically do it for you, so you don’t have to open the web page every day.

3. FlashMXML and SVGAnnotation: New graphics devices for R

They are also written by Duncan Temple Lang. FlashMXML can record R graphics in MXML (a kind of XML language) and we can compile the XML file to Flash output. SVGAnnotation enables us to save R graphics in SVG format, which also supports animation. The function animate() will be of great help to my animation package, I think.

You may check the Omegahat website for more interesting packages: http://www.omegahat.org

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> 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)

While these facts are revealing their great efforts in helping R users, we can see their work hours in committing revisions to R. For example, the answer to my question is clear in the graph below:

Prof Ripley Never Sleeps

## R code borrowed from Romain Francios
process_chunk <- function(txt) {
    if (length(txt) == 1L)
        return(NULL)
    header_line <- strsplit(txt[2L], " | ", fixed = TRUE)[[1]][c(1L,
        2L, 3L)]
    revision <- substring(header_line[1], 2)
    author <- header_line[2]
    if (author %in% c("apache", "root"))
        return(NULL)
    date <- substring(header_line[3], 1, 25)
    nlines <- length(date)
    matrix(c(rep.int(revision, nlines), rep.int(author, nlines),
        rep.int(date, nlines)), nrow = nlines)
}
data <- local({
    lines <- readLines("rsvn.log")
    index <- cumsum(grepl("^-+$", lines))
    commits <- split(lines, index)
    do.call(rbind, lapply(commits, process_chunk))
})
colnames(data) <- c("revision", "author", "date")
simple <- data[!duplicated(data[, "revision"]), ]
hour.data = data.frame(author = simple[, "author"],
      hour = as.integer(substr(simple[, "date"], 12, 13)), year = as.integer(substr(simple[,
          "date"], 1, 4)))
hour.data = subset(hour.data, year >= 1997 & (author %in%
    c("hornik", "maechler", "pd", "ripley")))
library(ggplot2)
# png("ripley-work-hour.png")
qplot(hour, data = subset(hour.data, author == "ripley"),
    main = "Does Prof Ripley Ever Sleep?") + stat_bin(binwidth = 1)
# dev.off()

Here I only selected four authors who have largest number of commits during 1997~2009. We can see the changes of working hours along these years:

Working hours of four R core members

hour.max = max(with(hour.data, table(author, year,
    hour)))
library(animation)
# you need ImageMagick to create the GIF animation!
saveMovie({
for (i in sort(unique(hour.data$year))) {
    print(qplot(hour, data = subset(hour.data, year == i), xlim = c(0,
        23), ylim = c(0, hour.max), main = i) + facet_wrap(~author) +
        stat_bin(binwidth = 1))
}
}, interval = 1.5, moviename = "r-core-work-hour", outdir = getwd())

The patterns are clear: Kurt does not like burning night oil; Martin tends to work very early in the morning (esp during 2000~2004); Peter always work at mid-night (highly centered around 12pm); and for Prof Ripley, he works round the clock but most in the morning (probably that’s when he begins to “Ripley” users? after that time, less people dare to report bugs so his work decays exponentially?)

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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); …

2. Data as Pure Text

A quick solution to the problem is to save your Excel data in a pure text format, e.g. CSV (comma-separated value) or tab-delimited. If you have ever thumbed through Dr Murrell’s book “Introduction to Data Technologies”, you probably know that the CSV format is NOT an Excel-specific format, although Windows users always find the Excel icon is associated with the *.csv files. Pure text is a ridiculously simple data format, but it’s amazing that there are still many people who do not know anything about it. The basic idea is to separate data columns with a delimiter (e.g. “,” or “;”) and rows with a usual line-break symbol (e.g. carriage-return, which can be different in Windows and Linux). In this case, we can identify all data values as we do in the spreadsheet. Here is an example with data in a spreadsheet:

Data in the Spreadsheet

If we save this data as a CSV file, and open it with a pure text editor (e.g. Notepad), we will see:

"","Sepal.Length","Sepal.Width","Petal.Length","Petal.Width","Species"
"1",5,3.2,1.2,0.2,"setosa"
"2",5.1,3.8,1.9,0.4,"setosa"
"3",5.1,3.3,1.7,0.5,"setosa"
"4",6.7,3.1,4.7,1.5,"versicolor"
"5",5.1,3.7,1.5,0.4,"setosa"
"6",5,3,1.6,0.2,"setosa"
"7",5.3,3.7,1.5,0.2,"setosa"
"8",5,3.4,1.6,0.4,"setosa"
"9",4.9,2.4,3.3,1,"versicolor"
"10",6.3,2.5,5,1.9,"virginica"

Or save as tab-delimited text:

""	"Sepal.Length"	"Sepal.Width"	"Petal.Length"	"Petal.Width"	"Species"
"1"	5	3.2	1.2	0.2	"setosa"
"2"	5.1	3.8	1.9	0.4	"setosa"
"3"	5.1	3.3	1.7	0.5	"setosa"
"4"	6.7	3.1	4.7	1.5	"versicolor"
"5"	5.1	3.7	1.5	0.4	"setosa"
"6"	5	3	1.6	0.2	"setosa"
"7"	5.3	3.7	1.5	0.2	"setosa"
"8"	5	3.4	1.6	0.4	"setosa"
"9"	4.9	2.4	3.3	1	"versicolor"
"10"	6.3	2.5	5	1.9	"virginica"

Then use read.table() or read.csv() in R to read these pure text files (as data.frames).

A hint for lazy users: you can also select all the data cells, copy it (into clipboard) and use read.table("clipboard") to get the data into R. In this case, what exists in your clipboard is the tab-delimited pure text.

3. What If I Insist on Using Excel

All right, you don’t bother to save the excel sheet into pure text and even don’t want to copy it into clipboard, then you can treat Excel files as databases, although they are indeed bad databases. You must guarantee that the data is “clean” and well-formatted, i.e. observations in each row and variables in each column (no merged cells, better no graphs). We can use the RODBC package to establish a connection to the Excel file, and execute SQL commands in the connection to make queries to data. Functions related to this task are odbcConnectExcel() or odbcConnectExcel2007() (again, Excel is stupid — they always change the standard in order that their products can be inconsistent). This is described in details in the manual R-data (“R Data Import/Export”).

As *.xls (or *.xlsx) is a binary format, never try to read.table("*.xls"). Meanwhile, read.xls() in the gdata package might be what you want if you are looking for the read.*-style R functions. [Thanks, Doug!]

In most cases, pure text format suffices to work, although it is ridiculously simple. Take a look at the “source code” and you will know everything. By the way, the extension of a file name is not that important: *.csv does not have to be a comma-separated text file, and *.doc can be something other than a Word document. It’s just a matter of convention. Again, open it and see what on earth is inside.

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1. find out where are the decimals in the character string;
2. format them;
3. replace the original decimals with formatted values;

Given a character vector, we can format the decimals with the code below:

# x: equations; FUN: formatting function; ...: passed to FUN
coefFormat = function(x, FUN, ...) {
    sapply(x, function(s) {
        dig = sapply(gregexpr("\\.[0-9]+", s), function(m) {
            sapply(seq(along = m), function(i) {
                substr(s, m[i], m[i] + attr(m, "match.length")[i] - 1)
            })
        })
        for (j in {
            if (is.null(dim(dig)))
                NULL
            else 1:dim(dig)[1]
        }) {
            s = sub(dig[j, 1], substring(FUN(as.numeric(dig[j, 1]), ...),
                2), s, fixed = TRUE)
        }
        s
    })
}

I used sapply() for 3 times to avoid explicit loops but consequently the code might be difficult to read. The critical part is the regular expression “\\.[0-9]+” which means one of more (controlled by “+” after “[0-9]”) digits (“[0-9]” or “[:digit:]”) after a decimal point “.”. As “.” is a metacharacter in regular expressions, we need to use a backslash before it, and again, “\” is a special character in R, so we need another backslash to denote a backslash.

x = readLines(zz <- textConnection(
"CC = 16.5547557654 + 0.0173022117998 * PP + 0.216234040485 * PP(-1) + 0.810182697599 * (WP + WG)

II = 20.2782089394 + 0.150221823899 * PP + 0.61594357734 * PP(-1) - 0.157787636546 * KK

WP = 1.50029688603 + C(10) * XX + 0.146673821502 * XX(-1) + 0.130395687204 * AA
"))
close(zz)

writeLines(coefFormat(x, round, digits = 3))
#  CC = 16.555 + 0.017 * PP + 0.216 * PP(-1) + 0.81 * (WP + WG)
#
#  II = 20.278 + 0.15 * PP + 0.616 * PP(-1) - 0.158 * KK
#
#  WP = 1.5 + C(10) * XX + 0.147 * XX(-1) + 0.13 * AA
#
writeLines(coefFormat(x, formatC, digits = 3, format = "f"))
#  CC = 16.555 + 0.017 * PP + 0.216 * PP(-1) + 0.810 * (WP + WG)
#  
#  II = 20.278 + 0.150 * PP + 0.616 * PP(-1) - 0.158 * KK
#  
#  WP = 1.500 + C(10) * XX + 0.147 * XX(-1) + 0.130 * AA
#

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It seems that the two results are equivalent, as H and T occurs with equal probability 0.5, so we naturally believe the average numbers of steps to HTH and HTT are the same, but the fact is not as we imagined.

## smart guys use math formulae to solve the problem,
## but *lazy* guys like me use simulations with R
coin.seq = function(v) {
    x = NULL
    n = 0
    while (!identical(x, v)) {
        x = append(x[length(x) - 1:0], rbinom(1, 1, 0.5))
        n = n + 1
    }
    n
}
set.seed(919)
mean(htt <- replicate(1e+05, coin.seq(c(1, 0, 0))))
# [1] 8.00304
mean(hth <- replicate(1e+05, coin.seq(c(1, 0, 1))))
# [1] 10.0062

png("coin-htt-hth.png", height = 150, width = 500)
par(mar = c(3, 2.5, 0.1, 0), mgp = c(2, 0.8, 0))
boxplot(list(HTT = htt, HTH = hth), horizontal = T,
    xlab = "n", ylim = range(boxplot(list(HTT = htt, HTH = hth),
        plot = FALSE)$stats))
points(c(mean(htt), mean(hth)), 1:2, pch = 19)
dev.off()

The answer is counterintuitive, isn’t it?

Number of times needed to get HTT and HTH (bold segments are for median; dots denote mean)

Well, mathematicians certainly do not like my solution (I guess they even hate such an imprecise approach). I hope some smart guys can give me some hints on working out the probability distribution and hence the expectation.

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Simulation of Burning Fire in R

And code here:

Fire <- function(row = 100, col = 100, time = 500, fade = 0.03) {
  fire <- matrix(0, col, row);
  fire[,1] <- runif(col);

  for (t in 1:time) {
    image(fire, col = rev(heat.colors(row)),
      axes = FALSE, main = "Welcome to COS Forum!");

    fire <- (
      fire +
      cbind(fire[,1], fire[c(col,1:(col-1)), 1:(row - 1)]) +
      cbind(fire[,1], fire[                , 1:(row - 1)]) +
      cbind(fire[,1], fire[c(2:col,1)      , 1:(row - 1)])
      ) / 4;
    fire <- cbind(fire[,1], (fire + fade / 5 - runif(1, max = fade))[,-1]);
    fire[fire < 0] <- 0;

    r <- runif(1);
    if (r < .1) fire[,1] <- fire[,1][c(2:col, 1)];
    if (r > .9) fire[,1] <- fire[,1][c(col, 1:(col-1))];
  };
  NULL;
}

The speed of drawing animation frames is rather slow in my computer, but it doesn’t matter since we can use the animation package (hey, you are advertising!) to save all the image frames and convert them to a single animation file.

library(animation)
# set row=50 instead of the default 100 to let the fire burn to the ceiling
# make sure you have installed the SWF Tools and the command 'png2swf' can
#    be executed (with or w/o it installation path); see ?saveSWF
saveSWF(Fire(50), interval = 0.05, dev = "png", outdir = getwd(),
    para = list(mar = c(0, 0, 2, 0)))

Now the animation is much more smooth than what we saw in R graphics window.

Thanks, awesome Linlin.

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