From 278b1622ea8159fc42c65f0545f56c0152ff0de7 Mon Sep 17 00:00:00 2001 From: lewy Date: Wed, 14 Jun 2023 09:12:06 +0200 Subject: [PATCH] Changes --- projekt_1_housing.R | 288 ++++++++++++++++++++-------------------- projekt_1_housing_new.r | 219 ++++++++++++++++++++++++++++++ projekt_2_housing_new.R | 177 ++++++++++++------------ webapp.R | 104 +++++++++++++++ 4 files changed, 554 insertions(+), 234 deletions(-) create mode 100644 projekt_1_housing_new.r create mode 100644 webapp.R diff --git a/projekt_1_housing.R b/projekt_1_housing.R index 4b6e600..6136ce1 100644 --- a/projekt_1_housing.R +++ b/projekt_1_housing.R @@ -1,4 +1,4 @@ -<<<<<<< HEAD + library(dplyr) # install.packages("lifecycle") library(ggplot2) @@ -143,147 +143,145 @@ ggplot(merged_df, aes(x = TIME_PERIOD, y = house_prices_wo_hicp, color = geo)) + labs(x = "Year", y = 'Indeks cen nieruchomości zdyskontowany o wartość inflacji [2015 = 100]') ======= -library(dplyr) -# install.packages("ggplot2") -library(ggplot2) -countries = c( 'PL', 'DE', 'CZ', 'NL', 'RO') - -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)) - -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 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) - -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)) - - -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) - -merged_df <- filter(merged_df, geo %in% countries) - -# 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]') - ->>>>>>> 9cd7ec6d23058242fa1b48bf76dd80c927b5ef48 +library(dplyr) +# install.packages("ggplot2") +library(ggplot2) +countries = c( 'PL', 'DE', 'CZ', 'NL', 'RO') + +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)) + +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 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) + +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)) + + +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) + +merged_df <- filter(merged_df, geo %in% countries) + +# 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]') \ No newline at end of file diff --git a/projekt_1_housing_new.r b/projekt_1_housing_new.r new file mode 100644 index 0000000..7ed4be4 --- /dev/null +++ b/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, "
", + "Rok: ", TIME_PERIOD, "
", + "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, "
", + "Rok: ", TIME_PERIOD, "
", + "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]]$mode <- "lines+markers" + } +} +plotly_plot + +house_price_index +# ###################################### + +# HICP - Harmonised Index for Consumer Prices +df +hicp_index <- df %>% select( TIME_PERIOD, geo, OBS_VALUE) +hicp_index <- na.omit(hicp_index) + +colnames(hicp_index) + +df %>% group_by(geo) %>% str() + +str(hicp_index) + +hicp_index <- filter(hicp_index, geo %in% countries) + +# Plot +ggplot(hicp_index, aes(x = TIME_PERIOD, y = OBS_VALUE, color = geo, label = geo)) + + geom_line() + + geom_point() + + geom_text(data = hicp_index %>% + group_by(geo) %>% + slice(n()), + aes(label = geo, hjust = -0.2, size = 4)) + + labs(x = "Rok", y = 'Indeks inflacji konsumenckiej HICP [2015 = 100]') + + scale_x_continuous(breaks=seq(2010,2024,2)) + + +plot <- ggplot(hicp_index, aes(x = TIME_PERIOD, y = OBS_VALUE, color = geo, text = paste("Kraj: ", geo, "
", + "Rok: ", TIME_PERIOD, "
", + "Warto?? wska?nika: ", OBS_VALUE))) + + geom_line() + + geom_point() + + labs(x = "Rok", y = 'Indeks inflacji konsumenckiej HICP [2015 = 100]') + + scale_x_continuous(breaks = seq(2010, 2024, 2)) + +plotly_plot <- ggplotly(plot, tooltip = "text") + +for (i in 1:length(plotly_plot$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$TIME_PERIOD-2015)) + +# View the resulting merged data frame with the divided values +merged_df +merged_df <- na.omit(merged_df) + +colnames(merged_df) + +merged_df %>% group_by(geo) %>% str() + +str(merged_df) + +unfiltered_df <- merged_df +merged_df <- filter(merged_df, geo %in% countries) + + + + +ggplot(merged_df, aes(x = TIME_PERIOD, y = house_prices_wo_hicp, color = geo)) + + geom_line(linetype="dotted", size=1) + + geom_point(aes(x=TIME_PERIOD, y=house_prices_wo_hicp)) + + geom_text(data = merged_df %>% + group_by(geo) %>% + slice(n()), + aes(label = geo, hjust = -0.2, size = 4)) + + stat_function(fun=function(x) 100*(1.04)^(x-2015), aes(colour = "4% Compounding")) + + stat_function(fun=function(x) 100*(1.07)^(x-2015), aes(colour = "7% Compounding")) + + scale_x_continuous(breaks=seq(2010,2024,2)) + + labs(x = "Year", y = 'Indeks cen nieruchomo?ci zdyskontowany o warto?? inflacji [2015 = 100]') + + +final_plot <- ggplot(merged_df, aes(x = TIME_PERIOD, y = house_prices_wo_hicp, color = geo, + text = paste("Kraj: ", geo, "
", "Rok: ", TIME_PERIOD, "
", + "Cena nieruchomo?ci: ", house_prices_wo_hicp))) + + geom_line(aes(group = geo), linetype = "dotted", size = 1) + + geom_point() + + geom_text(data = merged_df %>% group_by(geo) %>% slice(n()), + aes(label = "", hjust = -0.2, size = 4)) + + stat_function(fun = function(x) 100*(1.04)^(x-2015), aes(colour = "4% Compounding"), inherit.aes = FALSE) + + stat_function(fun = function(x) 100*(1.07)^(x-2015), aes(colour = "7% Compounding"), inherit.aes = FALSE) + + scale_x_continuous(breaks = seq(2010, 2024, 2)) + + labs(x = "Rok", y = "Indeks cen nieruchomości zdyskontowany o wartość inflacji [2015 = 100]") + +final_plot + +plotly_plot <- ggplotly(final_plot, tooltip = "text") + +for (i in 1:length(plotly_plot$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 + diff --git a/projekt_2_housing_new.R b/projekt_2_housing_new.R index cb85127..e03be51 100644 --- a/projekt_2_housing_new.R +++ b/projekt_2_housing_new.R @@ -1,89 +1,88 @@ -library(tidyverse) -library(eurostat) -# install.packages("ggthemes") -library(leaflet) -library(sf) -library(scales) -library(cowplot) -library(ggthemes) - -library(RColorBrewer) - -# Load results from project_1 -map_df = read.csv(".//data//compound_interest_housing.csv") -map_df - -# Load map/polygon data from eurostat -SHP_0 <- get_eurostat_geospatial(resolution = 10, - nuts_level = 0, - year = 2016) - -SHP_0 %>% - ggplot() + - geom_sf() - -EU28 <- eu_countries %>% - select(geo = code, name) - -SHP_28 <- SHP_0 %>% - select(geo = NUTS_ID, geometry) %>% - inner_join(EU28, by = "geo") %>% - arrange(geo) %>% - st_as_sf() - -SHP_28 %>% - ggplot() + - geom_sf() + - scale_x_continuous(limits = c(-10, 35)) + - scale_y_continuous(limits = c(35, 65)) - -# Join datasets - -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$wiki <- paste0( "https://en.wikipedia.org/wiki/", mapdata_new$name ) - -# Create a continuous palette function -qpal <- colorQuantile("YlOrBr", mapdata_new$substr_house_prices_wo_hicp, n = 5) - -qpal_colors <- unique(qpal(sort(mapdata_new$substr_house_prices_wo_hicp))) # hex codes -qpal_labs <- quantile(mapdata_new$substr_house_prices_wo_hicp, seq(0, 1, .2)) # depends on n from pal -qpal_labs <- paste(lag(qpal_labs), qpal_labs, sep = " - ")[-1] # first lag is NA - -popup_content <- paste0( "
" - , "_______________________________
" - , "Country: ", mapdata_new$name, "

" - , "
" - - , "House prices index: ", mapdata_new$substr_house_prices_wo_hicp, "
" - - , "Compound interest equivalent: ", mapdata_new$compound_interest, "%
" - - , "
" - , "", "
" - , "Click here to view wiki", "
" - , "
" ) - -map <- 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 = 51, lng = 14, zoom = 4) %>% - addLegend("bottomright", colors = qpal_colors, - title = " Real house prices \n index (2022) ", - labels = qpal_labs, - opacity = 1) - -map - +library(tidyverse) +library(eurostat) +library(leaflet) +library(sf) +library(scales) +library(cowplot) +library(ggthemes) + +library(RColorBrewer) +R.Version() + +# Load results from project_1 +map_df = read.csv(".//data//compound_interest_housing.csv") +map_df + +# Load map/polygon data from eurostat +SHP_0 <- get_eurostat_geospatial(resolution = 10, + nuts_level = 0, + year = 2016) + +SHP_0 %>% + ggplot() + + geom_sf() + +EU28 <- eu_countries %>% + select(geo = code, name) + +SHP_28 <- SHP_0 %>% + select(geo = NUTS_ID, geometry) %>% + inner_join(EU28, by = "geo") %>% + arrange(geo) %>% + st_as_sf() + +SHP_28 %>% + ggplot() + + geom_sf() + + scale_x_continuous(limits = c(-10, 35)) + + scale_y_continuous(limits = c(35, 65)) + +# Join datasets + +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 +qpal <- colorQuantile("YlOrBr", mapdata_new$substr_house_prices_wo_hicp, n = 5) + +qpal_colors <- unique(qpal(sort(mapdata_new$substr_house_prices_wo_hicp))) # hex codes +qpal_labs <- quantile(mapdata_new$substr_house_prices_wo_hicp, seq(0, 1, .2)) # depends on n from pal +qpal_labs <- paste(lag(qpal_labs), qpal_labs, sep = " - ")[-1] # first lag is NA + +popup_content <- paste0( "
" + , "_______________________________
" + , "Country: ", mapdata_new$name, "

" + , "
" + + , "House prices index: ", mapdata_new$substr_house_prices_wo_hicp, "
" + + , "Compound interest equivalent: ", mapdata_new$compound_interest, "%
" + + , "
" + , "", "
" + , "Click here to view wiki", "
" + , "
" ) + +map <- 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 = 53, lng = 14, zoom = 4) %>% + addLegend("bottomright", colors = qpal_colors, + title = " Real house prices \n index (2022) ", + labels = qpal_labs, + opacity = 1) + +map diff --git a/webapp.R b/webapp.R new file mode 100644 index 0000000..2139ac7 --- /dev/null +++ b/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$countries), aes(x = TIME_PERIOD, y = house_prices_wo_hicp, color = geo, + text = paste("Kraj: ", geo, "
", "Rok: ", TIME_PERIOD, "
", + "Cena nieruchomości: ", house_prices_wo_hicp))) + + geom_line(aes(group = geo), linetype = "dotted", size = 1) + + geom_point() + + geom_text(data = merged_df %>% group_by(geo) %>% slice(n()), + aes(label = "", hjust = -0.2, size = 4)) + + stat_function(fun = function(x) 100*(1+input$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 = " Real house prices \n index (2022) ", + labels = qpal_labs, + opacity = 1) + }) # End of map module + +}# End of server + +# Run +shinyApp(ui, server)