Project.R

packages=c('corrplot',
           'ggpubr',
           'plotly',
           'tidyverse',
           'readxl',
           'Hmisc', 
           'geojsonio',
           'sf', 
           'tmap',
           'spData',
           'maptools',
           'shiny',
           'shinythemes',
           'leaflet',
           'RColorBrewer',
           'rnaturalearth',
           'rnaturalearthdata',
           'hrbrthemes',
           "WDI",
           "ggvis",
           'reshape2')

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



### This part is done by ZHU Honglu
### This part has not been merged.

indicator2 <- WDI(country="all", indicator=c("NY.GDP.PCAP.CD", "SP.POP.TOTL", "SP.DYN.LE00.IN"), start=2011, end=2018, extra = TRUE)
drops <- c("iso2c","iso3c", "capital", "longitude", "latitude", "income", "lending")
indicator2 <- indicator2[ , !(names(indicator2) %in% drops)]
colnames(indicator2) <- c("country","year", "GDP_per_capita", "population_total", "life_expectancy", "region")
indicator2 <- indicator2[-c(1, 2, 3, 4, 5, 6, 19, 66, 67, 159, 178, 179, 180, 181, 182, 201, 202, 203, 204, 205, 206, 207, 225, 226, 227, 228, 236, 237, 238, 239, 240, 241, 242, 243, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 262, 263), ]

ui <- fluidPage(
  sliderInput(inputId = "Year",
              label = h5("Select Year"),
              min = 2011,#as.Date(cv_min_date,"%Y-%m-%d"),
              max = 2018,#as.Date(current_date,"%Y-%m-%d"),
              value = 2018, #as.Date(current_date),
              timeFormat = '%Y',
              #timeFormat = "%d %b", 
              #animate=animationOptions(interval = 3000, loop = FALSE),
  ),
  plotOutput("graph")
)

server <- function(input, output) {
  output$graph = renderPlot({
    indicator2 %>%
      ggvis(~GDP_per_capita, ~life_expectancy, fill=~factor(region)) %>%
      layer_points(size= ~population_total/1000000,opacity:=0.6) %>%
      add_legend(scales = "size", properties = legend_props(legend = list(y = 200))) %>%
      scale_numeric("y", domain = c(0, 90), nice = FALSE) %>%
      add_axis("x", title = "GDP per Capita(USD)") %>%
      add_axis("x", orient = "top", ticks = 0, title = "GDP per Capita vs. Life Expectancy 2019",
               properties = axis_props(
                 axis = list(stroke = "white"),
                 labels = list(fontSize = 0)))
  })
}


shinyApp(ui = ui, server = server)

# load packages


# and next
# try to draw a map
# here, argument country can change the country 
# also we can choose the continent to see.
#world <- ne_countries(scale = 'medium', returnclass = "sf")
worldcountry = geojson_read("data/50m.geojson", what = "sp")
#worldcountry<-subset(worldcountry, NAME_LONG!="Antarctica")
#country_geoms = read_csv("data/country_geoms.csv")

HDI = read_csv('data/data_cleaned/HDI/0_HDI.csv') 
HDI_selected = subset(HDI,Year==2018)
worldCountry_HDI <- merge(worldcountry, HDI_selected, by.x = "NAME_LONG", by.y = "Country")

##### HDI Distribution   #####
distribution_HDI = worldcountry %>%
    merge(filter(HDI,Year==2018),by.x = "NAME_LONG", by.y = "Country")

distribution_HDI$HDI

ggplot(filter(HDI,Year==2018),aes(x = reorder(Country,-HDI), 
                                  y = HDI,#color = Country,#fill = region
                                  ))+
  geom_bar(position="stack", stat="identity",fill = "#cc4c02")+
  ylab("HDI INDEX") + 
  #scale_x_categorical(breaks=seq(0, 10, 1))
  theme_bw() + 
  scale_fill_manual(values=c("#cc4c02")) +
  #scale_y_continuous(labels = function(l) {trans = l / 1000; paste0(trans, "K")}) +
  scale_y_continuous(expand = c(0, 0))+
  theme(legend.title = element_blank(), 
        axis.text.x = element_blank(),
        axis.ticks = element_blank(),
        legend.position = "", 
        plot.title = element_text(size=10), 
        plot.margin = margin(5, 12, 5, 5))
        
  
  




#plot_map <- worldcountry
# create plotting parameters for map
#bins = c(0,1,10,50,100,500,1000,Inf)
#HDI_pal <- colorBin("Blues", domain = worldCountry_HDI$"2018")#, bins = bins)
HDI_pal <- colorQuantile("Blues", domain =  worldCountry_HDI$HDI)
#plot_map <- worldcountry[worldcountry$ADM0_A3 %in% cv_large_countries$alpha3, ]

labels <- sprintf(
  "<strong>%s</strong><br/> 
   Index: %g",
  worldCountry_HDI$NAME_LONG, worldCountry_HDI$HDI
) %>% lapply(htmltools::HTML)

popup = sprintf(
  "<strong>%g</strong><br/>",worldCountry_HDI$HDI
) %>% lapply(htmltools::HTML)



# weight: the thickness of the boundary lines in pixels
# color: the color of the polygons
# label: the information to appear on hover
# highlightOptions: options to highlight a polygon on hover

#++++++++++++++++++  WORLD MAP AREA  ++++++++++++++++++++++++++
basemap = leaflet(worldCountry_HDI) %>% 
  addProviderTiles(providers$CartoDB.Positron) %>%
  addPolygons(#stroke = FALSE, # use to turn off the broad 
              smoothFactor = 0.2, 
              fillColor = ~HDI_pal(worldCountry_HDI$HDI), # ÊÇÂÖÀªÄÚµÄÑÕÉ«
              fillOpacity = 0.7,
              color="white", #stroke color
              weight = 1, # stroke width in pixels
              highlight = highlightOptions(
                #weight = 5,
                color = "#666",
                #dashArray = "",
                fillOpacity = 0.7,
                bringToFront = TRUE #Whether the shape should be brought to front on hover
                ),
              label = labels,
              labelOptions = labelOptions(
                style = list("font-weight" = "normal", padding = "3px 8px"),
                textsize = "15px",
                direction = "auto"),
              popup = popup,
              popupOptions = popupOptions(maxWidth ="100%", closeOnClick = TRUE),
              group = "Human Development Index"
              ) %>%
  addLayersControl(
    position = "bottomright",
    baseGroups = c("Human Development Index","Gender Development Index"),
    #overlayGroups = c("Human Development Index","Gender Development Index"),
    options = layersControlOptions(collapsed = FALSE)) %>%
  hideGroup(c("Human Development Index","Gender Development Index"))  %>%
  addLegend("bottomright", pal = HDI_pal, values = ~worldCountry_HDI$HDI,title = "<small>Index Value</small>")

basemap
  #addPolygons(
    #smoothFactor = 0.2,
    #fillOpacity = 1,
    #fillColor = ~HDI_pal(worldCountry_HDI$"2018"),
    #weight = 0.5,
    #opacity = 0.1,
    #color = "white",
    #dashArray = "1",
    #fillOpacity = 0.7,
    #highlight = highlightOptions(
     # weight = 0.5,
      #color = "#666",
      #dashArray = "¡ª",
      #fillOpacity = 0.5,
      #bringToFront = TRUE),
    #label = labels,
    #labelOptions = labelOptions(
      #style = list("font-weight" = "normal", padding = "3px 8px"),
      #textsize = "15px",
      #direction = "auto")) %>%
  


#=================================================================
states <- 
  geojson_read( 
    x = "https://raw.githubusercontent.com/PublicaMundi/MappingAPI/master/data/geojson/us-states.json"
    , what = "list"
  )
class(states)


bins <- c(0, 10, 20, 50, 100, 200, 500, 1000, Inf)
pal <- colorBin("YlOrRd", domain = states$density, bins = bins)

labels <- sprintf(
  "<strong>%s</strong><br/>%g people / mi<sup>2</sup>",
  states$name, states$density
) %>% lapply(htmltools::HTML)

leaflet(states) %>%
  setView(-96, 37.8, 4) %>%
  addProviderTiles("MapBox", options = providerTileOptions(
    id = "mapbox.light",
    accessToken = Sys.getenv('MAPBOX_ACCESS_TOKEN'))) %>%
  addPolygons(
    fillColor = ~pal(density),
    weight = 2,
    opacity = 1,
    color = "white",
    dashArray = "3",
    fillOpacity = 0.7,
    highlight = highlightOptions(
      weight = 5,
      color = "#666",
      dashArray = "",
      fillOpacity = 0.7,
      bringToFront = TRUE),
    label = labels,
    labelOptions = labelOptions(
      style = list("font-weight" = "normal", padding = "3px 8px"),
      textsize = "15px",
      direction = "auto")) %>%
  addLegend(pal = pal, values = ~density, opacity = 0.7, title = NULL,
            position = "bottomright")
#========================================================================================================
# count the number of countries in different level of development of HDI
temp=all_data %>%
  filter(Year == 2018) %>%
  group_by(Level) %>%
  summarise(
    HDI = mean(HDI),
    number_of_distinct_orders = length(unique(Country))) %>%
  ungroup()

#______________Draw a density graph___________________

density_plot_2018=all_data %>%
  filter(Year==2018) %>%
  ggplot(aes(x=Gender_Inequality_Index)) +
  geom_density(fill="Region", color="#e9ecef", alpha=0.8) +
  ggtitle("Night price distribution of Airbnb appartements") +
  theme_ipsum()
ggplotly(density_plot_2018)

#___________Draw a sparkline__________________________

# data wraggling:
Country_all_years_data = all_data %>%
  filter(Country == 'China')

# at first , we keep all the variables and 
# later we will decide which part of these variables will be kept.
# Now transform the data in a tidy form.
# indicator, year and value.
Country_all_years_data = all_data %>%
  filter(Country == 'China') %>%
  subset(select = -c(Continent,Region,Country,Level)) %>%
  melt(id=c("Year"))

# now we can try to creat a sparkline
Country_all_years_data = all_data %>%
  filter(Country == 'China') %>%
  #filter(Year >= 2010) %>%
  subset(select = c(Year,HDI,
                    Gender_Development_Index,
                    Gender_Inequality_Index,
                    Total_Unemployment_Rate,
                    Education_Index
                    )) %>%
  melt(id=c("Year")) %>%
  drop_na() %>%
  ggplot(aes(x=Year, y=value)) +
  facet_grid(variable ~ ., scales = "free_y") +
  geom_line()+
  theme_bw()+
  theme(axis.text.x = element_text(angle = 60, hjust = 1))

ggplotly(Country_all_years_data)


#=========
worldcountry %>%
  merge(filter(all_data,Year==2018) %>%
          select(Country,Year,HDI),by.x = "NAME_LONG", by.y = "Country") #%>%# here we can change the input of data

#______________________-
Sys.Date()

#__________
heatmap_data <- all_data %>%
  filter(Year == 2018) %>%
  filter(Country %in% c('China','Australia'))%>%
  select(Country,HDI,Gender_Development_Index,Gender_Inequality_Index)

row.names(heatmap_data) <- heatmap_data$Country

heatmap_data <- heatmap_data %>%
  select(-Country)

heatmap_matrix <- data.matrix(heatmap_data)

row.names(heatmap_matrix) <- heatmap_data$Country

heatmap_data <- heatmap_data %>%
  select(-Country)

heatmap_matrix <-heatmap_matrix[,-1]

normalize(heatmap_matrix)

temp= heatmaply(heatmap_matrix,
          scale = 'none',
          dist_method = 'euclidean',
          hclust_method = 'ward.D', 
          seriate = 'OLO',
          colors = Blues,
          Colv=NA,
          #k_row = 2,
          margins = c(NA,200,60,NA),
          fontsize_row = 4,
          fontsize_col = 5,
          main="World Happiness Score and Variables by Country, 2018 \nDataTransformation using Normalise Method",
          xlab = "World Happiness Indicators",
          ylab = "World Countries")
temp
#______________________________________--
heatmap <- all_data %>%
  filter(Year == 2018) %>%
  column_to_rownames(var = "Country") %>%
  normalize() %>%
  heatmaply(scale = 'none',
            dist_method = 'euclidean',
            hclust_method = 'ward.D', 
            seriate = 'OLO',
            colors = Blues,
            Colv=NA,
            k_row = 5,
            #k_col = 3,
            #margins = c(NA,800,1000,NA),
            fontsize_row = 4,
            fontsize_col = 5,
            #main="World Happiness Score and Variables by Country, 2018 \nDataTransformation using Normalise Method",
            xlab = "Indicators",
            ylab = "Countries") %>%
  layout(height=800,width=1000)
            
  #select(Country,HDI,Gender_Development_Index,Gender_Inequality_Index)
heatmap
##=______________________________________
dev.new()
scatter_plot <- ggplotly(all_data %>% 
  filter(Year == 2018) %>%
  ggplot(aes(x = HDI, 
             y = Gender_Development_Index, 
             col = Region, 
             size = Total_GDP,
             text = Country)) + 
  geom_point(alpha = 0.5) + 
  theme_classic()+
  geom_text(aes(x = 0.5, y= 0.5, label = Year), size = 10, color = 'lightgrey',family = 'Oswald'))

  ggplotly()

scatter_plot