Zaktualizuj 'projekt.R'
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projekt.R
382
projekt.R
@ -1,159 +1,223 @@
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library(shiny) # Main library
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library(ggplot2) # Plots
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library(dplyr) # Data manipulate
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library(shinythemes)
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library(plotly)
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CO_data <- read.csv("./data.csv", header= TRUE)
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col_names = colnames(CO_data)
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countries <- unique(CO_data['country'])
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ui <- navbarPage(
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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')),
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tabPanel("Linear Chart",
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sidebarLayout(
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sidebarPanel(
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selectInput('country',
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'Select Country',
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selected = 'Afghanistan',
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choices = countries
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),
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selectInput('category',
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'Select Category',
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selected = 'population',
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choices = col_names
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)
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),
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mainPanel(
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plotlyOutput('linear_chart')
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),
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)
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),
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tabPanel(
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'GDP',
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plotlyOutput('gdp')
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),
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tabPanel(
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'TOP Production',
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fluidRow(
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column(6,plotlyOutput(outputId="the_most_1")),
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column(6,plotlyOutput(outputId="the_most_2")),
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column(6,plotlyOutput(outputId="the_most_3"))
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)
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),
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tabPanel(
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'Smallest production',
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fluidRow(
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column(6,plotlyOutput(outputId="the_least_1")),
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column(6,plotlyOutput(outputId="the_least_2")),
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column(6,plotlyOutput(outputId="the_least_3"))
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)
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),
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tabPanel(
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'Data',
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DT::dataTableOutput('tableData')
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),
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tabPanel(
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'Theme',
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shinythemes::themeSelector(),
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theme = shinythemes::shinytheme('flatly'),
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)
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)
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server <- function(input, output, session) {
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output$tableData <- DT::renderDataTable({
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CO_data %>%
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filter(country == input$country) %>%
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DT::datatable()
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})
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output$linear_chart <- renderPlotly({
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CO_data %>%
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filter(country == input$country) %>%
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ggplot(aes(x = year, y = get(input$category))) +
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geom_line()
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})
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output$gdp <- renderPlotly({
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CO_data %>%
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filter(year == 2011) %>%
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ggplot(aes(x = gdp, y = co2, label = country)) +
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geom_line() +
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geom_point() +
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ggtitle('Placement of countries by CO2 and GDP production')
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})
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output$the_most_1 = renderPlotly({
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CO_data %>%
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filter(year==2011) %>%
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slice_max(n=7, order_by = co2_per_gdp) %>%
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ggplot(aes(x=country, y=co2_per_gdp, fill=country)) +
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xlab('Country') +
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ylab('CO2 per GDP') +
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ggtitle('The TOP production per GDP') +
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theme(axis.text.x = element_blank()) +
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geom_bar(stat='identity')
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})
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output$the_most_2 = renderPlotly({
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CO_data %>%
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filter(year==2011) %>%
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slice_max(n=7, order_by = co2_per_capita) %>%
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ggplot(aes(x=country, y=co2_per_capita, fill=country), custom) +
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xlab('Country') +
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ylab('CO2 per capita') +
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ggtitle('The TOP production per capita') +
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theme(axis.text.x = element_blank()) +
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geom_bar(stat='identity')
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})
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output$the_most_3 = renderPlotly({
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CO_data %>%
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filter(year==2011) %>%
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slice_max(n=7, order_by = co2_per_capita) %>%
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ggplot(aes(x=country, y=co2_per_capita, fill=country)) +
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xlab('Country') +
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ylab('CO2 overall') +
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ggtitle('The TOP production overall') +
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theme(axis.text.x = element_blank()) +
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geom_bar(stat='identity')
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})
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output$the_least_1 = renderPlotly({
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CO_data %>%
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filter(year==2011) %>%
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slice_min(n=7, order_by = co2_per_gdp) %>%
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ggplot(aes(x=country, y=co2_per_gdp, fill=country)) +
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xlab('Country') +
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ylab('CO2 per GDP') +
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ggtitle('The smalest production per GDP') +
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theme(axis.text.x = element_blank()) +
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geom_bar(stat='identity')
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})
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output$the_least_2 = renderPlotly({
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CO_data %>%
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filter(year==2011) %>%
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slice_min(n=7, order_by = co2_per_capita) %>%
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ggplot(aes(x=country, y=co2_per_capita, fill=country)) +
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xlab('Country') +
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ylab('CO2 per capita') +
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ggtitle('The smalest production per capita') +
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theme(axis.text.x = element_blank()) +
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geom_bar(stat='identity')
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})
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output$the_least_3 = renderPlotly({
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CO_data %>%
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filter(year==2011) %>%
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slice_min(n=7, order_by = co2) %>%
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ggplot(aes(x=country, y=co2, fill=country)) +
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xlab('Country') +
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ylab('CO2 overall') +
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ggtitle('The smalest production overall') +
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theme(axis.text.x = element_blank()) +
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geom_bar(stat='identity')
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})
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}
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shinyApp(ui = ui, server = server)
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library(shiny) # Main library
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library(ggplot2) # Plots
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library(dplyr) # Data manipulate
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library(shinythemes)
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library(plotly)
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library(sf)
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library(rnaturalearth)
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library(ggspatial)
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library(ggrepel)
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options(scipen=999)
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CO_data <- read.csv("./data.csv", header= TRUE)
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CO_data2 <- CO_data[,-c(1,2,3)]
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col_names = colnames(CO_data2)
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countries <- unique(CO_data['country'])
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years <- unique(sort(CO_data$year))
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world <- ne_countries(scale = 'medium', returnclass = 'sf')
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country_list <- unique(sort(world$name))
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CO_data_filtered <- subset(CO_data, country %in% country_list)
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only_co2_and_year <- CO_data[,c('year', 'country', 'co2')]
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ui <- navbarPage(
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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')),
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tabPanel("Linear Chart",
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sidebarLayout(
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sidebarPanel(
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selectInput('country',
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'Select Country',
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selected = 'Afghanistan',
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choices = countries
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),
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selectInput('category',
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'Select Category',
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selected = 'population',
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choices = col_names
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)
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),
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mainPanel(
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plotlyOutput('linear_chart')
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),
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)
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),
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tabPanel(
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'GDP',
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plotlyOutput('gdp')
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),
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tabPanel("Map: CO2 by year",
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sidebarLayout(
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sidebarPanel(
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selectInput('year',
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'Select year',
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selected = '2011',
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choices = years
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)
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),
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mainPanel(
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plotlyOutput('map'),
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),
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)
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),
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tabPanel("Map: statistics in year 2011",
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sidebarLayout(
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sidebarPanel(
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selectInput('category2',
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'Select category',
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selected = 'population',
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choices = col_names
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)
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),
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mainPanel(
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plotlyOutput('map2'),
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),
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)
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),
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tabPanel(
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'Biggest CO2 Production',
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fluidRow(
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column(6,plotlyOutput(outputId="the_most_1")),
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column(6,plotlyOutput(outputId="the_most_2")),
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column(6,plotlyOutput(outputId="the_most_3"))
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)
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),
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tabPanel(
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'Smallest CO2 production',
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fluidRow(
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column(6,plotlyOutput(outputId="the_least_1")),
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column(6,plotlyOutput(outputId="the_least_2")),
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column(6,plotlyOutput(outputId="the_least_3"))
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)
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),
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tabPanel(
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'Data',
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DT::dataTableOutput('tableData')
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),
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tabPanel(
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'Theme',
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shinythemes::themeSelector(),
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theme = shinythemes::shinytheme('flatly'),
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)
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)
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server <- function(input, output, session) {
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output$tableData <- DT::renderDataTable({
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CO_data %>%
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filter(country == input$country) %>%
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DT::datatable()
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})
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output$linear_chart <- renderPlotly({
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CO_data %>%
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filter(country == input$country) %>%
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ggplot(aes(x = year, y = get(input$category))) +
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ylab(input$category) +
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geom_line()
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})
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output$gdp <- renderPlotly({
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CO_data_filtered %>%
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filter(year == 2011) %>%
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ggplot(aes(x = gdp, y = co2, label = country)) +
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geom_line() +
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geom_point() +
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ylim(0,10000) +
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ggtitle('Placement of countries by CO2 and GDP production')
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})
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output$map = renderPlotly({
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countries_data <- filter(only_co2_and_year, year==input$year)
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data <- merge(world, countries_data, by.y="country", by.x="name")
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ggplot(data = data) +
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geom_sf(aes(fill = co2, label = name)) +
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scale_fill_viridis_c(option = "plasma", trans = "sqrt") # colorblind-friendly palette
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})
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output$map2 = renderPlotly({
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countries_data <- filter(CO_data, year==2011)
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data2 <- merge(world, countries_data, by.y="country", by.x="name")
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ggplot(data = data2) +
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geom_sf(aes(fill = get(input$category2), label = input$category2)) +
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labs(title=input$category2) +
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scale_fill_discrete(labels = input$category2) +
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scale_fill_viridis_c(option = "plasma", trans = "sqrt") # colorblind-friendly palette
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})
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output$the_most_1 = renderPlotly({
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CO_data_filtered %>%
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filter(year==2011) %>%
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slice_max(n=7, order_by = co2_per_gdp) %>%
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ggplot(aes(x=country, y=co2_per_gdp, fill=country)) +
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xlab('Country') +
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ylab('CO2 per GDP') +
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ggtitle('the biggest CO2 production per GDP') +
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theme(axis.text.x = element_blank()) +
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geom_bar(stat='identity')
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})
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output$the_most_2 = renderPlotly({
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CO_data_filtered %>%
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filter(year==2011) %>%
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slice_max(n=7, order_by = co2_per_capita) %>%
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ggplot(aes(x=country, y=co2_per_capita, fill=country), custom) +
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xlab('Country') +
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ylab('CO2 per capita') +
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ggtitle('the biggest CO2 production per capita') +
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theme(axis.text.x = element_blank()) +
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geom_bar(stat='identity')
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})
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output$the_most_3 = renderPlotly({
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CO_data_filtered %>%
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filter(year==2011) %>%
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slice_max(n=7, order_by = co2) %>%
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ggplot(aes(x=country, y=co2, fill=country)) +
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xlab('Country') +
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ylab('CO2 overall') +
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ggtitle('the biggest CO2 production overall') +
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theme(axis.text.x = element_blank()) +
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geom_bar(stat='identity')
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})
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output$the_least_1 = renderPlotly({
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CO_data_filtered %>%
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filter(year==2011) %>%
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slice_min(n=7, order_by = co2_per_gdp) %>%
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ggplot(aes(x=country, y=co2_per_gdp, fill=country)) +
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xlab('Country') +
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ylab('CO2 per GDP') +
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ggtitle('the smallest CO2 production per GDP') +
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theme(axis.text.x = element_blank()) +
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geom_bar(stat='identity')
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})
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output$the_least_2 = renderPlotly({
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CO_data_filtered %>%
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filter(year==2011) %>%
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slice_min(n=7, order_by = co2_per_capita) %>%
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ggplot(aes(x=country, y=co2_per_capita, fill=country)) +
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xlab('Country') +
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ylab('CO2 per capita') +
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ggtitle('the smallest CO2 production per capita') +
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theme(axis.text.x = element_blank()) +
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geom_bar(stat='identity')
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})
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output$the_least_3 = renderPlotly({
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CO_data_filtered %>%
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filter(year==2011) %>%
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slice_min(n=7, order_by = co2) %>%
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ggplot(aes(x=country, y=co2, fill=country)) +
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xlab('Country') +
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ylab('CO2 overall') +
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ggtitle('the smallest CO2 production overall') +
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theme(axis.text.x = element_blank()) +
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geom_bar(stat='identity')
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})
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}
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shinyApp(ui = ui, server = server)
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