<<<<<<< HEAD library(dplyr) # library(tidyverse) # install.packages("tidyverse") 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 ======= 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 >>>>>>> 9cd7ec6d23058242fa1b48bf76dd80c927b5ef48