---
title: "Vaibhav: Dashboard"
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)
```


```{r}
datas <- read.csv("car.csv")
```

```{r}
mycolors <- c("blue", "#FFC125", "darkgreen", "darkorange")
```

Interactive Data Visualization
=====================================

Row
-------------------------------------
### Total number of observation

```{r}
valueBox(length(datas$Type),
         icon = "fa-user")
```

### Gender split

```{r}
h5=datas %>% 
  group_by(`Gender`) %>% 
  summarise(count=n())

p5=plot_ly(h5) %>% 
  add_pie(labels=h5$`Gender`,values=h5$count,hole=0.5)
p5
```

### Average insured declared Value of cars

```{r}
valueBox(round(mean(datas$IDV), digits = 0),
         icon = 'fa-money')
```

### Total Number of Claims

```{r}
valueBox(sum(datas$ClaimsInd),
         icon = 'fa-thumbs-up')
```



### Total profit 

```{r}
valueBox(round(sum(datas$profit), digits = 0),
         icon = 'fa-money')
```

Row
-------------------------------

### Statewise Total settled claim


```{r}
car <- datas %>%
         group_by(State) %>%
         summarize(total_claim = sum(Claim.Amount))

p4=plot_ly(car) %>% 
  add_pie(labels=car$State,values=car$total_claim,hole=0.5)
p4
```

### Profitable car models

```{r}
model <- datas %>%
  group_by(Mfr.Model) %>%
  summarize(average_claim = mean(Claim.Amount), average_profit=mean(profit), counts=n())%>%
  arrange(desc(average_claim))
colors <- c('#4AC6B7', '#1972A4', '#965F8A', '#FF7070', '#C61951')
fig <- plot_ly(model, y = ~average_profit, x = ~average_claim, color = ~Mfr.Model, size = ~counts/1000, colors = colors,
               type = 'scatter', mode = 'markers',
               marker = list(symbol = 'circle', sizemode = 'diameter',
                             line = list(width = 2, color = '#FFFFFF')),
               text = ~paste('Car model:', Mfr.Model, '
Average Claim amount:',average_claim,'
Average Profit:', average_profit,
                             '
Total Number of claims:', counts)) %>%
  layout(title = 'Average Claim Amount v. Average Profit, by Car models',
         yaxis = list(title = 'Average claim in Rs'),
         xaxis = list(title = 'Average profit in Rs'))
         
fig
```

Row
------------------------------------
### Year on Year Settled Claim Vs profit

```{r}
year <- datas %>%
  group_by(Year) %>%
  summarize(total_claim = sum(Claim.Amount), profit=sum(profit))

p5 <- plot_ly(year, x=~Year) %>%
  add_bars(y = ~total_claim,
           text = ~paste("total_claim: ", total_claim),
           showlegend = F) %>%
  add_lines(y = ~fitted(loess(profit ~ Year)),
            name = "Total Profit",
            color = I("#FFC125"),
            showlegend = F,
            line = list(width=5)) %>%
  layout(xaxis = list(title = "Year"),
         yaxis = list(title = "Money"))
p5
```

### Cubic capacity based Premium Vs Profit

```{r}
capacity<- datas %>%
  group_by(Cubic.Capacity)%>% 
  summarise(average_premium= mean(Premium), profit= mean(profit))

p6 <- plot_ly(capacity, x=~Cubic.Capacity) %>%
  add_bars(y = ~average_premium,
           text = ~paste("Average premium: ", average_premium),
           showlegend = F) %>%
  add_lines(y = ~fitted(loess(profit ~ Cubic.Capacity)),
            name = "Total Profit",
            color = I("#FFC125"),
            showlegend = F,
            line = list(width=5)) %>%
  layout(xaxis = list(title = "Cubic Capacity"),
         yaxis = list(title = "Average Profit"))
p6

```

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

```{r}
datatable(datas,
          caption = "Insurance",
          rownames = T,
          filter = "top",
          options = list(pageLength = 25))
```

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

```{r}
rpivotTable(datas,
            aggregatorName = "Year",
            cols= "Year",
            rows = "Mfr.Model",
            rendererName = "Heatmap")
```