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
+