R : des premiers pas au modèle linéaire

# R : des premiers pas au modèle linéaire
## Visualisation de données
### Marie-Pierre Etienne
### <a href="https://marieetienne.github.io/premierspas/" class="uri">https://marieetienne.github.io/premierspas/</a>
### Mars 2021

---

# Introduction

---
template: intro

## Pourquoi la visualisation des données est importante ?

- La chronique de Constance par Constance dans  Par Jupiter

---
template: intro

## Un exemple inspirant : le réchauffement climatique en une image

[Extrait du journal Le Monde le 3 janvier 2020](https://www.lemonde.fr/les-decodeurs/article/2020/01/03/2019-est-la-troisieme-annee-la-plus-chaude-en-france-visualisez-la-hausse-dans-votre-ville_6024699_4355770.html?xtor=CS2-33281034-%5BKW%5D-%5Bgratuit%5D-%5Barticleacquisition%5D&utm_campaign=keywee_acquisition&utm_medium=PaidSocial&utm_source=Facebook&kwp_0=1527732&kwp_4=4629376&kwp_1=1961091)

Qu'est ce qui est efficace dans cette visualisation ?

---
template: intro

## Le musée des horreurs

- [Le pourcentage de fumeurs aux Pays-Bas](https://viz.wtf/image/630887965804150786)

---
template: intro 
count: false

## Le musée des horreurs

- [Le Tumblr WTFViz](https://viz.wtf/)
- [Probabilité d'infection par le coronavirus par G. Forestier](https://i.redd.it/b3vvel2xtsv51.jpg)

---
template: intro 
count: false

## Le musée des horreurs

- [Le Tumblr WTFViz](https://viz.wtf/)
- [Probabilité d'infection par le coronavirus par G. Forestier](https://i.redd.it/b3vvel2xtsv51.jpg)
- [Le coronavirus par Jean Castex](https://www.ouest-france.fr/sante/virus/coronavirus/covid-19-couvre-feu-que-voulaient-dire-les-droles-de-graphiques-montres-par-jean-castex-7016245)

---
template: intro

## Quels sont les éléments d'une visualisation efficace ?

- $$ $$
- $$ $$ 
- $$ $$
- $$ $$
- $$ $$ 
- $$ $$
- $$ $$

---
template: intro

## Some resources

- [Data visualization with R](https://rkabacoff.github.io/datavis/)
- [R for Data Science](https://r4ds.had.co.nz/) 
- [R Graphics Cookbook](http://www.cookbook-r.com/)
- [ggplot2: Elegant Graphics for Data Analysis](https://ggplot2-book.org/index.html) 
- [R graph gallery](https://www.r-graph-gallery.com/ggplot2-package.html)
- [Tutoriel de Raphaelle Momal et Marie Perrot Dockes](https://stateofther.github.io/post/fancy-plotting/)
- [Don’t cha wish your ggplot had colours like me?](https://www.dataembassy.co.nz/Liza-colours-in-R#22)
- [Valoriser ses données avec R](https://mtes-mct.github.io/parcours-r/m5/) (formation du  Ministere de la Transition Écologique et Solidaire (MTES)  et du Ministère de la Cohésion des Territoires et des relations avec les collectivités territoriales (MCTRCT))

---

# ggplot presentation

gg stands for Grammar of Graphics  and  ggplot2  is *based on the Grammar of Graphics , that allows you to compose graphs by combining independent components* .

*[The] grammar tells us that a graphic maps the*
- *data* 
- *to the aesthetic attributes (colour, shape, size)* 
- *of geometric objects (points, lines, bars)*. 
- *The plot may also include statistical transformations of the data and information about the plot’s coordinate system*.
- *Facetting can be used to plot for different subsets of the data.* 
*The combination of these independent components are what make up a graphic.*  .

---
# ggplot presentation

gg stands for Grammar of Graphics  and  ggplot2  is *based on the Grammar of Graphics , that allows you to compose graphs by combining independent components* .

```r
library(tidyverse)
library(ggplot2)
```

---
name: exemple

# Premier exemple : les manchots de Palmers

---
template: exemple

## Un peu de révision

* Installer le package `palmerpenguins`
* Charger ce package.

Ce package contient un jeu de données qu'on peut charger à l'aide de

```r
#remotes::install_github("allisonhorst/palmerpenguins")
data(penguins,package = "palmerpenguins")
```

* Renommer les colonnes en `bill_l`,  `bill_d`, `flip_l`, `bm` pour raccourcir les noms et stocker le résultat dans la table penguins

```r
penguins <- penguins %>%
 rename(bill_l = bill_length_mm, bill_d = bill_depth_mm, flip_l = flipper_length_mm, bm = body_mass_g)
penguins %>%
 print(n=2)
```

---

# The Palmer Penguins dataset : overview

```r
penguins %>%
  group_by(species, sex, year, island) %>%
  mutate(n = n()) %>%
  summarise_if(is.numeric, mean, na.rm = TRUE) %>%
  print(n=10)
```

```
## # A tibble: 35 x 9
## # Groups: species, sex, year [22]
## species sex year island bill_l bill_d flip_l bm n
## <fct> <fct> <int> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Adelie female 2007 Biscoe 37.5 18.6 182. 3470 5
## 2 Adelie female 2007 Dream 37.9 17.8 185 3269. 9
## 3 Adelie female 2007 Torgersen 38.3 18.2 188. 3475 8
## 4 Adelie female 2008 Biscoe 36.6 17.2 187. 3244. 9
## 5 Adelie female 2008 Dream 36.3 17.8 189 3412. 8
## 6 Adelie female 2008 Torgersen 36.6 17.4 190 3519. 8
## 7 Adelie female 2009 Biscoe 38.1 17.7 191. 3447. 8
## 8 Adelie female 2009 Dream 36.6 17.3 190. 3358. 10
## 9 Adelie female 2009 Torgersen 37.8 17.1 187. 3194. 8
## 10 Adelie male 2007 Biscoe 39.2 18.3 182. 3770 5
## # … with 25 more rows
```

---
name: simple
# Des graphiques simples

---
template: simple
## Un histogramme

*pour visualiser la distribution des valeurs*. L'ordonnée porte sur les comptages dans chaque classe de valeurs.

```r
penguins %>%
  ggplot() + 
  aes(x = bill_l) + 
  geom_histogram() 
```

```
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
```

```
## Warning: Removed 2 rows containing non-finite values (stat_bin).
```

---

```r
*penguins
```
]
 
.panel2-simple_hist-auto[

```
## # A tibble: 344 x 8
## species island bill_l bill_d flip_l bm sex year
## <fct> <fct> <dbl> <dbl> <int> <int> <fct> <int>
## 1 Adelie Torgersen 39.1 18.7 181 3750 male 2007
## 2 Adelie Torgersen 39.5 17.4 186 3800 female 2007
## 3 Adelie Torgersen 40.3 18 195 3250 female 2007
## 4 Adelie Torgersen NA NA NA NA <NA> 2007
## 5 Adelie Torgersen 36.7 19.3 193 3450 female 2007
## 6 Adelie Torgersen 39.3 20.6 190 3650 male 2007
## 7 Adelie Torgersen 38.9 17.8 181 3625 female 2007
## 8 Adelie Torgersen 39.2 19.6 195 4675 male 2007
## 9 Adelie Torgersen 34.1 18.1 193 3475 <NA> 2007
## 10 Adelie Torgersen 42 20.2 190 4250 <NA> 2007
## # … with 334 more rows
```
]

---
count: false

```r
penguins %>%
* ggplot()
```
]
 
.panel2-simple_hist-auto[
<img src="visu_files/figure-html/simple_hist_auto_02_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
count: false

```r
penguins %>%
 ggplot() +
* aes(x = bill_l)
```
]
 
.panel2-simple_hist-auto[
<img src="visu_files/figure-html/simple_hist_auto_03_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
count: false

```r
penguins %>%
  ggplot() +
  aes(x = bill_l) +
* geom_histogram()
```
]
 
.panel2-simple_hist-auto[

```
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
```

```
## Warning: Removed 2 rows containing non-finite values (stat_bin).
```

<img src="visu_files/figure-html/simple_hist_auto_04_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
template: simple
## Un histogramme

pour visualiser la distribution des valeurs. L'ordonnée porte sur la fréquence dans chaque classe de valeurs.

---

```r
*penguins
```
]
 
.panel2-simple_hist_dens-auto[

---
count: false

```r
penguins %>%
* ggplot()
```
]
 
.panel2-simple_hist_dens-auto[
<img src="visu_files/figure-html/simple_hist_dens_auto_02_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
count: false

```r
penguins %>%
 ggplot() +
* aes(x = bill_l)
```
]
 
.panel2-simple_hist_dens-auto[
<img src="visu_files/figure-html/simple_hist_dens_auto_03_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
count: false

```r
penguins %>%
  ggplot() +
  aes(x = bill_l) +
* geom_histogram()
```
]
 
.panel2-simple_hist_dens-auto[

```
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
```

```
## Warning: Removed 2 rows containing non-finite values (stat_bin).
```

<img src="visu_files/figure-html/simple_hist_dens_auto_04_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
count: false

```r
penguins %>%
  ggplot() +
  aes(x = bill_l) +
  geom_histogram() +
* aes(y = ..density..)
```
]
 
.panel2-simple_hist_dens-auto[

```
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
```

```
## Warning: Removed 2 rows containing non-finite values (stat_bin).
```

<img src="visu_files/figure-html/simple_hist_dens_auto_05_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
template: simple
## Un histogramme

pour visualiser la distribution des valeurs. L'ordonnée porte sur la fréquence dans chaque classe de valeurs.

```r
penguins %>%
  ggplot() + 
  geom_histogram(aes(x= bill_l, y = ..density..))
```

```
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
```

```
## Warning: Removed 2 rows containing non-finite values (stat_bin).
```

<img src="visu_files/figure-html/simple_hist_dens_simple-1.png" width="30%" style="display: block; margin: auto;" />
---
template: simple

## Un nuage de points (scatter plot)

*pour  visualiser le lien entre deux variables quantitatives*.

```r
penguins %>% 
  ggplot() + 
  aes( x= bill_l, y=bill_d) + 
  geom_point()
```

```
## Warning: Removed 2 rows containing missing values (geom_point).
```

---

```r
*penguins
```
]
 
.panel2-simple_scatter-auto[

---
count: false

```r
penguins %>%
* ggplot()
```
]
 
.panel2-simple_scatter-auto[
<img src="visu_files/figure-html/simple_scatter_auto_02_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
count: false

```r
penguins %>%
 ggplot() +
* aes( x= bill_l, y=bill_d)
```
]
 
.panel2-simple_scatter-auto[
<img src="visu_files/figure-html/simple_scatter_auto_03_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
count: false

```r
penguins %>%
  ggplot() +
  aes( x= bill_l, y=bill_d) +
* geom_point()
```
]
 
.panel2-simple_scatter-auto[

```
## Warning: Removed 2 rows containing missing values (geom_point).
```

<img src="visu_files/figure-html/simple_scatter_auto_04_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
template: simple

## Des boîtes à moustache

pour visualiser le lien entre une variable qualitative et une variable quantitative

```r
penguins %>% 
  ggplot() + 
  aes( x= species, y=bill_d) + 
  geom_boxplot()
```

```
## Warning: Removed 2 rows containing non-finite values (stat_boxplot).
```

---

```r
*penguins
```
]
 
.panel2-simple_boxplot-auto[

---
count: false

```r
penguins %>%
* ggplot()
```
]
 
.panel2-simple_boxplot-auto[
<img src="visu_files/figure-html/simple_boxplot_auto_02_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
count: false

```r
penguins %>%
 ggplot() +
* aes( x= species, y=bill_d)
```
]
 
.panel2-simple_boxplot-auto[
<img src="visu_files/figure-html/simple_boxplot_auto_03_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
count: false

```r
penguins %>%
  ggplot() +
  aes( x= species, y=bill_d) +
* geom_boxplot()
```
]
 
.panel2-simple_boxplot-auto[

```
## Warning: Removed 2 rows containing non-finite values (stat_boxplot).
```

<img src="visu_files/figure-html/simple_boxplot_auto_04_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---

```r
gg <- penguins %>%
 ggplot() + 
 aes(x = bill_l) + 
 geom_histogram() 
gg + 
 aes(y = ..density..)
```

---

```r
*gg <- penguins
```
]
 
.panel2-gg_save-auto[

]

---
count: false

```r
gg <- penguins %>%
* ggplot()
```
]
 
.panel2-gg_save-auto[

]

---
count: false

```r
gg <- penguins %>%
 ggplot() +
* aes(x = bill_l)
```
]
 
.panel2-gg_save-auto[

]

---
count: false

```r
gg <- penguins %>%
 ggplot() +
 aes(x = bill_l) +
* geom_histogram()
```
]
 
.panel2-gg_save-auto[

]

---
count: false

```r
gg <- penguins %>%
 ggplot() +
 aes(x = bill_l) +
 geom_histogram()
*gg
```
]
 
.panel2-gg_save-auto[

```
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
```

```
## Warning: Removed 2 rows containing non-finite values (stat_bin).
```

<img src="visu_files/figure-html/gg_save_auto_05_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
count: false

```r
gg <- penguins %>%
 ggplot() +
 aes(x = bill_l) +
 geom_histogram()
gg +
* aes(y = ..density..)
```
]
 
.panel2-gg_save-auto[

```
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
```

```
## Warning: Removed 2 rows containing non-finite values (stat_bin).
```

<img src="visu_files/figure-html/gg_save_auto_06_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---

```r
gg <- penguins %>% 
 ggplot() +
 aes( x= bill_l, y=bill_d) +
 geom_point() 
gg <- gg + 
 aes(col = species) 
print(gg)
```

```
## Warning: Removed 2 rows containing missing values (geom_point).
```

---

```r
*gg <- penguins
```
]
 
.panel2-gg1_save-auto[

]

---
count: false

```r
gg <- penguins %>%
* ggplot()
```
]
 
.panel2-gg1_save-auto[

]

---
count: false

```r
gg <- penguins %>%
 ggplot() +
* aes( x= bill_l, y=bill_d)
```
]
 
.panel2-gg1_save-auto[

]

---
count: false

```r
gg <- penguins %>%
 ggplot() +
 aes( x= bill_l, y=bill_d) +
* geom_point()
```
]
 
.panel2-gg1_save-auto[

]

---
count: false

```r
gg <- penguins %>%
 ggplot() +
 aes( x= bill_l, y=bill_d) +
 geom_point()
*gg <- gg
```
]
 
.panel2-gg1_save-auto[

]

---
count: false

```r
gg <- penguins %>%
 ggplot() +
 aes( x= bill_l, y=bill_d) +
 geom_point()
gg <- gg +
* aes(col = species)
```
]
 
.panel2-gg1_save-auto[

]

---
count: false

```r
gg <- penguins %>%
 ggplot() +
 aes( x= bill_l, y=bill_d) +
 geom_point()
gg <- gg +
 aes(col = species)
*print(gg)
```
]
 
.panel2-gg1_save-auto[

```
## Warning: Removed 2 rows containing missing values (geom_point).
```

<img src="visu_files/figure-html/gg1_save_auto_07_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
name: color
# Enrichir les graphiques avec la couleur

---
template: color
## Ajouter une information de groupes sur un nuage de points

```r
penguins %>% 
  ggplot() +
  aes( x= bill_l, y=bill_d) +
  geom_point() + #BREAK
  aes(col = species)  #BREAK
```

---

```r
*penguins %>%
* ggplot() +
* aes( x= bill_l, y=bill_d) +
* geom_point()
```
]
 
.panel2-scatter_plot_species-user[

```
## Warning: Removed 2 rows containing missing values (geom_point).
```

<img src="visu_files/figure-html/scatter_plot_species_user_01_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
count: false

```r
penguins %>%
  ggplot() +
  aes( x= bill_l, y=bill_d) +
  geom_point() +
* aes(col = species)
```
]
 
.panel2-scatter_plot_species-user[

```
## Warning: Removed 2 rows containing missing values (geom_point).
```

<img src="visu_files/figure-html/scatter_plot_species_user_02_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
template: color
## Ajouter une information de groupes sur un histogramme

```r
penguins %>%
  ggplot() + aes(x = bill_l, fill = species) + 
  geom_histogram() 
```

---

```r
*penguins
```
]
 
.panel2-hist_color_stack-auto[

---
count: false

```r
penguins %>%
* ggplot() + aes(x = bill_l, fill = species)
```
]
 
.panel2-hist_color_stack-auto[
<img src="visu_files/figure-html/hist_color_stack_auto_02_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
count: false

```r
penguins %>%
  ggplot() + aes(x = bill_l, fill = species) +
* geom_histogram()
```
]
 
.panel2-hist_color_stack-auto[

```
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
```

```
## Warning: Removed 2 rows containing non-finite values (stat_bin).
```

<img src="visu_files/figure-html/hist_color_stack_auto_03_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
template: color
## Ajouter une information de groupes sur un histogramme

Attention les couleurs représentent la part de chaque groupe dans la barre de l'histogramme.

```r
penguins %>%
  ggplot() + aes(x = bill_l, fill = species) + 
  geom_histogram() 
```

On peut vouloir faire 3 histogrammes différents

```r
penguins %>%
  ggplot() + aes(x = bill_l, fill = species) + 
  geom_histogram(position = 'identity') 
```
---

```r
*penguins
```
]
 
.panel2-hist_color_diff-auto[

---
count: false

```r
penguins %>%
* ggplot() + aes(x = bill_l, fill = species)
```
]
 
.panel2-hist_color_diff-auto[
<img src="visu_files/figure-html/hist_color_diff_auto_02_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
count: false

```r
penguins %>%
  ggplot() + aes(x = bill_l, fill = species) +
* geom_histogram(position = 'identity')
```
]
 
.panel2-hist_color_diff-auto[

```
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
```

```
## Warning: Removed 2 rows containing non-finite values (stat_bin).
```

<img src="visu_files/figure-html/hist_color_diff_auto_03_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
template: color
## une palette pour daltoniens

---

```r
*gg
```
]
 
.panel2-scatter_viridis-auto[

```
## Warning: Removed 2 rows containing missing values (geom_point).
```

<img src="visu_files/figure-html/scatter_viridis_auto_01_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
count: false

```r
gg +
* scale_color_viridis_d()
```
]
 
.panel2-scatter_viridis-auto[

```
## Warning: Removed 2 rows containing missing values (geom_point).
```

<img src="visu_files/figure-html/scatter_viridis_auto_02_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
template: color
##  with a color tribute to [Wes Anderson](https://en.wikipedia.org/wiki/Wes_Anderson)

Following  a Tumblr blog  [Wes Anderson Palettes](https://wesandersonpalettes.tumblr.com/), Karthik Ram proposes the [wesanderson palette](https://github.com/karthik/wesanderson) on Github.

```r
## remotes::install_github("wesanderson")
color_darj <- wesanderson::wes_palette(name = "Darjeeling1")
```

```r
gg + 
  scale_color_manual(values = color_darj)
```

---

```r
*gg
```
]
 
.panel2-scatter_wesanderson-auto[

```
## Warning: Removed 2 rows containing missing values (geom_point).
```

<img src="visu_files/figure-html/scatter_wesanderson_auto_01_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
count: false

```r
gg +
* scale_color_manual(values = color_darj)
```
]
 
.panel2-scatter_wesanderson-auto[

```
## Warning: Removed 2 rows containing missing values (geom_point).
```

<img src="visu_files/figure-html/scatter_wesanderson_auto_02_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
template: color
## Exercice

* Proposer un graphique  pour visulaliser le relation entre longueur d'ailes et poids du corps. 
* Illustrer également un potentiel effet de l'année.

```r
penguins %>% ggplot() +
  aes(x= bm, y = flip_l) + 
  geom_point() + 
  aes(col = as_factor(year)) +
  scale_color_manual(values = wesanderson::wes_palette(name = "Darjeeling1")) 
```

---
count: false

```r
*penguins %>% ggplot()
```
]
 
.panel2-exo_relation_cor-auto[
<img src="visu_files/figure-html/exo_relation_cor_auto_01_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
count: false

```r
penguins %>% ggplot() +
* aes(x= bm, y = flip_l)
```
]
 
.panel2-exo_relation_cor-auto[
<img src="visu_files/figure-html/exo_relation_cor_auto_02_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
count: false

```r
penguins %>% ggplot() +
  aes(x= bm, y = flip_l) +
* geom_point()
```
]
 
.panel2-exo_relation_cor-auto[

```
## Warning: Removed 2 rows containing missing values (geom_point).
```

<img src="visu_files/figure-html/exo_relation_cor_auto_03_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
count: false

```r
penguins %>% ggplot() +
  aes(x= bm, y = flip_l) +
  geom_point() +
* aes(col = as_factor(year))
```
]
 
.panel2-exo_relation_cor-auto[

```
## Warning: Removed 2 rows containing missing values (geom_point).
```

<img src="visu_files/figure-html/exo_relation_cor_auto_04_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
count: false

```r
penguins %>% ggplot() +
  aes(x= bm, y = flip_l) +
  geom_point() +
  aes(col = as_factor(year)) +
* scale_color_manual(values = wesanderson::wes_palette(name = "Darjeeling1"))
```
]
 
.panel2-exo_relation_cor-auto[

```
## Warning: Removed 2 rows containing missing values (geom_point).
```

<img src="visu_files/figure-html/exo_relation_cor_auto_05_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
count: false

```r
penguins %>% ggplot() +
  aes(x= bm, y = flip_l) +
  geom_point() +
  aes(col = as_factor(year)) +
  scale_color_manual(values = wesanderson::wes_palette(name = "Darjeeling1"))
```
]
 
.panel2-exo_relation_cor-auto[

```
## Warning: Removed 2 rows containing missing values (geom_point).
```

<img src="visu_files/figure-html/exo_relation_cor_auto_06_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
name: pimp
# Pimp my graph

---
template: pimp
## Some nice color palette
- [Inspired by Ghibli](https://github.com/ewenme/ghibli)
- [The yarr pirate palette](https://cran.r-project.org/web/packages/yarrr/vignettes/piratepal.html)
- [Harry Potter inspiration](https://github.com/aljrico/harrypotter)![](https://github.com/aljrico/harrypotter/blob/master/man/figures/logo.png)

---
template: pimp
## Des annotations choisies

```r
gg <- gg + scale_color_manual(values = color_darj) 
```

---

```r
*gg
```
]
 
.panel2-scatter_labels-auto[

```
## Warning: Removed 2 rows containing missing values (geom_point).
```

<img src="visu_files/figure-html/scatter_labels_auto_01_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
count: false

```r
gg  +
* labs( x = 'Bill length in mm')
```
]
 
.panel2-scatter_labels-auto[

```
## Warning: Removed 2 rows containing missing values (geom_point).
```

<img src="visu_files/figure-html/scatter_labels_auto_02_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
count: false

```r
gg  +
  labs( x = 'Bill length in mm') +
* labs(y = 'Bill depth in mm')
```
]
 
.panel2-scatter_labels-auto[

```
## Warning: Removed 2 rows containing missing values (geom_point).
```

<img src="visu_files/figure-html/scatter_labels_auto_03_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
count: false

```r
gg  +
  labs( x = 'Bill length in mm') +
  labs(y = 'Bill depth in mm') +
* labs(color = "Species")
```
]
 
.panel2-scatter_labels-auto[

```
## Warning: Removed 2 rows containing missing values (geom_point).
```

<img src="visu_files/figure-html/scatter_labels_auto_04_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
template: pimp
## Choisir le theme

```r
gg  +
  labs( x = 'Bill length in mm') +
  labs(y = 'Bill depth in mm') +
  labs(color = "Species") + #BREAK
  theme_light() #BREAK
```

---
count: false

```r
*gg  +
* labs( x = 'Bill length in mm') +
* labs(y = 'Bill depth in mm') +
* labs(color = "Species")
```
]
 
.panel2-scatter_themelight-user[

```
## Warning: Removed 2 rows containing missing values (geom_point).
```

<img src="visu_files/figure-html/scatter_themelight_user_01_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
count: false

```r
gg  +
  labs( x = 'Bill length in mm') +
  labs(y = 'Bill depth in mm') +
  labs(color = "Species") +
* theme_light()
```
]
 
.panel2-scatter_themelight-user[

```
## Warning: Removed 2 rows containing missing values (geom_point).
```

<img src="visu_files/figure-html/scatter_themelight_user_02_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
template: scatter_label
count: false

---
template: pimp
## Choisir le theme

```r
gg + scale_color_manual(values = color_darj) +
  labs( x = 'Bill length in mm',  
        y = 'Bill depth in mm',  color = "Species") +
  theme_light() + #BREAK 
  theme_minimal() #BREAK 
```

---

```r
*gg + scale_color_manual(values = color_darj) +
* labs( x = 'Bill length in mm',
*       y = 'Bill depth in mm',  color = "Species") +
* theme_light()
```
]
 
.panel2-scatter_thememinimal-user[

```
## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
```

```
## Warning: Removed 2 rows containing missing values (geom_point).
```

<img src="visu_files/figure-html/scatter_thememinimal_user_01_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
count: false

```r
gg + scale_color_manual(values = color_darj) +
  labs( x = 'Bill length in mm',
        y = 'Bill depth in mm',  color = "Species") +
  theme_light() +
* theme_minimal()
```
]
 
.panel2-scatter_thememinimal-user[

```
## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
```

```
## Warning: Removed 2 rows containing missing values (geom_point).
```

<img src="visu_files/figure-html/scatter_thememinimal_user_02_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
template: pimp
## Choisir le theme

Si on est heureux de notre graphique, on le garde

```r
gg <- gg + scale_color_manual(values = color_darj) +
 labs( x = 'Bill length in mm', y = 'Bill depth in mm', color = "Species") +
 theme_light()
```

```
## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
```

---
template: pimp
## Choisir le theme

The default theme might not be the best option

```
## Warning: Removed 2 rows containing missing values (geom_point).
```

---

```r
*gg
```
]
 
.panel2-scatter_below-auto[

```
## Warning: Removed 2 rows containing missing values (geom_point).
```

<img src="visu_files/figure-html/scatter_below_auto_01_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
count: false

```r
gg +
* theme(legend.position="bottom")
```
]
 
.panel2-scatter_below-auto[

```
## Warning: Removed 2 rows containing missing values (geom_point).
```

<img src="visu_files/figure-html/scatter_below_auto_02_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
template: pimp
## Choisir le theme

The legend position can also be specified by a vector ` c(x,y)`.  Their values should be between 0 and 1. c(0,0) corresponds to the “bottom left” and c(1,1) corresponds to the “top right” position. We may want to change the size.

```r
gg + 
  theme(legend.position=c(.9, .6))
```

```
## Warning: Removed 2 rows containing missing values (geom_point).
```

---
template: pimp
## Choisir le theme

```r
gg + theme(legend.position=c(.9, .6),
           text = element_text(size = 10, face = "italic"),
           axis.text.x = element_text(angle=90, hjust=1),
           legend.text = element_text(size = 9, face = 'plain'),
           legend.title = element_text(size = 11, face = "bold") )
```

```
## Warning: Removed 2 rows containing missing values (geom_point).
```

---
template: pimp
## Choisir le theme par défaut

---

```r
*theme_set(theme_light())
*theme_update(legend.position="bottom")
```
]
 
.panel2-theme_setup_2-user[

]

---
count: false

```r
theme_set(theme_light())
theme_update(legend.position="bottom")
*penguins %>%
* ggplot() +
* aes( x= bill_l, y=bill_d, col = species) +
* geom_point()
```
]
 
.panel2-theme_setup_2-user[

```
## Warning: Removed 2 rows containing missing values (geom_point).
```

<img src="visu_files/figure-html/theme_setup_2_user_02_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
name: stat
# Ajouter des résumés statistiques

---
template: stat
## pour illustrer la relation entre 2 variables

---

```r
*gg
```
]
 
.panel2-stat_info_lm-auto[

```
## Warning: Removed 2 rows containing missing values (geom_point).
```

<img src="visu_files/figure-html/stat_info_lm_auto_01_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
count: false

```r
gg +
* geom_smooth(method = 'lm', se = FALSE)
```
]
 
.panel2-stat_info_lm-auto[

```
## `geom_smooth()` using formula 'y ~ x'
```

```
## Warning: Removed 2 rows containing non-finite values (stat_smooth).
```

```
## Warning: Removed 2 rows containing missing values (geom_point).
```

<img src="visu_files/figure-html/stat_info_lm_auto_02_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
count: false

```r
gg +
  geom_smooth(method = 'lm', se = FALSE) +
* geom_smooth(method = 'loess', se = TRUE)
```
]
 
.panel2-stat_info_lm-auto[

```
## `geom_smooth()` using formula 'y ~ x'
```

```
## Warning: Removed 2 rows containing non-finite values (stat_smooth).
```

```
## `geom_smooth()` using formula 'y ~ x'
```

```
## Warning: Removed 2 rows containing non-finite values (stat_smooth).
```

```
## Warning: Removed 2 rows containing missing values (geom_point).
```

<img src="visu_files/figure-html/stat_info_lm_auto_03_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
template: stat
## Exercice

* Proposer un joli graphique pour visualiser la relation entre la longueur de l'aile et le poids. 
* Illustrer les changements potentiels au cours du temps ?
* En utilisant de belles couleurs
* en utilisant la palette `Zissou1` du package `wesanderson`.

```r
penguins %>% ggplot() + 
  aes(x= bm, y = flip_l, col = as_factor(year) ) + 
  geom_point() + geom_smooth(method = 'lm') +
  scale_color_manual(values = wesanderson::wes_palette(name = "Darjeeling1"))
```

---
name: autres
# de nombreuses autres possibilités à explorer

---
template: autres
## Comparing plots

---

```r
*penguins
```
]
 
.panel2-color_hist_facet-auto[

---
count: false

```r
penguins %>%
* ggplot() + aes(x = bill_l, fill = species)
```
]
 
.panel2-color_hist_facet-auto[
<img src="visu_files/figure-html/color_hist_facet_auto_02_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
count: false

```r
penguins %>%
  ggplot() + aes(x = bill_l, fill = species) +
* geom_histogram()
```
]
 
.panel2-color_hist_facet-auto[

```
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
```

```
## Warning: Removed 2 rows containing non-finite values (stat_bin).
```

<img src="visu_files/figure-html/color_hist_facet_auto_03_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
count: false

```r
penguins %>%
  ggplot() + aes(x = bill_l, fill = species) +
  geom_histogram() +
* facet_wrap(~species)
```
]
 
.panel2-color_hist_facet-auto[

```
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
```

```
## Warning: Removed 2 rows containing non-finite values (stat_bin).
```

<img src="visu_files/figure-html/color_hist_facet_auto_04_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
count: false

```r
penguins %>%
  ggplot() + aes(x = bill_l, fill = species) +
  geom_histogram() +
  facet_wrap(~species) +
* labs( x = 'Bill length in mm')
```
]
 
.panel2-color_hist_facet-auto[

```
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
```

```
## Warning: Removed 2 rows containing non-finite values (stat_bin).
```

<img src="visu_files/figure-html/color_hist_facet_auto_05_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
count: false

```r
penguins %>%
  ggplot() + aes(x = bill_l, fill = species) +
  geom_histogram() +
  facet_wrap(~species) +
  labs( x = 'Bill length in mm') +
* scale_fill_manual(values = wesanderson::wes_palette('Darjeeling1'))
```
]
 
.panel2-color_hist_facet-auto[

```
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
```

```
## Warning: Removed 2 rows containing non-finite values (stat_bin).
```

<img src="visu_files/figure-html/color_hist_facet_auto_06_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
template: autres
## Comparing plots - exercise

Change the previous graph to add a fitted density line

---

```r
*penguins %>%  ggplot() + aes(x = bill_l, y = ..density..) +
* facet_wrap(~species) +
* geom_histogram(alpha=0.5, aes( fill = species)) +
*  geom_density(aes(col = species)) +
* labs( x = 'Bill length in mm')
```
]
 
.panel2-color_hist_dens_cor-user[

```
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
```

```
## Warning: Removed 2 rows containing non-finite values (stat_bin).
```

```
## Warning: Removed 2 rows containing non-finite values (stat_density).
```

<img src="visu_files/figure-html/color_hist_dens_cor_user_01_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
count: false

```r
 penguins %>%  ggplot() + aes(x = bill_l, y = ..density..) +
  facet_wrap(~species) +
  geom_histogram(alpha=0.5, aes( fill = species)) +
   geom_density(aes(col = species)) +
  labs( x = 'Bill length in mm') +
* scale_fill_manual(values = wesanderson::wes_palette('Darjeeling1'))
```
]
 
.panel2-color_hist_dens_cor-user[

```
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
```

```
## Warning: Removed 2 rows containing non-finite values (stat_bin).
```

```
## Warning: Removed 2 rows containing non-finite values (stat_density).
```

<img src="visu_files/figure-html/color_hist_dens_cor_user_02_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
count: false

```r
 penguins %>%  ggplot() + aes(x = bill_l, y = ..density..) +
  facet_wrap(~species) +
  geom_histogram(alpha=0.5, aes( fill = species)) +
   geom_density(aes(col = species)) +
  labs( x = 'Bill length in mm') +
  scale_fill_manual(values = wesanderson::wes_palette('Darjeeling1')) +
* scale_color_manual(values = wesanderson::wes_palette('Darjeeling1'))
```
]
 
.panel2-color_hist_dens_cor-user[

```
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
```

```
## Warning: Removed 2 rows containing non-finite values (stat_bin).
```

```
## Warning: Removed 2 rows containing non-finite values (stat_density).
```

<img src="visu_files/figure-html/color_hist_dens_cor_user_03_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
template: autres

## des boxplots originaux

```r
 penguins %>%  ggplot() + aes(x = species,  y = bill_l) +
  geom_violin(alpha=0.5, aes( fill = species)) +
  labs( y = 'Bill length in mm') + #BREAK
  scale_fill_manual(values = wesanderson::wes_palette('Darjeeling1')) 
```
---

```r
*penguins %>%  ggplot() + aes(x = species,  y = bill_l) +
* geom_violin(alpha=0.5, aes( fill = species)) +
* labs( y = 'Bill length in mm')
```
]
 
.panel2-color_boxplot_2-user[

```
## Warning: Removed 2 rows containing non-finite values (stat_ydensity).
```

<img src="visu_files/figure-html/color_boxplot_2_user_01_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
count: false

```r
 penguins %>%  ggplot() + aes(x = species,  y = bill_l) +
  geom_violin(alpha=0.5, aes( fill = species)) +
  labs( y = 'Bill length in mm') +
* scale_fill_manual(values = wesanderson::wes_palette('Darjeeling1'))
```
]
 
.panel2-color_boxplot_2-user[

```
## Warning: Removed 2 rows containing non-finite values (stat_ydensity).
```

<img src="visu_files/figure-html/color_boxplot_2_user_02_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---

---
template: autres

## des boxplots originaux, qui font apparaître les valeurs

```r
penguins %>%  ggplot() + aes(x = species,  y = bill_l) +
  geom_boxplot(alpha=0.5, aes( fill = species)) +
  geom_jitter(color="black", size=0.4, alpha=0.8) +
  labs( y = 'Bill length in mm') + #BREAK
  scale_fill_manual(values = wesanderson::wes_palette('Darjeeling1')) 
```

---
count: false

```r
*penguins %>%  ggplot() + aes(x = species,  y = bill_l) +
* geom_boxplot(alpha=0.5, aes( fill = species)) +
* geom_jitter(color="black", size=0.4, alpha=0.8) +
* labs( y = 'Bill length in mm')
```
]
 
.panel2-color_boxplot_3-user[

```
## Warning: Removed 2 rows containing non-finite values (stat_boxplot).
```

```
## Warning: Removed 2 rows containing missing values (geom_point).
```

<img src="visu_files/figure-html/color_boxplot_3_user_01_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
count: false

```r
penguins %>%  ggplot() + aes(x = species,  y = bill_l) +
  geom_boxplot(alpha=0.5, aes( fill = species)) +
  geom_jitter(color="black", size=0.4, alpha=0.8) +
  labs( y = 'Bill length in mm') +
* scale_fill_manual(values = wesanderson::wes_palette('Darjeeling1'))
```
]
 
.panel2-color_boxplot_3-user[

```
## Warning: Removed 2 rows containing non-finite values (stat_boxplot).
```

```
## Warning: Removed 2 rows containing missing values (geom_point).
```

<img src="visu_files/figure-html/color_boxplot_3_user_02_output-1.png" width="50%" style="display: block; margin: auto;" />
]

---
template: autres

## Gérer les indices et les exposants

```r
penguins %>%  mutate(mu = bill_l * bill_d) %>% 
  ggplot() + aes(y= mu ) +
  geom_boxplot(alpha=0.5, aes( fill = species)) +
  labs( y = bquote(mu~(mm^2))) +
  scale_fill_manual(values = wesanderson::wes_palette('Darjeeling1')) 
```

```
## Warning: Removed 2 rows containing non-finite values (stat_boxplot).
```

---
name: useful_package

# Useful packages for publication

The `ggpubr`  package is very helpful for publication.

---
template: useful_package

```r
gg_p1 <- gg
gg_p1
```

```
## Warning: Removed 2 rows containing missing values (geom_point).
```

---
template: useful_package

```r
gg_p2 <- penguins %>%
 ggplot() + aes(x = bill_l, y = ..density..) + geom_histogram(alpha=0.5, aes( fill = species)) +
 geom_density(aes(col = species)) +
 labs( x = 'Bill length in mm') +
 scale_fill_manual(values = wesanderson::wes_palette('Darjeeling1')) +
 scale_color_manual(values = wesanderson::wes_palette('Darjeeling1'))
gg_p2
```

```
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
```

```
## Warning: Removed 2 rows containing non-finite values (stat_bin).
```

```
## Warning: Removed 2 rows containing non-finite values (stat_density).
```

---
template: useful_package

```r
##install.packages('ggpubr')
ggpubr::ggarrange(gg_p1, gg_p2, nrow=2, ncol = 1)
```

```
## Warning: Removed 2 rows containing missing values (geom_point).
```

```
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
```

```
## Warning: Removed 2 rows containing non-finite values (stat_bin).
```

```
## Warning: Removed 2 rows containing non-finite values (stat_density).
```

---
template: useful_package

```r
ggpubr::ggarrange(gg_p1, gg_p2, nrow=2, ncol = 1, common.legend = TRUE)
```

```
## Warning: Removed 2 rows containing missing values (geom_point).

## Warning: Removed 2 rows containing missing values (geom_point).
```

```
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
```

```
## Warning: Removed 2 rows containing non-finite values (stat_bin).
```

```
## Warning: Removed 2 rows containing non-finite values (stat_density).
```

---
name: bivariate_explore

# Useful packages for publications/explorations

The `GGally`  package contains plenty of very useful and nice functions

---
template: bivariate_explore
count: false

```r
##install.packages('GGally')
library(GGally)
penguins %>% ggpairs(columns = c(1,3,4,5), mapping = aes(col = species)) +
scale_color_manual(values = wesanderson::wes_palette('Darjeeling1'))+
scale_fill_manual(values = wesanderson::wes_palette('Darjeeling1')) + theme(text = element_text(size = 6))
```

---
template: bivariate_explore
count: false

```r
penguins %>% ggpairs(columns = c(1,3,4,5), mapping = aes(col = species),
                     upper = list(continuous = wrap( "cor",size = 2)),
                     lower = list(continuous = wrap('points', size = .5))) +
  scale_color_manual(values = wesanderson::wes_palette('Darjeeling1'))+
  scale_fill_manual(values = wesanderson::wes_palette('Darjeeling1')) + theme(text = element_text(size = 6))
```

---

# Useful packages for presentation

The `gganimate`  package produces animated graph (html only).

```r
library(gganimate)
gg  +
  transition_states(year)  +
  geom_text(x = 56 , y = 15,
            aes(label = as.character(year)),
            size = 8, col = "grey50") +
  theme(legend.position="bottom") 
```

---
name: plotly

# Useful packages for online reporting

The `plotly`  package produces interactive plots  (html only).

```r
library(plotly)
gg  %>%  ggplotly()
```

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---

# Inspiring websites

- [R Graph Gallery](https://www.r-graph-gallery.com/)
- [Top50 ggplot Visualisation](http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html)

---

# References

Cornillon, P., A. Guyader, F. Husson, et al. (2018). _R pour la
statistique et la science des données_. Presses universitaires de
Rennes.

Daudin, J. (2015). _Le modèle linéaire et ses extensions-Modèle
linéaire général, modèle linéaire généralisé, modèle mixte, plans
d'expériences (Niveau C)_. Edition Ellipses.

Faraway, J. J. (2014). _Linear models with R_. CRC press.

Hutcheon, J. M., J. A. W. Kirsch, and T. Garland (2002). "A Comparative
Analysis of Brain Size in Relation to Foraging Ecology and Phylogeny in
the Chiroptera". In: _Brain, Behavior and Evolution_ 60.3, pp. 165-180.
ISSN: 1421-9743. DOI:
[10.1159/000065938](https://doi.org/10.1159%2F000065938). URL:
[http://dx.doi.org/10.1159/000065938](http://dx.doi.org/10.1159/000065938).

R Core Team (2017). _R: A Language and Environment for Statistical
Computing_. R Foundation for Statistical Computing. Vienna, Austria.
URL: [https://www.R-project.org/](https://www.R-project.org/).

Wickham, H. and G. Grolemund (2016). _R for data science: import, tidy,
transform, visualize, and model data_. " O'Reilly Media, Inc.".