HDRnotebook.Rmd

---
title: "HDRnotebook"
author: "GENG Minghong"
date: "Last Updated: `r Sys.Date()`"
output: html_document
---

# 1. Load Data and Data Preparation
The data of this project can be accessed here: 
http://hdr.undp.org/en/data
Done beofre 4.17
```{r Introduction, eval=TRUE, echo=TRUE, message=FALSE, warning=FALSE}
# echo : the result
# eval : run or not. Remember if here you set it as false, the code here won't run. So don't put `library( )` here
# fig.height is about the graph of this chunk.
packages = c('corrplot','ggpubr','plotly','tidyverse','readxl')

for(p in packages){library
if (!require(p,character.only = T)){
  install.packages(p)
  }
  library(p,character.only = T)
}  
```

```{r}
data1=read_excel('data/Table1.HDI&Components.xlsx')
```

# 2. Exploratory Data Analysis




# 3. Data Visulisation Design
some thoughts here.

# 4. shiny


```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
### Install Packages
```{r}
packages = c('tidyverse','reshape','ggplot2','tidyr','gganimate','stringr','plotly','RColorBrewer','adegenet','NAM','snow','doSNOW','parallel','circular','d3heatmap','plotly','viridis','hrbrthemes','grid','gtable','leaflet')
for (p in packages){
  if(!require(p,character.only = T)){
    install.packages(p)
  }
  library(p, character.only = T)
}
install.packages('leaflet')
library(leaflet)
library(shiny)
```

### Read File


```{r}
# Prepare data
all <- read.csv('data/All.csv', header = TRUE)
names(all)[4]="Country"
names(all)[1]='Continent'
choice <- colnames(all)[1:4]
head(all)
print(choice)
```


### Shiny 

```{r}
#### User Interface
ui <- bootstrapPage(
    #shinythemes::themeSelector(),
    tags$head("Human Development Report"),
    navbarPage(theme = shinytheme("flatly"), collapsible = TRUE,
               "Human Development Report", id="nav",
               tabPanel("World mapper",
                        div(leafletOutput("mymap"),
                            p(),
                            actionButton("recalc", "New points")
                            )),
               tabPanel("HDI"),
               tabPanel("Gender Development Index"),
               tabPanel("Poverty Index"),
               tabPanel("Population"),
               tabPanel("Health"),
               tabPanel("Education"),
               tabPanel("Data"),
               tabPanel("About us")),

  sidebarLayout(
    sidebarPanel(width = 3,
      selectInput('level','Choose a Level', choices = choice),
      selectInput("country","Choose countries",choices = unique(HDI$Country), multiple = TRUE),
      sliderInput("year",'choose year'锛? min = 1990, max = 2018, value = c(1990,2018),step = 1),
      actionButton("Search", "Search"),
      actionButton("Help","About")
    ),
    mainPanel(
      fluidRow(column(plotly::plotlyOutput(outputId = "LEtrend"),width = 4, height = 3),
               column(plotly::plotlyOutput(outputId = "MStrend"),width = 4),
               column(plotly::plotlyOutput(outputId = "EStrend"),width = 4)),
      fluidRow(column(plotly::plotlyOutput(outputId = "GNItrend"),width = 4),
               column(plotly::plotlyOutput(outputId = "HDItrend"),width = 4))
    )
  )
)


#### Server
server<-function(input, output, session){
  
## filter data
  extract_data <- reactive({
    all %>%
    filter(Country == input$country,
           Year >= input$year[1],
           Year <= input$year[2])
  })
  
## HDI trend plot
  reactive_HDI <- eventReactive(input$Search,{
    extract_data()%>%
         plot_ly(x = ~Year, y=~HDI, color = ~Country, hoverinfo = "text",
                 text = ~paste(input$country, HDI)) %>%
         add_lines()%>%
        layout(showlegend=TRUE)
      })
  
  output$HDItrend <- renderPlotly({reactive_HDI()})
  
## Life Expectancy trend plot
  reactive_LifeExpectancy <- eventReactive(input$Search,{
       extract_data()%>%
         plot_ly(x = ~Year, y=~Life_Expectancy, color = ~Country, hoverinfo = "text",
                  text = ~paste(input$country, Life_Expectancy)) %>%
         add_lines()%>%
         layout(showlegend=TRUE)
      })
  output$LEtrend <- renderPlotly({reactive_LifeExpectancy()})

## Expected Schooling trend plot
  reactive_ExpectedSchooling <- eventReactive(input$Search,{
       extract_data()%>%
         plot_ly(x = ~Year, y=~Expected_Years_of_Schooling, color = ~Country, hoverinfo = "text",
                  text = ~paste(input$country, Expected_Years_of_Schooling)) %>%
         add_lines()%>%
         layout(showlegend=TRUE)
      })
  output$EStrend <- renderPlotly({reactive_ExpectedSchooling()})

## Mean Schooling trend plot
  reactive_MeanSchooling <- eventReactive(input$Search,{
       extract_data()%>%
         plot_ly(x = ~Year, y=~Mean_Years_of_Schooling, color = ~Country, hoverinfo = "text",
                  text = ~paste(input$country, Mean_Years_of_Schooling)) %>%
         add_lines()%>%
         layout(showlegend=TRUE)
      })
  output$MStrend <- renderPlotly({reactive_MeanSchooling()})

## GNI per capita trend plot
  reactive_GNI <- eventReactive(input$Search,{
       extract_data()%>%
         plot_ly(x = ~Year, y=~GNI_per_capita, color = ~Country, hoverinfo = "text",
                  text = ~paste(input$country, GNI_per_capita)) %>%
         add_lines()%>%
         layout(showlegend=TRUE)
      })
  output$GNItrend <- renderPlotly({reactive_GNI()})
}

shinyApp(ui = ui, server = server)

```

```{r}
  output$duration_table <- renderTable({
    HDI %>%
      filter(
        Country == input$country,
        Year >= input$year[1],
        Year <= input$year[2]
      ) %>%
      group_by(shape) %>%
      summarize(
        nb_sighted = n(),
        avg_duration = mean(duration_sec),
        median_duration = median(duration_sec),
        min_duration = min(duration_sec),
        max_duration = max(duration_sec)
      )
  })
```

```{r}
install.packages("shinyWidgets")
library(shinyWidgets)
shinyWidgetsGallery()
```