initialize and customize some charts

projekt.R

@@ -0,0 +1,159 @@
+library(shiny) # Main library
+library(ggplot2) # Plots
+library(dplyr) # Data manipulate
+library(shinythemes)
+library(plotly)
+
+CO_data <- read.csv("./data.csv",  header= TRUE)
+
+col_names = colnames(CO_data)
+
+countries <- unique(CO_data['country'])
+
+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')),
+  tabPanel("Linear Chart", 
+           sidebarLayout(
+             sidebarPanel(
+               selectInput('country', 
+                           'Select Country', 
+                           selected = 'Afghanistan',
+                           choices = countries
+               ),
+               selectInput('category', 
+                           'Select Category', 
+                           selected = 'population',
+                           choices = col_names
+               )
+             ),
+             
+             mainPanel(
+               plotlyOutput('linear_chart')
+             ),
+           )
+  ),
+  tabPanel(
+    'GDP',
+    plotlyOutput('gdp')
+  ),  
+  tabPanel(
+    'TOP Production',
+    fluidRow(
+      column(6,plotlyOutput(outputId="the_most_1")),  
+      column(6,plotlyOutput(outputId="the_most_2")),
+      column(6,plotlyOutput(outputId="the_most_3"))
+    )
+  ),
+  tabPanel(
+    'Smallest production',
+    fluidRow(
+      column(6,plotlyOutput(outputId="the_least_1")),  
+      column(6,plotlyOutput(outputId="the_least_2")),
+      column(6,plotlyOutput(outputId="the_least_3"))
+    )
+  ),
+  
+  tabPanel(
+    'Data',
+    DT::dataTableOutput('tableData')
+  ),  
+  
+  tabPanel(
+    'Theme',
+    shinythemes::themeSelector(),
+    theme = shinythemes::shinytheme('flatly'),
+  )
+)
+
+
+server <- function(input, output, session) {
+  output$tableData <- DT::renderDataTable({
+    CO_data %>% 
+      filter(country == input$country) %>% 
+      DT::datatable()
+  })
+  
+  output$linear_chart <- renderPlotly({
+    CO_data %>%
+      filter(country == input$country) %>%
+      ggplot(aes(x = year, y = get(input$category))) + 
+      geom_line()
+  })
+  output$gdp <- renderPlotly({
+    CO_data %>%
+      filter(year == 2011) %>%
+      ggplot(aes(x = gdp, y = co2, label = country)) + 
+      geom_line() +
+      geom_point() +
+      ggtitle('Placement of countries by CO2 and GDP production')
+  })
+  output$the_most_1 = renderPlotly({
+    CO_data %>%
+      filter(year==2011) %>%
+      slice_max(n=7, order_by = co2_per_gdp) %>%
+      ggplot(aes(x=country, y=co2_per_gdp, fill=country)) +
+      xlab('Country') +
+      ylab('CO2 per GDP') +
+      ggtitle('The TOP production per GDP') +
+      theme(axis.text.x = element_blank()) +
+      geom_bar(stat='identity')
+  })
+  output$the_most_2 = renderPlotly({
+    CO_data %>%
+      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') +
+      theme(axis.text.x = element_blank()) +
+      geom_bar(stat='identity')
+  })
+  output$the_most_3 = renderPlotly({
+    CO_data %>%
+      filter(year==2011) %>%
+      slice_max(n=7, order_by = co2_per_capita) %>%
+      ggplot(aes(x=country, y=co2_per_capita, fill=country)) +
+      xlab('Country') +
+      ylab('CO2 overall') +
+      ggtitle('The TOP production overall') +
+      theme(axis.text.x = element_blank()) +
+      geom_bar(stat='identity')
+  })
+  output$the_least_1 = renderPlotly({
+    CO_data %>%
+      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') +
+      theme(axis.text.x = element_blank()) +
+      geom_bar(stat='identity')
+  })
+  output$the_least_2 = renderPlotly({
+    CO_data %>%
+      filter(year==2011) %>%
+      slice_min(n=7, order_by = co2_per_capita) %>%
+      ggplot(aes(x=country, y=co2_per_capita, fill=country)) +
+      xlab('Country') +
+      ylab('CO2 per capita') +
+      ggtitle('The smalest production per capita') +
+      theme(axis.text.x = element_blank()) +
+      geom_bar(stat='identity')
+  })
+  output$the_least_3 = renderPlotly({
+    CO_data %>%
+      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') +
+      theme(axis.text.x = element_blank()) +
+      geom_bar(stat='identity')
+  })
+}
+
+
+shinyApp(ui = ui, server = server)