Visualisation de données

# Visualisation de données
## Pourquoi et comment ?
### Marie-Pierre Etienne
### <a href="https://marieetienne.github.io/" class="uri">https://marieetienne.github.io/</a>
### 2020/09/11 (updated: 2022-10-28)

---

# 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)

<img src="../resources/img/corona_gf.png" width="1836" height="40%" />
- [Incidence par G. Forestier (CovidTracker)](https://raw.githubusercontent.com/CovidTrackerFr/covidtracker-data/master/../resources/img/charts/france/heatmaps_deps/heatmap_taux_35.jpeg)

---
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/) [WG17]
- [R Graphics Cookbook](http://www.cookbook-r.com/)
- [ggplot2: Elegant Graphics for Data Analysis](https://ggplot2-book.org/index.html) [Wic16]
- [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))

---
name: manip
# Data manipulation

---
template: manip

The tidyverse is designed to facilitate data manipulation and we will use  the `%>%` operator.

```r
library(tidyverse)
```

---
template: manip

The tidyverse is designed to facilitate data manipulation and we will use  the `%>%` operator.

```r
library(tidyverse)
```

<div class="figure">
<img src="../resources/img/tidyverse.png" alt="Extract from https://mtes-mct.github.io/parcours-r/m5/package-ggplot2.html" width="80%" height="60%" />
Extract from https://mtes-mct.github.io/parcours-r/m5/package-ggplot2.html
</div>
---
template: manip

## The %>% operator

```r
set.seed(1)
n <- 8
data.frame(x = sample(1:10, size = n, replace = TRUE), 
 y = 3*rnorm(n)) %>% 
 mutate(xplusy = x + y ) %>% 
 filter( xplusy > 4) %>% 
 select(x, y)
```

---

```r
*set.seed(1)
```
]
 
.panel2-tidy_ex-auto[

]

---
count: false

```r
set.seed(1)
*n <- 8
```
]
 
.panel2-tidy_ex-auto[

]

---
count: false

```r
set.seed(1)
n <- 8
*data.frame(x = sample(1:10, size = n, replace = TRUE), y = 3*rnorm(n))
```
]
 
.panel2-tidy_ex-auto[

```
##   x          y
## 1 9  1.4622872
## 2 4  2.2149741
## 3 7  1.7273441
## 4 1 -0.9161652
## 5 2  4.5353435
## 6 7  1.1695297
## 7 2 -1.8637217
## 8 3 -6.6440997
```
]

---
count: false

```r
set.seed(1)
n <- 8
data.frame(x = sample(1:10, size = n, replace = TRUE), y = 3*rnorm(n)) %>%
* mutate(xplusy = x + y )
```
]
 
.panel2-tidy_ex-auto[

```
##   x          y      xplusy
## 1 9  1.4622872 10.46228716
## 2 4  2.2149741  6.21497412
## 3 7  1.7273441  8.72734405
## 4 1 -0.9161652  0.08383484
## 5 2  4.5353435  6.53534351
## 6 7  1.1695297  8.16952971
## 7 2 -1.8637217  0.13627826
## 8 3 -6.6440997 -3.64409966
```
]

---
count: false

```r
set.seed(1)
n <- 8
data.frame(x = sample(1:10, size = n, replace = TRUE), y = 3*rnorm(n)) %>%
 mutate(xplusy = x + y ) %>%
* filter( xplusy > 4)
```
]
 
.panel2-tidy_ex-auto[

```
##   x        y    xplusy
## 1 9 1.462287 10.462287
## 2 4 2.214974  6.214974
## 3 7 1.727344  8.727344
## 4 2 4.535344  6.535344
## 5 7 1.169530  8.169530
```
]

---
count: false

```r
set.seed(1)
n <- 8
data.frame(x = sample(1:10, size = n, replace = TRUE), y = 3*rnorm(n)) %>%
 mutate(xplusy = x + y ) %>%
 filter( xplusy > 4) %>%
* select(x, y)
```
]
 
.panel2-tidy_ex-auto[

```
##   x        y
## 1 9 1.462287
## 2 4 2.214974
## 3 7 1.727344
## 4 2 4.535344
## 5 7 1.169530
```
]

---
template: manip

## Useful commands

- `mutate` to create a new column,
- `filter` to select lines according to some conditions,
- `select` to select some columss,

- `rename` to rename variables,
- `group_by`  for grouping factor,
- `summarise` to cumputate summary statistics,
- ...

---
template: manip
## First running example:  The Palmer Penguins dataset

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

---
template: manip
## A case study running example:  The bats dataset

```r
data(bats,package = "coursesdata")
bats %>%   as_tibble() %>% print(n = 2, na.print = NULL)
```

```
## # A tibble: 63 × 8
## Species Diet Clade BOW BRW AUD MOB HIP
## <chr> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 " Rousettus aegyptiacus " 1 I 136. 2070 9.88 106. 126.
## 2 " Epomops franqueti " 1 I 120 2210 10.4 108. 160.
## # … with 61 more rows
```

---
template: manip

##  Combining group_by, mutate and summarise

```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 × 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
```

---

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

```
## # A tibble: 344 × 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 %>%
* group_by(species, sex, year, island)
```
]
 
.panel2-show_data_palmer_flip-auto[

```
## # A tibble: 344 × 8
## # Groups: species, sex, year, island [35]
## 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 %>%
  group_by(species, sex, year, island) %>%
* mutate(n = n())
```
]
 
.panel2-show_data_palmer_flip-auto[

```
## # A tibble: 344 × 9
## # Groups: species, sex, year, island [35]
## species island bill_l bill_d flip_l bm sex year n
## <fct> <fct> <dbl> <dbl> <int> <int> <fct> <int> <int>
## 1 Adelie Torgersen 39.1 18.7 181 3750 male 2007 7
## 2 Adelie Torgersen 39.5 17.4 186 3800 female 2007 8
## 3 Adelie Torgersen 40.3 18 195 3250 female 2007 8
## 4 Adelie Torgersen NA NA NA NA <NA> 2007 5
## 5 Adelie Torgersen 36.7 19.3 193 3450 female 2007 8
## 6 Adelie Torgersen 39.3 20.6 190 3650 male 2007 7
## 7 Adelie Torgersen 38.9 17.8 181 3625 female 2007 8
## 8 Adelie Torgersen 39.2 19.6 195 4675 male 2007 7
## 9 Adelie Torgersen 34.1 18.1 193 3475 <NA> 2007 5
## 10 Adelie Torgersen 42 20.2 190 4250 <NA> 2007 5
## # … with 334 more rows
```
]

---
count: false

```r
penguins %>%
  group_by(species, sex, year, island) %>%
  mutate(n = n()) %>%
* summarise_if(is.numeric, mean, na.rm = TRUE)
```
]
 
.panel2-show_data_palmer_flip-auto[

---
count: false

```r
penguins %>%
  group_by(species, sex, year, island) %>%
  mutate(n = n()) %>%
  summarise_if(is.numeric, mean, na.rm = TRUE) %>%
* print(n=10)
```
]
 
.panel2-show_data_palmer_flip-auto[

---
template: manip
class: inverse

---
template: manip
class: inverse

##  Combining group_by, mutate and summarise - Your turn!

1. create a Diet_fact colum, where Diet is understood as a factor
2. count the number of species by Diet
3. Compute the average body weight per Diet, and per Diet x Clade
4. Compute the mean and standard deviation for Brain weight per Diet

---
name: ggplot
# Visualization thanks to ggplot

---
template: ggplot

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* [Wic16].

*[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.*  .

---
 
# Simple plots

---
name: scatter_slide
# A scatter plot

---

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

---
count: false

```r
penguins %>%
* ggplot()
```
]
 
.panel2-simple_scatter-auto[
![](visu_intro_files/figure-html/simple_scatter_auto_02_output-1.png)
]

---
count: false

```r
penguins %>%
 ggplot() +
* aes( x= bill_l, y=bill_d)
```
]
 
.panel2-simple_scatter-auto[
![](visu_intro_files/figure-html/simple_scatter_auto_03_output-1.png)
]

---
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).
```

![](visu_intro_files/figure-html/simple_scatter_auto_04_output-1.png)
]

---

---
name: scatter_slide_col
# A colored scatter plot

---

```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).
```

![](visu_intro_files/figure-html/scatter_plot_species_user_01_output-1.png)
]

---
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).
```

![](visu_intro_files/figure-html/scatter_plot_species_user_02_output-1.png)
]

---
template: scatter_slide_col
count: false

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

## with a color blind compliant palette

---

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

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

![](visu_intro_files/figure-html/scatter_viridis_auto_01_output-1.png)
]

---
count: false

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

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

![](visu_intro_files/figure-html/scatter_viridis_auto_02_output-1.png)
]

---
template: scatter_slide_col
count: false

##  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
```
]
 
.panel2-scatter_wesanderson-auto[

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

![](visu_intro_files/figure-html/scatter_wesanderson_auto_01_output-1.png)
]

---
count: false

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

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

![](visu_intro_files/figure-html/scatter_wesanderson_auto_02_output-1.png)
]

---
template: scatter_slide_col
class: inverse

---
class: inverse
template: scatter_slide_col
count: true

## Visualizing the relation between Body weight and Brain weight among bats species

Propose a fancy graph to visualize the relation between Brain weight and Body weight. Does this relation differs between Diets ?

---
template: scatter_slide_col
count: true

Propose a fancy graph to visualize the relation between flipper and body mass. Does this relation change over year ?

```r
penguins %>% ggplot() +
  aes(x= bm, y = flip_l, col = as_factor(year) ) + 
  geom_point() + 
  scale_color_manual(values = wesanderson::wes_palette(name = "Zissou1"))
```

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

![](visu_intro_files/figure-html/exo_relation_cor-1.png)

---
template: scatter_slide_col

## 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)

---
name: scatter_slide_lab
count: true

# Properly labeled colored graph

---
template: scatter_slide_lab

```r
gg <- gg + scale_color_manual(values = wesanderson::wes_palette(name = "Zissou1"))
```

---

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

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

![](visu_intro_files/figure-html/scatter_labels_auto_01_output-1.png)
]

---
count: false

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

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

![](visu_intro_files/figure-html/scatter_labels_auto_02_output-1.png)
]

---
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).
```

![](visu_intro_files/figure-html/scatter_labels_auto_03_output-1.png)
]

---
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).
```

![](visu_intro_files/figure-html/scatter_labels_auto_04_output-1.png)
]

---
template: scatter_slide_lab
count: true

## using  a pre-set general theme

---

```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).
```

![](visu_intro_files/figure-html/scatter_themelight_user_01_output-1.png)
]

---
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).
```

![](visu_intro_files/figure-html/scatter_themelight_user_02_output-1.png)
]

---
template: scatter_slide_lab
count: false

## and another pre set general theme

---

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

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

![](visu_intro_files/figure-html/scatter_thememinimal_show-1.png)

---

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

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

![](visu_intro_files/figure-html/scatter_thememinimal_user_01_output-1.png)
]

---
count: false

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

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

![](visu_intro_files/figure-html/scatter_thememinimal_user_02_output-1.png)
]

---
template: scatter_slide_lab
count: false

## and a pre set general theme

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

---
template: scatter_slide_lab
count: true

## Defining s specific theme

The default theme might not be the best option

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

![](visu_intro_files/figure-html/preset_theme-1.png)

---

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

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

![](visu_intro_files/figure-html/scatter_below_auto_01_output-1.png)
]

---
count: false

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

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

![](visu_intro_files/figure-html/scatter_below_auto_02_output-1.png)
]

---
template: scatter_slide_lab
count: false

## and a custom  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).
```

![](visu_intro_files/figure-html/scatter_within-1.png)

---
template: scatter_slide_lab
count: false

## and a custom  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).
```

![](visu_intro_files/figure-html/scatter_custom-1.png)

---
name: sum_info

# Summarizing information for easier reading

---
template: sum_info
count: true

## with fitted statistical models

---

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

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

![](visu_intro_files/figure-html/stat_info_lm_auto_01_output-1.png)
]

---
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).
```

![](visu_intro_files/figure-html/stat_info_lm_auto_02_output-1.png)
]

---
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).
```

![](visu_intro_files/figure-html/stat_info_lm_auto_03_output-1.png)
]

---
template: scatter_slide_col
class: inverse

---
class: inverse
template: scatter_slide_lab
count: true

## Visualizing the relation between Body weight and Brain weight among bats species

Propose a fancy graph to visualize the relation between Brain weight and Body weight. Does this relation differs between Diets ?

---
template: scatter_slide_lab
count: true

Propose a fancy graph to visualize the relation between flipper and body mass. Does this relation change over year ?

```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")) +
  labs( x = 'Bill length in mm',  y = 'Bill depth in mm',  color = "Species") 
```

```
## `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).
```

![](visu_intro_files/figure-html/exo_relation_cor2-1.png)

---

To keep the theme preferences for all plots to come

---

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

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

![](visu_intro_files/figure-html/theme_setup_user_01_output-1.png)
]

---
count: false

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

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

![](visu_intro_files/figure-html/theme_setup_user_02_output-1.png)
]

---

```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).
```

![](visu_intro_files/figure-html/theme_setup_2_user_02_output-1.png)
]

---
name: plot_univar
# Representing univariate distribution

---
template: plot_univar
count: false

```r
penguins %>%
  ggplot() + aes(x = bill_l) + geom_histogram() +
  labs( x = 'Bill length in mm') +
  theme_minimal()
```

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

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

![](visu_intro_files/figure-html/simple_hist-1.png)

---
template: plot_univar
class: inverse

---
template: plot_univar
class: inverse

Represent the distribution of body weight according to diet among bats species.

---
template: plot_univar

Represent the distribution of bill length according to species.

---

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

---
count: false

```r
penguins %>%
* ggplot() + aes(x = bill_l, fill = species)
```
]
 
.panel2-color_hist_cor-auto[
![](visu_intro_files/figure-html/color_hist_cor_auto_02_output-1.png)
]

---
count: false

```r
penguins %>%
  ggplot() + aes(x = bill_l, fill = species) +
* geom_histogram(  position = "identity", alpha = 0.5)
```
]
 
.panel2-color_hist_cor-auto[

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

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

![](visu_intro_files/figure-html/color_hist_cor_auto_03_output-1.png)
]

---
count: false

```r
penguins %>%
  ggplot() + aes(x = bill_l, fill = species) +
  geom_histogram(  position = "identity", alpha = 0.5) +
* labs( x = 'Bill length in mm')
```
]
 
.panel2-color_hist_cor-auto[

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

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

![](visu_intro_files/figure-html/color_hist_cor_auto_04_output-1.png)
]

---
count: false

```r
penguins %>%
  ggplot() + aes(x = bill_l, fill = species) +
  geom_histogram(  position = "identity", alpha = 0.5) +
  labs( x = 'Bill length in mm') +
* scale_fill_manual(values = wesanderson::wes_palette('Zissou1', n = 3))
```
]
 
.panel2-color_hist_cor-auto[

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

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

![](visu_intro_files/figure-html/color_hist_cor_auto_05_output-1.png)
]

---
template: plot_univar

Represent the distribution of bill length according to species.

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

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

![](visu_intro_files/figure-html/color_hist_cor_show-1.png)

.care[Note the use of the position and alpha parameters.]
---
name: compare_plot
# Comparing plots : facetting

---

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

---
count: false

```r
penguins %>%
* ggplot() + aes(x = bill_l, fill = species)
```
]
 
.panel2-color_hist_facet-auto[
![](visu_intro_files/figure-html/color_hist_facet_auto_02_output-1.png)
]

---
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).
```

![](visu_intro_files/figure-html/color_hist_facet_auto_03_output-1.png)
]

---
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).
```

![](visu_intro_files/figure-html/color_hist_facet_auto_04_output-1.png)
]

---
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).
```

![](visu_intro_files/figure-html/color_hist_facet_auto_05_output-1.png)
]

---
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).
```

![](visu_intro_files/figure-html/color_hist_facet_auto_06_output-1.png)
]

---
template: compare_plot

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

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

![](visu_intro_files/figure-html/color_hist_facet_sho-1.png)

---
template: compare_plot
count: false
class: inverse

---
template: compare_plot
count: false

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).
```

![](visu_intro_files/figure-html/color_hist_dens_cor_user_01_output-1.png)
]

---
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).
```

![](visu_intro_files/figure-html/color_hist_dens_cor_user_02_output-1.png)
]

---
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).
```

![](visu_intro_files/figure-html/color_hist_dens_cor_user_03_output-1.png)
]

---
name: boxplot
# Comparing distribution

---
template: boxplot
count: false

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

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

![](visu_intro_files/figure-html/color_boxplot-1.png)

---

```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).
```

![](visu_intro_files/figure-html/color_boxplot_2_user_01_output-1.png)
]

---
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).
```

![](visu_intro_files/figure-html/color_boxplot_2_user_02_output-1.png)
]

---

```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).
```

![](visu_intro_files/figure-html/color_boxplot_3_user_01_output-1.png)
]

---
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).
```

![](visu_intro_files/figure-html/color_boxplot_3_user_02_output-1.png)
]

---
name: annotating
# Precise labelling

---
template: annotating
count: false

## Handling superscript and subscript

```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).
```

![](visu_intro_files/figure-html/color_boxplot_sup_sub-1.png)

---
name: useful_package

# Useful packages for publication

The `ggpubr`  package is very helpful for publication.

---
template: useful_package
count: false

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

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

![](visu_intro_files/figure-html/ggpubr_p1-1.png)

---
template: useful_package
count: false

```r
gg_p2 <- penguins %>%
 ggplot() + aes(x = bill_l, y = ..density..) + geom_histogram(alpha=0.5, position = "identity", aes( fill = species)) +
 geom_density(aes(col = species)) +
 labs( x = 'Bill length in mm') +
 scale_fill_manual(values = wesanderson::wes_palette('Zissou1')) +
 scale_color_manual(values = wesanderson::wes_palette('Zissou1'))
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).
```

![](visu_intro_files/figure-html/ggpubr_p2-1.png)

---
template: useful_package
count: false

```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).
```

![](visu_intro_files/figure-html/ggpubr_p3-1.png)

---
template: useful_package
count: false

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

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

![](visu_intro_files/figure-html/ggpubr_p4-1.png)

---
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)
```

```
## Warning: package 'GGally' was built under R version 4.0.5
```

```
## Registered S3 method overwritten by 'GGally':
##   method from   
##   +.gg   ggplot2
```

```r
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))
```

```
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 2 rows containing missing values
```

```
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 2 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 2 rows containing missing values
```

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