ADD About
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lewy
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webapp.R
204
webapp.R
@ -2,114 +2,138 @@ library(shiny)
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library(leaflet)
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library(leaflet)
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library(ggplot2)
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library(ggplot2)
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library(dplyr)
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library(dplyr)
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library(shinyalert)
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library(DT)
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# install.packages("DT")
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# Frontend
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# Frontend
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ui <- fluidPage(
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ui <- fluidPage(
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sidebarLayout(
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sidebarPanel(
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# Compound interest
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sliderInput("range", "Compound interest:",min = 0, max = 10, value = c(4,8)),textOutput("Compound interest slider"),
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# Checkboxes
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#tags$head(tags$style(HTML(".checkbox {margin-left:15px}"))),
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checkboxGroupInput("countries", "Chosen countries:",
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choiceNames = c(map_df$region),
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choiceValues = c(map_df$geo),
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selected = c("PL", "CZ", "DE"),
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inline = TRUE,
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width = "75%"
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),
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width=3
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sidebarLayout(
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sidebarPanel(
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# Compound interest
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sliderInput("range", "Compound interest:",min = 0, max = 10, value = c(4,8)),textOutput("Compound interest slider"),
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# Checkboxes
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#tags$head(tags$style(HTML(".checkbox {margin-left:15px}"))),
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checkboxGroupInput("countries", "Chosen countries:",
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choiceNames = c(map_df$region),
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choiceValues = c(map_df$geo),
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selected = c("PL", "CZ", "DE"),
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inline = TRUE,
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width = "75%"
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),
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),
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mainPanel(
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# About
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h1("Real house prices index"),
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useShinyalert(),
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actionButton("about", "?"),
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tabsetPanel(
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tabPanel("Plot", plotlyOutput("final_plot")),
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tabPanel("Map", leafletOutput("mymap")),
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tabPanel("Table", dataTableOutput('table'))
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),
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width = 9
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),
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fluid = TRUE
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)
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width=3
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),
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mainPanel(
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h1("Real house prices index"),
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tabsetPanel(
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tabPanel("Plot", plotlyOutput("final_plot")),
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tabPanel("Map", leafletOutput("mymap")),
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tabPanel("Table", dataTableOutput('table'))
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),
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width = 9
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),
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fluid = TRUE
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)
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)
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)
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# Backend
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# Backend
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server <- function(input, output, session) {
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server <- function(input, output, session) {
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output$table <- renderDataTable({
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observeEvent(input$about, {
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merged_df[, !names(merged_df) %in% c("OBS_VALUE.x", "OBS_VALUE.y", "geometry")]
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# Show a modal when the button is pressed
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}, options = list(pageLength = 10))
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shinyalert("About project:",
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"Authors: Paweł Lewicki, Patryk Kaszuba\n
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The aim of the project is to present growth of house prices as index of Purchasing Power Parity (PPP) in countries of European Union. 2015 is a benchmark value (100 for each country).
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# Plot module
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Datasets used:
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output$final_plot <- renderPlotly({
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- Eurostat Inflation 2022 - prc_hicp_aind_page_linear
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final_plot <- ggplot(filter(merged_df, geo %in% input$countries),
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- Eurostat House Price Index - prc_hpi_a__custom_3617733_page_linear
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aes(x = TIME_PERIOD, y = house_prices_wo_hicp, color = geo,
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text = paste("Kraj: ", geo, "<br>", "Rok: ", TIME_PERIOD, "<br>",
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Libraries used:
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"Cena nieruchomości: ", house_prices_wo_hicp))) +
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shiny, leaflet, ggplot2, dplyr, shinyalert, plotly, dplyr,
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geom_line(aes(group = geo), linetype = "dotted", size = 1) +
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tidyverse, eurostat, sf, scales, cowplot, ggthemes, RColorBrewer
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geom_point() +
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geom_text(data = filter(merged_df, geo %in% input$countries) %>% group_by(geo) %>% slice(n()),
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Subject: Data Wizualisation
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aes(label = "", hjust = -0.2, size = 4)) +
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Adam Mickiewicz University,
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stat_function(fun = function(x) 100*(1+input$range[1]/100)^(x-2015), aes(colour = paste0(as.character(input$range[1]), "% Compounding")), inherit.aes = FALSE) +
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Poznan, Poland, June 2023"
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stat_function(fun = function(x) 100*(1+input$range[2]/100)^(x-2015), aes(colour = paste0(as.character(input$range[2]), "% Compounding")), inherit.aes = FALSE) +
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, type = "info")
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scale_x_continuous(breaks = seq(2010, 2024, 2)) +
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})
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labs(x = "Rok", y = "Indeks cen nieruchomości zdyskontowany o wartość inflacji [2015 = 100]",
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color = "Countries") +
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output$table <- DT::renderDataTable({
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theme(axis.title = element_blank())
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datatable(merged_df[, !names(merged_df) %in% c("OBS_VALUE.x", "OBS_VALUE.y", "geometry")], options = list(pageLength = 10))
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})
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plotly_plot <- ggplotly(final_plot, tooltip = "text")
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for (i in 1:length(plotly_plot$x$data)) {
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# Plot module
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if (plotly_plot$x$data[[i]]$name == paste0(as.character(input$range[1]), "% Compounding") || plotly_plot$x$data[[i]]$name == paste0(as.character(input$range[1]), "% Compounding")) {
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output$final_plot <- renderPlotly({
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plotly_plot$x$data[[i]]$hoverinfo <- "name+y"
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final_plot <- ggplot(filter(merged_df, geo %in% input$countries),
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}
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aes(x = TIME_PERIOD, y = house_prices_wo_hicp, color = geo,
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text = paste("Kraj: ", geo, "<br>", "Rok: ", TIME_PERIOD, "<br>",
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"Cena nieruchomości: ", house_prices_wo_hicp))) +
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geom_line(aes(group = geo), linetype = "dotted", size = 1) +
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geom_point() +
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geom_text(data = filter(merged_df, geo %in% input$countries) %>% group_by(geo) %>% slice(n()),
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aes(label = "", hjust = -0.2, size = 4)) +
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stat_function(fun = function(x) 100*(1+input$range[1]/100)^(x-2015), aes(colour = paste0(as.character(input$range[1]), "% Compounding")), inherit.aes = FALSE) +
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stat_function(fun = function(x) 100*(1+input$range[2]/100)^(x-2015), aes(colour = paste0(as.character(input$range[2]), "% Compounding")), inherit.aes = FALSE) +
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scale_x_continuous(breaks = seq(2010, 2024, 2)) +
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labs(x = "Rok", y = "Indeks cen nieruchomości zdyskontowany o wartość inflacji [2015 = 100]",
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color = "Countries") +
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theme(axis.title = element_blank())
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plotly_plot <- ggplotly(final_plot, tooltip = "text")
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for (i in 1:length(plotly_plot$x$data)) {
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if (plotly_plot$x$data[[i]]$name == paste0(as.character(input$range[1]), "% Compounding") || plotly_plot$x$data[[i]]$name == paste0(as.character(input$range[1]), "% Compounding")) {
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plotly_plot$x$data[[i]]$hoverinfo <- "name+y"
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}
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}
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}
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plotly_plot %>% layout(showlegend = TRUE, legend = list(title = list(text = "Countries")))
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})
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# Map module
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output$mymap <- renderLeaflet({
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leaflet() %>%
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addProviderTiles(providers$CartoDB.Positron) %>%
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addPolygons(data=mapdata_new,
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fillOpacity = 0.6, # Przezroczystość
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stroke = TRUE, # Borders visible
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color = "grey", # Border color
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weight = 1,
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fillColor = ~qpal(mapdata_new$substr_house_prices_wo_hicp),
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popup = popup_content,
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popupOptions = popupOptions(maxWidth ="100%", closeOnClick = TRUE)
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) %>%
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setView( lat = 49, lng = 14, zoom = 4) %>%
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addLegend("bottomright", colors = qpal_colors,
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title = "<span style='white-space: pre-line;'> Real house prices \n index (2022) </span>",
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labels = qpal_labs,
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opacity = 1)
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}) # End of map module
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plotly_plot %>% layout(showlegend = TRUE, legend = list(title = list(text = "Countries")))
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})
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# Map module
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output$mymap <- renderLeaflet({
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leaflet() %>%
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addProviderTiles(providers$CartoDB.Positron) %>%
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addPolygons(data=mapdata_new,
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fillOpacity = 0.6, # Przezroczystość
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stroke = TRUE, # Borders visible
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color = "grey", # Border color
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weight = 1,
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fillColor = ~qpal(mapdata_new$substr_house_prices_wo_hicp),
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popup = popup_content,
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popupOptions = popupOptions(maxWidth ="100%", closeOnClick = TRUE)
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) %>%
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setView( lat = 49, lng = 14, zoom = 4) %>%
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addLegend("bottomright", colors = qpal_colors,
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title = "<span style='white-space: pre-line;'> Real house prices \n index (2022) </span>",
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labels = qpal_labs,
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opacity = 1)
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}) # End of map module
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}# End of server
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}# End of server
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# Run
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# Run
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