Zaktualizuj 'projekt.R'

projekt.R

@@ -4,11 +4,28 @@ library(dplyr) # Data manipulate
 library(shinythemes)
 library(plotly)
 
+library(sf)
+library(rnaturalearth)
+library(ggspatial) 
+library(ggrepel) 
+
+options(scipen=999)
+
 CO_data <- read.csv("./data.csv",  header= TRUE)
 
-col_names = colnames(CO_data)
+CO_data2 <- CO_data[,-c(1,2,3)]
+col_names = colnames(CO_data2)
+
 
 countries <- unique(CO_data['country'])
+years <- unique(sort(CO_data$year))
+world <- ne_countries(scale = 'medium', returnclass = 'sf')
+country_list <- unique(sort(world$name))
+
+CO_data_filtered <- subset(CO_data, country %in% country_list)
+
+only_co2_and_year <- CO_data[,c('year', 'country', 'co2')]
+
 
 ui <- navbarPage(
   titlePanel(title=div(img(src="https://siw.amu.edu.pl/__data/assets/file/0004/162751/logo_wersja-podstawowa_granat_1.jpg", width = 50, height = 50), 'explore CO2 data')),
@@ -36,8 +53,38 @@ ui <- navbarPage(
     'GDP',
     plotlyOutput('gdp')
   ),  
+  tabPanel("Map: CO2 by year", 
+           sidebarLayout(
+             sidebarPanel(
+               selectInput('year', 
+                           'Select year', 
+                           selected = '2011',
+                           choices = years
+               )
+             ),
+             
+             mainPanel(
+               plotlyOutput('map'),
+             ),
+           )
+  ), 
+  tabPanel("Map: statistics in year 2011", 
+           sidebarLayout(
+             sidebarPanel(
+               selectInput('category2', 
+                           'Select category', 
+                           selected = 'population',
+                           choices = col_names
+               )
+             ),
+             
+             mainPanel(
+               plotlyOutput('map2'),
+             ),
+           )
+  ), 
   tabPanel(
-    'TOP Production',
+    'Biggest CO2 Production',
     fluidRow(
       column(6,plotlyOutput(outputId="the_most_1")),  
       column(6,plotlyOutput(outputId="the_most_2")),
@@ -45,7 +92,7 @@ ui <- navbarPage(
     )
   ),
   tabPanel(
-    'Smallest production',
+    'Smallest CO2 production',
     fluidRow(
       column(6,plotlyOutput(outputId="the_least_1")),  
       column(6,plotlyOutput(outputId="the_least_2")),
@@ -77,83 +124,100 @@ server <- function(input, output, session) {
     CO_data %>%
       filter(country == input$country) %>%
       ggplot(aes(x = year, y = get(input$category))) + 
+      ylab(input$category) +
       geom_line()
   })
   output$gdp <- renderPlotly({
-    CO_data %>%
+    CO_data_filtered %>%
       filter(year == 2011) %>%
       ggplot(aes(x = gdp, y = co2, label = country)) + 
       geom_line() +
       geom_point() +
+      ylim(0,10000) +
       ggtitle('Placement of countries by CO2 and GDP production')
   })
+  output$map = renderPlotly({
+    countries_data <- filter(only_co2_and_year, year==input$year)
+    data <- merge(world, countries_data, by.y="country", by.x="name")
+    ggplot(data = data) +
+      geom_sf(aes(fill = co2, label = name)) +
+      scale_fill_viridis_c(option = "plasma", trans = "sqrt") # colorblind-friendly palette
+  })
+  output$map2 = renderPlotly({
+    countries_data <- filter(CO_data, year==2011)
+    data2 <- merge(world, countries_data, by.y="country", by.x="name")
+    ggplot(data = data2) +
+      geom_sf(aes(fill = get(input$category2), label = input$category2)) +
+      labs(title=input$category2) +
+      scale_fill_discrete(labels = input$category2) +
+      scale_fill_viridis_c(option = "plasma", trans = "sqrt") # colorblind-friendly palette
+  })
   output$the_most_1 = renderPlotly({
-    CO_data %>%
+    CO_data_filtered %>%
       filter(year==2011) %>%
       slice_max(n=7, order_by = co2_per_gdp) %>%
       ggplot(aes(x=country, y=co2_per_gdp, fill=country)) +
       xlab('Country') +
       ylab('CO2 per GDP') +
-      ggtitle('The TOP production per GDP') +
+      ggtitle('the biggest CO2 production per GDP') +
       theme(axis.text.x = element_blank()) +
       geom_bar(stat='identity')
   })
   output$the_most_2 = renderPlotly({
-    CO_data %>%
+    CO_data_filtered %>%
       filter(year==2011) %>%
       slice_max(n=7, order_by = co2_per_capita) %>%
       ggplot(aes(x=country, y=co2_per_capita, fill=country), custom) +
       xlab('Country') +
       ylab('CO2 per capita') +
-      ggtitle('The TOP production per capita') +
+      ggtitle('the biggest CO2 production per capita') +
       theme(axis.text.x = element_blank()) +
       geom_bar(stat='identity')
   })
   output$the_most_3 = renderPlotly({
-    CO_data %>%
+    CO_data_filtered %>%
       filter(year==2011) %>%
-      slice_max(n=7, order_by = co2_per_capita) %>%
+      slice_max(n=7, order_by = co2) %>%
-      ggplot(aes(x=country, y=co2_per_capita, fill=country)) +
+      ggplot(aes(x=country, y=co2, fill=country)) +
       xlab('Country') +
       ylab('CO2 overall') +
-      ggtitle('The TOP production overall') +
+      ggtitle('the biggest CO2 production overall') +
       theme(axis.text.x = element_blank()) +
       geom_bar(stat='identity')
   })
   output$the_least_1 = renderPlotly({
-    CO_data %>%
+    CO_data_filtered %>%
       filter(year==2011) %>%
       slice_min(n=7, order_by = co2_per_gdp) %>%
       ggplot(aes(x=country, y=co2_per_gdp, fill=country)) +
       xlab('Country') +
       ylab('CO2 per GDP') +
-      ggtitle('The smalest production per GDP') +
+      ggtitle('the smallest CO2 production per GDP') +
       theme(axis.text.x = element_blank()) +
       geom_bar(stat='identity')
   })
   output$the_least_2 = renderPlotly({
-    CO_data %>%
+    CO_data_filtered %>%
       filter(year==2011) %>%
       slice_min(n=7, order_by = co2_per_capita) %>%
       ggplot(aes(x=country, y=co2_per_capita, fill=country)) +
       xlab('Country') +
       ylab('CO2 per capita') +
-      ggtitle('The smalest production per capita') +
+      ggtitle('the smallest CO2 production per capita') +
       theme(axis.text.x = element_blank()) +
       geom_bar(stat='identity')
   })
   output$the_least_3 = renderPlotly({
-    CO_data %>%
+    CO_data_filtered %>%
       filter(year==2011) %>%
       slice_min(n=7, order_by = co2) %>%
       ggplot(aes(x=country, y=co2, fill=country)) +
       xlab('Country') +
       ylab('CO2 overall') +
-      ggtitle('The smalest production overall') +
+      ggtitle('the smallest CO2 production overall') +
       theme(axis.text.x = element_blank()) +
       geom_bar(stat='identity')
   })
 }
 
-
 shinyApp(ui = ui, server = server)