From 9cd7ec6d23058242fa1b48bf76dd80c927b5ef48 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Pawe=C5=82=20Lewicki?= Date: Tue, 30 May 2023 14:42:23 +0200 Subject: [PATCH] Upload files to '' --- projekt_1_housing.R | 286 ++++++++++++++++++++++---------------------- projekt_2_housing.R | 78 ++++++------ 2 files changed, 183 insertions(+), 181 deletions(-) diff --git a/projekt_1_housing.R b/projekt_1_housing.R index 680c679..f5aeeef 100644 --- a/projekt_1_housing.R +++ b/projekt_1_housing.R @@ -1,143 +1,143 @@ -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]') - +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]') + diff --git a/projekt_2_housing.R b/projekt_2_housing.R index 4a04268..0fc4131 100644 --- a/projekt_2_housing.R +++ b/projekt_2_housing.R @@ -1,38 +1,40 @@ -library(dplyr) -library(tidyverse) -# install.packages("colorRamps") -library(ggplot2) -library(colorRamps) -# countries = c( 'PL', 'DE', 'CZ', 'NL', 'RO') - -map_df = read.csv(".//data//compound_interest_housing.csv") -map_df - -ggplot(map_df, aes(x = TIME_PERIOD, y = compound_interest, color = geo)) + - geom_point(aes(x=TIME_PERIOD, y=compound_interest)) + - geom_text(data = map_df %>% - group_by(geo) %>% - slice(n()), - aes(label = geo, hjust = -0.2, size = 4)) + - scale_x_continuous(breaks=seq(2010,2024,2)) + - labs(x = "Year", y = 'wartość mieszkania jako % składany powyżej inflacji') - - -# filter data to have both coordinates and value -geo_list <- c("Belarus", "Greece", "Latvia", "Albania", - "Switzerland", "Bosnia and Herzegovina", "Ukraine", - "UK", "Turkey", "Serbia", "Kosovo", "Moldova", "North Macedonia", - "Montenegro", "cyprus", "Malta") - -mapdata <- map_data("world") -mapdata1 <- left_join(mapdata, map_df, by="region", relationship = "many-to-many") -mapdata2 <- mapdata1 %>% filter(!is.na(mapdata1$compound_interest)| mapdata1$region %in% geo_list) - -mapdata2 - -map1 <- ggplot(mapdata2, aes(x = long, y = lat, group = group)) + - geom_polygon(aes(fill = compound_interest), color = "black") + - scale_fill_gradient(name="compound interest", low = "white", high = "black", na.value = "yellow") - -map1 - +library(dplyr) +library(tidyverse) +# install.packages("colorRamps") +library(ggplot2) +library(colorRamps) +# countries = c( 'PL', 'DE', 'CZ', 'NL', 'RO') + +map_df = read.csv(".//data//compound_interest_housing.csv") +map_df + +ggplot(map_df, aes(x = TIME_PERIOD, y = compound_interest, color = geo)) + + geom_point(aes(x=TIME_PERIOD, y=compound_interest)) + + geom_text(data = map_df %>% + group_by(geo) %>% + slice(n()), + aes(label = geo, hjust = -0.2, size = 4)) + + scale_x_continuous(breaks=seq(2010,2024,2)) + + labs(x = "Year", y = 'wartość mieszkania jako % składany powyżej inflacji') + + +# filter data to have both coordinates and value +geo_list <- c("Belarus", "Greece", "Latvia", "Albania", + "Switzerland", "Bosnia and Herzegovina", "Ukraine", + "UK", "Turkey", "Serbia", "Kosovo", "Moldova", "North Macedonia", + "Montenegro", "Cyprus", "Malta", "Georgia", "Armenia") + +mapdata <- map_data("world") +mapdata1 <- left_join(mapdata, map_df, by="region", relationship = "many-to-many") +mapdata2 <- mapdata1 %>% filter(!is.na(mapdata1$compound_interest)| mapdata1$region %in% geo_list) + +mapdata2 + +map1 <- ggplot(mapdata2, aes(x = long, y = lat, group = group)) + + geom_polygon(aes(fill = compound_interest), color = "black") + + #geom_text(aes(label = region), size = 3, nudge_y = 1) + # Add labels + #scale_fill_gradient(name="compound interest", low = "white", high = "black", na.value = "yellow") + + scale_fill_viridis_c(name="compound interest", option = "plasma", trans = "sqrt", na.value = "grey") + # colorblind-friendly palette + labs(title = "World Map with Compound Interest") # Set plot title +map1 +