---
title: "Ebola Sierra Leone"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: fill
    social:
      - Twitter
      - Facebook
      - Menu
    source_code: embed
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE, warning = FALSE, message = FALSE)
library(flexdashboard)
library(ggplot2)
library(ggvis)
library(highcharter)
library(openintro)
library(rpivotTable)
library(dplyr)
library(DT)
library(plotly)
library(knitr)
library("mice")
library(readr)
library(shiny)
```

```{r  include=FALSE,echo=FALSE}
ebola_data <- read.csv("C:/Users/hp/Desktop/Project 1.0/Data/ebola_sierra_leone.csv" )
ebola1_data<-mice(ebola_data,n = 5,method =c("","pmm","","","","","") ,maxit = 20)
ebola1_data$imp$age
ebola2_data<-complete(ebola1_data,5)
ebola2_data



```


```{r}
mycolors<-c("blue","red","orange","brown")
```


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

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


### Siere-leone ebola affectees


```{r}
valueBox(
  value = 200,
  caption = "Total",
  icon = "fa-building",
  color=("warning")
)

```


### District affected


```{r}
valueBox(
  value = 7,
  caption = "Districts Examined",
  icon = "fa-building"
)

```


```{r echo=FALSE,include=FALSE}
ebola3_data <- data.frame(district = c("Bo","Kailahun","kambia","kenema","kono","port Loko","western Urban"),
  cases = c(2,155,1,34,2,2,4))
cases_by_district <- ebola3_data %>%
  group_by(district) %>%
  summarise(total_cases = sum(cases))
ebola3_data

```


### Cases in Kailahun


```{r}
fluidRow(
  column(4, 
         valueBox(
           value = 155,
           caption = "Kailahun",
           icon = "fa-building"
         )
  )
)


```


### cases in B0


```{r}
fluidRow(
  column(4, 
         valueBox(
           value = 2,
           caption = "Bo",
           icon = "fa-building"
         )
  )
)


```


### Cases in Port Loko


```{r}
fluidRow(
  column(4, 
         valueBox(
           value = 2,
           caption = "port loko",
           icon = "fa-building"
         )
  )
)


```


### cases in kenema


```{r}
fluidRow(
  column(4, 
         valueBox(
           value = 34,
           caption = "Kenema",
           icon = "fa-building"
         )
  )
)


``` 


### cases in kambia


```{r}
fluidRow(
  column(4, 
         valueBox(
           value = 1,
           caption = "Kambia",
           icon = "fa-building"
         )
  )
)


```



### kono


```{r}
fluidRow(
  column(4, 
         valueBox(
           value = 2,
           caption = "Kono",
           icon = "fa-building"
         )
  )
)


```


### western urban


```{r}
fluidRow(
  column(4, 
         valueBox(
           value = 155,
           caption = "Kailahun",
           icon = "fa-building"
         )
  )
)

```



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



###Top districts



```{r}
p1<-ebola2_data %>% group_by(district)%>%summarise(count =n())%>%filter(count>3) %>%  plot_ly(labels=~district,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)) 
p1
```




###Affected by districts



```{r}
p2<-ebola2_data %>% group_by(district)%>%summarise(count =n())%>% plot_ly(x=~district,y=~count,color = "red",type = "bar")%>% layout(xaxis=list(title="Affected by district"),yaxis=list(title="count")) 
p2
```


###District vs Age

```{r}
ebola2_data_grouped <- ebola2_data %>%
 group_by(age, sex, district) %>%
  summarise(count = n())
p3 <- plot_ly(ebola2_data_grouped, 
             x = ~age, 
             y = ~count, 
             color = ~sex,
             text = ~paste("District:", district),
             type = "bar",
             colors = c("red", "blue")) %>%
  layout(xaxis = list(title = "Age"), 
         yaxis = list(title = "Count"),
         barmode = "group")
p3
```


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

###scatter point of status vs age

```{r}
color_map <- c(
  "M" = "red",
  "F" = "blue")
p4 <- plot_ly(ebola2_data, 
             x = ~age, 
             y = ~status, 
             color = ~sex,
             colors = color_map,
             type = "scatter",
             mode = "markers") %>%
  layout(xaxis = list(title = "Age"), 
         yaxis = list(title = "Status"),
         title = "Scatter Plot of Age vs. Status by Sex")

p4    
p4_interactive <- ggplotly(p4)
```


###Box plot of top districts

```{r}
p5<-ebola2_data %>%
  ggplot(aes(x = district, y = age, fill = district)) +
  geom_boxplot() +
  labs(x = "District", y = "Age", title = "Box Plot of Age by District") +
  theme(legend.position = "none")
p5
p5_interactive <- ggplotly(p5)
```






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

###Map

```{r}
treatment<-ebola2_data %>% group_by(district) %>% summarise(total=n())
treatment$district<-abbr2state(treatment$district)
highchart() %>% hc_title(text="ebola ") %>% hc_subtitle(text="source:Ebola treatment.csv") %>%  hc_add_series_map(usgeojson,treatment,name="district" ,value = "total",joinBy = c("woename","district")) %>% hc_mapNavigation(enabled=T)
```




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

###Data Table

```{r}
datatable(ebola2_data,caption = "Affected Data",rownames = T,filter = "top",options = list(pagelength=25))
```





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

###Pivot Table

```{r}
rpivotTable(ebola2_data,aggregatorName = "count",cols = "count",rows = "district",rendererName = "Heatmap")
```



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

Created by:Data Scientist @BADHREY
Confidential:HIGHLY!