Changes

projekt_1_housing.R

@@ -1,4 +1,4 @@
-<<<<<<< HEAD
+
 library(dplyr)
 # install.packages("lifecycle")
 library(ggplot2)
@@ -285,5 +285,3 @@ ggplot(merged_df, aes(x = TIME_PERIOD, y = house_prices_wo_hicp, color = geo)) +
   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]')
-
->>>>>>> 9cd7ec6d23058242fa1b48bf76dd80c927b5ef48

projekt_1_housing_new.r

@@ -0,0 +1,219 @@
+library(dplyr)
+# install.packages("ggplot2")
+library(ggplot2)
+library(plotly)
+countries = c( 'PL', 'DE', 'CZ', 'NL', 'RO', 'XD')
+
+countries <- unique(map_df$geo)
+
+df = read.csv(".//data//prc_hicp_aind_page_linear.csv")
+df[,c("geo", "TIME_PERIOD", "OBS_VALUE")]
+
+
+df1 = read.csv(".//data//prc_hpi_a__custom_3617733_page_linear.csv")
+df1[,c("geo", "TIME_PERIOD", "OBS_VALUE")]
+
+colnames(df1)
+
+
+df2 = read.csv(".//data//sdg_08_10_page_linear.csv")
+df2[,c("geo", "TIME_PERIOD", "OBS_VALUE")]
+
+colnames(df2)
+
+df3 = read.csv(".//data//tec00114_page_linear.csv")
+df3[,c("geo", "TIME_PERIOD", "OBS_VALUE")]
+
+colnames(df3)
+print(df3)
+
+# ##################################################
+# Single Country GDP graph
+
+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)
+
+year_country_gdp <- filter(year_country_gdp, geo %in% countries)
+
+# Plot
+ggplot(year_country_gdp, aes(x = TIME_PERIOD, y = OBS_VALUE, color = geo, label = geo)) +
+  geom_line() +
+  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))
+
+plot <- ggplot(year_country_gdp, aes(x = TIME_PERIOD, y = OBS_VALUE, color = geo, text = paste("Kraj: ", geo, "<br>",
+                                                                                            "Rok: ", TIME_PERIOD, "<br>",
+                                                                                            "Warto?? wska?nika: ", OBS_VALUE))) +
+  geom_line(aes(group = geo)) +
+  geom_point() +
+  labs(x = "Rok", y = 'PKB per capita w PPP [PPS_EU27_2020=100]') +
+  scale_x_continuous(breaks = seq(2010, 2024, 2))
+
+plotly_plot <- ggplotly(plot, tooltip = "text")
+
+plotly_plot
+
+
+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)
+
+house_price_index <- filter(house_price_index, geo %in% countries)
+
+# Plot
+ggplot(house_price_index, aes(x = TIME_PERIOD, y = OBS_VALUE, color = geo, label = geo)) +
+  geom_line() +
+  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 nieruchomo?ci [cena z 2015 roku = 100]')
+
+plot <- ggplot(house_price_index, aes(x = TIME_PERIOD, y = OBS_VALUE, color = geo, text = paste("Kraj: ", geo, "<br>",
+                                                                                            "Rok: ", TIME_PERIOD, "<br>",
+                                                                                            "Warto?? wska?nika: ", OBS_VALUE))) +
+  geom_line() +
+  geom_point() +
+  scale_x_continuous(breaks = seq(2010, 2024, 2)) +
+  labs(x = "Rok", y = 'Indeks Cen nieruchomo?ci [cena z 2015 roku = 100]')
+plotly_plot <- ggplotly(plot, tooltip = "text")
+for (i in 1:length(plotly_plot$x$data)) {
+  if (plotly_plot$x$data[[i]]$type == "scatter") {
+    plotly_plot$x$data[[i]]$mode <- "lines+markers"
+  }
+}
+plotly_plot
+
+house_price_index
+# ######################################
+
+# HICP - Harmonised Index for Consumer Prices
+df
+hicp_index <- df %>% select( TIME_PERIOD, geo, OBS_VALUE)
+hicp_index <- na.omit(hicp_index)
+
+colnames(hicp_index)
+
+df %>% group_by(geo) %>% str()
+
+str(hicp_index)
+
+hicp_index <- filter(hicp_index, geo %in% countries)
+
+# Plot
+ggplot(hicp_index, aes(x = TIME_PERIOD, y = OBS_VALUE, color = geo, label = geo)) +
+  geom_line() +
+  geom_point() +
+  geom_text(data = hicp_index %>% 
+              group_by(geo) %>% 
+              slice(n()),
+            aes(label = geo, hjust = -0.2, size = 4)) +
+  labs(x = "Rok", y = 'Indeks inflacji konsumenckiej HICP [2015 = 100]') +
+  scale_x_continuous(breaks=seq(2010,2024,2))
+
+
+plot <- ggplot(hicp_index, aes(x = TIME_PERIOD, y = OBS_VALUE, color = geo, text = paste("Kraj: ", geo, "<br>",
+                                                                                      "Rok: ", TIME_PERIOD, "<br>",
+                                                                                      "Warto?? wska?nika: ", OBS_VALUE))) +
+  geom_line() +
+  geom_point() +
+  labs(x = "Rok", y = 'Indeks inflacji konsumenckiej HICP [2015 = 100]') +
+  scale_x_continuous(breaks = seq(2010, 2024, 2))
+
+plotly_plot <- ggplotly(plot, tooltip = "text")
+
+for (i in 1:length(plotly_plot$x$data)) {
+  if (plotly_plot$x$data[[i]]$type == "scatter") {
+    plotly_plot$x$data[[i]]$mode <- "lines+markers"
+  }
+}
+plotly_plot
+
+
+hicp_index
+
+# ########################
+# Show data discounting inflation rate
+
+# Merge the two data frames using the 'country' and 'date' columns
+merged_df <- merge(house_price_index, hicp_index, by = c("geo", "TIME_PERIOD"))
+
+merged_df
+# Create a new column that divides 'value1' by 'value2'
+merged_df$house_prices_wo_hicp <- merged_df$OBS_VALUE.x / merged_df$OBS_VALUE.y*100
+
+merged_df$TIME_PERIOD
+merged_df$compound_growth <- 1 * (1 + 0.02) ^ (1:(merged_df$TIME_PERIOD-2015))
+
+# View the resulting merged data frame with the divided values
+merged_df
+merged_df <- na.omit(merged_df)
+
+colnames(merged_df)
+
+merged_df %>% group_by(geo) %>% str()
+
+str(merged_df)
+
+unfiltered_df <- merged_df
+merged_df <- filter(merged_df, geo %in% countries)
+
+
+
+
+ggplot(merged_df, aes(x = TIME_PERIOD, y = house_prices_wo_hicp, color = geo)) +
+  geom_line(linetype="dotted", size=1) +
+  geom_point(aes(x=TIME_PERIOD, y=house_prices_wo_hicp)) +
+  geom_text(data = merged_df %>% 
+              group_by(geo) %>% 
+              slice(n()),
+            aes(label = geo, hjust = -0.2, size = 4)) +
+  stat_function(fun=function(x) 100*(1.04)^(x-2015), aes(colour = "4% Compounding")) +
+  stat_function(fun=function(x) 100*(1.07)^(x-2015), aes(colour = "7% Compounding")) +
+  scale_x_continuous(breaks=seq(2010,2024,2)) +
+  labs(x = "Year", y = 'Indeks cen nieruchomo?ci zdyskontowany o warto?? inflacji [2015 = 100]')
+
+
+final_plot <- ggplot(merged_df, aes(x = TIME_PERIOD, y = house_prices_wo_hicp, color = geo, 
+                              text = paste("Kraj: ", geo, "<br>", "Rok: ", TIME_PERIOD, "<br>", 
+                                           "Cena nieruchomo?ci: ", house_prices_wo_hicp))) +
+  geom_line(aes(group = geo), linetype = "dotted", size = 1) +
+  geom_point() +
+  geom_text(data = merged_df %>% group_by(geo) %>% slice(n()),
+            aes(label = "", hjust = -0.2, size = 4)) +
+  stat_function(fun = function(x) 100*(1.04)^(x-2015), aes(colour = "4% Compounding"), inherit.aes = FALSE) +
+  stat_function(fun = function(x) 100*(1.07)^(x-2015), aes(colour = "7% Compounding"), inherit.aes = FALSE) +
+  scale_x_continuous(breaks = seq(2010, 2024, 2)) +
+  labs(x = "Rok", y = "Indeks cen nieruchomości zdyskontowany o wartość inflacji [2015 = 100]")
+
+final_plot
+
+plotly_plot <- ggplotly(final_plot, tooltip = "text")
+
+for (i in 1:length(plotly_plot$x$data)) {
+  if (plotly_plot$x$data[[i]]$name == "4% Compounding" || plotly_plot$x$data[[i]]$name == "7% Compounding") {
+    plotly_plot$x$data[[i]]$hoverinfo <- "name+y"
+  }
+}
+
+plotly_plot
+

projekt_2_housing_new.R

@@ -1,6 +1,5 @@
 library(tidyverse)
 library(eurostat)
-# install.packages("ggthemes")
 library(leaflet)
 library(sf)
 library(scales)
@@ -8,6 +7,7 @@ library(cowplot)
 library(ggthemes)
 
 library(RColorBrewer)
+R.Version()
 
 # Load results from project_1
 map_df = read.csv(".//data//compound_interest_housing.csv")
@@ -43,7 +43,7 @@ mapdata_new <- left_join(SHP_28, map_df, by="geo", relationship = "many-to-many"
 
 # Delete Greece (null values)
 mapdata_new <- mapdata_new[-9,]
-
+mapdata_new
 mapdata_new$wiki <- paste0( "https://en.wikipedia.org/wiki/", mapdata_new$name )
 
 # Create a continuous palette function
@@ -79,11 +79,10 @@ map <- leaflet() %>%
               popup = popup_content,
               popupOptions = popupOptions(maxWidth ="100%", closeOnClick = TRUE),
               ) %>% 
-  setView( lat = 51, lng = 14, zoom = 4) %>%
+  setView( lat = 53, 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)
 
 map
-

webapp.R

@@ -0,0 +1,104 @@
+library(shiny)
+library(leaflet)
+library(ggplot2)
+library(dplyr)
+
+# 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 = unique(map_df$geo),
+                         choiceValues = unique(map_df$geo),
+                         selected = c("PL", "DE", "CZ")
+      ),
+      
+
+      width=3
+    ),
+    
+    mainPanel(
+      h1("Real house prices index"),
+      
+      tabsetPanel(
+        tabPanel("Plot", plotOutput("final_plot")),
+        tabPanel("Map", leafletOutput("mymap")),
+        tabPanel("Table", dataTableOutput('table'))
+      ),
+     
+      width = 9
+    ),
+    
+    fluid = TRUE
+  )
+  
+
+  
+)
+
+# Backend
+server <- function(input, output, session) {
+  
+  # Używanie wektora indeksu do indeksowania innych danych
+  output$table <- renderDataTable(merged_df[,!names(merged_df) %in% c("OBS_VALUE.x", "OBS_VALUE.y")])
+  
+  # Plot module
+  output$final_plot <- renderPlot({
+  
+    # final_plot <- 
+      ggplot(filter(merged_df, geo %in% input$countries), aes(x = TIME_PERIOD, y = house_prices_wo_hicp, color = geo, 
+                          text = paste("Kraj: ", geo, "<br>", "Rok: ", TIME_PERIOD, "<br>", 
+                                       "Cena nieruchomości: ", house_prices_wo_hicp))) +
+      geom_line(aes(group = geo), linetype = "dotted", size = 1) +
+      geom_point() +
+      geom_text(data = merged_df %>% group_by(geo) %>% slice(n()),
+                aes(label = "", hjust = -0.2, size = 4)) +
+      stat_function(fun = function(x) 100*(1+input$range[1]/100)^(x-2015), aes(colour = "4% Compounding"), inherit.aes = FALSE) +
+      stat_function(fun = function(x) 100*(1+input$range[2]/100)^(x-2015), aes(colour = "7% Compounding"), inherit.aes = FALSE) +
+      scale_x_continuous(breaks = seq(2010, 2024, 2)) +
+      # labs(x = "Rok", y = "Indeks cen nieruchomości zdyskontowany o wartość inflacji [2015 = 100]") +
+      theme( axis.title = element_blank() )
+    
+    # ggplotly(final_plot, tooltip = "text")
+    
+    #for (i in 1:length(plotly_plot$x$data)) {
+    #  if (plotly_plot$x$data[[i]]$name == "4% Compounding" || plotly_plot$x$data[[i]]$name == "7% Compounding") {
+    #    plotly_plot$x$data[[i]]$hoverinfo <- "name+y"
+    #  }
+    #}
+    
+    #plotly_plot
+    
+    })
+  
+  
+  # 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)