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6ba812c8b193df42eeb5de9ab7cfb35d
mooc-rr
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2aa9afbd
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2aa9afbd
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Feb 28, 2021
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migvasc
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module2/exo4/exercice_python_en.org
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#+TITLE:
Your title
#+TITLE:
Analysis of custom data - Module 2 - Exercise 4
#+AUTHOR:
Your name
#+AUTHOR:
Miguel Felipe Silva Vasconcelos
#+DATE:
Today's date
#+DATE:
28/02/2021
#+LANGUAGE: en
#+LANGUAGE: en
# #+PROPERTY: header-args :eval never-export
# #+PROPERTY: header-args :eval never-export
...
@@ -11,84 +11,18 @@
...
@@ -11,84 +11,18 @@
#+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
*
Introduction
This is an org-mode document with code examples in R. Once opened in
For the purpose of only solving this exercise, I'm using data that
Emacs, this document can easily be exported to HTML, PDF, and Office
was randomly generated. The file /data.csv/ contains two columns:
formats. For more information on org-mode, see
- The first column represents the day of the month
https://orgmode.org/guide/.
- The second column represents how many minutes were spent doing
determined task (in this case, studying for this MOOC)
When you type the shortcut =C-c C-e h o=, this document will be
The analysis will present the following metrics: median, average,
exported as HTML. All the code in it will be re-executed, and the
standard deviation, maximum,
results will be retrieved and included into the exported document. If
and minimum value, regarding the time spent on each day.
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, Python code is included as follows (and
is exxecuted by typing ~C-c C-c~):
#+begin_src python :results output :exports both
* Results of the experiments
print("Hello world!")
#+end_src
#+RESULTS:
: Hello world!
And now the same but in an Python session. With a session, Python'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 python :results output :session :exports both
import numpy
x=numpy.linspace(-15,15)
print(x)
#+end_src
#+RESULTS:
#+begin_example
[-15. -14.3877551 -13.7755102 -13.16326531 -12.55102041
-11.93877551 -11.32653061 -10.71428571 -10.10204082 -9.48979592
-8.87755102 -8.26530612 -7.65306122 -7.04081633 -6.42857143
-5.81632653 -5.20408163 -4.59183673 -3.97959184 -3.36734694
-2.75510204 -2.14285714 -1.53061224 -0.91836735 -0.30612245
0.30612245 0.91836735 1.53061224 2.14285714 2.75510204
3.36734694 3.97959184 4.59183673 5.20408163 5.81632653
6.42857143 7.04081633 7.65306122 8.26530612 8.87755102
9.48979592 10.10204082 10.71428571 11.32653061 11.93877551
12.55102041 13.16326531 13.7755102 14.3877551 15. ]
#+end_example
Finally, an example for graphical output:
#+begin_src python :results output file :session :var matplot_lib_filename="./cosxsx.png" :exports results
import matplotlib.pyplot as plt
plt.figure(figsize=(10,5))
plt.plot(x,numpy.cos(x)/x)
plt.tight_layout()
plt.savefig(matplot_lib_filename)
print(matplot_lib_filename)
#+end_src
#+RESULTS:
[[file:./cosxsx.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
the org document. It's a plain file, here named ~cosxsx.png~. You have
to commit it explicitly if you want your analysis to be legible and
understandable on GitLab.
Finally, don't forget that we provide in the resource section of this
MOOC a configuration with a few keyboard shortcuts that allow you to
quickly create code blocks in Python by typing ~<p~, ~<P~ or ~<PP~
followed by ~Tab~.
Now it's your turn! You can delete all this information and replace it
by your computational document.
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