BlogPost.R

#global.R

library(shiny)
library(dplyr)
library(ggplot2)
library(shinydashboard)
library(rworldmap)
library(googleVis)
library(DT)

commodity <- read.csv(file = "./commodity_trade_statistics_data.csv")

#Top_4 - USA, China, Canada, Mexico
commodity_T4 <- commodity %>%
  filter(., country_or_area == "USA" | country_or_area == "China, Hong Kong SAR" | country_or_area == "Canada" | country_or_area == "Mexico")

#tidy data
commodity_T4[,'comm_code']<-NULL
commodity_T4[,'weight_kg']<-NULL
commodity_T4[,'quantity_name']<-NULL
commodity_T4[,'quantity']<-NULL


commodity_T4 <- rename(commodity_T4, country = `country_or_area`)
commodity_T4 <- rename(commodity_T4, trade.in.usd = `trade_usd`)
commodity_T4 <- rename(commodity_T4, year.trade = `year`)


class(commodity_T4$category)  ##factor
commodity_T4$category<-as.character(commodity_T4$category)

commodity_T4 <- mutate(commodity_T4, flow = gsub(pattern = "Re-Export", replacement = "Export", x = flow, ignore.case = F, fixed = T)) %>%
  mutate(flow = gsub(pattern = "Re-Import", replacement = "Import", x = flow, ignore.case = F))%>%
  mutate(country = gsub(pattern = "China, Hong Kong SAR", replacement = "China", x = country, ignore.case = F))


## Data Set for Map## 
commodity_T4_maps<-commodity_T4%>%
  filter(.,year.trade==2016)%>%
  mutate(.,flow_sign = ifelse(flow=="Import",-1,1))%>%
  mutate(.,trade.in.sign = trade.in.usd*flow_sign)%>%
  group_by(.,country)%>%
  summarize(., balance = sum(trade.in.sign), balance2 = mean(trade.in.sign))





#ui.R

sidebar <- dashboardSidebar(
  width = 250,
  sidebarUserPanel("Michael Tarino"), 
  sidebarMenu(
    menuItem("Maps", tabName = "maps", icon = icon("fire")),
    menuItem("Graphs", tabName = "graphs", icon = icon("list-alt"),
             menuItem("Bar Graph: Trade", tabName = "bargraph", icon=icon('bar-chart-o')),
             menuItem("Histogram: Trade", tabName = "histogram", icon=icon('bar-chart-o'))
    ),
    menuItem("Statistics", tabName = "statistics", icon = icon("gear")),
    menuItem("Table", tabName = "table", icon = icon("table"))
  )
)

body <- dashboardBody(
  tabItems(
    tabItem(tabName = "maps",
            h2("Global Commodity Trade Balances for 2016"),
            fluidPage(htmlOutput("map"))
    ),
    
    tabItem(tabName = "bargraph",
            h2("Global Commodity Trade Data"),
            fluidPage(
              titlePanel("Bar Graph: Trade by Country and Flow"),
              sidebarLayout(
                sidebarPanel(
                  selectizeInput(inputId = "year.trade",
                                 label = "Year",
                                 choices = unique(commodity_T4[, 'year.trade'])),
                  selectizeInput(inputId = "category",
                                 label = "Category",
                                 choices = unique(commodity_T4[, 'category']))
                ),
                mainPanel(
                  fluidRow(
                    column(6, plotOutput("bar_graph", width = "200%"))
                  )
                )
              )
            )
            
    ),
    
    tabItem(tabName = "histogram",
            h2("Global Commodity Trade Data"),
            fluidPage(titlePanel("Histogram: Sum of Commodity transaction grouped by Year")),
            fluidRow(column(6, plotOutput("histogram_graph", width = "200%")))
    ),
    
    tabItem(tabName = "statistics",
            h2("Statistics: Linear Model and LOESS Regression"),
            fluidRow(column(3, selectizeInput('country', 
                                              label='Country:', 
                                              choices= unique(commodity_T4[, 'country'])))),
            fluidRow(column(5, plotOutput('statistics', width = "250%")))
            
    ),
    
    tabItem(tabName = "table",
            h2("Table from Commodity Database"),
            fluidRow(column(3, selectizeInput('country1', label='Country:', choices= unique(commodity_T4[, 'country']))),
                     column(3, selectizeInput('year.trade1', label='Year:', choices= unique(commodity_T4[, 'year.trade']))),
                     dataTableOutput('table'))
            
    )
    
  )
)


dashboardPage(skin='red',
              dashboardHeader(title = "Commodity Data"),
              sidebar,
              body
)





#server.R

function(input, output, session) {
  
  ########  MAP ########
  output$map <- renderGvis({
    map<-gvisGeoChart(commodity_T4_maps, locationvar='country', colorvar='balance',
                      options=list(title='Trade Balance by Country', width='100%', projection="kavrayskiy-vii", colorAxis="{colors:['#0000ff', '#FF0000']}"))
    return(map)
  })
  
  
  ########  BAR GRAPH 1 ########
  observe(
    print(paste("year.trade:", input$year.trade, "category:", input$category))
  )
  
  observe({
    category <- unique(commodity_T4 %>%
                         filter(commodity_T4$year.trade == input$year.trade) %>%
                         .$category)
    updateSelectizeInput(
      session, "category",
      choices = category,
      selected = category[1])
  })
  
  output$bar_graph <- renderPlot({
    commodity_T4 %>%
      filter(.,commodity_T4$year.trade == input$year.trade & commodity_T4$category == input$category)%>%
      group_by(.,country, flow)%>% 
      summarize(., sum_trade_yr = sum(trade.in.usd, na.rm=TRUE)) %>%
      ggplot(., aes(x=flow, y=sum_trade_yr)) +
      geom_bar(aes(x=country,fill=flow), stat="identity", position='dodge') +
      labs(title='Trade by country and flow', x='Country', y='Sum Trade in USD') + 
      theme_bw()
  })
  
  ########  HISTOGRAM 1 ########
  output$histogram_graph <- renderPlot({
    commodity_T4%>%
      group_by(country, year.trade)%>%
      summarize(sum.trade=sum(trade.in.usd))%>%
      ggplot(aes(x=sum.trade)) + geom_histogram(aes(fill=country), color="black", bins = 40, position = position_stack(reverse=TRUE)) + coord_cartesian(xlim=c(66000000000,5e+12),ylim=c(0,15)) + ##from 5000000, 4500000000
      xlab("Sum of Commodity transactions in USD grouped by Year") + ylab("Count")
  })
  
  ########  STATISTICS ########
  observe(
    print(paste("country:", input$country))
  )
  
  output$statistics <- renderPlot({
    commodity_T4 %>%
      filter(.,commodity_T4$country == input$country, year.trade==1996|year.trade==1998|year.trade==2000|year.trade==2002|year.trade==2004|year.trade==2006|year.trade==2008|year.trade==2010|year.trade==2012|year.trade==2014|year.trade==2016)%>%
      group_by(., year.trade)%>%
      summarize(., balance = sum(trade.in.usd), balance2 = mean(trade.in.usd))%>%
      ggplot(., aes(x=year.trade, y=balance)) + geom_point(color='red1', shape=2 , size=4) + geom_smooth(se=FALSE, col = "green") + stat_smooth(method = "lm", col = "blue", se=FALSE) +
      labs(title='Trade Volume in USD', x='Year', y='Trade in USD') + 
      theme(legend.key=element_blank(), legend.position="bottom") +
      theme_bw()
  })
  
  
  ########  FILTERING TABLE ########
  observe(
    print(paste("year.trade:", input$year.trade1, "country:", input$country1))
  )
  
  observe({
    country <- unique(commodity_T4 %>%
                        filter(commodity_T4$year.trade == input$year.trade1) %>%
                        .$country1)
    updateSelectizeInput(
      session, "country",
      choices = country,
      selected = country[1])
  })
  
  trade_filter<-reactive({
    commodity_T4%>%
      filter(., year.trade == input$year.trade1)%>%
      filter(., country == input$country1)
  })
  
  output$table <- renderDataTable({
    datatable(trade_filter(), rownames=TRUE) %>%
      formatStyle(input$selected, background="red", fontWeight='bold')
  })
}