#+TITLE:  L'emploi recule
:preamble:
#+AUTHOR: V. Ledda
#+DATE:   <2024-04-13 sam.>
#+LANGUAGE: fr

#+startup:overview indent inlineimages

#+PROPERTY: header-args:jupyter-R :exports both
#+PROPERTY: header-args:jupyter-R+ :session /jpy::ir

#+EXPORT_EXCLUDE_TAGS: noexport
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:end:
* Emploi et chomage en Europe entre 2006 et 2011.

** Chargement des données

#+begin_src jupyter-R   :exports code :results raw drawer
  packages<-c("tidyverse","skimr")
  for(package in packages)
  {
   
    if(!require(package,character.only=TRUE)){
      print(c("Installation de ",package))
      install.packages(package)
    }
    library(package,character.only=TRUE)
  }

  donnees<-as_tibble(read.csv2("Lemploirecule.csv",sep=";",dec=","))
  donnees$Date<-as.Date(donnees$Date,"%d/%m/%y")
#+end_src

#+RESULTS:



** Aperçu
#+begin_src jupyter-R   :exports both :results raw drawer
glimpse(donnees)
#+end_src

#+RESULTS:
: Rows: 19
: Columns: 3
: $ Date                <date> 2006-01-01, 2006-04-01, 2006-07-01, 2006-10-01, 2…
: $ Personnes.employées <dbl> 218.947, 220.053, 220.955, 221.632, 222.985, 224.0…
: $ Chomeurs            <dbl> 20.1053, 19.5865, 18.9098, 18.2782, 17.4887, 16.92…

#+begin_src jupyter-R 
  skim(donnees)
#+end_src

#+RESULTS:
:RESULTS:
#+begin_example
  ── Data Summary ────────────────────────
                             Values 
  Name                       donnees
  Number of rows             19     
  Number of columns          3      
  _______________________           
  Column type frequency:            
    Date                     1      
    numeric                  2      
  ________________________          
  Group variables            None   

  ── Variable type: Date ─────────────────────────────────────────────────────────
    skim_variable n_missing complete_rate min        max        median    
  1 Date                  0             1 2006-01-01 2010-07-01 2008-04-01
    n_unique
  1       19

  ── Variable type: numeric ──────────────────────────────────────────────────────
    skim_variable       n_missing complete_rate  mean   sd    p0   p25   p50   p75
  1 Personnes.employées         0             1 223.  2.45 219.  221.  223.  225. 
  2 Chomeurs                    0             1  19.2 2.52  16.0  16.9  18.9  21.4
     p100 hist 
  1 227.  ▂▇▂▃▆
  2  23.2 ▇▃▅▁▇
  Warning message in is.null(text_repr) || nchar(text_repr) == 0L:
  “‘length(x) = 16 > 1’ dans la conversion automatique vers ‘logical(1)’”
#+end_example
#+begin_export html
<table class="dataframe">
<caption>A skim_df: 3 × 16</caption>
<thead>
	<tr><th></th><th scope=col>skim_type</th><th scope=col>skim_variable</th><th scope=col>n_missing</th><th scope=col>complete_rate</th><th scope=col>Date.min</th><th scope=col>Date.max</th><th scope=col>Date.median</th><th scope=col>Date.n_unique</th><th scope=col>numeric.mean</th><th scope=col>numeric.sd</th><th scope=col>numeric.p0</th><th scope=col>numeric.p25</th><th scope=col>numeric.p50</th><th scope=col>numeric.p75</th><th scope=col>numeric.p100</th><th scope=col>numeric.hist</th></tr>
	<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;date&gt;</th><th scope=col>&lt;date&gt;</th><th scope=col>&lt;date&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>
</thead>
<tbody>
	<tr><th scope=row>1</th><td>Date   </td><td>Date               </td><td>0</td><td>1</td><td>2006-01-01</td><td>2010-07-01</td><td>2008-04-01</td><td>19</td><td>       NA</td><td>      NA</td><td>      NA</td><td>       NA</td><td>      NA</td><td>     NA</td><td>      NA</td><td>NA   </td></tr>
	<tr><th scope=row>2</th><td>numeric</td><td>Personnes.employées</td><td>0</td><td>1</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>223.11079</td><td>2.447718</td><td>218.9470</td><td>221.24800</td><td>222.9850</td><td>225.286</td><td>226.7740</td><td>▂▇▂▃▆</td></tr>
	<tr><th scope=row>3</th><td>numeric</td><td>Chomeurs           </td><td>0</td><td>1</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td> 19.22913</td><td>2.521522</td><td> 16.0451</td><td> 16.89095</td><td> 18.9098</td><td> 21.391</td><td> 23.1504</td><td>▇▃▅▁▇</td></tr>
</tbody>
</table>
#+end_export
:END:



* Représentation graphique

#+begin_src jupyter-R   :exports both :results raw drawer
  donnees |>
    mutate(Date=Date+45)|>
    pivot_longer(!Date,names_to="Variables")|>
    ggplot(aes(x=Date,y=value,group=Variables))+
    geom_line(aes(color=Variables),linewidth=2)+
    geom_point(size=1)+
    labs(title="Évolution du nombre de personnes employées\n et du nombre de personnes au chomage")
#+end_src

#+RESULTS:
:RESULTS:
#+attr_org: :width 420 :height 420
[[./.ob-jupyter/e802e54060b7411127a1be1573f26d4883ac8db4.png]]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
:END: