UE_house_prices_wizualizacja/projekt_1_housing_new.r
2023-06-14 10:58:06 +02:00

215 lines
7.2 KiB
R

library(dplyr)
# install.packages("ggplot2")
library(ggplot2)
library(plotly)
countries = c( 'PL', 'DE', 'CZ', 'NL', 'RO')
countries = unique(SHP_28$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>",
"Wartoœæ wskaŸnika: ", 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 nieruchomoœci [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>",
"Wartoœæ wskaŸnika: ", OBS_VALUE))) +
geom_line() +
geom_point() +
scale_x_continuous(breaks = seq(2010, 2024, 2)) +
labs(x = "Rok", y = 'Indeks Cen nieruchomoœci [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]]$mode <- "lines+markers"
}
}
plotly_plot
house_price_index
# ######################################
# HICP - Harmonised Index for Consumer Prices
df
hicp_index <- df %>% select( TIME_PERIOD, geo, OBS_VALUE)
hicp_index <- na.omit(hicp_index)
colnames(hicp_index)
df %>% group_by(geo) %>% str()
str(hicp_index)
hicp_index <- filter(hicp_index, geo %in% countries)
# Plot
ggplot(hicp_index, aes(x = TIME_PERIOD, y = OBS_VALUE, color = geo, label = geo)) +
geom_line() +
geom_point() +
geom_text(data = hicp_index %>%
group_by(geo) %>%
slice(n()),
aes(label = geo, hjust = -0.2, size = 4)) +
labs(x = "Rok", y = 'Indeks inflacji konsumenckiej HICP [2015 = 100]') +
scale_x_continuous(breaks=seq(2010,2024,2))
plot <- ggplot(hicp_index, aes(x = TIME_PERIOD, y = OBS_VALUE, color = geo, text = paste("Kraj: ", geo, "<br>",
"Rok: ", TIME_PERIOD, "<br>",
"Wartoœæ wskaŸnika: ", OBS_VALUE))) +
geom_line() +
geom_point() +
labs(x = "Rok", y = 'Indeks inflacji konsumenckiej HICP [2015 = 100]') +
scale_x_continuous(breaks = seq(2010, 2024, 2))
plotly_plot <- ggplotly(plot, tooltip = "text")
for (i in 1:length(plotly_plot$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$TIME_PERIOD-2015))
# View the resulting merged data frame with the divided values
merged_df
merged_df <- na.omit(merged_df)
colnames(merged_df)
merged_df %>% group_by(geo) %>% str()
str(merged_df)
merged_df <- filter(merged_df, geo %in% countries)
ggplot(merged_df, aes(x = TIME_PERIOD, y = house_prices_wo_hicp, color = geo)) +
geom_line(linetype="dotted", size=1) +
geom_point(aes(x=TIME_PERIOD, y=house_prices_wo_hicp)) +
geom_text(data = merged_df %>%
group_by(geo) %>%
slice(n()),
aes(label = geo, hjust = -0.2, size = 4)) +
stat_function(fun=function(x) 100*(1.04)^(x-2015), aes(colour = "4% Compounding")) +
stat_function(fun=function(x) 100*(1.07)^(x-2015), aes(colour = "7% Compounding")) +
scale_x_continuous(breaks=seq(2010,2024,2)) +
labs(x = "Year", y = 'Indeks cen nieruchomoœci zdyskontowany o wartoœæ inflacji [2015 = 100]')
final_plot <- ggplot(merged_df, aes(x = TIME_PERIOD, y = house_prices_wo_hicp, color = geo,
text = paste("Kraj: ", geo, "<br>", "Rok: ", TIME_PERIOD, "<br>",
"Cena nieruchomoœci: ", house_prices_wo_hicp))) +
geom_line(aes(group = geo), linetype = "dotted", size = 1) +
geom_point() +
geom_text(data = merged_df %>% group_by(geo) %>% slice(n()),
aes(label = "", hjust = -0.2, size = 4)) +
stat_function(fun = function(x) 100*(1.04)^(x-2015), aes(colour = "4% Compounding"), inherit.aes = FALSE) +
stat_function(fun = function(x) 100*(1.07)^(x-2015), aes(colour = "7% Compounding"), inherit.aes = FALSE) +
scale_x_continuous(breaks = seq(2010, 2024, 2)) +
labs(x = "Rok", y = "Indeks cen nieruchomoœci zdyskontowany o wartoœæ inflacji [2015 = 100]")
final_plot
plotly_plot <- ggplotly(final_plot, tooltip = "text")
for (i in 1:length(plotly_plot$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