# -*- mode: org -*-
# -*- coding: utf-8 -*-
#+STARTUP: overview indent inlineimages logdrawer
#+TITLE:       Journal
#+AUTHOR:      Put your name here
#+LANGUAGE:    en

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* 2021
** 2021-03 mars
*** 2021-03-02 mardi
**** Premier pas avec org mode

-note1
-note2

#+begin_src R :results output :session *R* :exports both
summary(cars)
#+end_src

#+RESULTS:
:      speed           dist       
:  Min.   : 4.0   Min.   :  2.00  
:  1st Qu.:12.0   1st Qu.: 26.00  
:  Median :15.0   Median : 36.00  
:  Mean   :15.4   Mean   : 42.98  
:  3rd Qu.:19.0   3rd Qu.: 56.00  
:  Max.   :25.0   Max.   :120.00

#+begin_src R :results output graphics :file (org-babel-temp-file "figure" ".png") :exports both :width 600 :height 400 :session *R*
plot(cars)
#+end_src

#+RESULTS:
#+begin_src R :results output graphics :file (org-babel-temp-file "figure" ".png") :exports both :width 600 :height 400 :session *R* 
plot(cars)
#+end_src

#+RESULTS:
[[file:/tmp/babel-cFH86o/figureNFJuq7.png]]
#+begin_src python :results output :exports both
print("hello word")
#+end_src

#+RESULTS:
: hello word

#+begin_src python :results output :session :exports both
x=10
#+end_src

#+RESULTS:

#+begin_src python :results file :session :var matplot_lib_filename="foo.png") :exports both
import matplotlib.pyplot as plt

import numpy
x=numpy.linspace(-15,15)
plt.figure(figsize=(10,5))
plt.plot(x,numpy.cos(x)/x)
plt.tight_layout()

plt.savefig(matplot_lib_filename)
matplot_lib_filename
#+end_src

#+RESULTS:
[[file:foo.png]]

Entered on [2021-03-02 mar. 14:09]
  
  [[file:~/org/journal.org::*Org-mode Babel (for literate programming)][Org-mode Babel (for literate programming)]]
*** 2021-03-03 mercredi
**** FUN MOOC reproductibility
***** Exercice 2.4

I worked on the Cars data from R, i trained myself on the usage of
orgmode en the realisation of a journal following the Mooc
recommendation.

Firts I create a csv file in R with the Cars data 

 #+begin_src R :results output :session *R* :exports both
data <- cars
write.csv(data, 'data_cars_exercice2_3.csv')
 #+end_src

Then i load to make statistic on it,  the goal here is
to see if the distance cross by a car is influenced by its speed.
 

#+begin_src R :results output graphics :file data_cars_graph.png :exports both :width 600 :height 400 :session *R* 
data <- read.csv('data_cars_exercice2_3.csv')
scatter.smooth(x=data$speed, y=data$dist, main="Dist ~ Speed")
#+end_src

#+RESULTS:
file:data_cars_graph

Here the graph is almost a line, so it will be insteresting to made a
correlation analasys of this data


#+begin_src R :results output :session *R* :exports both
cor(data$speed, data$dist)
#+end_src


#+RESULTS:
: [1] 0.8068949

The correlation between speed and distance is high ( < 0.5) and
positive, which mean higher is the speed, higher will be the
distance. Now to be sure that my hypthesis is good i will apply a the
linear regression.

#+begin_src R :results output :session *R* :exports both
linearMod <- lm(dist ~ speed, data=data)
summary(linearMod) 
#+end_src

#+RESULTS:
#+begin_example

Call:
lm(formula = dist ~ speed, data = data)

Residuals:
    Min      1Q  Median      3Q     Max 
-29.069  -9.525  -2.272   9.215  43.201 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -17.5791     6.7584  -2.601   0.0123 *  
speed         3.9324     0.4155   9.464 1.49e-12 ***
---
codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 15.38 on 48 degrees of freedom
Multiple R-squared:  0.6511,	Adjusted R-squared:  0.6438 
F-statistic: 89.57 on 1 and 48 DF,  p-value: 1.49e-12
#+end_example

LinearMod, both these p-Values are well below the 0.05
threshold, so I can conclude our model is indeed statistically
significant, which mean, now i'm sure that the speed influence the distance