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@ -1,143 +1,143 @@
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library(dplyr)
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# install.packages("ggplot2")
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library(ggplot2)
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countries = c( 'PL', 'DE', 'CZ', 'NL', 'RO')
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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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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 nieruchomości [cena z 2015 roku = 100]')
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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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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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merged_df <- filter(merged_df, geo %in% countries)
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# Plot
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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 nieruchomości zdyskontowany o wartość inflacji [2015 = 100]')
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library(dplyr)
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# install.packages("ggplot2")
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library(ggplot2)
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countries = c( 'PL', 'DE', 'CZ', 'NL', 'RO')
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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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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 nieruchomości [cena z 2015 roku = 100]')
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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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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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merged_df <- filter(merged_df, geo %in% countries)
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# Plot
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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 nieruchomości zdyskontowany o wartość inflacji [2015 = 100]')
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@ -1,38 +1,40 @@
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library(dplyr)
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library(tidyverse)
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# install.packages("colorRamps")
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library(ggplot2)
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library(colorRamps)
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# countries = c( 'PL', 'DE', 'CZ', 'NL', 'RO')
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map_df = read.csv(".//data//compound_interest_housing.csv")
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map_df
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ggplot(map_df, aes(x = TIME_PERIOD, y = compound_interest, color = geo)) +
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geom_point(aes(x=TIME_PERIOD, y=compound_interest)) +
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geom_text(data = map_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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scale_x_continuous(breaks=seq(2010,2024,2)) +
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labs(x = "Year", y = 'wartość mieszkania jako % składany powyżej inflacji')
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# filter data to have both coordinates and value
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geo_list <- c("Belarus", "Greece", "Latvia", "Albania",
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"Switzerland", "Bosnia and Herzegovina", "Ukraine",
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"UK", "Turkey", "Serbia", "Kosovo", "Moldova", "North Macedonia",
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"Montenegro", "cyprus", "Malta")
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mapdata <- map_data("world")
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mapdata1 <- left_join(mapdata, map_df, by="region", relationship = "many-to-many")
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mapdata2 <- mapdata1 %>% filter(!is.na(mapdata1$compound_interest)| mapdata1$region %in% geo_list)
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mapdata2
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map1 <- ggplot(mapdata2, aes(x = long, y = lat, group = group)) +
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geom_polygon(aes(fill = compound_interest), color = "black") +
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scale_fill_gradient(name="compound interest", low = "white", high = "black", na.value = "yellow")
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map1
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library(dplyr)
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library(tidyverse)
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# install.packages("colorRamps")
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library(ggplot2)
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library(colorRamps)
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# countries = c( 'PL', 'DE', 'CZ', 'NL', 'RO')
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map_df = read.csv(".//data//compound_interest_housing.csv")
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map_df
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ggplot(map_df, aes(x = TIME_PERIOD, y = compound_interest, color = geo)) +
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geom_point(aes(x=TIME_PERIOD, y=compound_interest)) +
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geom_text(data = map_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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scale_x_continuous(breaks=seq(2010,2024,2)) +
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labs(x = "Year", y = 'wartość mieszkania jako % składany powyżej inflacji')
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# filter data to have both coordinates and value
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geo_list <- c("Belarus", "Greece", "Latvia", "Albania",
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"Switzerland", "Bosnia and Herzegovina", "Ukraine",
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"UK", "Turkey", "Serbia", "Kosovo", "Moldova", "North Macedonia",
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"Montenegro", "Cyprus", "Malta", "Georgia", "Armenia")
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mapdata <- map_data("world")
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mapdata1 <- left_join(mapdata, map_df, by="region", relationship = "many-to-many")
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mapdata2 <- mapdata1 %>% filter(!is.na(mapdata1$compound_interest)| mapdata1$region %in% geo_list)
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mapdata2
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map1 <- ggplot(mapdata2, aes(x = long, y = lat, group = group)) +
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geom_polygon(aes(fill = compound_interest), color = "black") +
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#geom_text(aes(label = region), size = 3, nudge_y = 1) + # Add labels
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#scale_fill_gradient(name="compound interest", low = "white", high = "black", na.value = "yellow") +
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scale_fill_viridis_c(name="compound interest", option = "plasma", trans = "sqrt", na.value = "grey") + # colorblind-friendly palette
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labs(title = "World Map with Compound Interest") # Set plot title
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map1
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