---
title: "flexdashboard Template"
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(shiny)
```


```{r}
library(data.table)
n <- 1000
dt <- data.table(
    id = 1:n
  , score = rnorm(n, mean = 200, sd = 40)
  , group = sample(c("A", "B", "C"), size = n, replace = TRUE, prob = c(0.2, 0.5, 0.3))
  , grade = sample(1:15, size = n, replace = TRUE)
    , state = sample(c("Tennessee", "Georgia", "Kentucky", "Alibama"), size = n, replace = TRUE)
)

```

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

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

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

### Project A Analysis

```{r}
valueBox(paste("Project A"), color = "#448833")
```

### Number of Enrollments

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

### **Average Score**

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

### Group A

```{r}
valueBox(sum(dt$group == "A"), icon = 'fa-building')
```

### Group B

```{r}
valueBox(sum(dt$group == "B"), icon = 'fa-building')
```

### Group C

```{r}
valueBox(sum(dt$group == "C"), icon = 'fa-building')
```


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

### Enrollments By Group: Bar Plot

```{r}
p1 <- dt %>%
         group_by(group) %>%
         summarise(count = n()) %>%
         plot_ly(x = ~group,
                 y = ~count,
                 color = "blue",
                 type = 'bar') %>%
layout(xaxis = list(title = "Number by Group"),
yaxis = list(title = 'Count'))
p1
```

### Pie Plot

```{r}

colors <- c("#009999", "#990099", "#999900")
p2 <- dt %>%
         group_by(group) %>%
         summarise(count = n()) %>%
         filter(count>50) %>%
         plot_ly(labels = ~group,
                 values = ~count,
                 marker = list(colors = colors)) %>%
         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
```

### Histogram/Bar Plot

```{r}
p3 <- plot_ly(dt,
              x = ~grade,
              y = ~score,
              text = paste("Grade:", dt$grade,
                           "Score:",
                           dt$score),
              type = "bar") %>%
         layout(xaxis = list(title="Grade"),
                yaxis = list(title = "Score"))
p3
```

Row
------------------------------------
### Scatter Plot

```{r}
p4 <- plot_ly(dt, x=~grade) %>%
         add_markers(y = ~score,
                     text = ~paste("score: ", score),
                     showlegend = F) %>%
         add_lines(y = ~fitted(loess(score ~ grade)),
                   name = "Loess Smoother",
                   color = I("#119999"),
                   showlegend = T,
                   line = list(width=5)) %>%
         layout(xaxis = list(title = "Grade"),
                yaxis = list(title = "Score"))
p4
```

### Box Plot

```{r}
dt %>%
         group_by(group) %>%
         ggvis(~group, ~score, fill = ~group) %>%
         layer_boxplots()
```

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

### Map

```{r}
library("dplyr")
library(highcharter)

data("USArrests", package = "datasets")
data("usgeojson")

USArrests <- mutate(USArrests, state = rownames(USArrests))

highchart() %>%
  hc_title(text = "Violent Crime Rates by US State") %>%
  hc_subtitle(text = "Source: USArrests data") %>%
  hc_add_series_map(usgeojson, USArrests,
    name = "Murder arrests (per 100,000)",
    value = "Murder", joinBy = c("woename", "state"),
    dataLabels = list(
      enabled = TRUE,
      format = "{point.properties.postalcode}"
    )
  ) %>%
  hc_colorAxis(stops = color_stops()) %>%
  hc_legend(valueDecimals = 0, valueSuffix = "%") %>%
  hc_mapNavigation(enabled = TRUE)



```

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

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

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

```{r}
rpivotTable(dt,
            aggregatorName = "Count",
            cols= "grade",
            rows = "group",
            rendererName = "Heatmap")
```

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

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

### Max Grade

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

### Average Score
```{r}
valueBox(round(mean(dt$score),
               digits = 2),
         icon = "fa-area-chart")
```

### Average Grade

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

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

Report

* This is a report on `r length(dt$grade)` enrollments.

* The average score was `r mean(dt$score)`.

* The average grade was `r mean(dt$grade)`.

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

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

Created by: 


```{r}
sessionInfo()

```