This commit is contained in:
lewy 2023-06-14 09:12:06 +02:00
parent 0d28556d38
commit 278b1622ea
4 changed files with 554 additions and 234 deletions

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@ -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]')

219
projekt_1_housing_new.r Normal file
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@ -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

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@ -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$substr_house_prices_wo_hicp, seq(0, 1, .2)) # depends on n from pal
qpal_labs <- paste(lag(qpal_labs), qpal_labs, sep = " - ")[-1] # first lag is NA
popup_content <- paste0( "<div style='text-align: center'>"
, "_______________________________<br>"
, "Country: ", mapdata_new$name, "<br><br>"
, "</div>"
, "House prices index: ", mapdata_new$substr_house_prices_wo_hicp, "<br>"
, "Compound interest equivalent: ", mapdata_new$compound_interest, "%<br>"
, "<div style='text-align: center'>"
, "<a href='", mapdata_new$wiki, "' target='_blank'>", "<br>"
, "Click here to view wiki</a>", "<br>"
, "</div>" )
map <- leaflet() %>%
addProviderTiles(providers$CartoDB.Positron) %>%
addPolygons(data=mapdata_new,
fillOpacity = 0.6, # Przezroczystość
stroke = TRUE, # Borders visible
color = "grey", # Border color
weight = 1,
fillColor = ~qpal(mapdata_new$substr_house_prices_wo_hicp),
popup = popup_content,
popupOptions = popupOptions(maxWidth ="100%", closeOnClick = TRUE),
) %>%
setView( lat = 51, lng = 14, zoom = 4) %>%
addLegend("bottomright", colors = qpal_colors,
title = "<span style='white-space: pre-line;'> Real house prices \n index (2022) </span>",
labels = qpal_labs,
opacity = 1)
map
library(tidyverse)
library(eurostat)
library(leaflet)
library(sf)
library(scales)
library(cowplot)
library(ggthemes)
library(RColorBrewer)
R.Version()
# Load results from project_1
map_df = read.csv(".//data//compound_interest_housing.csv")
map_df
# Load map/polygon data from eurostat
SHP_0 <- get_eurostat_geospatial(resolution = 10,
nuts_level = 0,
year = 2016)
SHP_0 %>%
ggplot() +
geom_sf()
EU28 <- eu_countries %>%
select(geo = code, name)
SHP_28 <- SHP_0 %>%
select(geo = NUTS_ID, geometry) %>%
inner_join(EU28, by = "geo") %>%
arrange(geo) %>%
st_as_sf()
SHP_28 %>%
ggplot() +
geom_sf() +
scale_x_continuous(limits = c(-10, 35)) +
scale_y_continuous(limits = c(35, 65))
# Join datasets
mapdata_new <- left_join(SHP_28, map_df, by="geo", relationship = "many-to-many")
# Delete Greece (null values)
mapdata_new <- mapdata_new[-9,]
mapdata_new
mapdata_new$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( "<div style='text-align: center'>"
, "_______________________________<br>"
, "Country: ", mapdata_new$name, "<br><br>"
, "</div>"
, "House prices index: ", mapdata_new$substr_house_prices_wo_hicp, "<br>"
, "Compound interest equivalent: ", mapdata_new$compound_interest, "%<br>"
, "<div style='text-align: center'>"
, "<a href='", mapdata_new$wiki, "' target='_blank'>", "<br>"
, "Click here to view wiki</a>", "<br>"
, "</div>" )
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 = "<span style='white-space: pre-line;'> Real house prices \n index (2022) </span>",
labels = qpal_labs,
opacity = 1)
map

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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)