287 lines
8.0 KiB
R
287 lines
8.0 KiB
R
|
|
library(dplyr)
|
|
# install.packages("lifecycle")
|
|
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]')
|
|
|
|
=======
|
|
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]') |