---
title: "Vehicle Failure Data Analysis Report"
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(dplyr)
library(openintro)
library(highcharter)
library(ggplot2)
library(plotly)
library(ggvis)

```





```{r}
mydata<-read.csv("VehicleFailure.csv")
mycolor<-c("darkred","darkorange","blue","darkgreen","pink")

c<-c("blue", "#FFC125", "darkgreen", "darkorange")
attach(mydata)

```

Iterative Data Visualization
============================================

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


### Car Failure Analysis

```{r}

valueBox(paste("Failure"), color = "warning")

```


### Car failure in US

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

```


### **Labor cost**

```{r}

gauge(round(mean(mydata$lc),digits = 2),
      min = 0,
      max = 350,
      gaugeSectors(success = c(0,150),
                   warning = c(150,240),
                   danger = c(240,350),
                   colors = c("green","orange","red")))


```


### Massachusetts


```{r}
valueBox(sum(mydata$State=="MA"),icon = "fa-building")

```


### california


```{r}
#head(mydata$State)

valueBox(sum(mydata$State=="CA"),icon = "fab fa-amazon-pay")


```


### Texas


```{r}

valueBox(sum(mydata$State=="TX"),icon ="fas fa-dollar-sign" )

```



### Florida


```{r}

valueBox(sum(mydata$State=="FL"),icon ="fas fa-wrench" )

```












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


### Scatterplot 


```{r}
p4<-plot_ly(mydata,x=~fm) %>% 
  add_markers(y=~Mileage,text=~paste("Mileage:",Mileage))%>% 
  add_lines(y = ~fitted(loess(Mileage~fm)),name="loss Smoother",color=I("#FFC125"),
            line=list(width=5)) %>% 
  layout(xaxis=list(title="Month"),yaxis=list(title="Mileage"))

p4


```

### Top State


```{r}

p2<-mydata %>%
  group_by(State) %>% 
  summarise(count=n()) %>% 
  filter(count>50) %>% 
  plot_ly(labels=~State,
          values=~count,
          marker=list(color=c))%>%  
  add_pie(hole=0.2) %>% 
  layout(xasis=list(zeroline = F,
                    showline = F,
                    showticklabels = F,
                    showgrid = F),
         yaxis=list(zeroline = F,
                    showline = F,
                    showticklabels= F,
                    showgrid = F))
p2

```



### FM Vs Millage


```{r}

p3<- plot_ly(mydata, x=~fm,y=~Mileage,
             text=paste("FM:",mydata$fm,"Mileage:",mydata$Mileage),
             type = "bar") %>% layout(xaxis=list(title="FM"),yaxis=list(title="Failure Mileage"))
  
p3

```









Row
-------------------------------------
### Failure by state

```{r}


p1<-mydata %>% 
  group_by(State) %>% 
  summarise(count=n())%>%
  filter(count<100) %>% 
  plot_ly(x=~State,
          y=~count,
          type="bar",
          color = rainbow(48)) %>% 
  layout(xaxis=list(title="Failure by State"),yaxis=list(title="count"))
p1

```



2nd page
=========================================


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

### Boxplot


```{r}

p5<-mydata %>%
  group_by(State) %>% 
   ggvis(~State,~lc,fill=~State) %>%
  layer_boxplots()
p5
  


```





Map
========================================

### Map

```{r}


car <- mydata %>% 
  group_by(State) %>% 
  summarize(total=n()) 
  
car$State<-abbr2state(car$State)
  


highchart() %>% 
  hc_title(text="Car filure in Us") %>% 
  hc_subtitle(text="source: VehicleFailure.csv") %>% 
  hc_add_series_map(usgeojson,car,name="State",value = "total",
                    joinBy = c("woename","State")) %>% 
  hc_mapNavigation(enabled=T)
  
 
```




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


```{r}
datatable(mydata,
          caption = "Failure Data",
          rownames = T,
          filter = "top",
          options = list(pageLength=25))


```




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

```{r}


rpivotTable(mydata,
            aggregatorName = "Count",
            cols="fm",
            rows = "State",
            rendererName = "Stacked Bar Chart"
           
            )


```



Summary {data-orientation=columns} 
===========================================


Column
---------------------------

Report

* This is a report on `r length(mydata$fm)` car failures.

* The average labor cost was `r mean(mydata$lc)`.

* The average material cost was `r mean(mydata$mc)`.

This report was generated on `r format(Sys.Date(), format = "%B %d, %Y")`.


About Report
========================================

Created by: 

**Md Sojibul Islam**

**Department of Statistics**

**Mawlana Bhashani Science and Technology University**

**Tangail, Bangladesh.**



Confidential: HIGHLY!