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mooc-rr-ressources
Commit b80c5178 authored by Laurence Farhi's avatar Laurence Farhi
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Ajoute de notebooks utilisés dans les docs de Marie-Gabrielle

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documents/notebooks/cars.csv 0 → 100644
View file @ b80c5178
"","speed","dist"
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"2",4,10
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"23",14,80
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"30",17,40
"31",17,50
"32",18,42
"33",18,56
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"38",19,68
"39",20,32
"40",20,48
"41",20,52
"42",20,56
"43",20,64
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"46",24,70
"47",24,92
"48",24,93
"49",24,120
"50",25,85
documents/notebooks/notebook_Jupyter_Python_R.ipynb 0 → 100644
View file @ b80c5178
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Notebook Python R\n",
"\n",
"## Import des données dans Python"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>speed</th>\n",
" <th>dist</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>7</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>7</td>\n",
" <td>22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>8</td>\n",
" <td>16</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 speed dist\n",
"0 1 4 2\n",
"1 2 4 10\n",
"2 3 7 4\n",
"3 4 7 22\n",
"4 5 8 16"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"# data_url = \"https://forge.scilab.org/index.php/p/rdataset/source/file/master/csv/datasets/cars.csv\"\n",
"data_url = \"cars.csv\"\n",
"df_python = pd.read_csv(data_url)\n",
"df_python.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Supression de la première colonne"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>speed</th>\n",
" <th>dist</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>7</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>7</td>\n",
" <td>22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>8</td>\n",
" <td>16</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" speed dist\n",
"0 4 2\n",
"1 4 10\n",
"2 7 4\n",
"3 7 22\n",
"4 8 16"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_python.drop(df_python.columns[[0]], axis=1, inplace=True)\n",
"df_python.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Summary avec Python"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>speed</th>\n",
" <th>dist</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>50.000000</td>\n",
" <td>50.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>15.400000</td>\n",
" <td>42.980000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>5.287644</td>\n",
" <td>25.769377</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>4.000000</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>12.000000</td>\n",
" <td>26.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>15.000000</td>\n",
" <td>36.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>19.000000</td>\n",
" <td>56.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>25.000000</td>\n",
" <td>120.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" speed dist\n",
"count 50.000000 50.000000\n",
"mean 15.400000 42.980000\n",
"std 5.287644 25.769377\n",
"min 4.000000 2.000000\n",
"25% 12.000000 26.000000\n",
"50% 15.000000 36.000000\n",
"75% 19.000000 56.000000\n",
"max 25.000000 120.000000"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_python.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Summary avec R"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"from rpy2.robjects import pandas2ri\n",
"pandas2ri.activate()\n",
"from rpy2.robjects.packages import importr"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" speed dist \n",
"\r\n",
" Min. : 4.0 Min. : 2.00 \n",
"\r\n",
" 1st Qu.:12.0 1st Qu.: 26.00 \n",
"\r\n",
" Median :15.0 Median : 36.00 \n",
"\r\n",
" Mean :15.4 Mean : 42.98 \n",
"\r\n",
" 3rd Qu.:19.0 3rd Qu.: 56.00 \n",
"\r\n",
" Max. :25.0 Max. :120.00 \n",
"\n"
]
}
],
"source": [
"base = importr('base')\n",
"print(base.summary(df_python))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\r\n",
" Numeric \n",
"\r\n",
" mean median var sd valid.n\n",
"\r\n",
"speed 15.40 15 27.96 5.29 50\n",
"\r\n",
"dist 42.98 36 664.06 25.77 50\n",
"\n"
]
}
],
"source": [
"prettyR = importr('prettyR')\n",
"print(prettyR.describe(df_python))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Liens utiles\n",
"\n",
"- http://rpy.sourceforge.net/rpy2/doc-2.4/html/introduction.html\n",
"- https://rpy2.readthedocs.io/en/version_2.8.x/"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
documents/notebooks/notebook_Jupyter_Python_SAS.ipynb 0 → 100644
View file @ b80c5178
This diff is collapsed.
documents/notebooks/notebook_Jupyter_R.ipynb 0 → 100644
View file @ b80c5178
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Notebook R"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
" speed dist \n",
" Min. : 4.0 Min. : 2.00 \n",
" 1st Qu.:12.0 1st Qu.: 26.00 \n",
" Median :15.0 Median : 36.00 \n",
" Mean :15.4 Mean : 42.98 \n",
" 3rd Qu.:19.0 3rd Qu.: 56.00 \n",
" Max. :25.0 Max. :120.00 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"summary(cars)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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kBIg7NqNwdCKsA+0vQJif8z3k+jaPBf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\n0A6vdAAAAABJRU5ErkJggg==",
"text/plain": [
"plot without title"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot(cars)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "R",
"language": "R",
"name": "ir"
},
"language_info": {
"codemirror_mode": "r",
"file_extension": ".r",
"mimetype": "text/x-r-source",
"name": "R",
"pygments_lexer": "r",
"version": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
documents/notebooks/notebook_Jupyter_SAS.ipynb 0 → 100644
View file @ b80c5178
This source diff could not be displayed because it is too large. You can view the blob instead.
documents/notebooks/notebook_Jupyter_html.ipynb 0 → 100644
View file @ b80c5178
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<h3>Hello</h3>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from IPython.display import display, HTML\n",
"display(HTML('<h3>Hello</h3>'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Liens utiles :\n",
"\n",
"- https://stackoverflow.com/questions/43965823/convert-notebook-generated-html-snippet-to-latex-and-pdf\n",
"- https://github.com/jupyter/nbconvert/issues/474"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
documents/notebooks/notebook_RStudio_SASmarkdown.md 0 → 100644
View file @ b80c5178
SASmarkdown notebook
====================
Reference manual
================
[SASmarkdown.pdf](https://cran.r-project.org/web/packages/SASmarkdown/SASmarkdown.pdf)
Configuration
=============
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
require(SASmarkdown)
saspath <- "C:/Program Files/SASHome/SASFoundation/9.4/sas.exe"
sasopts <- "-nosplash -ls 75"
knitr::opts_chunk$set(engine='sas', engine.path=saspath,
engine.opts=sasopts, comment="")
```
Listing output
==============
```{r, engine='sas', collectcode=TRUE, error=FALSE}
proc means data=sashelp.class;
run;
ods listing gpath='D:\figures';
ods graphics / imagename="fig" imagefmt=png;
title 'Student Weight by Student Height';
proc sgplot data=sashelp.class noautolegend;
pbspline y=weight x=height;
run;
```
Procédure MEANS
Variable N Moyenne Ecart-type Minimum Maximum
-------------------------------------------------------------------------
Age 19 13.3157895 1.4926722 11.0000000 16.0000000
Height 19 62.3368421 5.1270752 51.3000000 72.0000000
Weight 19 100.0263158 22.7739335 50.5000000 150.0000000
-------------------------------------------------------------------------
`![figure](D:\figures\fig.png)`
![figure](images/fig.png)
HTML output (vue du fichier pdf)
===========
```{r, engine='sashtml', collectcode=TRUE, error=FALSE}
proc means data=sashelp.class;
run;
```
Tableau mal converti en pdf
Variable
N
Moyenne
Ecart-type
Minimum
Maximum
Age
Height
Weight
19
19
19
13.3157895
62.3368421
100.0263158
1.4926722
5.1270752
22.7739335
11.0000000
51.3000000
50.5000000
16.0000000
72.0000000
150.0000000
```{r, engine='sashtml', echo=FALSE, collectcode=TRUE, error=FALSE}
title 'Student Weight by Student Height';
proc sgplot data=sashelp.class noautolegend;
pbspline y=weight x=height;
run;
```
Pas de sortie graphique dans le fichier pdf.
\ No newline at end of file
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