#+TITLE: An Org-mode Demo
#+AUTHOR: Eric Schulte
#+OPTIONS: num:nil ^:nil f:nil
#+STARTUP: hideblocks
#+BABEL: :session *R* :results silent

# some minor customization for nicer looking LaTeX output
#+begin_LaTeX
\hypersetup{
pdfborder={0 0 0 0}
}
\lstset{basicstyle=\ttfamily\bfseries\small}
#+end_LaTeX

#+begin_center
Adapted from /[[http://www.stat.umn.edu/~charlie/Sweave/foo.Rnw][An Sweave Demo]]/ by Charles J. Geyer.
#+end_center

This is a demo for using Org-babel to produce LaTeX documents with
embedded R code.  To get started fire up Emacs and create a text file
with the =.org= suffix.  You should see Org-mode become your major
mode -- denoted by =Org= in your status bar.

Press =C-c C-e= while viewing this Org-mode buffer and you will see a
menu appear with options for export to a variety target formats --
herein we'll only consider export to LaTeX.

So now we have a more complicated file chain
$$\begin{CD} \texttt{foo.org} @>\texttt{Sweave}>> \texttt{foo.tex} @>\texttt{latex}>> \texttt{foo.dvi} @>\texttt{xdvi}>> \text{view of document} \end{CD}$$
and what have we accomplished other than making it twice as annoying
as the WYSIWYG crows (having to use both =Org-mode= and =latex= to get
anything that looks like the document)?

Well, we can now include =R= in our document.  Here's a simple example
#+begin_src R :exports both
2 + 2
#+end_src
What I actually typed in =foo.org= was
: #+begin_src R :exports both
:   2 + 2
: #+end_src

This is a "code block" to be processed by Org-babel.  When Org-babel
hits such a thing, it processes it, runs R to get the results, and
stuffs the output in the LaTeX file it is creating.  The LaTeX between
code chunks is copied verbatim (except for in-line src code, about
which see below).  Hence to create a /active/ document you just write
plain old text interspersed with "code blocks" which are plain old R.

#+LaTeX: \pagebreak[3]

Plots get a little more complicated.  First we make something to plot
(simulated regression data).
#+source: reg
#+begin_src R :results output :exports both
n <- 50
x <- seq(1, n)
a.true <- 3
b.true <- 1.5
y.true <- a.true + b.true * x
s.true <- 17.3
y <- y.true + s.true * rnorm(n)
out1 <- lm(y ~ x)
summary(out1)
#+end_src
(for once we won't show the code chunk itself, look at =foo.org= if
you want to see what the actual code chunk was).

Figure \ref{fig:one} (p. \pageref{fig:one}) is produced by the following code
#+srcname: fig1plot
#+begin_src R :exports code
plot(x, y)
abline(out1)
#+end_src
Note that =x=, =y=, and =out1= are remembered from the preceding code
chunk.  We don't have to regenerate them.  All code chunks are part of
one R "session".
#+source: fig1
#+begin_src R :exports results :noweb yes :file fig1.pdf
<<fig1plot>>
#+end_src

#+attr_latex: width=0.8\textwidth,placement=[p]
#+label: fig:one
#+caption: Scatter Plot with Regression Line
#+results: fig1
[[file:fig1.pdf]]

Now this was a little tricky.  We did this with two code chunks,
one visible and one invisible.  First we did
: #+srcname: fig1plot
: #+begin_src R :exports code :file fig1plot.pdf
:   plot(x, y)
:   abline(out1)
: #+end_src
where the =:exports code= indicates that only the return value (not
code) should be exported and the =#+srcname: fig1plot= gives the code
block a name (to be used later).  And "later" is almost immediate.
Next we did
: #+source: fig1
: #+begin_src R :exports results :noweb yes :file fig1.pdf
:   <<fig1plot>>
: #+end_src

In this code block the =:file fig1.pdf= header argumentindicates that
the block generates a figure.  Org-babel automagically makes a PDF
file for the figure, and Org-mode handles the export to LaTeX.  The
=<<fig1plot>>= is an example of "code block reuse".  It means that we
reuse the code of the code chunk named =fig1plot=.  The =:exports
results= in the code block means just what it says (we've already seen
the code---it was produced by the preceding chunk---and we don't want
to see it again, we only want to see the results).  It is important
that we observe the DRY/SPOT rule (/don't repeat yourself/ or /single
point of truth/) and only have one bit of code for generating the
plot.  What the reader sees is guaranteed to be the code that made the
plot.  If we had used cut-and-paste, just repeating the code, the
duplicated code might get out of sync after edits.  The rest of this
should be recognizable to anyone who has ever done a LaTeX figure.

So making a figure is a bit more complicated in some ways, but much simpler
than others.  Note the following virtues
- The figure is guaranteed to be the one described by the text (at
least by the R in the text).
- No messing around with sizing or rotations.  It just works!

#+source: fig2
#+begin_src R :exports results :file fig2.pdf
out3 <- lm(y ~ x + I(x^2) + I(x^3))
plot(x, y)
#+end_src

Note that if you don't care to show the R code to make the figure, it
is simpler still.  Figure \ref{fig:two} shows another plot.  What I
actually typed in =foo.org= was
: #+srcname: fig2
: #+begin_src R :exports results :file fig2.pdf
:   out3 <- lm(y ~ x + I(x^2) + I(x^3))
:   plot(x, y)
:   curve(predict(out3, newdata=data.frame(x=x)), add = TRUE)
: #+end_src

#+attr_latex: width=0.8\textwidth,placement=[p]
#+label: fig:two
#+caption: Scatter Plot with Cubic Regression Curve
#+results: fig2
[[file:fig2.pdf]]

#+LaTeX: \pagebreak

Now we just excluded the code for the plot from the figure (with
=:exports results= so it doesn't show).

Also note that every time we re-export Figures \ref{fig:one}
and \ref{fig:two} change, the latter conspicuously (because the
simulated data are random).  Everything just works.  This should tell
you the main virtue of Org-babel.  It's always correct.  There is
never a problem with stale cut-and-paste.

#+begin_src R :exports none
options(scipen=10)
#+end_src

#+results:
: 0
Simple numbers can be plugged into the text with the =src_R= command,
for example, the quadratic and cubic regression coefficients in the
preceding regression were \beta_2 = src_R{round(out3$coef[3], 4)} and \beta_3 = src_R{round(out3$coef[4], 4)}.  Just magic!  What I actually typed
in =foo.org= was
: were \beta_2 = src_R{round(out3$coef[3], 4)} : and \beta_3 = src_R{round(out3$coef[4], 4)}
#+begin_src R :exports none
options(scipen=0)
#+end_src

The =xtable= command is used to make tables.  (The following is the
Org-babel output of another code block that we don't explicitly show.
Look at =foo.org= for details.)
#+begin_src R :exports both :results output
out2 <- lm(y ~ x + I(x^2))
foo <- anova(out1, out2, out3)
foo
#+end_src

#+begin_src R :exports both :results output
class(foo)
#+end_src

#+begin_src R :exports both :results output
dim(foo)
#+end_src

#+source: foo-as-matrix
#+begin_src R :exports both :results output
foo <- as.matrix(foo)
foo
#+end_src

#+LaTeX: \pagebreak

#+begin_src R :results output latex :exports results
library(xtable)
xtable(foo, caption = "ANOVA Table", label = "tab:one",
digits = c(0, 0, 2, 0, 2, 3, 3))
#+end_src

#+results: foo-as-matrix

So now we are ready to turn the matrix =foo= into Table \ref{tab:one}
using the R chunk
: #+begin_src R :results output latex :exports results
:   library(xtable)
:   xtable(foo, caption = "ANOVA Table", label = "tab:one",
:       digits = c(0, 0, 2, 0, 2, 3, 3))
: #+end_src

(note the difference between arguments to the =xtable= function and to
the =xtable= method of the =print= function)

To summarize, Org-babel is terrific, so important that soon we'll not
be able to get along without it.  Its virtues are
- The numbers and graphics you report are actually what they
are claimed to be.
- Your analysis is reproducible.  Even years later, when you've
completely forgotten what you did, the whole write-up, every single
number or pixel in a plot is reproducible.
- Your analysis actually works---at least in this particular instance.
The code you show actually executes without error.
- Toward the end of your work, with the write-up almost done you
discover an error.  Months of rework to do?  No!  Just fix the error
and rerun =Sweave= and =latex=.  One single problem like this and
you will have all the time invested in =Sweave= repaid.
- This methodology provides dicipline.  There's nothing that will make
you clean up your code like the prospect of actually revealing it to
the world.

Whether we're talking about homework, a consulting report, a textbook,
or a research paper.  If they involve computing and statistics, this
is the way to do it.