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    <title>Lab 07 - Données temporelles et textuelles</title>
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    <meta name="author" content="Antoine Neuraz" />
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## TODO

#### 1. charger le dataset `us_city_populations` de la librairie `vizoR`

#### 2. tracer un line chart de l'évolution de la population des villes US

#### 3. Mettez en évidence les 5 plus grandes villes (hint: package gghighlight)
[introduction gghighlight](https://cran.r-project.org/web/packages/gghighlight/vignettes/gghighlight.html)

#### 4. Appliquez les principes de design de Tufte 

##### 5. BONUS: affichez le nom des villes directement à la fin de la ligne

#### 6. Réalisez un streamgraph des 5 plus grandes villes US (hint: package ggTimeSeries)

---

## TODO 2

#### Trouver une 3e visualization pertinente pour montrer l'évolution de la population des villes US. 

---


```r
data("us_city_populations")

n_cities = 5

# top_cities &lt;- 
#   us_city_populations %&gt;% 
#   filter(Rank &lt;= n_cities) %&gt;%
#   select(City, State, Region) %&gt;% 
#   distinct()
# 
# to_plot &lt;- filter(us_city_populations, City %in% top_cities$City)

#to_plot &lt;- us_city_populations

last_ranks &lt;- us_city_populations %&gt;% 
  filter(Year == max(Year)) %&gt;%
  mutate(last_rank = Rank) %&gt;% 
  select(City, last_rank)

to_plot &lt;- left_join(us_city_populations, last_ranks, by= 'City')

right_axis &lt;-  to_plot %&gt;% 
  group_by(City) %&gt;% 
  top_n(1, Year) %&gt;% 
  ungroup() %&gt;%
  top_n(n_cities, -last_rank)

ends &lt;- right_axis %&gt;%
  pull(Population) 

labels &lt;-  right_axis %&gt;%
  pull(City)
```

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![](lab7-temporal_data_files/figure-html/unnamed-chunk-2-1.png)&lt;!-- --&gt;
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![](lab7-temporal_data_files/figure-html/unnamed-chunk-3-1.png)&lt;!-- --&gt;
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![](bar_race.gif)
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