---
title: "CAR ANALYSIS DASHBOARD"
output: 
  flexdashboard::flex_dashboard:
    css: styles.css
    theme: default
    orientation: rows
    vertical_layout: scroll
    source_code: embed
    social: ["menu"]
---

```{r setup, include=FALSE}
library(flexdashboard)
library(knitr)
library(DT)
library(rpivotTable)
library(tidyverse)
library(plotly)
library(openintro)
library(highcharter)

```




```{r, include=FALSE}
data <- read.csv("vehicle.csv")
str(data)
```


Interactive Visualization
=====================================================

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

### Car Failure Analysis.

```{r}
valueBox(paste("Failure",
               color = "warning"))
```



### Car Failure in US

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



### Labor Cost

```{r}
gauge(round(mean(data$lc),
            digits = 2),
            min = 0,
            max = 350,
            gaugeSectors(success = c(0,150),
                         warning = c(150,240),
                         danger = c(240,350),
                         colors = c('green', 'yellow', 'red')))
```



### Massachusetts

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

### California

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


### Texas

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


### Florida

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


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

### State With Minimum Failure

```{r}
p1 <- data %>% 
  group_by(State) %>% 
  summarise(count = n()) %>% filter(count <= 20 &  count > 1) %>% 
  ggplot(aes(State,count, fill = State))+geom_col()+
  theme_classic()+theme(legend.position = "none")+
  labs(title = "Failure By State",
       caption = "** By Julius**")
ggplotly(p1)
```


### Top 5 States
```{r}
p2 <- data %>% 
  group_by(State) %>% 
  summarise(count = n()) %>% 
  filter(count>50) %>% 
  plot_ly(labels = ~State,
          values = ~count) %>% 
  add_pie(hole = 0.4)
p2
```


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

### FM vs Mileage

```{r}
p3 <- data %>% 
  ggplot(aes(fm, Mileage))+geom_col(fill = "blue")+theme_classic()
ggplotly(p3)
```


### Scatter Plot of Month vs Mileage

```{r}
p4 <- data %>% 
  ggplot(aes(fm, Mileage))+geom_point()+geom_smooth(se = FALSE, color ="blue")
ggplotly(p4)
```



Map
=====================================================
### Map

```{r}
car <- data %>% 
  group_by(State) %>% 
  summarize(total = n())
car$State <- abbr2state(car$State)
highchart() %>% 
  hc_title(text = "Car Failure in US") %>% 
  hc_subtitle(text = "source: vehicle failure.csv") %>% 
  hc_add_series_map(usgeojson, car,
                    name = "State",
                    value = "total",
                    joinBy = c("woename", "State")) %>% 
  hc_mapNavigation(enabled = T)
```

DATA TABLE
============================================================
```{r}
datatable(data,
          caption = "Failure data",
          rownames = T,
          filter = "top",
          options = list(pagelength = 25))
```

Pivot Table
============================================================
```{r}
rpivotTable(data,
            aggregatorName = "count",
            cols = "fm",
            rows = "State",
            rendererName = "Heatmap")
```



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

column
----------------------------------------------

### Max Failure Month


```{r}
valueBox(max(data$fm),
         icon = "fa-user")
```


### Average Labour cost

```{r}
valueBox(round(mean(data$lc),digits = 2),
         icon = "fa-area-chart")
```


### Average Mileage at Failure

```{r}
valueBox(round(mean(data$Mileage),digits = 2),
         icon = "fa-area-chart")
```



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


Report


* This report on `r length(data$fm)` car failures.

* The average labour cost was `r mean(data$lc)`.

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

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





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

Created by: Analyst Julius