Changes
This commit is contained in:
lewy
parent
0d28556d38
commit
278b1622ea
@ -1,4 +1,4 @@
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<<<<<<< HEAD
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library(dplyr)
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# install.packages("lifecycle")
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library(ggplot2)
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@ -285,5 +285,3 @@ ggplot(merged_df, aes(x = TIME_PERIOD, y = house_prices_wo_hicp, color = geo)) +
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stat_function(fun=function(x) 100*(1.07)^(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 nieruchomości zdyskontowany o wartość inflacji [2015 = 100]')
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>>>>>>> 9cd7ec6d23058242fa1b48bf76dd80c927b5ef48
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219
projekt_1_housing_new.r
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219
projekt_1_housing_new.r
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@ -0,0 +1,219 @@
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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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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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df1 = read.csv(".//data//prc_hpi_a__custom_3617733_page_linear.csv")
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df1[,c("geo", "TIME_PERIOD", "OBS_VALUE")]
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colnames(df1)
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df2 = read.csv(".//data//sdg_08_10_page_linear.csv")
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df2[,c("geo", "TIME_PERIOD", "OBS_VALUE")]
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colnames(df2)
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df3 = read.csv(".//data//tec00114_page_linear.csv")
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df3[,c("geo", "TIME_PERIOD", "OBS_VALUE")]
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colnames(df3)
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print(df3)
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# ##################################################
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# Single Country GDP graph
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year_country_gdp <- df3 %>% select( TIME_PERIOD, geo, OBS_VALUE)
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year_country_gdp <- na.omit(year_country_gdp)
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colnames(year_country_gdp)
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df3 %>% group_by(geo) %>% str()
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str(year_country_gdp)
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year_country_gdp <- filter(year_country_gdp, geo %in% countries)
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# Plot
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ggplot(year_country_gdp, aes(x = TIME_PERIOD, y = OBS_VALUE, color = geo, label = geo)) +
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geom_line() +
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geom_point() +
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geom_text(aes(label = geo), hjust = -0.1, size = 3)+
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labs(x = "Rok", y = 'PKB per capita w PPP [PPS_EU27_2020=100]') +
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scale_x_continuous(breaks=seq(2010,2024,2))
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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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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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scale_x_continuous(breaks = seq(2010, 2024, 2))
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plotly_plot <- ggplotly(plot, tooltip = "text")
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plotly_plot
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year_country_gdp
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# ##################################################
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# House price index HPI
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df1
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house_price_index <- df1 %>% select( TIME_PERIOD, geo, OBS_VALUE)
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house_price_index <- na.omit(house_price_index)
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colnames(house_price_index)
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df1 %>% group_by(geo) %>% str()
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str(house_price_index)
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house_price_index <- filter(house_price_index, geo %in% countries)
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# Plot
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ggplot(house_price_index, aes(x = TIME_PERIOD, y = OBS_VALUE, color = geo, label = geo)) +
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geom_line() +
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geom_point() +
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geom_text(data = house_price_index %>%
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group_by(geo) %>%
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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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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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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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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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plotly_plot$x$data[[i]]$mode <- "lines+markers"
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}
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}
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plotly_plot
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house_price_index
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# ######################################
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# HICP - Harmonised Index for Consumer Prices
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df
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hicp_index <- df %>% select( TIME_PERIOD, geo, OBS_VALUE)
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hicp_index <- na.omit(hicp_index)
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colnames(hicp_index)
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df %>% group_by(geo) %>% str()
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str(hicp_index)
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hicp_index <- filter(hicp_index, geo %in% countries)
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# Plot
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ggplot(hicp_index, aes(x = TIME_PERIOD, y = OBS_VALUE, color = geo, label = geo)) +
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geom_line() +
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geom_point() +
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geom_text(data = hicp_index %>%
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group_by(geo) %>%
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slice(n()),
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aes(label = geo, hjust = -0.2, size = 4)) +
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labs(x = "Rok", y = 'Indeks inflacji konsumenckiej HICP [2015 = 100]') +
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scale_x_continuous(breaks=seq(2010,2024,2))
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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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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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scale_x_continuous(breaks = seq(2010, 2024, 2))
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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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plotly_plot$x$data[[i]]$mode <- "lines+markers"
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}
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}
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plotly_plot
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hicp_index
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# ########################
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# Show data discounting inflation rate
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# Merge the two data frames using the 'country' and 'date' columns
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merged_df <- merge(house_price_index, hicp_index, by = c("geo", "TIME_PERIOD"))
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merged_df
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# Create a new column that divides 'value1' by 'value2'
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merged_df$house_prices_wo_hicp <- merged_df$OBS_VALUE.x / merged_df$OBS_VALUE.y*100
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merged_df$TIME_PERIOD
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merged_df$compound_growth <- 1 * (1 + 0.02) ^ (1:(merged_df$TIME_PERIOD-2015))
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# View the resulting merged data frame with the divided values
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merged_df
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merged_df <- na.omit(merged_df)
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colnames(merged_df)
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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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geom_text(data = merged_df %>%
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group_by(geo) %>%
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slice(n()),
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aes(label = geo, hjust = -0.2, size = 4)) +
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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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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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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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aes(label = "", hjust = -0.2, size = 4)) +
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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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final_plot
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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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plotly_plot
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@ -1,6 +1,5 @@
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library(tidyverse)
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library(eurostat)
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# install.packages("ggthemes")
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library(leaflet)
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library(sf)
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library(scales)
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@ -8,6 +7,7 @@ library(cowplot)
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library(ggthemes)
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library(RColorBrewer)
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R.Version()
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# Load results from project_1
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map_df = read.csv(".//data//compound_interest_housing.csv")
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@ -43,7 +43,7 @@ mapdata_new <- left_join(SHP_28, map_df, by="geo", relationship = "many-to-many"
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# Delete Greece (null values)
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mapdata_new <- mapdata_new[-9,]
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mapdata_new
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mapdata_new$wiki <- paste0( "https://en.wikipedia.org/wiki/", mapdata_new$name )
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# Create a continuous palette function
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@ -79,11 +79,10 @@ map <- leaflet() %>%
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popup = popup_content,
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popupOptions = popupOptions(maxWidth ="100%", closeOnClick = TRUE),
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) %>%
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setView( lat = 51, lng = 14, zoom = 4) %>%
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setView( lat = 53, lng = 14, zoom = 4) %>%
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addLegend("bottomright", colors = qpal_colors,
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title = "<span style='white-space: pre-line;'> Real house prices \n index (2022) </span>",
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labels = qpal_labs,
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opacity = 1)
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map
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104
webapp.R
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104
webapp.R
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library(shiny)
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library(leaflet)
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library(ggplot2)
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library(dplyr)
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# Frontend
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ui <- fluidPage(
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sidebarLayout(
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sidebarPanel(
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# Compound interest
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sliderInput("range", "Compound interest:",min = 0, max = 10, value = c(4,8)),textOutput("Compound interest slider"),
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# Checkboxes
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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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),
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width=3
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),
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mainPanel(
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h1("Real house prices index"),
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tabsetPanel(
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tabPanel("Plot", plotOutput("final_plot")),
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tabPanel("Map", leafletOutput("mymap")),
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tabPanel("Table", dataTableOutput('table'))
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),
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width = 9
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),
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fluid = TRUE
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)
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)
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# Backend
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server <- function(input, output, session) {
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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$countries), aes(x = TIME_PERIOD, y = house_prices_wo_hicp, color = geo,
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text = paste("Kraj: ", geo, "<br>", "Rok: ", TIME_PERIOD, "<br>",
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"Cena nieruchomości: ", house_prices_wo_hicp))) +
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geom_line(aes(group = geo), linetype = "dotted", size = 1) +
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geom_point() +
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geom_text(data = merged_df %>% group_by(geo) %>% slice(n()),
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aes(label = "", hjust = -0.2, size = 4)) +
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stat_function(fun = function(x) 100*(1+input$range[1]/100)^(x-2015), aes(colour = "4% Compounding"), inherit.aes = FALSE) +
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stat_function(fun = function(x) 100*(1+input$range[2]/100)^(x-2015), aes(colour = "7% Compounding"), inherit.aes = FALSE) +
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scale_x_continuous(breaks = seq(2010, 2024, 2)) +
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# labs(x = "Rok", y = "Indeks cen nieruchomości zdyskontowany o wartość inflacji [2015 = 100]") +
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theme( axis.title = element_blank() )
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# ggplotly(final_plot, tooltip = "text")
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#for (i in 1:length(plotly_plot$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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#plotly_plot
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})
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# Map module
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output$mymap <- renderLeaflet({
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leaflet() %>%
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addProviderTiles(providers$CartoDB.Positron) %>%
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addPolygons(data=mapdata_new,
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fillOpacity = 0.6, # Przezroczystość
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stroke = TRUE, # Borders visible
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color = "grey", # Border color
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weight = 1,
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fillColor = ~qpal(mapdata_new$substr_house_prices_wo_hicp),
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popup = popup_content,
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popupOptions = popupOptions(maxWidth ="100%", closeOnClick = TRUE)
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) %>%
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setView( lat = 49, lng = 14, zoom = 4) %>%
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addLegend("bottomright", colors = qpal_colors,
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title = "<span style='white-space: pre-line;'> Real house prices \n index (2022) </span>",
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labels = qpal_labs,
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opacity = 1)
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}) # End of map module
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}# End of server
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# Run
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shinyApp(ui, server)
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