diff --git a/.Rhistory b/.Rhistory
index 74b0558..65be83f 100644
--- a/.Rhistory
+++ b/.Rhistory
@@ -1,512 +1,512 @@
-colnames(df3)
-print(df3)
-year_country_gdp <- df3 %>% select( TIME_PERIOD, geo, OBS_VALUE)
-year_country_gdp <- na.omit(year_country_gdp)
-colnames(year_country_gdp)
-df3 %>% group_by(geo) %>% str()
-str(year_country_gdp)
-# Plot
-ggplot(year_country_gdp, aes(x = TIME_PERIOD, y = OBS_VALUE, color = geo, label = geo)) +
-geom_line() +
+tabPanel("Table", dataTableOutput('table'))
+),
+width = 9
+),
+fluid = TRUE
+)
+)
+# Backend
+server <- function(input, output, session) {
+output$table <- renderDataTable({
+merged_df[, !names(merged_df) %in% c("OBS_VALUE.x", "OBS_VALUE.y", "geometry")]
+}, options = list(pageLength = 10))
+# Plot module
+output$final_plot <- renderPlotly({
+final_plot <- ggplot(filter(merged_df, geo %in% input$countries),
+aes(x = TIME_PERIOD, y = house_prices_wo_hicp, color = geo,
+text = paste("Kraj: ", geo, "<br>", "Rok: ", TIME_PERIOD, "<br>",
+"Cena nieruchomości: ", house_prices_wo_hicp))) +
+geom_line(aes(group = geo), linetype = "dotted", size = 1) +
 geom_point() +
-geom_text(aes(label = geo), hjust = -0.1, size = 3)+
-labs(x = "Rok", y = 'PKB per capita w PPP [PPS_EU27_2020=100]') +
-scale_x_continuous(breaks=seq(2010,2024,2))
-year_country_gdp
-# ##################################################
-# House price index HPI
-df1
-house_price_index <- df1 %>% select( TIME_PERIOD, geo, OBS_VALUE)
-house_price_index <- na.omit(house_price_index)
-colnames(house_price_index)
-df1 %>% group_by(geo) %>% str()
-str(house_price_index)
-# Plot
-ggplot(house_price_index, aes(x = TIME_PERIOD, y = OBS_VALUE, color = geo, label = geo)) +
-geom_line() +
+geom_text(data = filter(merged_df, geo %in% input$countries) %>% group_by(geo) %>% slice(n()),
+aes(label = "", hjust = -0.2, size = 4)) +
+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) +
+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) +
+scale_x_continuous(breaks = seq(2010, 2024, 2)) +
+labs(x = "Rok", y = "Indeks cen nieruchomości zdyskontowany o wartość inflacji [2015 = 100]",
+color = "Countries") +
+theme(axis.title = element_blank())
+plotly_plot <- ggplotly(final_plot, tooltip = "text")
+for (i in 1:length(plotly_plot$x$data)) {
+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")) {
+plotly_plot$x$data[[i]]$hoverinfo <- "name+y"
+}
+}
+plotly_plot %>% layout(showlegend = TRUE, legend = list(title = list(text = "Countries")))
+})
+# Map module
+output$mymap <- renderLeaflet({
+leaflet() %>%
+addProviderTiles(providers$CartoDB.Positron) %>%
+addPolygons(data=mapdata_new,
+fillOpacity = 0.6, # Przezroczystość
+stroke = TRUE, # Borders visible
+color = "grey", # Border color
+weight = 1,
+fillColor = ~qpal(mapdata_new$substr_house_prices_wo_hicp),
+popup = popup_content,
+popupOptions = popupOptions(maxWidth ="100%", closeOnClick = TRUE)
+) %>%
+setView( lat = 49, lng = 14, zoom = 4) %>%
+addLegend("bottomright", colors = qpal_colors,
+title = "<span style='white-space: pre-line;'> Real house prices \n index (2022) </span>",
+labels = qpal_labs,
+opacity = 1)
+}) # End of map module
+}# End of server
+# Run
+shinyApp(ui, server)
+print(unique(map_df$geo))
+print(unique(map_df$region))
+library(shiny)
+library(leaflet)
+library(ggplot2)
+library(dplyr)
+print(unique(map_df$geo))
+print(unique(map_df$region))
+# print(length(map_df$geo))
+# Frontend
+ui <- fluidPage(
+sidebarLayout(
+sidebarPanel(
+# Compound interest
+sliderInput("range", "Compound interest:",min = 0, max = 10, value = c(4,8)),textOutput("Compound interest slider"),
+# Checkboxes
+checkboxGroupInput("countries", "Chosen countries:",
+choiceNames = map_df$region,
+choiceValues = map_df$geo,
+selected = c("PL", "DE", "CZ"),
+inline = TRUE,
+width = "75%"
+),
+width=3
+),
+mainPanel(
+h1("Real house prices index"),
+tabsetPanel(
+tabPanel("Plot", plotlyOutput("final_plot")),
+tabPanel("Map", leafletOutput("mymap")),
+tabPanel("Table", dataTableOutput('table'))
+),
+width = 9
+),
+fluid = TRUE
+)
+)
+# Backend
+server <- function(input, output, session) {
+output$table <- renderDataTable({
+merged_df[, !names(merged_df) %in% c("OBS_VALUE.x", "OBS_VALUE.y", "geometry")]
+}, options = list(pageLength = 10))
+# Plot module
+output$final_plot <- renderPlotly({
+final_plot <- ggplot(filter(merged_df, geo %in% input$countries),
+aes(x = TIME_PERIOD, y = house_prices_wo_hicp, color = geo,
+text = paste("Kraj: ", geo, "<br>", "Rok: ", TIME_PERIOD, "<br>",
+"Cena nieruchomości: ", house_prices_wo_hicp))) +
+geom_line(aes(group = geo), linetype = "dotted", size = 1) +
 geom_point() +
-geom_text(data = house_price_index %>%
-group_by(geo) %>%
-slice(n() - 1),
-aes(label = geo, hjust = -1, size = 4))+
-scale_x_continuous(breaks=seq(2010,2024,2))
-labs(x = "Rok", y = 'Indeks Cen nieruchomości [cena z 2015 roku = 100]')
-house_price_index
-# HICP - Harmonised Index for Consumer Prices
-df
-hicp_index <- df %>% select( TIME_PERIOD, geo, OBS_VALUE)
-hicp_index <- na.omit(hicp_index)
-colnames(hicp_index)
-df %>% group_by(geo) %>% str()
-str(hicp_index)
-# Plot
-ggplot(hicp_index, aes(x = TIME_PERIOD, y = OBS_VALUE, color = geo, label = geo)) +
-geom_line() +
+geom_text(data = filter(merged_df, geo %in% input$countries) %>% group_by(geo) %>% slice(n()),
+aes(label = "", hjust = -0.2, size = 4)) +
+stat_function(fun = function(x) 100*(1+input$range[1]/100)^(x-2015), aes(colour = paste0(as.character(input$range[1]), "% Compounding")), inherit.aes = FALSE) +
+stat_function(fun = function(x) 100*(1+input$range[2]/100)^(x-2015), aes(colour = paste0(as.character(input$range[2]), "% Compounding")), inherit.aes = FALSE) +
+scale_x_continuous(breaks = seq(2010, 2024, 2)) +
+labs(x = "Rok", y = "Indeks cen nieruchomości zdyskontowany o wartość inflacji [2015 = 100]",
+color = "Countries") +
+theme(axis.title = element_blank())
+plotly_plot <- ggplotly(final_plot, tooltip = "text")
+for (i in 1:length(plotly_plot$x$data)) {
+if (plotly_plot$x$data[[i]]$name == paste0(as.character(input$range[1]), "% Compounding") || plotly_plot$x$data[[i]]$name == paste0(as.character(input$range[1]), "% Compounding")) {
+plotly_plot$x$data[[i]]$hoverinfo <- "name+y"
+}
+}
+plotly_plot %>% layout(showlegend = TRUE, legend = list(title = list(text = "Countries")))
+})
+# Map module
+output$mymap <- renderLeaflet({
+leaflet() %>%
+addProviderTiles(providers$CartoDB.Positron) %>%
+addPolygons(data=mapdata_new,
+fillOpacity = 0.6, # Przezroczystość
+stroke = TRUE, # Borders visible
+color = "grey", # Border color
+weight = 1,
+fillColor = ~qpal(mapdata_new$substr_house_prices_wo_hicp),
+popup = popup_content,
+popupOptions = popupOptions(maxWidth ="100%", closeOnClick = TRUE)
+) %>%
+setView( lat = 49, lng = 14, zoom = 4) %>%
+addLegend("bottomright", colors = qpal_colors,
+title = "<span style='white-space: pre-line;'> Real house prices \n index (2022) </span>",
+labels = qpal_labs,
+opacity = 1)
+}) # End of map module
+}# End of server
+# Run
+shinyApp(ui, server)
+library(shiny)
+library(leaflet)
+library(ggplot2)
+library(dplyr)
+print(unique(map_df$geo))
+print(unique(map_df$region))
+# print(length(map_df$geo))
+# Frontend
+ui <- fluidPage(
+sidebarLayout(
+sidebarPanel(
+# Compound interest
+sliderInput("range", "Compound interest:",min = 0, max = 10, value = c(4,8)),textOutput("Compound interest slider"),
+# Checkboxes
+checkboxGroupInput("countries", "Chosen countries:",
+choiceNames = map_df$region,
+choiceValues = map_df$geo,
+selected = c("PL", "DE", "CZ"),
+inline = TRUE,
+width = "75%"
+)
+width=3
+library(shiny)
+library(leaflet)
+library(ggplot2)
+library(dplyr)
+print(unique(map_df$geo))
+print(unique(map_df$region))
+# print(length(map_df$geo))
+# Frontend
+ui <- fluidPage(
+sidebarLayout(
+sidebarPanel(
+# Compound interest
+sliderInput("range", "Compound interest:",min = 0, max = 10, value = c(4,8)),textOutput("Compound interest slider"),
+# Checkboxes
+checkboxGroupInput("countries", "Chosen countries:",
+choiceNames = map_df$region,
+choiceValues = map_df$geo,
+selected = c("PL", "DE", "CZ"),
+inline = TRUE,
+width = "75%"
+),
+width=3
+),
+mainPanel(
+h1("Real house prices index"),
+tabsetPanel(
+tabPanel("Plot", plotlyOutput("final_plot")),
+tabPanel("Map", leafletOutput("mymap")),
+tabPanel("Table", dataTableOutput('table'))
+),
+width = 9
+),
+fluid = TRUE
+)
+)
+# Backend
+server <- function(input, output, session) {
+output$table <- renderDataTable({
+merged_df[, !names(merged_df) %in% c("OBS_VALUE.x", "OBS_VALUE.y", "geometry")]
+}, options = list(pageLength = 10))
+# Plot module
+output$final_plot <- renderPlotly({
+final_plot <- ggplot(filter(merged_df, geo %in% input$countries),
+aes(x = TIME_PERIOD, y = house_prices_wo_hicp, color = geo,
+text = paste("Kraj: ", geo, "<br>", "Rok: ", TIME_PERIOD, "<br>",
+"Cena nieruchomości: ", house_prices_wo_hicp))) +
+geom_line(aes(group = geo), linetype = "dotted", size = 1) +
 geom_point() +
-geom_text(data = hicp_index %>%
-group_by(geo) %>%
-slice(n()),
-aes(label = geo, hjust = -0.2, size = 4)) +
-labs(x = "Rok", y = 'Indeks inflacji konsumenckiej HICP [2015 = 100]') +
-scale_x_continuous(breaks=seq(2010,2024,2))
-hicp_index
-# Merge the two data frames using the 'country' and 'date' columns
-merged_df <- merge(house_price_index, hicp_index, by = c("geo", "TIME_PERIOD"))
-merged_df
-# Create a new column that divides 'value1' by 'value2'
-merged_df$house_prices_wo_hicp <- merged_df$OBS_VALUE.x - merged_df$OBS_VALUE.y
-merged_df$TIME_PERIOD
-merged_df$compound_growth <- 1 * (1 + 0.02) ^ (1:(merged_df$TIME_PERIOD-2015))
-# View the resulting merged data frame with the divided values
-merged_df
-merged_df <- na.omit(merged_df)
-colnames(merged_df)
-merged_df %>% group_by(geo) %>% str()
-str(merged_df)
-merged_df <- merged_df %>% filter(!geo %in% c("TR"))
-# Plot
-ggplot(merged_df, aes(x = TIME_PERIOD, y = house_prices_wo_hicp, color = geo)) +
-geom_line(linetype="dotted", size=1) +
-geom_point(aes(x=TIME_PERIOD, y=house_prices_wo_hicp)) +
-geom_text(data = merged_df %>%
-group_by(geo) %>%
-slice(n()),
-aes(label = geo, hjust = -0.2, size = 4)) +
-#stat_function(fun=function(x) 100*(1.04)^(x-2015), aes(colour = "4% Compounding")) +
-#stat_function(fun=function(x) 100*(1.07)^(x-2015), aes(colour = "7% Compounding")) +
-scale_x_continuous(breaks=seq(2010,2024,2)) +
-labs(x = "Year", y = 'Indeks cen nieruchomości zdyskontowany o wartość inflacji [2015 = 100]')
-map_df <- merged_df %>% select( geo, house_prices_wo_hicp, TIME_PERIOD)
-map_df <- filter(map_df, TIME_PERIOD == 2022)
-map_df
-merged_df
-# Plot
-ggplot(hicp_index, aes(x = TIME_PERIOD, y = OBS_VALUE, color = geo, label = geo)) +
-geom_line() +
+geom_text(data = filter(merged_df, geo %in% input$countries) %>% group_by(geo) %>% slice(n()),
+aes(label = "", hjust = -0.2, size = 4)) +
+stat_function(fun = function(x) 100*(1+input$range[1]/100)^(x-2015), aes(colour = paste0(as.character(input$range[1]), "% Compounding")), inherit.aes = FALSE) +
+stat_function(fun = function(x) 100*(1+input$range[2]/100)^(x-2015), aes(colour = paste0(as.character(input$range[2]), "% Compounding")), inherit.aes = FALSE) +
+scale_x_continuous(breaks = seq(2010, 2024, 2)) +
+labs(x = "Rok", y = "Indeks cen nieruchomości zdyskontowany o wartość inflacji [2015 = 100]",
+color = "Countries") +
+theme(axis.title = element_blank())
+plotly_plot <- ggplotly(final_plot, tooltip = "text")
+for (i in 1:length(plotly_plot$x$data)) {
+if (plotly_plot$x$data[[i]]$name == paste0(as.character(input$range[1]), "% Compounding") || plotly_plot$x$data[[i]]$name == paste0(as.character(input$range[1]), "% Compounding")) {
+plotly_plot$x$data[[i]]$hoverinfo <- "name+y"
+}
+}
+plotly_plot %>% layout(showlegend = TRUE, legend = list(title = list(text = "Countries")))
+})
+# Map module
+output$mymap <- renderLeaflet({
+leaflet() %>%
+addProviderTiles(providers$CartoDB.Positron) %>%
+addPolygons(data=mapdata_new,
+fillOpacity = 0.6, # Przezroczystość
+stroke = TRUE, # Borders visible
+color = "grey", # Border color
+weight = 1,
+fillColor = ~qpal(mapdata_new$substr_house_prices_wo_hicp),
+popup = popup_content,
+popupOptions = popupOptions(maxWidth ="100%", closeOnClick = TRUE)
+) %>%
+setView( lat = 49, lng = 14, zoom = 4) %>%
+addLegend("bottomright", colors = qpal_colors,
+title = "<span style='white-space: pre-line;'> Real house prices \n index (2022) </span>",
+labels = qpal_labs,
+opacity = 1)
+}) # End of map module
+}# End of server
+# Run
+shinyApp(ui, server)
+library(shiny)
+library(leaflet)
+library(ggplot2)
+library(dplyr)
+print(unique(map_df$geo))
+print(unique(map_df$region))
+# print(length(map_df$geo))
+# Frontend
+ui <- fluidPage(
+sidebarLayout(
+sidebarPanel(
+# Compound interest
+sliderInput("range", "Compound interest:",min = 0, max = 10, value = c(4,8)),textOutput("Compound interest slider"),
+# Checkboxes
+tags$head(tags$style(HTML(".checkbox {margin-left:15px}"))),
+checkboxGroupInput("countries", "Chosen countries:",
+choiceNames = map_df$region,
+choiceValues = map_df$geo,
+selected = c("PL", "DE", "CZ"),
+inline = TRUE,
+width = "75%"
+),
+width=3
+),
+mainPanel(
+h1("Real house prices index"),
+tabsetPanel(
+tabPanel("Plot", plotlyOutput("final_plot")),
+tabPanel("Map", leafletOutput("mymap")),
+tabPanel("Table", dataTableOutput('table'))
+),
+width = 9
+),
+fluid = TRUE
+)
+)
+# Backend
+server <- function(input, output, session) {
+output$table <- renderDataTable({
+merged_df[, !names(merged_df) %in% c("OBS_VALUE.x", "OBS_VALUE.y", "geometry")]
+}, options = list(pageLength = 10))
+# Plot module
+output$final_plot <- renderPlotly({
+final_plot <- ggplot(filter(merged_df, geo %in% input$countries),
+aes(x = TIME_PERIOD, y = house_prices_wo_hicp, color = geo,
+text = paste("Kraj: ", geo, "<br>", "Rok: ", TIME_PERIOD, "<br>",
+"Cena nieruchomości: ", house_prices_wo_hicp))) +
+geom_line(aes(group = geo), linetype = "dotted", size = 1) +
 geom_point() +
-geom_text(data = hicp_index %>%
-group_by(geo) %>%
-slice(n()),
-aes(label = geo, hjust = -0.2, size = 4)) +
-labs(x = "Rok", y = 'Indeks inflacji konsumenckiej HICP [2015 = 100]') +
-scale_x_continuous(breaks=seq(2010,2024,2))
-write.csv(merged_df, ".//merged_df.csv")
-hicp_index
-write.csv(hicp_index, ".//hicp_index.csv")
-df = read.csv(".//data//compound_interest_housing")
+geom_text(data = filter(merged_df, geo %in% input$countries) %>% group_by(geo) %>% slice(n()),
+aes(label = "", hjust = -0.2, size = 4)) +
+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) +
+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) +
+scale_x_continuous(breaks = seq(2010, 2024, 2)) +
+labs(x = "Rok", y = "Indeks cen nieruchomości zdyskontowany o wartość inflacji [2015 = 100]",
+color = "Countries") +
+theme(axis.title = element_blank())
+plotly_plot <- ggplotly(final_plot, tooltip = "text")
+for (i in 1:length(plotly_plot$x$data)) {
+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")) {
+plotly_plot$x$data[[i]]$hoverinfo <- "name+y"
+}
+}
+plotly_plot %>% layout(showlegend = TRUE, legend = list(title = list(text = "Countries")))
+})
+# Map module
+output$mymap <- renderLeaflet({
+leaflet() %>%
+addProviderTiles(providers$CartoDB.Positron) %>%
+addPolygons(data=mapdata_new,
+fillOpacity = 0.6, # Przezroczystość
+stroke = TRUE, # Borders visible
+color = "grey", # Border color
+weight = 1,
+fillColor = ~qpal(mapdata_new$substr_house_prices_wo_hicp),
+popup = popup_content,
+popupOptions = popupOptions(maxWidth ="100%", closeOnClick = TRUE)
+) %>%
+setView( lat = 49, lng = 14, zoom = 4) %>%
+addLegend("bottomright", colors = qpal_colors,
+title = "<span style='white-space: pre-line;'> Real house prices \n index (2022) </span>",
+labels = qpal_labs,
+opacity = 1)
+}) # End of map module
+}# End of server
+# Run
+shinyApp(ui, server)
+library(shiny)
+library(leaflet)
+library(ggplot2)
 library(dplyr)
-# install.packages("ggplot2")
+print(unique(map_df$geo))
+print(unique(map_df$region))
+# print(length(map_df$geo))
+# Frontend
+ui <- fluidPage(
+sidebarLayout(
+sidebarPanel(
+# Compound interest
+sliderInput("range", "Compound interest:",min = 0, max = 10, value = c(4,8)),textOutput("Compound interest slider"),
+# Checkboxes
+# tags$head(tags$style(HTML(".checkbox {margin-left:15px}"))),
+checkboxGroupInput("countries", "Chosen countries:",
+choiceNames = map_df$region,
+choiceValues = map_df$geo,
+selected = c("PL", "DE", "CZ"),
+inline = TRUE,
+width = "75%"
+),
+width=3
+),
+mainPanel(
+h1("Real house prices index"),
+tabsetPanel(
+tabPanel("Plot", plotlyOutput("final_plot")),
+tabPanel("Map", leafletOutput("mymap")),
+tabPanel("Table", dataTableOutput('table'))
+),
+width = 9
+),
+fluid = TRUE
+)
+)
+# Backend
+server <- function(input, output, session) {
+output$table <- renderDataTable({
+merged_df[, !names(merged_df) %in% c("OBS_VALUE.x", "OBS_VALUE.y", "geometry")]
+}, options = list(pageLength = 10))
+# Plot module
+output$final_plot <- renderPlotly({
+final_plot <- ggplot(filter(merged_df, geo %in% input$countries),
+aes(x = TIME_PERIOD, y = house_prices_wo_hicp, color = geo,
+text = paste("Kraj: ", geo, "<br>", "Rok: ", TIME_PERIOD, "<br>",
+"Cena nieruchomości: ", house_prices_wo_hicp))) +
+geom_line(aes(group = geo), linetype = "dotted", size = 1) +
+geom_point() +
+geom_text(data = filter(merged_df, geo %in% input$countries) %>% group_by(geo) %>% slice(n()),
+aes(label = "", hjust = -0.2, size = 4)) +
+stat_function(fun = function(x) 100*(1+input$range[1]/100)^(x-2015), aes(colour = paste0(as.character(input$range[1]), "% Compounding")), inherit.aes = FALSE) +
+stat_function(fun = function(x) 100*(1+input$range[2]/100)^(x-2015), aes(colour = paste0(as.character(input$range[2]), "% Compounding")), inherit.aes = FALSE) +
+scale_x_continuous(breaks = seq(2010, 2024, 2)) +
+labs(x = "Rok", y = "Indeks cen nieruchomości zdyskontowany o wartość inflacji [2015 = 100]",
+color = "Countries") +
+theme(axis.title = element_blank())
+plotly_plot <- ggplotly(final_plot, tooltip = "text")
+for (i in 1:length(plotly_plot$x$data)) {
+if (plotly_plot$x$data[[i]]$name == paste0(as.character(input$range[1]), "% Compounding") || plotly_plot$x$data[[i]]$name == paste0(as.character(input$range[1]), "% Compounding")) {
+plotly_plot$x$data[[i]]$hoverinfo <- "name+y"
+}
+}
+plotly_plot %>% layout(showlegend = TRUE, legend = list(title = list(text = "Countries")))
+})
+# Map module
+output$mymap <- renderLeaflet({
+leaflet() %>%
+addProviderTiles(providers$CartoDB.Positron) %>%
+addPolygons(data=mapdata_new,
+fillOpacity = 0.6, # Przezroczystość
+stroke = TRUE, # Borders visible
+color = "grey", # Border color
+weight = 1,
+fillColor = ~qpal(mapdata_new$substr_house_prices_wo_hicp),
+popup = popup_content,
+popupOptions = popupOptions(maxWidth ="100%", closeOnClick = TRUE)
+) %>%
+setView( lat = 49, lng = 14, zoom = 4) %>%
+addLegend("bottomright", colors = qpal_colors,
+title = "<span style='white-space: pre-line;'> Real house prices \n index (2022) </span>",
+labels = qpal_labs,
+opacity = 1)
+}) # End of map module
+}# End of server
+# Run
+shinyApp(ui, server)
+library(shiny)
+library(leaflet)
 library(ggplot2)
-df = read.csv(".//data//compound_interest_housing")
-df[,c("geo", "TIME_PERIOD", "OBS_VALUE")]
 library(dplyr)
-# install.packages("ggplot2")
-library(ggplot2)
-map_df = read.csv(".//data//compound_interest_housing")
-map_df = read.csv(".//data//compound_interest_housing.csv")
-map_df
-map_df = read.csv(".//data//compound_interest_housing_2.csv")
-map_df
-map_df = read.csv(".//data//compound_interest_housing_3.csv")
-map_df
-map_df = read.csv(".//data//compound_interest_housing.csv")
-map_df
-map_df = read.csv(".//data//compound_interest_housing.csv")
-map_df
-ggplot(map_df, aes(x = TIME_PERIOD, y = compound_interest, color = geo)) +
-geom_line(linetype="dotted", size=1) +
-geom_point(aes(x=TIME_PERIOD, y=compound_interest)) +
-geom_text(data = map_df %>%
-group_by(geo) %>%
-slice(n()),
-aes(label = geo, hjust = -0.2, size = 4)) +
-stat_function(fun=function(x) 100*(1.04)^(x-2015), aes(colour = "4% Compounding")) +
-stat_function(fun=function(x) 100*(1.07)^(x-2015), aes(colour = "7% Compounding")) +
-scale_x_continuous(breaks=seq(2010,2024,2)) +
-labs(x = "Year", y = 'Indeks cen nieruchomości zdyskontowany o wartość inflacji [2015 = 100]')
-ggplot(map_df, aes(x = TIME_PERIOD, y = compound_interest, color = geo)) +
-geom_line(linetype="dotted", size=1) +
-geom_point(aes(x=TIME_PERIOD, y=compound_interest)) +
-geom_text(data = map_df %>%
-group_by(geo) %>%
-slice(n()),
-aes(label = geo, hjust = -0.2, size = 4)) +
-scale_x_continuous(breaks=seq(2010,2024,2)) +
-labs(x = "Year", y = 'Indeks cen nieruchomości zdyskontowany o wartość inflacji [2015 = 100]')
-ggplot(map_df, aes(x = TIME_PERIOD, y = compound_interest, color = geo)) +
-geom_line(linetype="dotted", size=1) +
-geom_point(aes(x=TIME_PERIOD, y=compound_interest)) +
-geom_text(data = map_df %>%
-group_by(geo) %>%
-slice(n()),
-aes(label = geo, hjust = -0.2, size = 4)) +
-scale_x_continuous(breaks=seq(2010,2024,2)) +
-labs(x = "Year", y = 'wartość mieszkania jako % składany powyżej inflacji')
-ggplot(map_df, aes(x = TIME_PERIOD, y = compound_interest, color = geo)) +
-geom_point(aes(x=TIME_PERIOD, y=compound_interest)) +
-geom_text(data = map_df %>%
-group_by(geo) %>%
-slice(n()),
-aes(label = geo, hjust = -0.2, size = 4)) +
-scale_x_continuous(breaks=seq(2010,2024,2)) +
-labs(x = "Year", y = 'wartość mieszkania jako % składany powyżej inflacji')
-plot(map_df$compound_interest)
-plot(map_df$geo,map_df$compound_interest)
-barplot(map_df$geo, map_df$compound_interest)
-ggplot(map_df, aes(x = TIME_PERIOD, y = compound_interest, color = geo)) +
-geom_point(aes(x=TIME_PERIOD, y=compound_interest)) +
-geom_text(data = map_df %>%
-group_by(geo) %>%
-slice(n()),
-aes(label = geo, hjust = -0.2, size = 4)) +
-scale_x_continuous(breaks=seq(2010,2024,2)) +
-labs(x = "Year", y = 'wartość mieszkania jako % składany powyżej inflacji')
-# install.packages("tidyverse")
-library(ggplot2)
-install.packages("tidyverse")
-install.packages("tidyverse")
-library(tidyverse)
-library(tidyverse)
-install.packages("tidyverse")
-install.packages("tidyverse")
-library(tidyverse)
-install.packages("tidyverse")
-install.packages("tidyverse")
-library(tidyverse)
-# install.packages("tidyverse")
-library(ggplot2)
-view(mapdata)
-map_df = read.csv(".//data//compound_interest_housing.csv")
-map_df
-ggplot(map_df, aes(x = TIME_PERIOD, y = compound_interest, color = geo)) +
-geom_point(aes(x=TIME_PERIOD, y=compound_interest)) +
-geom_text(data = map_df %>%
-group_by(geo) %>%
-slice(n()),
-aes(label = geo, hjust = -0.2, size = 4)) +
-scale_x_continuous(breaks=seq(2010,2024,2)) +
-labs(x = "Year", y = 'wartość mieszkania jako % składany powyżej inflacji')
-view(mapdata)
-mapdata <- map_data("world")
-library(tidyverse)
-# install.packages("tidyverse")
-library(ggplot2)
-map_df = read.csv(".//data//compound_interest_housing.csv")
-map_df
-ggplot(map_df, aes(x = TIME_PERIOD, y = compound_interest, color = geo)) +
-geom_point(aes(x=TIME_PERIOD, y=compound_interest)) +
-geom_text(data = map_df %>%
-group_by(geo) %>%
-slice(n()),
-aes(label = geo, hjust = -0.2, size = 4)) +
-scale_x_continuous(breaks=seq(2010,2024,2)) +
-labs(x = "Year", y = 'wartość mieszkania jako % składany powyżej inflacji')
-mapdata <- map_data("world")
-ggplot(map_df, aes(x = TIME_PERIOD, y = compound_interest, color = geo)) +
-geom_point(aes(x=TIME_PERIOD, y=compound_interest)) +
-geom_text(data = map_df %>%
-group_by(geo),
-aes(label = geo, hjust = -0.2, size = 4)) +
-scale_x_continuous(breaks=seq(2010,2024,2)) +
-labs(x = "Year", y = 'wartość mieszkania jako % składany powyżej inflacji')
-library(dplyr)
-# install.packages("tidyverse")
-library(ggplot2)
-map_df = read.csv(".//data//compound_interest_housing.csv")
-map_df
-ggplot(map_df, aes(x = TIME_PERIOD, y = compound_interest, color = geo)) +
-geom_point(aes(x=TIME_PERIOD, y=compound_interest)) +
-geom_text(data = map_df %>%
-group_by(geo) %>%
-slice(n()),
-aes(label = geo, hjust = -0.2, size = 4)) +
-scale_x_continuous(breaks=seq(2010,2024,2)) +
-labs(x = "Year", y = 'wartość mieszkania jako % składany powyżej inflacji')
-mapdata <- map_data("world")
-view(mapdata)
-mapdata <- left_join(mapdata, map_df, by="region")
-test <- test %>% filter("Czech republic" )
-test
-test <- mapdata %>% filter("Czech republic" )
-test
-test <- mapdata %>% filter("Afghanistan" )
-test
-test <-  filter(mapdata, region="Afghanistan" )
-test
-test <-  filter(mapdata, region="Aruba" )
-test
-filter(mapdata, region="Aruba" )
-filter(mapdata, region="Aruba" )
-filter(mapdata, region=="Aruba" )
-filter(mapdata, region=="Czech republic" )
-filter(mapdata, region=="Czech Republic" )
-filter(mapdata, region=="Hungary
-" )
-write.csv(mapdata, "./mapdata.csv")
-map_df = read.csv(".//data//compound_interest_housing.csv")
-map_df
-ggplot(map_df, aes(x = TIME_PERIOD, y = compound_interest, color = geo)) +
-geom_point(aes(x=TIME_PERIOD, y=compound_interest)) +
-geom_text(data = map_df %>%
-group_by(geo) %>%
-slice(n()),
-aes(label = geo, hjust = -0.2, size = 4)) +
-scale_x_continuous(breaks=seq(2010,2024,2)) +
-labs(x = "Year", y = 'wartość mieszkania jako % składany powyżej inflacji')
-mapdata <- map_data("world")
-mapdata <- left_join(mapdata, map_df, by="region")
-mapdata <- left_join(mapdata, map_df, by="region", relation = "many-to-many")
-view(mapdata)
-mapdata <- mapdata %>% filter(!is.na(mapdata$compound_interest))
-view(mapdata)
-map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
-geom_polygon(aes(fill = compound_interest), color = "black")
-map1
-map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
-geom_polygon(aes(fill = compound_interest), color = "black") +
-scale_fill_gradient(name="compound interest", low="yellow", high = "red", na.value = "grey50")
-map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
-geom_polygon(aes(fill = compound_interest), color = "black") +
-scale_fill_gradient(name="compound interest", low="yellow", high = "red", na.value = "grey50")
-view(mapdata)
-map1
-map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
-geom_polygon(aes(fill = compound_interest), color = "black") +
-scale_fill_gradient(name="compound interest", low="blue", high = "yellow", na.value = "grey50")
-map1
-map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
-geom_polygon(aes(fill = compound_interest), color = "black") +
-scale_fill_gradient(name="compound interest", low="blue", high = "red", na.value = "grey50")
-map1
-map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
-geom_polygon(aes(fill = compound_interest), color = "black") +
-scale_fill_gradient(name="compound interest", palette(colorRamps::matlab.like), na.value = "grey50")
-map1
-library(colorRamps)
-install.packages("colorRamps")
-library(colorRamps)
-map_df = read.csv(".//data//compound_interest_housing.csv")
-map_df
-ggplot(map_df, aes(x = TIME_PERIOD, y = compound_interest, color = geo)) +
-geom_point(aes(x=TIME_PERIOD, y=compound_interest)) +
-geom_text(data = map_df %>%
-group_by(geo) %>%
-slice(n()),
-aes(label = geo, hjust = -0.2, size = 4)) +
-scale_x_continuous(breaks=seq(2010,2024,2)) +
-labs(x = "Year", y = 'wartość mieszkania jako % składany powyżej inflacji')
-# filter data to have both coordinates and value
-mapdata <- map_data("world")
-mapdata <- left_join(mapdata, map_df, by="region", relation = "many-to-many")
-# filter data to have both coordinates and value
-mapdata <- map_data("world")
-mapdata <- left_join(mapdata, map_df, by="region", relation = "many-to-many")
-mapdata <- mapdata %>% filter(!is.na(mapdata$compound_interest))
-map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
-geom_polygon(aes(fill = compound_interest), color = "black") +
-scale_fill_gradient(name="compound interest", palette(colorRamps::matlab.like), na.value = "grey50")
-map1
-view(mapdata)
-library(dplyr)
-library(tidyverse)
-# install.packages("colorRamps")
-library(ggplot2)
-library(colorRamps)
-map_df = read.csv(".//data//compound_interest_housing.csv")
-map_df
-ggplot(map_df, aes(x = TIME_PERIOD, y = compound_interest, color = geo)) +
-geom_point(aes(x=TIME_PERIOD, y=compound_interest)) +
-geom_text(data = map_df %>%
-group_by(geo) %>%
-slice(n()),
-aes(label = geo, hjust = -0.2, size = 4)) +
-scale_x_continuous(breaks=seq(2010,2024,2)) +
-labs(x = "Year", y = 'wartość mieszkania jako % składany powyżej inflacji')
-# filter data to have both coordinates and value
-mapdata <- map_data("world")
-mapdata <- left_join(mapdata, map_df, by="region", relation = "many-to-many")
-mapdata <- left_join(mapdata, map_df, by="region")
-, relation = "many-to-many"
-mapdata <- left_join(mapdata, map_df, by="region", relation = "many-to-many")
-map1
-library(dplyr)
-library(tidyverse)
-# install.packages("colorRamps")
-library(ggplot2)
-library(colorRamps)
-map_df = read.csv(".//data//compound_interest_housing.csv")
-map_df
-ggplot(map_df, aes(x = TIME_PERIOD, y = compound_interest, color = geo)) +
-geom_point(aes(x=TIME_PERIOD, y=compound_interest)) +
-geom_text(data = map_df %>%
-group_by(geo) %>%
-slice(n()),
-aes(label = geo, hjust = -0.2, size = 4)) +
-scale_x_continuous(breaks=seq(2010,2024,2)) +
-labs(x = "Year", y = 'wartość mieszkania jako % składany powyżej inflacji')
-# filter data to have both coordinates and value
-mapdata <- map_data("world")
-mapdata <- left_join(mapdata, map_df, by="region", relation = "many-to-many")
-mapdata <- left_join(mapdata, map_df, by="region")
-map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
-geom_polygon(aes(fill = compound_interest), color = "black") +
-scale_fill_gradient(name="compound interest", palette(colorRamps::matlab.like), na.value = "grey50")
-map1
-mapdata <- left_join(mapdata, map_df, by="region")
-mapdata
-mapdata <- mapdata %>% filter(!is.na(mapdata$compound_interest))
-View(mapdata)
-mapdata <- mapdata %>% filter(!is.na(mapdata$compound_interest.x))
-map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
-geom_polygon(aes(fill = compound_interest), color = "black") +
-scale_fill_gradient(name="compound interest", palette(colorRamps::matlab.like), na.value = "grey50")
-map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
-geom_polygon(aes(fill = compound_interest.x), color = "black") +
-scale_fill_gradient(name="compound interest", palette(colorRamps::matlab.like), na.value = "grey50")
-map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
-geom_polygon(aes(fill = compound_interest.x), color = "black")
-map1
-map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
-geom_polygon(aes(fill = compound_interest.x), color = "black") +
-scale_fill_gradient(name="compound interest", palette = matlab.like, na.value = "grey50")
-map1
-map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
-geom_polygon(aes(fill = compound_interest.x), color = "black") +
-scale_fill_gradient(name="compound interest", palette = "matlab.like", na.value = "grey50")
-map1
-map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
-geom_polygon(aes(fill = compound_interest.x), color = "black") +
-scale_fill_gradient(name="compound interest", low = "yellow", high = "red", na.value = "grey50")
-map1
-map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
-geom_polygon(aes(fill = compound_interest.x), color = "black") +
-scale_fill_gradient(name="compound interest", low = "white", high = "black", na.value = "grey50")
-map1
-# filter data to have both coordinates and value
-geo_list <- c("Belarus", "Greece", "United Kingdom")
-mapdata <- map_data("world")
-mapdata <- left_join(mapdata, map_df, by="region")
-mapdata <- mapdata %>% filter(!is.na(mapdata$compound_interest.x)| mapdata$region %in% geo_list)
-# filter data to have both coordinates and value
-geo_list <- c("Belarus", "Greece", "United Kingdom")
-mapdata <- map_data("world")
-mapdata <- left_join(mapdata, map_df, by="region")
-mapdata <- mapdata %>% filter(!is.na(mapdata$compound_interest.x)| mapdata$region %in% geo_list)
-mapdata <- map_data("world")
-mapdata1 <- left_join(mapdata, map_df, by="region")
-mapdata2 <- mapdata1 %>% filter(!is.na(mapdata1$compound_interest.x)| mapdata1$region %in% geo_list)
-mapdata1
-mapdata2 <- mapdata1 %>% filter(!is.na(mapdata1$compound_interest)| mapdata1$region %in% geo_list)
-mapdata <- map_data("world")
-mapdata1 <- left_join(mapdata, map_df, by="region")
-mapdata2 <- mapdata1 %>% filter(!is.na(mapdata1$compound_interest)| mapdata1$region %in% geo_list)
-map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
-geom_polygon(aes(fill = compound_interest), color = "black") +
-scale_fill_gradient(name="compound interest", low = "white", high = "black", na.value = "grey50")
-map1
-mapdata2
-mapdata2
-mapdata2
-View(mapdata2)
-mapdata2 <- mapdata1 %>% filter(mapdata1$region %in% geo_list)
-#!is.na(mapdata1$compound_interest)|
-mapdata2
-View(mapdata2)
-mapdata2 <- mapdata1 %>% filter(!is.na(mapdata1$compound_interest)| mapdata1$region %in% geo_list)
-mapdata2
-map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
-geom_polygon(aes(fill = compound_interest), color = "black") +
-scale_fill_gradient(name="compound interest", low = "white", high = "black", na.value = "grey50")
-map1
-map1 <- ggplot(mapdata2, aes(x = long, y = lat, group = group)) +
-geom_polygon(aes(fill = compound_interest), color = "black") +
-scale_fill_gradient(name="compound interest", low = "white", high = "black", na.value = "grey50")
-map1
-view(mapdata)
-map1 <- ggplot(mapdata2, aes(x = long, y = lat, group = group)) +
-geom_polygon(aes(fill = compound_interest), color = "black") +
-scale_fill_gradient(name="compound interest", low = "white", high = "black", na.value = "yellow")
-map1
-# filter data to have both coordinates and value
-geo_list <- c("Belarus", "Greece", "England")
-mapdata <- map_data("world")
-mapdata1 <- left_join(mapdata, map_df, by="region")
-mapdata2 <- mapdata1 %>% filter(!is.na(mapdata1$compound_interest)| mapdata1$region %in% geo_list)
-mapdata2
-map1 <- ggplot(mapdata2, aes(x = long, y = lat, group = group)) +
-geom_polygon(aes(fill = compound_interest), color = "black") +
-scale_fill_gradient(name="compound interest", low = "white", high = "black", na.value = "yellow")
-map1
-# filter data to have both coordinates and value
-geo_list <- c("Belarus", "Greece", "Latvia", "Albania",
-"Switzerland", "Bosnia and Herzegowina", "Ukraine", "Scotland")
-mapdata <- map_data("world")
-mapdata1 <- left_join(mapdata, map_df, by="region")
-mapdata2 <- mapdata1 %>% filter(!is.na(mapdata1$compound_interest)| mapdata1$region %in% geo_list)
-mapdata2
-map1 <- ggplot(mapdata2, aes(x = long, y = lat, group = group)) +
-geom_polygon(aes(fill = compound_interest), color = "black") +
-scale_fill_gradient(name="compound interest", low = "white", high = "black", na.value = "yellow")
-map1
-# filter data to have both coordinates and value
-geo_list <- c("Belarus", "Greece", "Latvia", "Albania",
-"Switzerland", "Bosnia and Herzegowina", "Ukraine", "Scotland", "Turkey")
-mapdata <- map_data("world")
-mapdata1 <- left_join(mapdata, map_df, by="region")
-mapdata1 <- left_join(mapdata, map_df, by="region", relationship = "many-to-many")
-mapdata2 <- mapdata1 %>% filter(!is.na(mapdata1$compound_interest)| mapdata1$region %in% geo_list)
-mapdata2
-map1 <- ggplot(mapdata2, aes(x = long, y = lat, group = group)) +
-geom_polygon(aes(fill = compound_interest), color = "black") +
-scale_fill_gradient(name="compound interest", low = "white", high = "black", na.value = "yellow")
-map1
-# filter data to have both coordinates and value
-geo_list <- c("Belarus", "Greece", "Latvia", "Albania",
-"Switzerland", "Bosnia and Herzegowina", "Ukraine",
-"UK", "Turkey", "Serbia", "Macedonia", "North Macedonia")
-mapdata <- map_data("world")
-mapdata1 <- left_join(mapdata, map_df, by="region", relationship = "many-to-many")
-mapdata2 <- mapdata1 %>% filter(!is.na(mapdata1$compound_interest)| mapdata1$region %in% geo_list)
-mapdata2
-map1 <- ggplot(mapdata2, aes(x = long, y = lat, group = group)) +
-geom_polygon(aes(fill = compound_interest), color = "black") +
-scale_fill_gradient(name="compound interest", low = "white", high = "black", na.value = "yellow")
-map1
-# filter data to have both coordinates and value
-geo_list <- c("Belarus", "Greece", "Latvia", "Albania",
-"Switzerland", "Bosnia and Herzegovina", "Ukraine",
-"UK", "Turkey", "Serbia", "Kosovo" "Moldova", "North Macedonia",
-mapdata <- map_data("world")
-mapdata1 <- left_join(mapdata, map_df, by="region", relationship = "many-to-many")
-mapdata2 <- mapdata1 %>% filter(!is.na(mapdata1$compound_interest)| mapdata1$region %in% geo_list)
-mapdata2
-map1 <- ggplot(mapdata2, aes(x = long, y = lat, group = group)) +
-geom_polygon(aes(fill = compound_interest), color = "black") +
-scale_fill_gradient(name="compound interest", low = "white", high = "black", na.value = "yellow")
-map1
-# filter data to have both coordinates and value
-geo_list <- c("Belarus", "Greece", "Latvia", "Albania",
-"Switzerland", "Bosnia and Herzegovina", "Ukraine",
-"UK", "Turkey", "Serbia", "Kosovo", "Moldova", "North Macedonia",
-"Montenegro")
-mapdata <- map_data("world")
-mapdata1 <- left_join(mapdata, map_df, by="region", relationship = "many-to-many")
-mapdata2 <- mapdata1 %>% filter(!is.na(mapdata1$compound_interest)| mapdata1$region %in% geo_list)
-mapdata2
-map1 <- ggplot(mapdata2, aes(x = long, y = lat, group = group)) +
-geom_polygon(aes(fill = compound_interest), color = "black") +
-scale_fill_gradient(name="compound interest", low = "white", high = "black", na.value = "yellow")
-map1
-# filter data to have both coordinates and value
-geo_list <- c("Belarus", "Greece", "Latvia", "Albania",
-"Switzerland", "Bosnia and Herzegovina", "Ukraine",
-"UK", "Turkey", "Serbia", "Kosovo", "Moldova", "North Macedonia",
-"Montenegro", "cyprus", "Malta")
-mapdata <- map_data("world")
-mapdata1 <- left_join(mapdata, map_df, by="region", relationship = "many-to-many")
-mapdata2 <- mapdata1 %>% filter(!is.na(mapdata1$compound_interest)| mapdata1$region %in% geo_list)
-mapdata2
-map1 <- ggplot(mapdata2, aes(x = long, y = lat, group = group)) +
-geom_polygon(aes(fill = compound_interest), color = "black") +
-scale_fill_gradient(name="compound interest", low = "white", high = "black", na.value = "yellow")
-map1
+# Frontend
+ui <- fluidPage(
+sidebarLayout(
+sidebarPanel(
+# Compound interest
+sliderInput("range", "Compound interest:",min = 0, max = 10, value = c(4,8)),textOutput("Compound interest slider"),
+# Checkboxes
+# tags$head(tags$style(HTML(".checkbox {margin-left:15px}"))),
+checkboxGroupInput("countries", "Chosen countries:",
+choiceNames = map_df$region,
+choiceValues = map_df$geo,
+selected = c("PL", "DE", "CZ"),
+inline = TRUE,
+width = "75%"
+),
+width=3
+),
+mainPanel(
+h1("Real house prices index"),
+tabsetPanel(
+tabPanel("Plot", plotlyOutput("final_plot")),
+tabPanel("Map", leafletOutput("mymap")),
+tabPanel("Table", dataTableOutput('table'))
+),
+width = 9
+),
+fluid = TRUE
+)
+)
+# Backend
+server <- function(input, output, session) {
+output$table <- renderDataTable({
+merged_df[, !names(merged_df) %in% c("OBS_VALUE.x", "OBS_VALUE.y", "geometry")]
+}, options = list(pageLength = 10))
+# Plot module
+output$final_plot <- renderPlotly({
+final_plot <- ggplot(filter(merged_df, geo %in% input$countries),
+aes(x = TIME_PERIOD, y = house_prices_wo_hicp, color = geo,
+text = paste("Kraj: ", geo, "<br>", "Rok: ", TIME_PERIOD, "<br>",
+"Cena nieruchomości: ", house_prices_wo_hicp))) +
+geom_line(aes(group = geo), linetype = "dotted", size = 1) +
+geom_point() +
+geom_text(data = filter(merged_df, geo %in% input$countries) %>% group_by(geo) %>% slice(n()),
+aes(label = "", hjust = -0.2, size = 4)) +
+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) +
+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) +
+scale_x_continuous(breaks = seq(2010, 2024, 2)) +
+labs(x = "Rok", y = "Indeks cen nieruchomości zdyskontowany o wartość inflacji [2015 = 100]",
+color = "Countries") +
+theme(axis.title = element_blank())
+plotly_plot <- ggplotly(final_plot, tooltip = "text")
+for (i in 1:length(plotly_plot$x$data)) {
+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")) {
+plotly_plot$x$data[[i]]$hoverinfo <- "name+y"
+}
+}
+plotly_plot %>% layout(showlegend = TRUE, legend = list(title = list(text = "Countries")))
+})
+# Map module
+output$mymap <- renderLeaflet({
+leaflet() %>%
+addProviderTiles(providers$CartoDB.Positron) %>%
+addPolygons(data=mapdata_new,
+fillOpacity = 0.6, # Przezroczystość
+stroke = TRUE, # Borders visible
+color = "grey", # Border color
+weight = 1,
+fillColor = ~qpal(mapdata_new$substr_house_prices_wo_hicp),
+popup = popup_content,
+popupOptions = popupOptions(maxWidth ="100%", closeOnClick = TRUE)
+) %>%
+setView( lat = 49, lng = 14, zoom = 4) %>%
+addLegend("bottomright", colors = qpal_colors,
+title = "<span style='white-space: pre-line;'> Real house prices \n index (2022) </span>",
+labels = qpal_labs,
+opacity = 1)
+}) # End of map module
+}# End of server
+# Run
+shinyApp(ui, server)
diff --git a/.Rproj.user/shared/notebooks/paths b/.Rproj.user/shared/notebooks/paths
index 69b8c12..f8ba964 100644
--- a/.Rproj.user/shared/notebooks/paths
+++ b/.Rproj.user/shared/notebooks/paths
@@ -1,3 +1,6 @@
-C:/Users/Lewy/Desktop/Wizualizacja danych/experiments.R="98FA7FC8"
-C:/Users/Lewy/Desktop/Wizualizacja danych/projekt_1_housing.R="B49036B1"
-C:/Users/Lewy/Desktop/Wizualizacja danych/projekt_2_housing.R="569F1072"
+/home/lewy/Downloads/projekt_1_housing.r="FDB568A6"
+/home/lewy/UE_house_prices_wizualizacja/projekt_1_housing.R="305524DE"
+/home/lewy/UE_house_prices_wizualizacja/projekt_1_housing_new.r="7F985654"
+/home/lewy/UE_house_prices_wizualizacja/test.R="0EE8C830"
+/home/lewy/UE_house_prices_wizualizacja/test_checkboxes.R="7F311D9A"
+/home/lewy/UE_house_prices_wizualizacja/webapp.R="7174B648"
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..5b6a065
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,4 @@
+.Rproj.user
+.Rhistory
+.RData
+.Ruserdata
diff --git a/test.R b/test.R
new file mode 100644
index 0000000..795e400
--- /dev/null
+++ b/test.R
@@ -0,0 +1,42 @@
+## Only run examples in interactive R sessions
+if (interactive()) {
+  
+  ui <- fluidPage(
+    checkboxGroupInput("variable", "Variables to show:",
+                       c("Cylinders" = "cyl",
+                         "Transmission" = "am",
+                         "Gears" = "gear")),
+    tableOutput("data")
+  )
+  
+  server <- function(input, output, session) {
+    output$data <- renderTable({
+      mtcars[, c("mpg", input$variable), drop = FALSE]
+    }, rownames = TRUE)
+  }
+  
+  shinyApp(ui, server)
+  
+  ui <- fluidPage(
+    checkboxGroupInput("icons", "Choose icons:",
+                       choiceNames =
+                         list(icon("calendar"), icon("bed"),
+                              icon("cog"), icon("bug")),
+                       choiceValues =
+                         list("calendar", "bed", "cog", "bug")
+    ),
+    textOutput("txt")
+  )
+  
+  server <- function(input, output, session) {
+    output$txt <- renderText({
+      icons <- paste(input$icons, collapse = ", ")
+      paste("You chose", icons)
+    })
+  }
+  
+  shinyApp(ui, server)
+}
+
+
+
diff --git a/test_checkboxes.R b/test_checkboxes.R
new file mode 100644
index 0000000..c38d2fa
--- /dev/null
+++ b/test_checkboxes.R
@@ -0,0 +1,28 @@
+  ## Only run examples in interactive R sessions
+  if (interactive()) {
+    
+    # Frontend
+    ui <- fluidPage(
+      # Checkboxes
+      checkboxGroupInput("countries", "Choose countries:",
+                         choiceNames = unique(merged_df$geo),
+                         choiceValues = unique(merged_df$geo)
+      ),
+      
+      # list of chosen
+      textOutput("txt")
+    )
+    
+    # Backend
+    server <- function(input, output, session) {
+      
+      # Printer
+      output$txt <- renderText({
+        chosen_countries <- paste(input$countries, collapse = ", ")
+        paste("You chose", chosen_countries)
+      })
+    
+      }
+    
+    shinyApp(ui, server)
+  }
\ No newline at end of file
diff --git a/webapp.R b/webapp.R
index 3168f24..1a44c1d 100644
--- a/webapp.R
+++ b/webapp.R
@@ -11,12 +11,13 @@ ui <- fluidPage(
       # Compound interest
       sliderInput("range", "Compound interest:",min = 0, max = 10, value = c(4,8)),textOutput("Compound interest slider"),
       
+      
       # Checkboxes
-      # tags$head(tags$style(HTML(".checkbox {margin-left:15px}"))),
+      #tags$head(tags$style(HTML(".checkbox {margin-left:15px}"))),
       checkboxGroupInput("countries", "Chosen countries:",
-                         choiceNames = map_df$region,
-                         choiceValues = map_df$geo,
-                         selected = c("PL", "DE", "CZ"),
+                         choiceNames = c(map_df$region),
+                         choiceValues = c(map_df$geo),
+                         selected = c("PL", "CZ", "DE"),
                          inline = TRUE,
                          width = "75%"
       ),
@@ -24,6 +25,7 @@ ui <- fluidPage(
       
       
       
+      
 
       width=3
     ),