Visualización de datos: ggplot2 strikes back

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# Visualización de datos:<br>*ggplot2 strikes back*
### Análisis estadístico utilizando R

UNQ UNTreF CONICET

Ignacio Spiousas
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Pablo Etchemendy
[<svg viewBox="0 0 496 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:black;" xmlns="http://www.w3.org/2000/svg">  <path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"></path></svg>](https://github.com/https://github.com/petcheme) [<svg viewBox="0 0 512 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:#black;" xmlns="http://www.w3.org/2000/svg">  <path d="M459.37 151.716c.325 4.548.325 9.097.325 13.645 0 138.72-105.583 298.558-298.558 298.558-59.452 0-114.68-17.219-161.137-47.106 8.447.974 16.568 1.299 25.34 1.299 49.055 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.112-72.772 6.498.974 12.995 1.624 19.818 1.624 9.421 0 18.843-1.3 27.614-3.573-48.081-9.747-84.143-51.98-84.143-102.985v-1.299c13.969 7.797 30.214 12.67 47.431 13.319-28.264-18.843-46.781-51.005-46.781-87.391 0-19.492 5.197-37.36 14.294-52.954 51.655 63.675 129.3 105.258 216.365 109.807-1.624-7.797-2.599-15.918-2.599-24.04 0-57.828 46.782-104.934 104.934-104.934 30.213 0 57.502 12.67 76.67 33.137 23.715-4.548 46.456-13.32 66.599-25.34-7.798 24.366-24.366 44.833-46.132 57.827 21.117-2.273 41.584-8.122 60.426-16.243-14.292 20.791-32.161 39.308-52.628 54.253z"></path></svg>](https://twitter.com/petcheme)

**Agosto 2021**

---
class: left, top, highlight-last-item
# ggplot2 📈

En esta breve presentación vamos a hablar un poco de geometrías más complejas, como algunas **geoms_** y **stats_**

---
class: left, top, highlight-last-item
# geom_bar

Con **geom_col()** hay que calcular la media antes.

.pull-left[

```r
penguins %>%
    group_by(species) %>%
    summarise(mMass = mean(body_mass_g,
                           na.rm = TRUE)) %>%
    ggplot(aes(x = species,
               y = mMass,
               fill = species)) +
*   geom_col()
```
]

.pull-right[
<img src="ggplot_2_files/figure-html/bar1-out-1.png" width="60%" style="display: block; margin: auto;" />
]

Usando **geom_bar()** se calcula la media de la magnitud en **y** para cada valor de **x**

.pull-left[

```r
penguins %>%
    ggplot(aes(x = species,
               y = body_mass_g,
               fill = species)) +
*   geom_bar(fun = "mean",
*            stat = "summary")
```
]

.pull-right[
<img src="ggplot_2_files/figure-html/bar2-out-1.png" width="60%" style="display: block; margin: auto;" />
]

---
class: left, top, highlight-last-item
# stat_count

**stat_count** cumple la misma tarea que **geom_bar()** pero contando los casos

.pull-left[

```r
penguins %>%
    group_by(species) %>%
    summarise(N = n()) %>%
    ggplot(aes(x = species,
               y = N,
               fill = species)) +
*   geom_col()
```
]

.pull-right[
<img src="ggplot_2_files/figure-html/count1-out-1.png" width="60%" style="display: block; margin: auto;" />
]

Podemos reemplazar ese pipe con esto:

.pull-left[

```r
penguins %>%
  ggplot(aes(x = species,
             fill = species)) +
* stat_count()
```
]

.pull-right[
<img src="ggplot_2_files/figure-html/count-out-1.png" width="60%" style="display: block; margin: auto;" />
]

---
class: left, top, highlight-last-item
# stat_summary

Usando **stat_summary()** agrega de alguna forma la magnitud en **y** para cada valor de **x**

Por ejemplo, podemos calcular la media y el error estándar:

.pull-left[

```r
penguins %>%
    ggplot(aes(x = species,
               y = body_mass_g,
               color = species)) +
*   stat_summary(fun.data = mean_se)
```
]

.pull-right[
<img src="ggplot_2_files/figure-html/summary1-out-1.png" width="55%" style="display: block; margin: auto;" />
]

O en formato barra:

.pull-left[

```r
penguins %>%
  ggplot(aes(x = species,
               y = body_mass_g,
               fill = species)) +
* stat_summary(fun.data = mean_se,
*                geom = "bar") +
* stat_summary(fun.data = mean_se,
*                geom = "errorbar")
```
]

.pull-right[
<img src="ggplot_2_files/figure-html/summary2-out-1.png" width="55%" style="display: block; margin: auto;" />
]

---
class: left, top, highlight-last-item
# geom_boxplot

Un boxplot muestra de forma compacta la distribución de una variable continua.

Es posible visualizar la **mediana**, el **IQR** y los **Outliers**

.pull-left[

```r
penguins %>%
  ggplot(aes(x = species,
             y = body_mass_g,
             color = species)) +
* geom_boxplot()
```
]

.pull-right[
<img src="ggplot_2_files/figure-html/boxplot1-out-1.png" width="55%" style="display: block; margin: auto;" />
]

También lo podemos usar para ver los datos y las estadísticas descriptivas

.pull-left[

```r
penguins %>%
  ggplot(aes(x = species,
               y = body_mass_g,
               color = species)) +
* geom_boxplot(outlier.shape = NA) +
* geom_jitter(width = 0.2)
```
]

.pull-right[
<img src="ggplot_2_files/figure-html/boxplot2-out-1.png" width="55%" style="display: block; margin: auto;" />
]

???
Mencionar que se puede ajustar el criterio de outliers en base al IQR
---
class: left, top, highlight-last-item
# geom_violin

**geom_violin()** nos permite graficar un **violin plot** de los datos

Un **Violin plot** es una forma compacta de mostrar una distribución continua

Es una mezcla entre un **geom_boxplot()** y **geom_density()**

.pull-left[

```r
penguins %>%
    ggplot(aes(x = species,
               y = body_mass_g,
               fill = species)) +
*   geom_violin()
```
]

.pull-right[
<img src="ggplot_2_files/figure-html/violin-out-1.png" width="80%" style="display: block; margin: auto;" />
]

Combinado con los datos crudos podemos hacer un **raincloud plot**, pero esa es historia para más adelnte

---
class: left, top, highlight-last-item
# geom_smooth

**geom_smooth** ayuda a ver patrones en nubes de puntos suporponiendo una capa suavizada

.pull-left[

```r
penguins %>%
  filter(species == "Adelie") %>%
  ggplot(aes(x = bill_length_mm,
             y = bill_depth_mm)) +
  geom_point() + 
* geom_smooth()
```
]

.pull-right[
<img src="ggplot_2_files/figure-html/smooth1-out-1.png" width="55%" style="display: block; margin: auto;" />
]

Se puede forzar la **geometrìa** de esa capa

.pull-left[

```r
penguins %>%
  filter(species == "Adelie") %>%
  ggplot(aes(x = bill_length_mm,
             y = bill_depth_mm)) +
  geom_point() + 
* geom_smooth(se = FALSE,
*             method = lm)
```
]

.pull-right[
<img src="ggplot_2_files/figure-html/smooth2-out-1.png" width="55%" style="display: block; margin: auto;" />
]

---
class: left, top, highlight-last-item
# geom_hex

**geom_hex** divide el plano en polígonos regulares y cuenta la candidad de casos en cada **bin**

Es una especie de **histograma bidimensional** o **heathmap discreto**

```r
penguins %>%
  filter(species == "Adelie") %>%
  ggplot(aes(x = bill_length_mm,
             y = bill_depth_mm)) +
* geom_hex(binwidth = c(1, 0.5))
```

---
class: left, top, highlight-last-item
# Para seguir investigando...

Pueden seguir investigando más **geoms**, capas y escalas [acá](https://ggplot2.tidyverse.org/reference/)

![:scale 60%](figs/referencia.png)
---
class: center, top
# Referencias

.left[.big[
- Nordmann, E., McAleer, P., Toivo, W., Paterson, H., & DeBruine, L. (2021). Data visualisation using R, for researchers who don't use R.

- Wickham, H. (2011). ggplot2. Wiley Interdisciplinary Reviews: Computational Statistics, 3(2), 180-185.

]]