---
title: "Dian's Dashboard"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: fill
    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(readxl)
library(ggvis)

```


```{r}
data <- read_excel("C:/Users/Asus/Downloads/Lab2/dashboard/VehicleFailure.xlsx")
```

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

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

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

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


### Car Failures in US

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

### **Labour 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")))
```

### Georgia
```{r}
valueBox(sum(data$State == "GA"),
         icon = 'fa-building')
```

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

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

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

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

### Failure By State

```{r}
p1 <- data %>%
  group_by(State) %>%
  summarise(count = n()) %>%
  plot_ly(x = ~State,
         y = ~count, 
         color = rainbow(51),
         type = 'bar') %>%
layout(xaxis = list(title = "Failure By State"),
yaxis = list(title = 'Count'))
p1
```

### Top States
```{r}
p2 <- data %>%
  group_by(State) %>%
  summarise(count = n()) %>%
  filter(count>50) %>%
  plot_ly(labels = ~State,
         values = ~count, 
         marker = list(colors = mycolors)) %>%
  add_pie(hole = 0.2) %>%
  layout(xaxis = list(zeroline = F,
                      showline = F, 
                      showticklabels = F, 
                      showgrid = F),
         yaxis = list(zeroline = F, 
                      showline = F,
                      showticklabels = F,
                      showgrid = F))
p2
```

### FM Vs Mileage

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

### Scatter Plot of Month Vs Mileage

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

Box Plot
================================================================================

### Box plot of Top State

```{r}
library(plotly)
library(dplyr)

box_plot <- data %>%
  group_by(State) %>%
  ggplot(aes(x = State, y = lc, fill = State)) +
  geom_boxplot()

interactive_box_plot <- ggplotly(box_plot)

interactive_box_plot

```

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

### Map
```{r}
car <- data %>%
  group_by(State) %>%
  summarize(total = n())
car$State <- abbr2state(car$State)

highchart() %>%
  hc_title(text = "Car Failures in US") %>%
  hc_subtitle(text = "Source: Vehiclefailure.xlsx") %>%
  hc_add_series_map(usgeojson, car,
                    name = "State",
                    value = "total",
                    joinBy = c("woename", "State")) %>%
  hc_mapNavigation(enables = 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",
            renderName = "Heatmap")
```

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

Column {data-width = 100}
--------------------------------------------------------------------------------

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

### Average Labor 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 is a report on `r length(data$fm)` car failures.

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

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

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