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    <title>Données tabulaires</title>
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class: center, middle, title

# UE Visualisation

### 2020-2021

## Dr. Maxime Wack

### AHU Informatique médicale
#### Hôpital Européen Georges Pompidou, &lt;/br&gt; Université de Paris

---

# Données tabulaires

.center[Chargement des données avec `read_csv`]


```r
read_csv("lab03-data/notes.csv") -&gt; notes
```

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---

# Pivot

.center[Données *wide* → *long*]


```r
pivot_longer(notes,
             Alice:David,
             names_to = "Prénom",
             values_to = "Note") -&gt; notes_long
```

<div id="htmlwidget-d3acf82e5da55982884c" style="width:100%;height:auto;" class="datatables html-widget"></div>
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---

# Pivot

.center[Données *long* → *wide*]


```r
pivot_wider(notes_long,
            names_from = Prénom,
            values_from = Note)
```

<div id="htmlwidget-75b346f1165091af2cbb" style="width:100%;height:auto;" class="datatables html-widget"></div>
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---

# Exercices

Utiliser les fonctions de `pivot_*` pour exprimer le dataset `gapminder` de différentes manières.

### Représenter l'intégralité sous forme clé-valeur

Chaque ligne ne doit porter qu'une valeur de `lifeExp`, `gdpPercap` ou `pop`, pour chaque pays et chaque année.

### Représenter un pays par ligne

Une seule ligne par pays, toutes les années × indicateur doivent donner lieu à une nouvelle colonne

### Reformer gapminder à partir du précédent exercice
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