Final changes
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lewy
parent
278b1622ea
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c3d100ece3
@ -2,9 +2,8 @@ library(dplyr)
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# install.packages("ggplot2")
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library(ggplot2)
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library(plotly)
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countries = c( 'PL', 'DE', 'CZ', 'NL', 'RO', 'XD')
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countries <- unique(map_df$geo)
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countries = c( 'PL', 'DE', 'CZ', 'NL', 'RO')
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countries = unique(SHP_28$geo)
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df = read.csv(".//data//prc_hicp_aind_page_linear.csv")
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df[,c("geo", "TIME_PERIOD", "OBS_VALUE")]
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@ -51,7 +50,7 @@ ggplot(year_country_gdp, aes(x = TIME_PERIOD, y = OBS_VALUE, color = geo, label
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plot <- ggplot(year_country_gdp, aes(x = TIME_PERIOD, y = OBS_VALUE, color = geo, text = paste("Kraj: ", geo, "<br>",
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"Rok: ", TIME_PERIOD, "<br>",
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"Warto?? wska?nika: ", OBS_VALUE))) +
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"Wartoœæ wskaŸnika: ", OBS_VALUE))) +
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geom_line(aes(group = geo)) +
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geom_point() +
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labs(x = "Rok", y = 'PKB per capita w PPP [PPS_EU27_2020=100]') +
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@ -86,15 +85,15 @@ ggplot(house_price_index, aes(x = TIME_PERIOD, y = OBS_VALUE, color = geo, label
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slice(n() - 1),
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aes(label = geo, hjust = -1, size = 4))+
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scale_x_continuous(breaks=seq(2010,2024,2))
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labs(x = "Rok", y = 'Indeks Cen nieruchomo?ci [cena z 2015 roku = 100]')
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labs(x = "Rok", y = 'Indeks Cen nieruchomoœci [cena z 2015 roku = 100]')
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plot <- ggplot(house_price_index, aes(x = TIME_PERIOD, y = OBS_VALUE, color = geo, text = paste("Kraj: ", geo, "<br>",
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"Rok: ", TIME_PERIOD, "<br>",
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"Warto?? wska?nika: ", OBS_VALUE))) +
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"Wartoœæ wskaŸnika: ", OBS_VALUE))) +
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geom_line() +
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geom_point() +
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scale_x_continuous(breaks = seq(2010, 2024, 2)) +
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labs(x = "Rok", y = 'Indeks Cen nieruchomo?ci [cena z 2015 roku = 100]')
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labs(x = "Rok", y = 'Indeks Cen nieruchomoœci [cena z 2015 roku = 100]')
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plotly_plot <- ggplotly(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]]$type == "scatter") {
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@ -133,7 +132,7 @@ ggplot(hicp_index, aes(x = TIME_PERIOD, y = OBS_VALUE, color = geo, label = geo)
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plot <- ggplot(hicp_index, aes(x = TIME_PERIOD, y = OBS_VALUE, color = geo, text = paste("Kraj: ", geo, "<br>",
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"Rok: ", TIME_PERIOD, "<br>",
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"Warto?? wska?nika: ", OBS_VALUE))) +
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"Wartoœæ wskaŸnika: ", OBS_VALUE))) +
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geom_line() +
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geom_point() +
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labs(x = "Rok", y = 'Indeks inflacji konsumenckiej HICP [2015 = 100]') +
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@ -174,12 +173,8 @@ merged_df %>% group_by(geo) %>% str()
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str(merged_df)
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unfiltered_df <- merged_df
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merged_df <- filter(merged_df, geo %in% countries)
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ggplot(merged_df, aes(x = TIME_PERIOD, y = house_prices_wo_hicp, color = geo)) +
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geom_line(linetype="dotted", size=1) +
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geom_point(aes(x=TIME_PERIOD, y=house_prices_wo_hicp)) +
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@ -190,12 +185,12 @@ ggplot(merged_df, aes(x = TIME_PERIOD, y = house_prices_wo_hicp, color = geo)) +
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stat_function(fun=function(x) 100*(1.04)^(x-2015), aes(colour = "4% Compounding")) +
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stat_function(fun=function(x) 100*(1.07)^(x-2015), aes(colour = "7% Compounding")) +
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scale_x_continuous(breaks=seq(2010,2024,2)) +
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labs(x = "Year", y = 'Indeks cen nieruchomo?ci zdyskontowany o warto?? inflacji [2015 = 100]')
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labs(x = "Year", y = 'Indeks cen nieruchomoœci zdyskontowany o wartoœæ inflacji [2015 = 100]')
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final_plot <- ggplot(merged_df, 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 nieruchomo?ci: ", house_prices_wo_hicp))) +
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"Cena nieruchomoœci: ", 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 = merged_df %>% group_by(geo) %>% slice(n()),
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@ -203,7 +198,7 @@ final_plot <- ggplot(merged_df, aes(x = TIME_PERIOD, y = house_prices_wo_hicp, c
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stat_function(fun = function(x) 100*(1.04)^(x-2015), aes(colour = "4% Compounding"), inherit.aes = FALSE) +
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stat_function(fun = function(x) 100*(1.07)^(x-2015), aes(colour = "7% 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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labs(x = "Rok", y = "Indeks cen nieruchomoœci zdyskontowany o wartoœæ inflacji [2015 = 100]")
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final_plot
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@ -40,6 +40,8 @@ SHP_28 %>%
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# Join datasets
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mapdata_new <- left_join(SHP_28, map_df, by="geo", relationship = "many-to-many")
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merged_df <- left_join(SHP_28, merged_df, by="geo", relationship = "many-to-many")
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merged_df
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# Delete Greece (null values)
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mapdata_new <- mapdata_new[-9,]
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62
webapp.R
62
webapp.R
@ -12,12 +12,18 @@ ui <- fluidPage(
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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$style(HTML(".checkbox {margin-left:15px}"))),
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checkboxGroupInput("countries", "Chosen countries:",
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choiceNames = unique(map_df$geo),
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choiceValues = unique(map_df$geo),
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selected = c("PL", "DE", "CZ")
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choiceNames = map_df$region,
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choiceValues = map_df$geo,
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selected = c("PL", "DE", "CZ"),
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inline = TRUE,
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width = "75%"
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),
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width=3
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),
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@ -26,7 +32,7 @@ ui <- fluidPage(
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h1("Real house prices index"),
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tabsetPanel(
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tabPanel("Plot", plotOutput("final_plot")),
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tabPanel("Plot", plotlyOutput("final_plot")),
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tabPanel("Map", leafletOutput("mymap")),
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tabPanel("Table", dataTableOutput('table'))
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),
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@ -43,38 +49,42 @@ ui <- fluidPage(
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# Backend
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server <- function(input, output, session) {
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output$table <- renderDataTable({
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merged_df[, !names(merged_df) %in% c("OBS_VALUE.x", "OBS_VALUE.y", "geometry")]
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}, options = list(pageLength = 10))
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# Używanie wektora indeksu do indeksowania innych danych
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output$table <- renderDataTable(merged_df[,!names(merged_df) %in% c("OBS_VALUE.x", "OBS_VALUE.y")])
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# Plot module
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output$final_plot <- renderPlot({
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# final_plot <-
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ggplot(filter(merged_df, geo %in% input$countries), aes(x = TIME_PERIOD, y = house_prices_wo_hicp, color = geo,
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text = paste("Kraj: ", geo, "<br>", "Rok: ", TIME_PERIOD, "<br>",
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"Cena nieruchomości: ", house_prices_wo_hicp))) +
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output$final_plot <- renderPlotly({
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final_plot <- ggplot(filter(merged_df, geo %in% input$countries),
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aes(x = TIME_PERIOD, y = house_prices_wo_hicp, color = geo,
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text = paste("Kraj: ", geo, "<br>", "Rok: ", TIME_PERIOD, "<br>",
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"Cena nieruchomości: ", house_prices_wo_hicp))) +
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geom_line(aes(group = geo), linetype = "dotted", size = 1) +
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geom_point() +
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geom_text(data = merged_df %>% group_by(geo) %>% slice(n()),
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geom_text(data = filter(merged_df, geo %in% input$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$range[1]/100)^(x-2015), aes(colour = "4% Compounding"), inherit.aes = FALSE) +
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stat_function(fun = function(x) 100*(1+input$range[2]/100)^(x-2015), aes(colour = "7% Compounding"), inherit.aes = FALSE) +
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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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theme( axis.title = element_blank() )
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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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# ggplotly(final_plot, tooltip = "text")
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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]]$name == "4% Compounding" || plotly_plot$x$data[[i]]$name == "7% Compounding") {
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# plotly_plot$x$data[[i]]$hoverinfo <- "name+y"
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# }
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#}
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for (i in 1:length(plotly_plot$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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#plotly_plot
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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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