---
title: "Visualização de dados com R"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: fill
    social: [ "twitter", "facebook", "menu"]
    source_code: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
library(knitr)
library(DT)
library(rpivotTable)
library(ggplot2)
library(plotly)
library(dplyr)
library(openintro)
library(highcharter)
library(ggvis)
library(plotly)
library(treemap)
library(ggridges)
library(corrplot)
```


```{r}
data <- read.csv("https://raw.githubusercontent.com/DATAUNIRIO/Base_de_dados/master/Fam%C3%ADlias.csv",sep=";",dec=",")
mycolors <- c("blue", "red", "darkgreen", "darkorange")
```

Visualização de Dados
=====================================

Row 
---------------------------
### Característica da turma 

```{r}
valueBox(paste("Turma Estatística"),
         color = "green")
```

### Quantidade de estudantes

```{r}
valueBox(dim(data)[1])
```

### **Média de renda**

```{r}
gauge(round(mean(data$renda),
            digits = 2),
            min = 0,
            max = 26)
```

### Ensino fundamental

```{r}
valueBox(sum(data$instr == "Ensino fundamental")
         )
```

### Ensino médio

```{r}
valueBox(sum(data$instr == "Ensino médio"),
         icon = 'fa-tag')
```

### Sem Instrução

```{r}
valueBox(sum(data$instr == "Sem Instrução"),
         icon = 'fa-building')
```

### Monte Verde 

```{r}
valueBox(sum(data$local == "Monte Verde"),
         icon = 'fa-building')
```


Row {.tabset .tabset-fade}
-------------------------------
  
### Quantidade de estudantes por bairro
  
```{r}
p1 <- data %>%
  group_by(local) %>%
  summarise(count = n()) %>%
  plot_ly(x = ~local,
          y = ~ count,
          color = "blue",
          type = 'bar') %>%
  layout(xaxis = list(title = "local"),
         yaxis = list(title = 'Frequência Absoluta'))
p1
```

### Boxplot Altura x Sexo

```{r}
p2 <- plot_ly(data, x = ~factor(p.a.p), y = ~renda) %>% 
      add_boxplot()
p2
```


### Dispersão

```{r}
p4 <- plot_ly(data, x=~renda) %>%
         add_markers(y = ~tam,
                     text = ~paste("Tamanho: ", tam),
                     showlegend = F) %>%
         layout(xaxis = list(title = "tam"),
                yaxis = list(title = "renda"))
p4

```


### Tree map
```{r}
# treemap
treemap(data,
        index=c("local","instr"),
        vSize="renda",
        type="index"
)
```




Data Table
========================================

```{r}
datatable(data,
          caption = "Familia",
          rownames = T,
          filter = "top",
          options = list(pageLength = 10))
```

Pivot Table
=========================================

```{r}
rpivotTable(data,
            rendererName = "Heatmap")
```


Dispersao
=========================================

```{r}

ggplot(data, aes(x=tam, y=renda, fill=renda)) +
  geom_label(label=rownames(data), color="white", size=5)


```


Joyplot
=========================================

```{r}
ggplot(data, aes(x = renda, y = local, fill = local)) +
  geom_density_ridges() +
  theme_ridges() + 
  theme(legend.position = "none")
```

Corrplot
=========================================
```{r}
data("mtcars")
M <- cor(mtcars[,c("disp","drat","hp","mpg","qsec","wt")],method="spearman")
corrplot.mixed(M)
```