interactive-dashboards-in-r.Rmd

---
title: "Dashboard"
knit: (function(input_file, encoding) {
  out_dir <- 'docs';
  rmarkdown::render(input_file,
 encoding=encoding,
 output_file=file.path(dirname(input_file), out_dir, 'index.html'))})
output: 
  flexdashboard::flex_dashboard:
    vertical_layout: scroll
    theme: cosmo
---

The current code is taken from Kaggle kernel [Interactive Dashboards in R](https://www.kaggle.com/philippsp/interactive-dashboards-in-r) to set up a Dashboard in R using [flexdashboard](http://rmarkdown.rstudio.com/flexdashboard/layouts.html), to demonstrate how flexdashboard can be hosted on github page. Furthermore it uses [highcharter](http://jkunst.com/highcharter/) to create interactive plots.

Credit for the dashboard code: [Philipp Spachtholz](https://www.kaggle.com/philippsp)




```{r setup, include=FALSE}
library(flexdashboard)
library(ggplot2)
library(readr)
library(highcharter)
library(dplyr)

train <- read_csv('./input/train.csv')
train$Pclass <- factor(train$Pclass, labels = c("1st", "2nd", "3rd"))
train$Embarked <- factor(train$Embarked, labels = c("Cherbourg", "Queenstown", "Southhampton"))
```

Sample Layout 1 {data-orientation=rows data-icon="fa-bar-chart"}
=====================================

## Row 1 {data-height=110}

### Passengers of the Titanic

```{r}
valueBox(2222, icon = "fa-ship", color="rgb(100,100,100)")
```

### Percentage of survivors

```{r}
valueBox("31.6 %", icon = "fa-heart", color="rgb(200,100,100)")
```

### Water temperature

```{r}
valueBox("-2°C", icon = "fa-life-ring",color="rgb(26,110,204)")
```
    

## Row 2 {data-height=400}  

### Age
    
```{r}
tmp_male <- train %>%  filter(Sex=="male", !is.na(Age)) %>% select(Age) %>% .[[1]]
b <- hist(tmp_male, 20, plot=FALSE)
tmp_female <- train %>%  filter(Sex=="female", !is.na(Age)) %>% select(Age) %>% .[[1]]
a <- hist(tmp_female, breaks = b$breaks, plot=FALSE)

df <- data.frame(Age=c(a$mids,b$mids),Density=c(a$density,b$density),Sex=c(rep("female",length(a$mids)),rep("male",length(b$mids))))

highchart() %>% 
  hc_add_series(name="female", select(filter(df,Sex=="female"),Density)[[1]], type="column", color='rgba(255, 192, 203, 0.30)', showInLegend=FALSE) %>% 
  hc_add_series(name="male", select(filter(df,Sex=="male"),Density)[[1]], type="column", color='rgba(68, 170, 255, 0.30)', showInLegend=FALSE) %>% 
  hc_add_series(name="male", select(filter(df,Sex=="male"),Density)[[1]], type="spline", color="#44AAFF") %>% 
  hc_add_series(name="female", select(filter(df,Sex=="female"),Density)[[1]], type="spline", color="#FFC0Cb") %>% 
hc_tooltip(pointFormat = "<span style=\"color:{series.color}\">{series.name}</span>:
             {point.y:.3f}<br/>",
             shared = FALSE) %>% 
  hc_yAxis(title=list(text='Density')) %>% 
  hc_xAxis(title=list(text='Age'))   
```
    


## Row 3 {data-height=400}  

### Port of Embarkation

```{r}
tmp <- train %>% filter(!(Embarked=="")) %>% group_by(Embarked) %>% tally() %>% mutate(Percent = n/sum(n))
tmp$colors <- c("#d35400", "#2980b9", "#2ecc71")
tmp <- arrange(tmp,desc(Percent))
highchart() %>% 
  hc_xAxis(categories = c("Southhampton", "Cherbourg", "Queenstown")) %>%
  hc_yAxis(title=list(text='Percentage')) %>%
  hc_add_series(tmp, "bar", hcaes(x = Embarked, y = Percent, color=colors)) %>% 
  hc_tooltip(pointFormat = "{point.y:.2f}</br>",shared = FALSE) %>% 
  hc_legend(enabled=FALSE)
```   

### Passenger Class
    
```{r}
tmp <- train %>%  group_by(Pclass) %>% summarize(Survived = mean(Survived))
tmp$colors <- c("#d35400", "#2980b9", "#2ecc71")
hchart(tmp, "column", hcaes(x = Pclass, y = Survived, color=colors)) %>% 
    hc_tooltip(pointFormat = "{point.y:.2f}</br>",shared = FALSE)
```


Sample Layout 2 {data-icon="fa-area-chart"}
=====================================  


Column {data-width=450}
-------------------------------------

### Kernel Finished

```{r}
rate <- 95
gauge(rate, min = 0, max = 100, symbol = '%', gaugeSectors(
  success = c(80, 100), warning = c(40, 79), danger = c(0, 39)
))
```


    
### Sex
    
```{r}
tmp <- train %>% group_by(Sex) %>% tally() %>% mutate(pct = n/sum(n))
tmp$colors <- c("#d35400", "#2980b9")

hchart(tmp, "pie", hcaes(x = Sex, y = pct, color=colors))
```
   
Column {data-width=450}
-------------------------------------

### Number of votes 

```{r}
gauge(59, min = 0, max = 75, gaugeSectors(
  success = c(65, 75), warning = c(30, 64), danger = c(0, 29)
))
```

### Port of Embarkation

```{r}
tmp <- train %>% filter(!(Embarked=="")) %>% group_by(Embarked) %>% summarize(Survived = mean(Survived))
tmp$colors <- c("#d35400", "#2980b9", "#2ecc71")
hchart(tmp, "column", hcaes(x = Embarked, y = Survived, color=colors))
```