meat_consumption.R

library(shiny)
library(plotly)
library(dplyr)
library(tidyr)
animals_slaughtered_for_meat <- read.csv("animals-slaughtered-for-meat.csv")
colnames(animals_slaughtered_for_meat) <- c("Entity","Code", "Year", "Cattle", "Goats", "Chicken", "Turkey", "Pigs", "Sheep")
animals_slaughtered_for_meat <- animals_slaughtered_for_meat[!is.na(animals_slaughtered_for_meat$Code),]
meat<-read.csv('per-capita-meat-consumption-by-type-kilograms-per-year.csv',sep = ',' )
colnames(meat)<-c("Entity","Code","Year","Mutton & Goat","Other meat","Poultry","Pigmeat","Cattle")
meat <- meat[!is.na(meat$Code),]




ui<-fluidPage(
  fluidRow(
    column(12,
           "Meat consumption in the world",
           fluidRow(
             column(6,
                    plotlyOutput("glob"),
                    fluidRow(
                      column(6, 
                             selectInput("gatunek",
                                         "Species:",
                                         choices =  c("Cattle","Goats", "Chicken", "Turkey","Pigs", "Sheep" ),
                                         selected = "Cattle") ),
                      column(6,
                             sliderInput("zakres",
                                         "Year:",
                                         value =  min(animals_slaughtered_for_meat$Year),
                                         min = min(animals_slaughtered_for_meat$Year),
                                         max = max(animals_slaughtered_for_meat$Year),
                                         step = 1,
                                         animate = animationOptions(interval = 500)))
                    ),
                    h4("Map above represents number of slaughtered animals of chosen species by country in the chosen year ")
             ),
             
             column(6,
                    plotlyOutput("glob1"),
                    fluidRow(
                      column(6, 
                             selectInput("typ",
                                         "Species:",
                                         choices =  c("Mutton & Goat", "Other meat" ,"Poultry","Pigs","Cattle"),
                                         selected = "Cattle")),
                      column(6,
                             sliderInput("zakres1",
                                         "Year:",
                                         value = min(meat$Year),
                                         min = min(meat$Year),
                                         max = max(meat$Year),
                                         step = 1,
                                         animate = animationOptions(interval = 500)))
                    )
             ),
             h4("Map above represents consumption per capita of chosen species by country in the chosen year")
           )
    )
    
  ),
  h2("   "),
  textOutput("text"),
  sliderInput("przed",
              "Time period:",
              value = c(min(meat$Year), max(meat$Year)),
              min = min(meat$Year),
              max = max(meat$Year),
              step = 1),
  DT::dataTableOutput("mytable"),
  h4("Table represents summary of the consumption of in the chosen country in the chosen time period")
)









server <- function(input, output) {
  output$glob1 <- renderPlotly({
    meat1<-meat %>% 
      filter(Year == input$zakres1)
    if (input$typ == "Mutton & Goat"){
      plot_ly(data = meat1,
              type = 'choropleth',
              locations = ~Code,
              z = meat1$`Mutton & Goat`,
              zmin = 0,
              zmax = 61.34,
              text = ~Entity,
              colorscale = "Reds") %>% 
        layout(title = "Mutton and goat meat consumption per capita per country in kgs",
               geo = list(projection = list(type = 'natural earth')))
    } 
    else if (input$typ == "Other meat"){
      plot_ly(data = meat1,
              type = 'choropleth',
              locations = ~Code,
              z = meat1$`Other meat`,
              zmin = 0,
              zmax = 65.18,
              text = ~Entity,
              colorscale = "Reds") %>% 
        layout(title = "Other meat consumption per capita per country in kgs",
               geo = list(projection = list(type = 'natural earth')))
    }else if (input$typ == "Poultry"){
      plot_ly(data = meat1,
              type = 'choropleth',
              locations = ~Code,
              z = meat1$Poultry,
              zmin = 0,
              zmax = 87.82,
              text = ~Entity,
              colorscale = "Reds") %>% 
        layout(title = "Poultry consumption per capita per country in kg",
               geo = list(projection = list(type = 'natural earth')))
    }else if (input$typ == "Pigmeat"){
      plot_ly(data = meat1,
              type = 'choropleth',
              locations = ~Code,
              z = meat1$Pigmeat,
              zmin = 0,
              zmax = 77.93,
              text = ~Entity,
              colorscale = "Reds") %>% 
        layout(title = "Pigmeat consumption per capita per country in kg",
               geo = list(projection = list(type = 'natural earth')))
    }else {
      plot_ly(data = meat1,
              type = 'choropleth',
              locations = ~Code,
              z = meat1$Cattle,
              zmin = 0,
              zmax = 93.25,
              text = ~Entity,
              colorscale = "Reds") %>% 
        layout(title = "Cattle consumption per capita per country in kg",
               geo = list(projection = list(type = 'natural earth')))}
  })
  
  output$glob <- renderPlotly({
    df <- animals_slaughtered_for_meat %>% 
      filter(Year == input$zakres)
    if (input$gatunek == "Cattle"){
      plot_ly(data = df,
              type = 'choropleth',
              locations = ~Code,
              z = ~Cattle,
              zmin = 0,
              zmax = 48726000,
              text = ~Entity,
              colors = "Reds") %>% 
        layout(title = "Number of cattle slaughtered per country",
               geo = list(projection = list(type = 'natural earth')))
    }
    else if (input$gatunek == "Goats") {
      plot_ly(data = df,
              type = 'choropleth',
              locations = ~Code,
              z = ~Goats,
              zmin = 0,
              zmax = 162761966,
              text = ~Entity,
              colors = "Reds") %>% 
        layout(title = "Number of goats slaughtered per country",
               geo = list(projection = list(type = 'natural earth')))
    }
    else if (input$gatunek == "Chicken") {
      plot_ly(data = df,
              type = 'choropleth',
              locations = ~Code,
              z = ~Chicken,
              zmin = 0,
              zmax = 10510737000,
              text = ~Entity,
              colors = "Reds") %>% 
        layout(title = "Number of chickens slaughtered per country",
               geo = list(projection = list(type = 'natural earth')))
    }
    else if (input$gatunek == "Turkey") {
      plot_ly(data = df,
              type = 'choropleth',
              locations = ~Code,
              z = ~Turkey,
              zmin = 0,
              zmax = 293290000,
              text = ~Entity,
              colors = "Reds") %>% 
        layout(title = "Number of turkeys slaughtered per country",
               geo = list(projection = list(type = 'natural earth')))
    }
    else if (input$gatunek == "Pigs") {
      plot_ly(data = df,
              type = 'choropleth',
              locations = ~Code,
              z = ~Pigs,
              zmin = 744899496,
              text = ~Entity,
              colors = "Reds") %>% 
        layout(title = "Number of pigs slaughtered per country",
               geo = list(projection = list(type = 'natural earth')))
    }
    else if (input$gatunek == "Sheep") {
      plot_ly(data = df,
              type = 'choropleth',
              locations = ~Code,
              z = ~Sheep,
              zmin = 0,
              zmax = 144265012,
              text = ~Entity,
              colors = "Reds") %>% 
        layout(title = "Number of sheep slaughtered per country",
               geo = list(projection = list(type = 'natural earth')))
      
    }
    
  })
  output$text <- renderText(({
    
    paste("Sum of all meat consumed by country in years ", input$przed[1], "-", input$przed[2])
    
  }))
  
  output$mytable = DT::renderDataTable({
    jak <- meat
    jak$Suma <-rowSums(jak[, c(4:8)], na.rm = TRUE)
    
    g <- jak %>% 
      select(Entity, Year, Suma) %>%
      filter(Year >= input$przed[1],
             Year <= input$przed[2]) %>% 
      group_by(Entity) %>% 
      summarise(Sum_avg = round(mean(Suma)),
                Sum_min = min(Suma),
                Sum_max = max(Suma))
    
    
    g
  })
  
  
}


shinyApp(ui = ui, server = server)