UE_house_prices_wizualizacja/projekt_2_housing.R

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2023-05-30 17:43:47 +02:00
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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
=======
2023-05-30 14:42:23 +02:00
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
2023-05-30 17:43:47 +02:00
>>>>>>> 9cd7ec6d23058242fa1b48bf76dd80c927b5ef48