Introdução ao uso de dados geoespaciais no R

# Introdução ao uso de dados geoespaciais no R 
## 5 Visualização de dados
### Maurício H. Vancine Milton C. Ribeiro
### UNESP - Rio Claro Laboratório de Ecologia Espacial e Conservação (LEEC)
### 25/10/2021-05/11/2021

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# 5 Visualização de dados

## Conteúdo

1. Contextualização
1. Pacotes para produção de gráficos
1. Gramática dos gráficos
1. Principal material de estudo 
1. Principais tipos de gráficos 
1. Histograma e Densidade
1. Gráfico de setores
1. Gráfico de barras
1. Gráfico de caixas
1. Gráfico de dispersão
1. Gráfico pareado
1. Combinando gráficos
1. Gráficos animados
1. Gráficos interativos
1. Gráficos usando interface

[ggplot2](https://ggplot2.tidyverse.org/)

---

# 5 Visualização de dados

## Script

## .center[`05_script_intro_geoespacial_r.R`]

---

# 1. Contextualização

## Descrição

### Melhor forma de **apresentar, sintetizar, discutir e interpretar** seus dados

### Necessário em quase todas as **análises estatísticas**

### Necessário em quase todas as **publicações**, trabalhos de consultoria, TCC, dissertação, tese, etc.

### Existem vários **tipos de gráficos** para representar os padrões em seus dados para **diferentes tipos de finalidades**

### De forma simplificada, os gráficos são **representações** dos nossos dados tabulares

---

# 2. Pacotes para produção de gráficos

### Principais pacotes

#### [graphics](http://search.r-project.org/R/library/graphics/html/00Index.html): simples, porém útil para visualizações rápidas de quase todos os formatos de arquivos

#### [ggplot2](https://ggplot2.tidyverse.org/): pacote integrado ao tidyverse, possui uma sintaxe própria baseada na gramática de gráficos por camadas (layers)

#### [ggplot2 extentions](https://exts.ggplot2.tidyverse.org/): conjunto de pacotes que adicionam diversas expansões ao pacote ggplot2. Exemplos: [GGally](https://ggobi.github.io/ggally/), [gganimate](https://gganimate.com/) e [esquisse](https://dreamrs.github.io/esquisse/)

#### [visdat](https://docs.ropensci.org/visdat/): cria visualizações preliminares de um conjunto de dados inteiro para identificar problemas ou recursos inesperados usando `ggplot2`

#### [DataExplorer](http://boxuancui.github.io/DataExplorer/): automatiza a maior parte do manuseio e visualização de dados, para Análise Exploratória de Dados usando `ggplot2`

#### [ggpubr](https://rpkgs.datanovia.com/ggpubr/): fornece algumas funções simplificadas para criar gráficos para publicação, baseados no `ggplot2`

#### [cowplot]((https://wilkelab.org/cowplot/): pacote que permite combinar vários gráficos de forma elaborada

#### [pacthwork](https://patchwork.data-imaginist.com/): pacote que permite combinar vários gráficos em um só de forma extremamente simples e com alta qualidade

#### [plotly](https://plotly.com/r/): pacote para criar gráficos interativos da web por meio da biblioteca gráfica de JavaScript de código aberto plotly.js

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[graphics](http://search.r-project.org/R/library/graphics/html/00Index.html)

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background-image: url(img/package_graphics.png)
background-size: 250px
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# 2. Principais pacotes para gráficos

## **graphics**

### Default do R e mais simples

### Pode ser utilizado para objetos de **diversas classes**

### Possui funções como:

`plot()` 
`hist()` 
`barplot()` 
`boxplot()` 
`abline()` 
`points()` 
`lines()` 
`polygon()`

---

# 2. Principais pacotes para gráficos

## **graphics**

```r
# graphics
plot(flipper_length_mm ~ body_mass_g, data = penguins)
```

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background-size: 400px
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[ggplot2](https://ggplot2.tidyverse.org/)

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background-size: 600px
background-position: 50% 50%

# 2. Principais pacotes para gráficos

## Data Visualization Cheatsheet

[Data Visualization with ggplot2 Cheatsheet](https://github.com/rstudio/cheatsheets/raw/master/data-visualization-2.1.pdf)

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# 2. Principais pacotes para gráficos

## **ggplot2**

### Integrado ao tidyverse, possui uma sintaxe própria

### Necessita de funções específicas para objetos de **classes diferentes**

### Estruturado dessa forma:

`ggplot() +` 
`aes() +` 
`geom_() +` 
`facet_() +` 
`stats_() +` 
`coord_() +` 
`theme_()`

---

# 2. Principais pacotes para gráficos

## **ggplot2**

```r
# ggplot2
library(ggplot2)
ggplot(data = penguins) + aes(x = body_mass_g, y = flipper_length_mm) + geom_point()
```

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[ggpubr](https://rpkgs.datanovia.com/ggpubr/)

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background-size: 250px
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# 2. Principais pacotes para gráficos

## **ggpubr**

### Funções fáceis de usar para criar e personalizar plots para publicações baseadas no "ggplot2"

### Necessita de funções específicas para gerar **gráficos específicos**

### Funções:

`gghistogram()` 
`ggdensity()` 
`ggboxplot()` 
`ggviolin()` 
`ggbarplot()` 
`ggscatter()`

---

# 2. Principais pacotes para gráficos

## **ggpubr**

```r
# ggpubr
library(ggpubr)
ggscatter(penguins, x = "body_mass_g", y = "flipper_length_mm")
```

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# 3. Gramática dos gráficos

## Descrição

### Wilkinson (2005) *Grammar of Graphics*: representação gráfica dos dados a partir de atributos estéticos (do inglês *aesthetic*)

[Wilkinson (2005)](https://www.springer.com/gp/book/9780387245447)

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# 3. Gramática dos gráficos

## Descrição

[Think About the Grammar of Graphics When Improving Your Graphs](https://medium.com/tdebeus/think-about-the-grammar-of-graphics-when-improving-your-graphs-18e3744d8d18)

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# 3. Gramática dos gráficos

## Descrição

### Wickham (2008) criou o pacote **ggplot2**, onde “gg” representa a contração de Grammar of Graphics

[Wickham (2008)](https://ggplot2-book.org/)

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# 4. Principal material de estudo

## Livros

### O idealizador e mantenedor do pacote **ggplot2** foi de novo o **Hadley Wickham**

[Hadley Wickham](http://hadley.nz/)

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# 4. Principal material de estudo

## Livros

### ggplot2 (2009, 2016)

[Wickham (2009, 2016)](https://ggplot2.tidyverse.org/)

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# 4. Principal material de estudo

## Livros

### R Graphics Cookbook (2018)

[Chang (2018)](http://www.cookbook-r.com/Graphs/)

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# 4. Principal material de estudo

## Livros

### R for Data Science (2017)

[Wickham & Grolemund (2017)](https://r4ds.had.co.nz/)

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# 4. Principal material de estudo

## Livros

### Data Visualization with R (2018)

[Kabacoff (2018)](https://rkabacoff.github.io/datavis/)

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# 4. Principal material de estudo

## Livros

### Fundamentals of Data Visualization (2019)

[Wilke (2019)](https://serialmentor.com/dataviz/)

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# 4. Principal material de estudo

## Livros

### Interactive web-based data visualization with R, plotly, and shiny (2019)

[Sievert (2019)](https://plotly-r.com/)

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# 4. Principal material de estudo

## Sites

### ggplot2: Reference

[ggplot2 reference](https://ggplot2.tidyverse.org/reference/)

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# 4. Principal material de estudo

## Sites

### R Graph Gallery

[R Graph Gallery](https://www.r-graph-gallery.com/)

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# 4. Principal material de estudo

## Sites

### from Data to Viz

[from Data to Viz](https://www.data-to-viz.com/)

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background-size: 300px
background-position: 50% 70%

# 4. Principal material de estudo

## Sites

### Statistical tools for high-throughput data analysis (STHDA)

[STHDA](http://www.sthda.com/english/)

---

# Dúvidas?

---

# Os elementos de um gráfico são representações das colunas (eixos) e linhas (elementos) de nossas matrizes de dados

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# 5. Principais tipos de gráficos

## Colunas se tornam eixos e valores a representação

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# 5. Principais tipos de gráficos

## Tipos de variáveis

[Matthiopoulos (2011)](http://greenmaths.st-andrews.ac.uk/)

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# 5. Principais tipos de gráficos

## Tipos de variáveis

[Matthiopoulos (2011)](http://greenmaths.st-andrews.ac.uk/), [@allison_horst](https://twitter.com/allison_horst)

---

# O **tipo e quantidade das variáveis** indicará o melhor tipo de gráfico para **representar** os dados

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# 5. Principais tipos de gráficos

## from Data to Viz

[from Data to Viz](https://www.data-to-viz.com/)

---

# Dúvidas?

---

# E que dados vamos usar?

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background-image: url(img/package_palmerpenguins.png)
background-size: 400px

[palmerpenguins](https://allisonhorst.github.io/palmerpenguins/)

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background-image: url(img/culmen_depth.png), url(img/lter_penguins.png)
background-size: 400px, 400px
background-position: 20% 85%, 80% 85%

# palmerpenguins

## Dados de medidas de pinguins chamados `palmerpenguins`

```r
# carregar
library(palmerpenguins)

# visualizar os dados
penguins

# glimpse
tibble::glimpse(penguins)
```

[palmerpenguins ](https://allisonhorst.github.io/palmerpenguins/)

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background-image: url(img/plot_histogram.png), url(img/plot_density.png)
background-size: 300px, 300px
background-position: 30% 80%, 70% 80%

# 6. Histograma (*Histogram*)

### **Representa os dados de**: uma coluna
### **Tipo de dado**: discreto ou contínuo
### Distribuição de frequência e densidade de dados discretos ou contínuos

---

class: clear
background-image: url(img/package_graphics.png)
background-size: 400px
background-position: 50% 50%

[graphics](http://search.r-project.org/R/library/graphics/html/00Index.html)

---

# 6. Histograma (*Histogram*)

## graphics
.pull-left[

```r
hist(penguins$flipper_length_mm)
```
]

---

# 6. Histograma (*Histogram*)

## graphics
.pull-left[

```r
hist(penguins$flipper_length_mm,
     col = "gray50",
     border = "gray")
```
]

---

# 6. Histograma (*Histogram*)

## graphics
.pull-left[

```r
hist(penguins$flipper_length_mm,
     col = "gray50",
     border = "gray",
     main = "Comprimento da nadadeira dos penguins")
```
]

---

# 6. Histograma (*Histogram*)

## graphics
.pull-left[

```r
hist(penguins$flipper_length_mm,
     col = "gray50",
     border = "gray",
     main = "Comprimento da nadadeira dos penguins",
     xlab = "Comprimento da nadadeira (mm)",
     ylab = "Frequência")
```
]

---

# 6. Histograma (*Histogram*)

## graphics
.pull-left[

---

# 6. Histograma (*Histogram*)

## graphics
.pull-left[

---

# 6. Densidade (*Density*)

## graphics
.pull-left[

```r
par(mar = c(5, 5, 5, 5))
hist(penguins$flipper_length_mm,
 col = "gray50",
 border = "gray",
 main = "Comprimento da nadadeira dos penguins",
 xlab = "Comprimento da nadadeira (mm)",
 ylab = "Densidade",
 br = 50,
 cex.main = 2,
 cex.lab = 2, 
 cex.axis = 1.5,
 prob = TRUE)
lines(density(na.omit(penguins$flipper_length_mm)))
```
]
.pull-right[
![](05_slides_intro_geoespacial_r_files/figure-html/unnamed-chunk-11-1.png)
]

---

# 6. Densidade (*Density*)

## graphics
.pull-left[

```r
par(mar = c(5, 5, 5, 5))
plot(density(na.omit(penguins$flipper_length_mm)),
 col = "gray50",
 main = "Comprimento da nadadeira dos penguins",
 xlab = "Comprimento da nadadeira (mm)",
 ylab = "Densidade",
 cex.main = 2,
 cex.lab = 2, 
 cex.axis = 1.5)
polygon(density(na.omit(penguins$flipper_length_mm)), 
 col = "gray50")
```
]
.pull-right[
![](05_slides_intro_geoespacial_r_files/figure-html/unnamed-chunk-12-1.png)
]

---

# 6. Densidade (*Density*)

## graphics

### Exportar

```r
png(here::here("03_dados", "graficos", "plot_densidade.png"), 
    wi = 15, he = 15, un = "cm", res = 300)
```

```r
par(mar = c(5, 5, 5, 5))
plot(density(na.omit(penguins$flipper_length_mm)),
     col = "gray50",
     main = "Comprimento da nadadeira dos penguins",
     xlab = "Comprimento da nadadeira (mm)",
     ylab = "Frequência",
     cex.main = 2,  cex.lab = 2, cex.axis = 1.5)
polygon(density(na.omit(penguins$flipper_length_mm)), col = "gray50")
```

```r
dev.off()
```

---

class: clear
background-image: url(img/package_ggplot2.png)
background-size: 400px
background-position: 50% 50%

[ggplot2](https://ggplot2.tidyverse.org/)

---

# 6. Histograma (*Histogram*)

## ggplot2
.pull-left[

```r
ggplot(data = penguins)
```
]

---

# 6. Histograma (*Histogram*)

## ggplot2
.pull-left[

```r
ggplot(data = penguins, aes(x = flipper_length_mm))
```
]

---

# 6. Histograma (*Histogram*)

## ggplot2
.pull-left[

```r
ggplot(data = penguins, aes(x = flipper_length_mm)) +
  geom_histogram()
```
]

---

# 6. Histograma (*Histogram*)

## ggplot2
.pull-left[

```r
ggplot(data = penguins, aes(x = flipper_length_mm)) +
  geom_histogram(color = "black", 
                 fill = "cyan4",
                 bins = 10)
```
]

---

# 6. Histograma (*Histogram*)

## ggplot2
.pull-left[

```r
ggplot(data = penguins, aes(x = flipper_length_mm)) +
  geom_histogram(color = "black", 
                 fill = "cyan4", 
                 bins = 10, 
                 alpha = .5) +
  labs(title = "Comprimento da nadadeira dos penguins",
       x = "Comprimento da nadadeira (mm)", 
       y = "Frequência") +
  theme_bw(base_size = 15)
```
]

---

# 6. Histograma (*Histogram*)

## ggplot2
.pull-left[

```r
ggplot(data = penguins, 
       aes(x = flipper_length_mm, fill = species)) +
  geom_histogram(bins = 10, alpha = .5) +
  labs(title = "Comprimento da nadadeira dos penguins",
       fill = "Espécies",
       x = "Comprimento da nadadeira (mm)", 
       y = "Frequência") +
  theme_bw(base_size = 15)
```
]

---

# 6. Histograma (*Histogram*)

## ggplot2
.pull-left[

```r
ggplot(data = penguins, 
       aes(x = flipper_length_mm, fill = species)) +
  geom_histogram(bins = 10, alpha = .5, 
                 position = "dodge") +
  labs(title = "Comprimento da nadadeira dos penguins",
       fill = "Espécies",
       x = "Comprimento da nadadeira (mm)", 
       y = "Frequência") +
  theme_bw(base_size = 15)
```
]

---

# 6. Histograma (*Histogram*)

## ggplot2
.pull-left[

```r
ggplot(data = penguins, 
       aes(x = flipper_length_mm, fill = species)) +
  geom_histogram(alpha = .5, position = "identity") +
  scale_fill_manual(values = c("darkorange", 
                               "darkorchid", "cyan4")) +
  labs(title = "Comprimento da nadadeira dos penguins",
       fill = "Espécies",
       x = "Comprimento da nadadeira (mm)", 
       y = "Frequência") +
  theme_bw(base_size = 15)
```
]

---

# 6. Histograma (*Histogram*)

## ggplot2
.pull-left[

```r
ggplot(data = penguins, 
       aes(x = flipper_length_mm, fill = species)) +
  geom_histogram() +
  scale_fill_manual(values = c("darkorange", 
                               "darkorchid", "cyan4")) +
  facet_wrap(~ species, ncol = 2, scale = "free_y") +
  labs(title = "Comprimento da nadadeira dos penguins",
       fill = "Espécies",
       x = "Comprimento da nadadeira (mm)", 
       y = "Frequência") +
  theme_bw(base_size = 15)
```
]

---

# 6. Histograma (*Histogram*)

## ggplot2
.pull-left[

```r
ggplot(data = penguins, 
       aes(x = flipper_length_mm, fill = species)) +
  geom_histogram() +
  scale_fill_manual(values = c("darkorange",
                               "darkorchid", "cyan4")) +
  facet_grid(species ~ .) +
  labs(title = "Comprimento da nadadeira dos penguins",
       fill = "Espécies",
       x = "Comprimento da nadadeira (mm)", 
       y = "Frequência") +
  theme_bw(base_size = 15)
```
]

---

# 6. Densidade (*Density*)

## ggplot2
.pull-left[

```r
ggplot(data = penguins) +
  aes(x = flipper_length_mm) +
  geom_density(color = "black", 
               fill = "cyan4", alpha = .5) + 
  labs(title = "Comprimento da nadadeira dos penguins",
       x = "Comprimento da nadadeira (mm)", 
       y = "Densidade") +
  theme_bw(base_size = 15)
```
]

---

# 6. Densidade (*Density*)

## ggplot2
.pull-left[

```r
ggplot(data = penguins, 
       aes(x = flipper_length_mm, 
           fill = species)) +
  geom_density(alpha = .5) +
  scale_fill_manual(values = c("darkorange", 
                               "darkorchid", "cyan4")) +
  labs(x = "Comprimento da nadadeira (mm)", 
       y = "Densidade", 
       fill = "Espécie") +
  theme_bw(base_size = 15)
```
]

---

# 6. Histograma (*Histogram*)

## ggplot2

### Exportar

```r
ggplot_densidade <- ggplot(data = penguins, aes(x = flipper_length_mm, fill = species)) +
 geom_density(alpha = .5) +
 scale_fill_manual(values = c("darkorange", "darkorchid", "cyan4")) +
 labs(x = "Comprimento da nadadeira (mm)", y = "Densidade", fill = "Espécie") +
 theme_bw(base_size = 15)

ggsave(filename = here::here("03_dados", "graficos", "histogram_ggplot2.png"), 
       plot = ggplot_densidade, wi = 20, he = 15, un = "cm", dpi = 300)
```

---

class: clear
background-image: url(img/package_ggpubr.png)
background-size: 400px
background-position: 50% 50%

[ggpubr](https://rpkgs.datanovia.com/ggpubr/)

---

# 6. Histograma (*Histogram*)

## ggpubr
.pull-left[

```r
gghistogram(data = penguins, 
            x = "flipper_length_mm",
            add = "median",
            color = "species",
            fill = "species",
            palette = c("darkorange", "darkorchid", "cyan4"),
            xlab = "Comprimento da nadadeira (mm)",
            ylab = "Frequência")
```
]

---

# 6. Densidade (*Density*)

## ggpubr
.pull-left[

```r
ggdensity(data = penguins, 
          x = "flipper_length_mm",
          add = "median",
          color = "species",
          fill = "species",
          palette = c("darkorange", "darkorchid", "cyan4"),
          xlab = "Comprimento da nadadeira (mm)",
          ylab = "Densidade")
```
]

---

# 6. Densidade (*Density*)

## ggpubr

### Exportar

```r
ggpubr_densidade <- ggdensity(data = penguins, 
 x = "flipper_length_mm",
 add = "median",
 fill = "cyan4",
 rug = TRUE,
 add_density = TRUE,
 xlab = "Comprimento da nadadeira (mm)",
 ylab = "Densidade")

ggsave(filename = here::here("03_dados", "graficos", "densidade_ggpubr.png"), 
       plot = ggpubr_densidade, wi = 20, he = 15, un = "cm", dpi = 300)
```

---

background-image: url(img/plot_pie.png)
background-size: 300px
background-position: 50% 90%

# 7. Gráfico de setores (*Pie chart*)

### **Representa os dados de**: uma coluna
### **Tipo de dado**: categórico
### Proporção ou porcentagem de dados categóricos

---

class: clear
background-image: url(img/package_graphics.png)
background-size: 400px
background-position: 50% 50%

[graphics](http://search.r-project.org/R/library/graphics/html/00Index.html)

---

# 7. Gráfico de setores (*Pie chart*)

### Clássico
.pull-left[

```r
par(mar = c(0, 1, 0, 1))
pie(c(280, 60, 20),
    c("Sky", "Sunny side of pyramid", "Shady side of pyramid"),
    col = c("#0292D8", "#F7EA39", "#C4B632"),
    init.angle = -50, border = NA)
```
]

---

# 7. Gráfico de setores (*Pie chart*)

### Tabela de frequência

```r
# calculo da proporcao
penguins_prop <- penguins %>%
 dplyr::count(species) %>% 
 dplyr::mutate(prop = round(n/sum(n), 4)*100)
penguins_prop
```

```
## # A tibble: 3 × 3
## species n prop
## <fct> <int> <dbl>
## 1 Adelie 152 44.2
## 2 Chinstrap 68 19.8
## 3 Gentoo 124 36.0
```

---

# 7. Gráfico de setores (*Pie chart*)

## graphics
.pull-left[

```r
par(mar = c(5, 5, 5, 5))
pie(penguins_prop$prop,
    labels = paste(penguins_prop$prop, "%"), 
    main = "Espécies",
    col = c("darkorange", "darkorchid", "cyan4"))
legend("topright", legend = penguins_prop$species, 
       fill = c("darkorange", "darkorchid", "cyan4"))
```
]

---

class: clear
background-image: url(img/package_ggplot2.png)
background-size: 400px
background-position: 50% 50%

[ggplot2](https://ggplot2.tidyverse.org/)

---

# 7. Gráfico de setores (*Pie chart*)

## ggplot2
.pull-left[

```r
ggplot(data = penguins_prop, 
       aes(x = "", y = prop, fill = species)) + 
  geom_bar(stat = "identity", color = "white") +
  coord_polar("y", start = 0) +
  geom_text(aes(label = paste0(prop, "%")), 
            color = "white", 
            position = position_stack(vjust = .5), size = 8) +
  scale_fill_manual(values = c("darkorange", "purple", "cyan4")) +
  theme_void() +
  labs(fill = "Espécie")
```
]

---

class: clear
background-image: url(img/package_ggpubr.png)
background-size: 400px
background-position: 50% 50%

[ggpubr](https://rpkgs.datanovia.com/ggpubr/)

---

# 7. Gráfico de setores (*Pie chart*)

## ggpubr
.pull-left[

```r
ggpie(penguins_prop,
      "prop", 
      label = "prop",
      lab.pos = "in", 
      lab.font = c(8, "white"),
      fill = "species", 
      color = "white",
      palette = c("darkorange", "purple", "cyan4"))
```
]

---

# Mas todos dizem para jogar o gráfico de pizza fora e pedir uma pizza...

---

# ... e eu concordo...

---

# Então vamos usar algo mais saudável: vamos retirar o recheio da pizza...

---

background-image: url(img/plot_donut.png)
background-size: 300px
background-position: 50% 90%

# 7. Gráfico de setores (*Donut chart*)

### **Representa os dados de**: uma coluna
### **Tipo de dado**: categórico
### Proporção ou porcentagem de dados categóricos

---

class: clear
background-image: url(img/package_ggplot2.png)
background-size: 400px
background-position: 50% 50%

[ggplot2](https://ggplot2.tidyverse.org/)

---

# 7. Gráfico de setores (*Donut chart*)

## ggplot2
.pull-left[

```r
ggplot(data = penguins_prop, 
       aes(x = 2, y = prop, fill = species)) +
  geom_bar(stat = "identity") +
  coord_polar(theta = "y", start = 0) +
  geom_text(aes(label = paste0(prop, "%")),
            color = "white",
            position = position_stack(vjust = .5), size = 5) +
  scale_fill_manual(values = c("darkorange", "purple", "cyan4")) +
  xlim(0, 2.5) +
  theme_void() +
  theme(legend.position = c(.5, .5),
        legend.title = element_text(size = 20),
        legend.text = element_text(size = 15)) +
  labs(fill = "Espécie")
```
]

---

class: clear
background-image: url(img/package_ggpubr.png)
background-size: 400px
background-position: 50% 50%

[ggpubr](https://rpkgs.datanovia.com/ggpubr/)

---

# 7. Gráfico de setores (*Donut chart*)

## ggpubr
.pull-left[

```r
ggdonutchart(penguins_prop,
             "prop", 
             label = "prop",
             lab.pos = "in", 
             lab.font = c(7, "white"),
             fill = "species", 
             color = "white",
             palette = c("darkorange", "purple", "cyan4"))
```
]

---

background-image: url(img/plot_barplot.png)
background-size: 300px
background-position: 50% 90%

# 8. Gráfico de barras (*Bar plot*)

### **Representa os dados de**: duas colunas
### **Modo das colunas**: X = categórico e Y = categórico
### Resume dados de contagens para uma coluna com diversos tipos de dados

---

class: clear
background-image: url(img/package_graphics.png)
background-size: 400px
background-position: 50% 50%

[graphics](http://search.r-project.org/R/library/graphics/html/00Index.html)

---

# 8. Gráfico de barras (*Bar plot*)

## Tabela de frequência

```r
# numero de individuos coletados
penguins_count <- penguins %>%
 dplyr::count(species)
penguins_count
```

```
## # A tibble: 3 × 2
## species n
## <fct> <int>
## 1 Adelie 152
## 2 Chinstrap 68
## 3 Gentoo 124
```

---

# 8. Gráfico de barras (*Bar plot*)

## graphics
.pull-left[

```r
barplot(n ~ species,
        data = penguins_count, 
        col = c("darkorange", "purple", "cyan4"),
        main = "Número de indivíduos medido por espécie",
        xlab = "Espécies",
        ylab = "Frequência absoluta",
        cex.main = 1.5,
        cex.lab = 1.3,
        cex.axis = 1.2)
```
]

---

class: clear
background-image: url(img/package_ggplot2.png)
background-size: 400px
background-position: 50% 50%

[ggplot2](https://ggplot2.tidyverse.org/)

---

# 8. Gráfico de barras (*Bar plot*)

## ggplot2
.pull-left[

```r
ggplot(data = penguins_count, 
       aes(x = species, y = n, fill = species)) +
  geom_bar(stat = "identity") +
  geom_label(aes(label = n), fill = "white") +
  scale_fill_manual(values = c("darkorange", "purple", "cyan4")) +
  theme_bw(base_size = 15) +
  theme(legend.position = "none") +
  labs(x = "Espécie", 
       y = "Número de indivíduos", 
       fill = "Espécie")
```
]

---

# 8. Gráfico de barras (*Bar plot*)

## ggplot2
.pull-left[

```r
ggplot(data = penguins_count, 
       aes(x = species, y = n, fill = species)) +
  geom_bar(stat = "identity") +
  geom_label(aes(label = n), fill = "white") +
  scale_fill_manual(values = c("darkorange", "purple", "cyan4")) +
  coord_flip() +
  theme_bw(base_size = 15) +
  theme(legend.position = "none") +
  labs(x = "Espécie", 
       y = "Número de indivíduos", 
       fill = "Espécie")
```
]

---

class: clear
background-image: url(img/package_ggpubr.png)
background-size: 400px
background-position: 50% 50%

[ggpubr](https://rpkgs.datanovia.com/ggpubr/)

---

# 9. Gráfico de barras (*Bar plot*)

### ggpubr
.pull-left[

```r
ggbarplot(penguins_count,
          x = "species",
          y = "n", 
          fill = "species", 
          color = "species",
          palette = c("darkorange", "purple", "cyan4"),
          label = TRUE, 
          lab.pos = "in", 
          lab.col = "white",
          lab.size = 8,
          main = "Número de indivíduos medido por espécie",
          xlab = "Espécies",
          ylab = "Frequência absoluta",
          legend = "none")
```
]

---

# 9. Gráfico de barras (*Bar plot*)

### ggpubr
.pull-left[

```r
ggbarplot(penguins_count,
          x = "species",
          y = "n", 
          fill = "species", 
          color = "species",
          palette = c("darkorange", "purple", "cyan4"),
          label = TRUE, 
          lab.pos = "out", 
          lab.col = "black",
          lab.size = 8,
          main = "Número de indivíduos medido por espécie",
          xlab = "Espécies",
          ylab = "Frequência absoluta",
          legend = "none",
          orientation = "horiz")
```
]

---

background-image: url(img/plot_boxplot.png)
background-size: 300px
background-position: 50% 90%

# 9. Gráfico de caixa (*Box plot*)

### **Representa os dados de**: duas colunas
### **Modo das colunas**: X = categórico e Y = contínuo
### Resume informações de medidas contínuas para dois ou mais fatores categóricos

---

background-image: url(img/plot_boxplot1.png)
background-size: 600px
background-position: 50% 70%

# 9. Gráfico de caixa (*Box plot*)

### Intervalo inter-quartil (*interquartile range* - IQR)
### Limite inferior e limite superipor (1.5 x IQR)
### Valores exteriores (*outliers*)

[Understanding Boxplots](https://towardsdatascience.com/understanding-boxplots-5e2df7bcbd51)

---

background-image: url(img/plot_boxplot2.png)
background-size: 500px
background-position: 50% 35%

# 9. Gráfico de caixa (*Box plot*)

[Understanding Boxplots](https://towardsdatascience.com/understanding-boxplots-5e2df7bcbd51)

---

class: clear
background-image: url(img/package_graphics.png)
background-size: 400px
background-position: 50% 50%

[graphics](http://search.r-project.org/R/library/graphics/html/00Index.html)

---

# 9. Gráfico de caixa (*Box plot*)

## graphics
.pull-left[

```r
boxplot(flipper_length_mm ~ as.factor(species),
        data = penguins,
        col = c("darkorange", "purple", "cyan4"),
        border = "black",
        main = "Espécies por amostragens",
        xlab = "Espécies",
        ylab = "Comprimento da nadadeira (mm)",
        cex.main = 1.5,
        cex.lab = 1.3,
        cex.axis = 1.2)
```
]

---

class: clear
background-image: url(img/package_ggplot2.png)
background-size: 400px
background-position: 50% 50%

[ggplot2](https://ggplot2.tidyverse.org/)

---

# 9. Gráfico de caixa (*Box plot*)

### ggplott2
.pull-left[

```r
ggplot(data = penguins, 
       aes(x = species, y = flipper_length_mm, fill = species)) +
  geom_boxplot(width = .3, 
               show.legend = FALSE) +
  scale_fill_manual(values = c("darkorange", "purple", "cyan4")) +
  theme_bw(base_size = 15) +
  labs(x = "Species", y = "Flipper length (mm)")
```
]

---

# 9. Gráfico de caixa (*Box plot*)

### ggplott2
.pull-left[

```r
ggplot(data = penguins, 
       aes(x = species, y = flipper_length_mm, fill = species)) +
  geom_boxplot(width = .3, 
               show.legend = FALSE) +
  geom_jitter(alpha = .5, 
              show.legend = FALSE, 
              position = position_jitter(width = .1, seed = 0)) +
  scale_fill_manual(values = c("darkorange", "purple", "cyan4")) +
  theme_bw(base_size = 15) +
  labs(x = "Species", y = "Flipper length (mm)")
```
]

---

# 10. Gráfico de caixa (*Violin plot*)

### ggplott2
.pull-left[

```r
ggplot(data = penguins, 
       aes(x = species, y = flipper_length_mm, fill = species)) +
  geom_violin(width = .3, 
              show.legend = FALSE) +
  geom_jitter(alpha = .5, 
              show.legend = FALSE, 
              position = position_jitter(width = .1, seed = 0)) +
  scale_fill_manual(values = c("darkorange", "purple", "cyan4")) +
  theme_bw(base_size = 15) +
  labs(title = "Pontos com jitter", x = "Species", y = "Flipper length (mm)")
```
]

---

class: clear
background-image: url(img/package_ggpubr.png)
background-size: 400px
background-position: 50% 50%

[ggpubr](https://rpkgs.datanovia.com/ggpubr/)

---

# 9. Gráfico de caixa (*Box plot*)

### ggpubr
.pull-left[

```r
ggboxplot(data = penguins, 
          x = "species", 
          y = "flipper_length_mm",
          add = "jitter", 
          shape = "species",
          fill = "species",
          color = "black",
          palette = c("darkorange", "purple", "cyan4"),
          xlab = "Comprimento da nadadeira (mm)",
          ylab = "Frequência absoluta",
          legend = "none")
```
]

---

# 10. Gráfico de caixa (*Violin plot*)

### ggpubr
.pull-left[

```r
ggviolin(data = penguins, 
         x = "species", 
         y = "flipper_length_mm",
         add = "jitter", 
         shape = "species",
         fill = "species",
         color = "black",
         palette = c("darkorange", "purple", "cyan4"),
         xlab = "Comprimento da nadadeira (mm)",
         ylab = "Frequência absoluta",
         legend = "none")
```
]

---

background-image: url(img/plot_scatter.png)
background-size: 300px
background-position: 50% 90%

# 10. Gráfico de dispersão (*Scatter plot*)

### **Representa os dados de**: duas colunas
### **Modo das colunas**: X = numérico e Y = numérico
### Plota a relação entre duas variáveis contínuas

---

class: clear
background-image: url(img/package_graphics.png)
background-size: 400px
background-position: 50% 50%

[graphics](http://search.r-project.org/R/library/graphics/html/00Index.html)

---

# 10. Gráfico de dispersão (*Scatter plot*)

## graphics
.pull-left[

```r
par(mar = c(5, 5, 1, 1))
plot(bill_depth_mm ~ bill_length_mm,
     data = penguins,
     pch = 20,
     cex = 1.5,
     xlab = "Comprimento do bico (mm)", 
     ylab = "Profundidade do bico (mm)",
     cex.lab = 1.5,
     cex.axis = 1.3,
     bty = "l")
```
]

---

class: clear
background-image: url(img/package_ggplot2.png)
background-size: 400px
background-position: 50% 50%

[ggplot2](https://ggplot2.tidyverse.org/)

---

# 10. Gráfico de dispersão (*Scatter plot*)

## ggplot2
.pull-left[

```r
ggplot(data = penguins, 
       aes(x = bill_length_mm, 
           y = bill_depth_mm,
           color = species,
           shape = species)) +
  geom_point(size = 3, alpha = .8) +
  scale_shape_manual(values = c(19, 15, 17)) +
  scale_color_manual(values = c("darkorange", "purple", "cyan4")) +
  theme_bw(base_size = 15) +
  labs(x = "Comprimento do bico (mm)", 
       y = "Profundidade do bico (mm)", 
       color = "Espécies", shape = "Espécies")
```
]

---

# 10. Gráfico de dispersão (*Scatter plot*)

## ggplot2
.pull-left[

```r
ggplot(data = penguins, 
       aes(x = bill_length_mm, 
           y = bill_depth_mm,
           color = species,
           shape = species)) +
  geom_point(size = 3, alpha = .8) +
  geom_smooth(method = "lm", se = FALSE) +
  scale_shape_manual(values = c(19, 15, 17)) +
  scale_color_manual(values = c("darkorange", "purple", "cyan4")) +
  theme_bw(base_size = 15) +
  labs(x = "Comprimento do bico (mm)", 
       y = "Profundidade do bico (mm)", 
       color = "Espécies", shape = "Espécies")
```
]

---

background-image: url(img/gif_data_dino_statistics.gif)
background-size: 600px
background-position: 50% 70%

# 10. Gráfico de dispersão (*Scatter plot*)

### Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing

#### Justin Matejka, George Fitzmaurice

[Matejka & Fitzmaurice (2017)](https://www.autodeskresearch.com/publications/samestats)

---

# 10. Gráfico de dispersão (*Scatter plot*)

## dino
.pull-left[

```r
# package
library(datasauRus)

# data + plot
datasaurus_dozen %>% 
  dplyr::filter(dataset == "dino") %>% 
  ggplot() +
  aes(x = x, y = y) +
  geom_point(colour = "black", fill = "black", 
             size = 5, alpha = .75, pch = 21) +
  theme_bw(base_size = 15)
```
]

---

# 10. Gráfico de dispersão (*Scatter plot*)

## Todos os plots
.pull-left[

```r
datasaurus_dozen %>% 
  ggplot() +
  aes(x = x, y = y) +
  geom_point(colour = "black", fill = "black", 
             size = 1, alpha = .75, pch = 21) +
  facet_wrap(~dataset) +
  theme_bw(base_size = 15)
```
]

---

class: clear
background-image: url(img/package_ggpubr.png)
background-size: 400px
background-position: 50% 50%

[ggpubr](https://rpkgs.datanovia.com/ggpubr/)

---

# 10. Gráfico de dispersão (*Scatter plot*)

## ggpubr
.pull-left[

```r
ggscatter(data = penguins,
          x = "flipper_length_mm", 
          y = "bill_depth_mm",
          color = "species",
          fill = "species",
          palette = c("darkorange", "purple", "cyan4"),
          shape = "species", 
          size = 5,
          xlab = "Comprimento do bico (mm)", 
          ylab = "Profundidade do bico (mm)")
```
]

---

# 10. Gráfico de dispersão (*Scatter plot*)

## ggpubr
.pull-left[

---

# 10. Gráfico de dispersão (*Scatter plot*)

## ggpubr
.pull-left[

```r
ggscatter(data = datasaurus_dozen %>% 
            dplyr::filter(dataset == "dino"),
          x = "x", 
          y = "y",
          size = 5,
          xlab = "X", 
          ylab = "Y")
```
]

---

# 10. Gráfico de dispersão (*Scatter plot*)

## ggpubr
.pull-left[

```r
ggscatter(data = datasaurus_dozen,
          x = "x", 
          y = "y",
          size = 1,
          xlab = "X", 
          ylab = "Y",
          facet.by = "dataset")
```
]

---

background-image: url(img/plot_correlogram.png)
background-size: 300px
background-position: 50% 90%

# 11. Gráfico pareado (*Pairs plot*)

### **Representa os dados de**: muitas colunas
### **Modo das colunas**: X = numérico e Y = numérico
### Plota a relação entre duas variáveis contínuas, mas para várias colunas

---

class: clear
background-image: url(img/package_graphics.png)
background-size: 400px
background-position: 50% 50%

[graphics](http://search.r-project.org/R/library/graphics/html/00Index.html)

---

# 11. Gráfico pareado (*Pairs plot*)

## graphics
.pull-left[

```r
penguins %>%
  dplyr::select(body_mass_g, ends_with("_mm")) %>%
  pairs(pch = 20,
        upper.panel = NULL)
```
]

---

class: clear
background-image: url(img/package_ggplot2.png)
background-size: 400px
background-position: 50% 50%

[GGally](http://ggobi.github.io/ggally/)

---

# 11. Gráfico pareado (*Pairs plot*)

## GGally
.pull-left[

```r
# pacote
# install.packages("GGally")
library(GGally)

penguins %>%
  dplyr::select(body_mass_g, ends_with("_mm")) %>%
  ggpairs() +
  theme_bw(base_size = 15)
```
]

---

# 11. Gráfico pareado (*Pairs plot*)

## GGally
.pull-left[

```r
penguins %>%
  dplyr::select(species, sex, body_mass_g, ends_with("_mm")) %>%
  GGally::ggpairs(aes(color = species)) +
  scale_colour_manual(values = c("darkorange", "purple", "cyan4")) +
  scale_fill_manual(values = c("darkorange", "purple", "cyan4")) +
  theme_bw(base_size = 15)
```
]

---

# 11. Gráfico pareado (*Pairs plot*)

## psych
.pull-left[

```r
# pacote
# install.packages("psych")
library(psych)

penguins %>%
  dplyr::select(body_mass_g, ends_with("_mm")) %>%
  pairs.panels(pch = 20, 
               ellipses = FALSE, 
               density = FALSE, 
               stars = TRUE, 
               hist.col = "gray",
               digits = 2,
               rug = FALSE,
               breaks = 10,
               ci = TRUE)
```
]

---

background-image: url(img/cowplot_graphics.png), url(img/patchwork_graphics.png)
background-size: 600px, 350px
background-position: 10% 97%, 85% 95%

# 12. Combinando gráficos

## Descrição

### Muitas vezes precisamos **combinar gráficos** para representar nossos dados

### Essa tarefa pode ser realizada depois da criação dos gráficos em aplicativos como [Inkscape](https://inkscape.org/pt-br/) ou [GIMP](https://www.gimp.org/)

### Outras vezes, podemos usar códigos e pacotes do R para fazer essa tarefa

---

class: clear
background-image: url(img/package_graphics.png)
background-size: 400px
background-position: 50% 50%

[graphics](http://search.r-project.org/R/library/graphics/html/00Index.html)

---

# 12. Combinando gráficos

## graphics

```r
par(mfrow = c(1, 2))

boxplot(flipper_length_mm ~ as.factor(species),
        data = penguins,
        col = c("darkorange", "purple", "cyan4"),
        main = "Espécies por amostragens",
        xlab = "Espécies",
        ylab = "Comprimento da nadadeira (mm)",
        cex.main = 1.5, cex.lab = 1.3, cex.axis = 1.2)

plot(bill_depth_mm ~ bill_length_mm,
     data = penguins,
     pch = 20,
     cex = 1.5,
     main = "Comprimento e profundidade do bico", 
     xlab = "Comprimento do bico (mm)", 
     ylab = "Profundidade do bico (mm)",
     cex.lab = 1.5, cex.axis = 1.3)
```
]

---

# 12. Combinando gráficos

## graphics

```r
par(mfrow = c(2, 1))

---

class: clear
background-image: url(img/package_ggplot2.png)
background-size: 400px
background-position: 50% 50%

[ggplot2](https://ggplot2.tidyverse.org/)

---

# 12. Combinando gráficos

## ggplot2

```r
ggplot_boxplot <- ggplot(data = penguins, 
 aes(x = species, y = flipper_length_mm, fill = species)) +
 geom_boxplot(width = .3, 
 show.legend = FALSE) +
 geom_jitter(alpha = .5, 
 show.legend = FALSE, 
 position = position_jitter(width = .1, seed = 0)) +
 scale_fill_manual(values = c("darkorange", "purple", "cyan4")) +
 theme_bw(base_size = 15) +
 labs(x = "Species", y = "Flipper length (mm)")
ggplot_boxplot
```
]

---

# 12. Combinando gráficos

## ggplot2

```r
ggplot_scatterplot <- ggplot(data = penguins, 
 aes(x = bill_length_mm, 
 y = bill_depth_mm,
 color = species,
 shape = species)) +
 geom_point(size = 3, alpha = .8) +
 scale_shape_manual(values = c(19, 15, 17)) +
 scale_color_manual(values = c("darkorange", "purple", "cyan4")) +
 theme_bw(base_size = 15) +
 labs(x = "Comprimento do bico (mm)", 
 y = "Profundidade do bico (mm)", 
 color = "Espécies", shape = "Espécies")
ggplot_scatterplot
```
]

---

class: clear
background-image: url(img/cowplot_package.png)
background-size: 400px
background-position: 50% 50%

[cowplot](https://wilkelab.org/cowplot/)

---

# 12. Combinando gráficos

## cowplot

```r
# pacote
# install.packages("cowplot")
library(cowplot)

# combinacao horizontal
plot_grid(ggplot_boxplot, ggplot_scatterplot, 
          align = "h", rel_widths = c(1, 1.5),
          labels = "AUTO")
```
]

---

# 12. Combinando gráficos

## cowplot

```r
# combinacao vertical
plot_grid(ggplot_boxplot, ggplot_scatterplot, 
          ncol = 1, align = "v", 
          labels = "AUTO")
```
]

---

class: clear
background-image: url(img/patchwork_package.png)
background-size: 300px
background-position: 50% 50%

[patchwork](https://patchwork.data-imaginist.com/)

---

# 12. Combinando gráficos

## patchwork

```r
# pacote
# install.packages("patchwork")
library(patchwork)

# combinacao horizontal
ggplot_boxplot + ggplot_scatterplot
```
]

---

# 12. Combinando gráficos

## patchwork

```r
# combinacao vertical
ggplot_boxplot / ggplot_scatterplot
```
]

---

class: clear
background-image: url(img/gganimate_package.png)
background-size: 400px
background-position: 50% 50%

[gganimate](https://gganimate.com/)

---

# 13. Gráficos animados

## gganimate

```r
# pacote
# devtools::install_github('thomasp85/gganimate')
library(gganimate)

plot_animate <- ggplot(data = penguins,
 aes(x = bill_length_mm, 
 y = bill_depth_mm, 
 color = species)) +
 geom_point() +
 scale_color_manual(values = c("darkorange", "purple", "cyan4")) +
 theme_bw(base_size = 15) +
 labs(x = "Comprimento do bico (mm)", 
 y = "Profundidade do bico (mm)", 
 color = "Espécies", shape = "Espécies") +
 labs(title = "{closest_state}") +
 transition_states(species) +
 enter_grow() + 
 exit_fade()
plot_animate
```
]

---

# 13. Gráficos animados

## gganimate

### Exportar

```r
gganimate::anim_save(filename = here::here("03_dados", 
                                           "graficos" ,
                                           "plot_animate.gif"),
                     animation = plot_animate)
```

---

class: clear
background-image: url(img/package_plotly.png)
background-size: 600px
background-position: 50% 50%

[plotly](https://plotly.com/r/)

---

# 14. Gráficos interativos

## plotly
.pull-left[

```r
# pacote
# install.packages("plotly")
library(plotly)

plot_ly(data = penguins,
        x = ~bill_length_mm, 
        y = ~bill_depth_mm, 
        type = "scatter",
        color = ~species,
        colors = c("darkorange", "purple", "cyan4")) %>% 
  layout(xaxis = list(title = "Comprimento do bico (mm)"),
         yaxis = list(title = "Profundidade do bico (mm)"))
```
]

.pull-right[
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]

---

# 14. Gráficos interativos

## plotly

```r
plot_ly(data = penguins,
        x = ~bill_length_mm, 
        y = ~bill_depth_mm, 
        z = ~body_mass_g,
        type = "scatter3d",
        color = ~species,
        colors = c("darkorange", "purple", "cyan4")) %>% 
  layout(scene = list(xaxis = list(title = "Comprimento do bico (mm)"),
                      yaxis = list(title = "Profundidade do bico (mm)"),
                      zaxis = list(title = "Massa (g)")))
```
]

.pull-right[
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]

---

# 14. Gráficos interativos

## plotly

```r
plot_penguins_scatter_int <- ggplotly(
 ggplot(data = penguins, 
 aes(x = bill_length_mm, 
 y = bill_depth_mm,
 color = species,
 shape = species)) +
 geom_point(size = 3, alpha = .8) +
 geom_smooth(method = "lm", se = FALSE) +
 scale_shape_manual(values = c(19, 15, 17)) +
 scale_color_manual(values = c("darkorange", "purple", "cyan4")) +
 theme_bw(base_size = 15) +
 labs(x = "Comprimento do bico (mm)", 
 y = "Profundidade do bico (mm)", 
 color = "Espécies", shape = "Espécies"))
plot_penguins_scatter_int
```
]

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]

---

# 14. Gráficos interativos

## plotly

### Exportar

```r
# pacote
# install.packages("htmlwidgets")
library(htmlwidgets)

# export
htmlwidgets::saveWidget(widget = plot_penguins_scatter_int, 
                        file = here::here("03_dados", "graficos" ,"plot_penguins_scatter_int.html"))
```

---

class: clear
background-image: url(img/esquisse_package.png)
background-size: 500px
background-position: 50% 50%

[gganimate](https://dreamrs.github.io/esquisse/)

---

# 15. Gráficos usando interface

## esquisse
.pull-left[

```r
# pacote
#install.packages("esquisse")
library(esquisse)

# iniciar
esquisse::esquisser(iris)
esquisse::esquisser(palmerpenguins::penguins)
```
]

---

# Dúvidas?

---

# Leitura

---

background-image: url(img/geo_geoprocessing02.png)
background-size: 800px
background-position: 50% 40%

# Leitura

[Xavier-da-Silva (2009)](http://www.ufrrj.br/lga/tiagomarino/artigos/oqueegeoprocessamento.pdf)

---

# Dúvidas?

---

class: clear, middle
background-image: url(img/gif_frog.gif),url(img/gif_frogs.gif), url(img/package_xaringan.png)
background-size: 250px, 500px, 130px
background-position: 35% 50%, 90% 55%, 5% 86%

## Maurício Vancine

Contatos:

Slides criados via pacote [xaringan](https://github.com/yihui/xaringan) e tema [Metropolis](https://github.com/pat-s/xaringan-metropolis). Animação dos sapos por [@probzz](https://twitter.com/probzz/status/1367613720294170627).