app.R

library(ggplot2)
library(shiny)
library(shinyjs)
library(shinydashboard)
library(ggplot2)
library(reshape2)
library(gridExtra)
library(dplyr)
library(tidyr)
library(DT)
library(data.table)
library(corrplot)
library(sf)
library(ggplot2)
library("rnaturalearth")
library("rnaturalearthdata")


df2015 = read.csv("2015.csv")
df2016 = read.csv("2016.csv")
df2017 = read.csv("2017.csv")
df2018 = read.csv("2018.csv")
df2019 = read.csv("2019.csv")
names(df2018) = c('Happiness.Rank','Country','Happiness.Score',
                  'Economy..GDP.per.Capita.', 'Family', 
                  'Health..Life.Expectancy.', 'Freedom', 'Generosity', 
                  'Trust..Government.Corruption.')
names(df2019) = c('Happiness.Rank','Country','Happiness.Score',
                  'Economy..GDP.per.Capita.', 'Family', 
                  'Health..Life.Expectancy.', 'Freedom', 'Generosity', 
                  'Trust..Government.Corruption.')
world <- ne_countries(scale = "medium", returnclass = "sf")
countries = sort( unique(c(df2015$Country,df2016$Country,df2017$Country,df2018$Country,df2019$Country)))
setdiff(countries,df2015$Country)
for (country in countries){
  if (!(country %in% df2015$Country)){
    df2015[nrow(df2015) + 1,] = c(country,rep(NA, ncol(df2015)-1))
  }
}
for (country in countries){
  if (!(country %in% df2016$Country)){
    df2016[nrow(df2016) + 1,] = c(country,rep(NA, ncol(df2016)-1))
  }
}
for (country in countries){
  if (!(country %in% df2017$Country)){
    df2017[nrow(df2017) + 1,] = c(country,rep(NA, ncol(df2017)-1))
  }
}
for (country in countries){
  if (!(country %in% df2018$Country)){
    df2018[nrow(df2018) + 1,] = c(NA,country,rep(NA, ncol(df2018)-2))
  }
}
for (country in countries){
  if (!(country %in% df2019$Country)){
    df2019[nrow(df2019) + 1,] = c(NA,country,rep(NA, ncol(df2019)-2))
  }
}
cdf2015 = df2015[order(df2015$Country),]
cdf2016 = df2016[order(df2016$Country),]
cdf2017 = df2017[order(df2017$Country),]
cdf2018 = df2018[order(df2018$Country),]
cdf2019 = df2019[order(df2019$Country),]


rdf = copy(df2016)
rdf[] = lapply(rdf, function(x) {
  x1 = type.convert(as.character(x), as.is=TRUE)
  ifelse(grepl("^[0-9.]+$", x1), round(as.numeric(x1), 3), x1)})
rdf$Country = NULL
rdf = rdf %>% group_by(Region) %>%
  summarise(Mean_Happiness = mean(Happiness.Score, na.rm=T),
            Mean_gdp = mean(Economy..GDP.per.Capita.,na.rm=T),
            Mean_family=mean(Family,na.rm=T),
            Mean_freedom=mean(Freedom,na.rm=T),
            Mean_LE = mean(Health..Life.Expectancy., na.rm=T))
rdf = rdf[1:10,]
names(rdf) = c("Region","Happiness", "GDP_per_capita", "Family", "Freedom", "Life_Expectancy")
rdf = melt(rdf, id='Region')
rdf[] = lapply(rdf, function(x) {
  x1 = type.convert(as.character(x), as.is=TRUE)
  ifelse(grepl("^[0-9.]+$", x1), round(as.numeric(x1), 3), x1)})


corr = df2019[1:156,3:(ncol(df2019)-2)]
names(corr) = c('Happiness','GDP_per_capita','Family','Life_Expectancy','Freedom')
corr[] = lapply(corr, function(x) {
  x = type.convert(as.character(x), as.is=TRUE)})


scatter = df2019[1:156,3:ncol(df2019)]
names(scatter) = c('Happiness','GDP_per_capita','Family','Life_Expectancy','Freedom',
                   'Generosity','Trust in Government')
scatter[] = lapply(scatter, function(x) {
  x = type.convert(as.character(x), as.is=TRUE)})








ui <- dashboardPage(
  dashboardHeader(title="World Happiness Data"),
  dashboardSidebar(
    sidebarMenu(
      menuItem("About", tabName = "about"),
      menuItem('Map Visualization', tabName='map'),
      menuItem("Regional Analysis", tabName = "region"),
      menuItem("Cross Factor Analysis", tabName = "scatter"),
      menuItem('Cross Country Analysis', tabName='country')
    )
  ),
  dashboardBody(
    tags$style(HTML(".sidebar-menu li a { font-size: 16px; }")),
    tabItems(
      tabItem('about',
              tags$h2('Overview', align='center'),
              tags$br(),
              tags$h4('The dashboard is a visual approach to analyze happiness across the world.
                     Through the use of multiple interactive plots, users can see the variance in 
                     happiness on a regional and country level, and also study the various factors
                     that affect it. The key variables that we will consider are:'),
              tags$h4(
                tags$ol(
                  tags$li('Happiness.Score - Measure of happiness that is calculated using the below factors'),
                  tags$li('Economy..GDP.per.Capita - Per capita GDP of the country'),
                  tags$li('Family - Measure of satisfaction provided by family'),
                  tags$li('Health..Life.Expectancy. - Life expectancy in the country'),
                  tags$li('Freedom - Measure of freedom provided to the individual by the government, society, etc.'),
                )
              ),
              tags$br(),
              HTML("<h4>The data can be found <a href='https://www.kaggle.com/datasets/unsdsn/world-happiness'> here</a>. 
                   We have 5 years worth data from 2015-2019, thereby making a time series analysis possible,
                   each of which has the countrywise data for that year
                   and every country is also divided into what region of the world it belongs to, so we can perform
                   both country and region wise analysis.</h4>"),
              tags$br(),
              tags$h4('The plots might take some time to render, apologies for that.')),
      tabItem("region",
              tabsetPanel(
                tabPanel("Region vs Region",
                         selectInput("regvar1", "Select Region 1", unique(df2016[,2])[1:10], selected='Australia and New Zealand'),
                         selectInput("regvar2", "Select Region 2", unique(df2016[,2])[1:10], selected='Eastern Asia'),
                         fluidRow(plotOutput("rvrplot"))
                ),
                tabPanel("Factor vs Factor",
                         selectInput("facvar1", "Select Factor 1", c("Happiness", "GDP_per_capita", "Family", "Freedom", "Life_Expectancy"),selected='Happiness'),
                         selectInput("facvar2", "Seelct Factor 2", c("Happiness", "GDP_per_capita", "Family", "Freedom", "Life_Expectancy"),selected='GDP_per_capita'),
                         fluidRow(plotOutput("fvfplot"))
                )
              )),
      tabItem("scatter",
              tabsetPanel(
                tabPanel("Factor vs Factor",
                         h3("Analyse 2 Factors against each other", align='center'),
                         fluidRow(plotOutput('corrplot'), align='center'),
                         selectInput("scatvar1", "Select Factor 1", c("Happiness", "GDP_per_capita", "Family", "Freedom", "Life_Expectancy"), selected='Happiness'),
                         selectInput("scatvar2", "Select Factor 2", c("Happiness", "GDP_per_capita", "Family", "Freedom", "Life_Expectancy"), selected='Family'),
                         fluidRow(plotOutput("scatplot")),
                         actionButton("fitline", "Fit Regression Line"),
                         actionButton("removefitline", "Remove Regression Line")
                )
              )),
      tabItem('country',
              tabsetPanel(
                tabPanel("Country",
                         h3("Trend in country's happiness across 5 years", align='center'),
                         selectInput('cvar','Select Country',countries, selected='Finland'),
                         selectInput('fvar1', 'Select Factor', names(cdf2019)[3:7],selected='Happiness.Score'),
                         fluidRow(plotOutput('lineplot'), align='center')
                ),
                tabPanel("Country vs Country",
                         h3("Compare the trends of two countries", align='center'),
                         selectInput('cvar1','Select Country 1',countries, selected='United States'),
                         selectInput('cvar2','Select Country 2',countries, selected='India'),
                         selectInput('fvar2', 'Select Factor', names(cdf2019)[3:7],selected='Happiness.Score'),
                         fluidRow(plotOutput('lvlplot'))
                ),
                tabPanel("Top and Bottom",
                         h3("Compare the trends of countries at the (current) top/bottom"),
                         radioButtons('tb','Select Top or Bottom',c('Top','Bottom'), selected='Top'),
                         numericInput('pvar','Select number of countries',5, min=1, max=100),
                         selectInput('fvar3', 'Select Factor', names(cdf2019)[3:7],selected='Happiness.Score'),
                         fluidRow(plotOutput('plvlplot'))
                )
              )),
      tabItem('map',
              h3("Visualize the trends globally", align='center'),
              selectInput('var', 'Select Factor', names(cdf2019)[3:7],selected='Happiness.Score'),
              fluidRow(plotOutput('mapplot'))
      )
    )
  )
)


server <- function(input, output) {
  rv = reactiveValues(ols=0)
  output$rvrplot = renderPlot({
    rvar1 = input$regvar1
    rvar2 = input$regvar2
    ggplot(rdf[rdf$Region %in% c(rvar1,rvar2),], aes(x=variable,y=value, fill=variable)) + 
      geom_bar(stat='identity', col='black', alpha=0.75) + facet_grid(Region~.)+
      geom_text( aes(label=value), vjust=1.25) + theme_classic() + 
      xlab('Factors')+ labs(fill='Factors')+
      ggtitle(paste0('Comparison plot of different factors between ', rvar1, ' and ', rvar2))+
      theme(axis.text.x  = element_blank(),
            text = element_text(size=11),
            legend.text = element_text(size=11),
            legend.title = element_text(size=13, hjust = 0.5), 
            strip.text.y = element_text(size=10),
            axis.text.y = element_text(size=15),
            legend.box.background=element_rect(),
            plot.title = element_text(hjust=0.5))
  })
  output$fvfplot = renderPlot({
    fvar1 = input$facvar1
    fvar2 = input$facvar2
    ggplot(rdf[rdf$variable %in% c(fvar1,fvar2),], aes(x=Region,y=value, fill=Region)) + 
      geom_bar(stat='identity', col='black', alpha=0.75) + facet_grid(variable~.)+
      geom_text( aes(label=value), vjust=1.25) + theme_classic() + 
      xlab('Regions')+labs(fill='Regions')+
      ggtitle(paste0('Comparison plot between ', fvar1, ' and ', fvar2, ' across regions'))+
      theme(axis.text.x  = element_blank(),
            legend.text = element_text(size=11),
            legend.title = element_text(size=13, hjust=0.5), 
            strip.text.y = element_text(size=10),
            axis.text.y = element_text(size=15),
            legend.box.background=element_rect(),
            plot.title = element_text(hjust=0.5))
  })
  output$corrplot = renderPlot({
    cormat = round(cor(corr),2)
    corrplot(cormat, method='square',type='lower', diag=FALSE
             ,addCoef.col = "black")
  }, width=500)
  output$scatplot = renderPlot({
    svar1 = input$scatvar1
    svar2 = input$scatvar2
    if(rv$ols) {
      g = ggplot(data=scatter, mapping=aes_string(x=svar1,y=svar2)) + 
        geom_point(color="black", alpha=0.5) + geom_smooth(method='lm', col='red')+ 
        theme_classic() + 
        ggtitle(paste0("Scatter plot between ", svar1, " and ", svar2))+
        theme(plot.title = element_text(hjust=0.5))
    }
    else{
      g = ggplot(data=scatter, mapping=aes_string(x=svar1,y=svar2)) + 
        geom_point(color="black", alpha=0.5) + theme_classic() + 
        ggtitle(paste0("Scatter plot between ", svar1, " and ", svar2)) +
        theme(plot.title = element_text(hjust=0.5))
    }
    g
  })
  observeEvent(input$fitline, {
    rv$ols = 1
  })
  observeEvent(input$removefitline, {
    rv$ols = 0
  })
  output$lineplot = renderPlot({
    fac1 = input$fvar1
    cvar = input$cvar
    df = as.data.frame(cbind(countries,cdf2015[fac1],cdf2016[fac1],
                             cdf2017[fac1],cdf2018[fac1],
                             cdf2019[fac1]))
    names(df) = c('Country','2015', '2016', '2017', '2018', '2019')
    for (country in countries){
      df[which(df$Country==country),][which(is.na(df[which(df$Country==country),]))]=median(as.numeric(df[which(df$Country==country),2:ncol(df)]), na.rm=T)
    }
    df = df[order(df$`2019`, decreasing =TRUE),]
    df[] = lapply(df, function(x) {
      x1 = type.convert(as.character(x), as.is=TRUE)
      ifelse(grepl("^[0-9.]+$", x1), round(as.numeric(x1), 3), x1)})
    ggplot(melt(df[df$Country==cvar,])) + geom_line(aes(x=variable, y=value,group=1)) + 
      geom_point(aes(x=variable, y=value, group=1),color='red') + 
      geom_text(aes(x=variable, y=value,label=value),vjust=2,hjust=1)+
      labs(x='Year',y=paste0(fac1), title=paste0('Trend in ',cvar, ' : ', fac1)) + 
      theme_classic()+ 
      theme(plot.title = element_text(hjust = 0.5),
            legend.box.background=element_rect())
  })
  output$lvlplot = renderPlot({
    fac2 = input$fvar2
    cvar1 = input$cvar1
    cvar2 = input$cvar2
    df = as.data.frame(cbind(countries,cdf2015[fac2],cdf2016[fac2],
                             cdf2017[fac2],cdf2018[fac2],
                             cdf2019[fac2]))
    names(df) = c('Country','2015', '2016', '2017', '2018', '2019')
    for (country in countries){
      df[which(df$Country==country),][which(is.na(df[which(df$Country==country),]))]=median(as.numeric(df[which(df$Country==country),2:ncol(df)]), na.rm=T)
    }
    df = df[order(df$`2019`, decreasing =TRUE),]
    df[] = lapply(df, function(x) {
      x1 = type.convert(as.character(x), as.is=TRUE)
      ifelse(grepl("^[0-9.]+$", x1), round(as.numeric(x1), 3), x1)})
    ggplot(melt(df[df$Country %in% c(cvar1,cvar2),])) + 
      geom_line(aes(x=variable, y=value,group=Country, color=Country)) + 
      geom_point(aes(x=variable, y=value, group=Country,color=Country)) + 
      geom_text(aes(x=variable, y=value,label=value),vjust=2,hjust=1)+
      labs(x='Year',y=paste0(fac2), title=paste0(cvar1 ,' vs ',cvar2, " : ", fac2, ' trend')) + 
      theme_classic()+ 
      theme(plot.title = element_text(hjust = 0.5),
            legend.box.background=element_rect())
  })
  output$plvlplot = renderPlot({
    n = as.integer(input$pvar)
    fac3 = input$fvar3
    df = as.data.frame(cbind(countries,cdf2015[fac3],cdf2016[fac3],
                             cdf2017[fac3],cdf2018[fac3],
                             cdf2019[fac3]))
    names(df) = c('Country','2015', '2016', '2017', '2018', '2019')
    for (country in countries){
      df[which(df$Country==country),][which(is.na(df[which(df$Country==country),]))]=median(as.numeric(df[which(df$Country==country),2:ncol(df)]), na.rm=T)
    }
    df = df[order(df$`2019`, decreasing =TRUE),]
    df[] = lapply(df, function(x) {
      x1 = type.convert(as.character(x), as.is=TRUE)
      ifelse(grepl("^[0-9.]+$", x1), round(as.numeric(x1), 3), x1)})
    if (input$tb == "Top"){
      ggplot(melt(df[1:n,])) + 
        geom_line(aes(x=variable, y=value,group=Country, color=Country)) + 
        geom_point(aes(x=variable, y=value, group=Country,color=Country)) + 
        geom_text(aes(x=variable, y=value,label=value),vjust=2,hjust=1)+
        labs(x='Year',y=paste0(fac3), 
             title=paste0(fac3,' : Trend in the top ', n ,' countries')) + 
        theme_classic()+
        theme(text = element_text(size=10)) + 
        theme(plot.title = element_text(hjust = 0.5),
              legend.box.background=element_rect())
    }
    else{
      ggplot(melt(df[(length(countries)+1-n):length(countries),])) + 
        geom_line(aes(x=variable, y=value,group=Country, color=Country)) + 
        geom_point(aes(x=variable, y=value, group=Country,color=Country)) + 
        geom_text(aes(x=variable, y=value,label=value),vjust=2,hjust=1)+
        labs(x='Year',y=paste0(fac3), 
             title=paste0(fac3,' : Trend in the bottom ', n ,' countries')) + 
        theme_classic() + 
        theme(plot.title = element_text(hjust = 0.5),
              legend.box.background=element_rect())
    }
  })
  output$mapplot = renderPlot({
    var = input$var
    fill = c()
    for (country in world$name){
      if (country %in% df2019$Country){
        fill = c(fill, df2019[df2019$Country==country,var])
      }
      else{
        fill = c(fill,NA)
      }
    }
    fill = lapply(fill, function(x){
      return (as.double(x))
    })
    fill=as.numeric(fill)
    ggplot(data = world) +
      geom_sf(aes(fill=fill)) +
      ggtitle(paste0("World map showing distribution of ", var, " in 2019"), 
              subtitle = paste0("(Grey countries are due a lack of data)")) +
      scale_fill_viridis_c(option='A')+
      theme(plot.title = element_text(hjust=0.5),
            plot.subtitle = element_text(hjust=0.5))
  }, height=800)
}

shinyApp(ui = ui, server = server)