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c2f48af02e33cf3438be65f47ee492df
mooc-rr
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6bc83479
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6bc83479
authored
Oct 14, 2020
by
boulanlo
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Module 2 exercise 1
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module2/exo1/toy_document_orgmode_R_en.org
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6bc83479
#+TITLE:
Your title
#+TITLE:
On the computation of pi
#+AUTHOR:
Your name
#+AUTHOR:
Louis Boulanger
#+DATE:
Today's date
#+DATE:
October 13th 2020
#+LANGUAGE: en
#+LANGUAGE: en
# #+PROPERTY: header-args :eval never-export
# #+PROPERTY: header-args :eval never-export
...
@@ -11,71 +11,55 @@
...
@@ -11,71 +11,55 @@
#+HTML_HEAD: <script type="text/javascript" src="http://www.pirilampo.org/styles/lib/js/jquery.stickytableheaders.js"></script>
#+HTML_HEAD: <script type="text/javascript" src="http://www.pirilampo.org/styles/lib/js/jquery.stickytableheaders.js"></script>
#+HTML_HEAD: <script type="text/javascript" src="http://www.pirilampo.org/styles/readtheorg/js/readtheorg.js"></script>
#+HTML_HEAD: <script type="text/javascript" src="http://www.pirilampo.org/styles/readtheorg/js/readtheorg.js"></script>
* Some explanations
* Asking the maths library
This is an org-mode document with code examples in R. Once opened in
Emacs, this document can easily be exported to HTML, PDF, and Office
formats. For more information on org-mode, see
https://orgmode.org/guide/.
When you type the shortcut =C-c C-e h o=, this document will be
exported as HTML. All the code in it will be re-executed, and the
results will be retrieved and included into the exported document. If
you do not want to re-execute all code each time, you can delete the #
and the space before ~#+PROPERTY:~ in the header of this document.
Like we showed in the video, R code is included as follows (and is
exxecuted by typing ~C-c C-c~):
My computer tells me that $\pi$ is /approximately/
#+begin_src R :results output :exports both
#+begin_src R :results output :exports both
p
rint("Hello world!")
p
i
#+end_src
#+end_src
#+RESULTS:
#+RESULTS:
: [1]
"Hello world!"
: [1]
3.141593
And now the same but in an R session. This is the most frequent
* Buffon's needle
situation, because R is really an interactive language. With a
Applying the method of [[https://en.wikipedia.org/wiki/Buffon%27s_needle_problem][Buffon's needle]], we get the *approximation*
session, R's state, i.e. the values of all the variables, remains
persistent from one code block to the next. The code is still executed
using ~C-c C-c~.
#+begin_src R :results output :session *R* :exports both
#+begin_src R :results output :exports both
summary(cars)
set.seed(42)
N = 100000
x = runif(N)
theta = pi/2*runif(N)
2/(mean(x+sin(theta)>1))
#+end_src
#+end_src
#+RESULTS:
#+RESULTS:
: speed dist
: [1] 3.14327
: Min. : 4.0 Min. : 2.00
: 1st Qu.:12.0 1st Qu.: 26.00
* Using a surface fraction argument
: Median :15.0 Median : 36.00
A method that is easier to understand and does not make use of the sin
: Mean :15.4 Mean : 42.98
function is base on the fact that if $X \sim U(0,1)$ and $Y \sim
: 3rd Qu.:19.0 3rd Qu.: 56.00
U(0,1)$, then $P\left[X^2 + Y^2 \le 1 \right] = \pi/4$ (see [[https://en.wikipedia.org/wiki/Monte_Carlo_method]["Monte
: Max. :25.0 Max. :120.00
Carlo method" on Wikipedia]]). The following code uses this approach:
Finally, an example for graphical output:
#+begin_src R :results output graphics file :file "./monte_carlo.png" :exports both :session *R*
#+begin_src R :results output graphics :file "./cars.png" :exports results :width 600 :height 400 :session *R*
set.seed(42)
plot(cars)
N = 1000
df = data.frame(X = runif(N), Y = runif(N))
df$Accept = (df$X**2 + df$Y**2 <=1)
library(ggplot2)
ggplot(df, aes(x=X,y=Y,color=Accept)) + geom_point(alpha=.2) + coord_fixed() + theme_bw()
#+end_src
#+end_src
#+RESULTS:
#+RESULTS:
[[file:./cars.png]]
[[file:./monte_carlo.png]]
Note the parameter ~:exports results~, which indicates that the code
will not appear in the exported document. We recommend that in the
context of this MOOC, you always leave this parameter setting as
~:exports both~, because we want your analyses to be perfectly
transparent and reproducible.
Watch out: the figure generated by the code block is /not/ stored in
It is then straightforward to obtain a (not really good) approximation
the org document. It's a plain file, here named ~cars.png~. You have
to $\pi$ by counting how many times, on average, $X^2 + Y^2$ is
to commit it explicitly if you want your analysis to be legible and
smaller than 1:
understandable on GitLab.
Finally, don't forget that we provide in the resource section of this
#+begin_src R :exports both :session *R*
MOOC a configuration with a few keyboard shortcuts that allow you to
4*mean(df$Accept)
quickly create code blocks in R by typing ~<r~ or ~<R~ followed by
#+end_src
~Tab~.
Now it's your turn! You can delete all this information and replace it
#+RESULTS:
by your computational document.
: 3.156
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