colnames(df3)
print(df3)
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)
# 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)
# 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)
# 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
# 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$OBS_VALUE.y
merged_df$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 <- merged_df %>% filter(!geo %in% c("TR"))
# 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]')
map_df <- merged_df %>% select( geo, house_prices_wo_hicp, TIME_PERIOD)
map_df <- filter(map_df, TIME_PERIOD == 2022)
map_df
merged_df
# 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))
write.csv(merged_df, ".//merged_df.csv")
hicp_index
write.csv(hicp_index, ".//hicp_index.csv")
df = read.csv(".//data//compound_interest_housing")
library(dplyr)
# install.packages("ggplot2")
library(ggplot2)
df = read.csv(".//data//compound_interest_housing")
df[,c("geo", "TIME_PERIOD", "OBS_VALUE")]
library(dplyr)
# install.packages("ggplot2")
library(ggplot2)
map_df = read.csv(".//data//compound_interest_housing")
map_df = read.csv(".//data//compound_interest_housing.csv")
map_df
map_df = read.csv(".//data//compound_interest_housing_2.csv")
map_df
map_df = read.csv(".//data//compound_interest_housing_3.csv")
map_df
map_df = read.csv(".//data//compound_interest_housing.csv")
map_df
map_df = read.csv(".//data//compound_interest_housing.csv")
map_df
ggplot(map_df, aes(x = TIME_PERIOD, y = compound_interest, color = geo)) +
geom_line(linetype="dotted", size=1) +
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)) +
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]')
ggplot(map_df, aes(x = TIME_PERIOD, y = compound_interest, color = geo)) +
geom_line(linetype="dotted", size=1) +
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 = 'Indeks cen nieruchomości zdyskontowany o wartość inflacji [2015 = 100]')
ggplot(map_df, aes(x = TIME_PERIOD, y = compound_interest, color = geo)) +
geom_line(linetype="dotted", size=1) +
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')
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')
plot(map_df$compound_interest)
plot(map_df$geo,map_df$compound_interest)
barplot(map_df$geo, map_df$compound_interest)
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')
# install.packages("tidyverse")
library(ggplot2)
install.packages("tidyverse")
install.packages("tidyverse")
library(tidyverse)
library(tidyverse)
install.packages("tidyverse")
install.packages("tidyverse")
library(tidyverse)
install.packages("tidyverse")
install.packages("tidyverse")
library(tidyverse)
# install.packages("tidyverse")
library(ggplot2)
view(mapdata)
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')
view(mapdata)
mapdata <- map_data("world")
library(tidyverse)
# install.packages("tidyverse")
library(ggplot2)
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')
mapdata <- map_data("world")
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),
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')
library(dplyr)
# install.packages("tidyverse")
library(ggplot2)
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')
mapdata <- map_data("world")
view(mapdata)
mapdata <- left_join(mapdata, map_df, by="region")
test <- test %>% filter("Czech republic" )
test
test <- mapdata %>% filter("Czech republic" )
test
test <- mapdata %>% filter("Afghanistan" )
test
test <-  filter(mapdata, region="Afghanistan" )
test
test <-  filter(mapdata, region="Aruba" )
test
filter(mapdata, region="Aruba" )
filter(mapdata, region="Aruba" )
filter(mapdata, region=="Aruba" )
filter(mapdata, region=="Czech republic" )
filter(mapdata, region=="Czech Republic" )
filter(mapdata, region=="Hungary
" )
write.csv(mapdata, "./mapdata.csv")
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')
mapdata <- map_data("world")
mapdata <- left_join(mapdata, map_df, by="region")
mapdata <- left_join(mapdata, map_df, by="region", relation = "many-to-many")
view(mapdata)
mapdata <- mapdata %>% filter(!is.na(mapdata$compound_interest))
view(mapdata)
map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
geom_polygon(aes(fill = compound_interest), color = "black")
map1
map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
geom_polygon(aes(fill = compound_interest), color = "black") +
scale_fill_gradient(name="compound interest", low="yellow", high = "red", na.value = "grey50")
map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
geom_polygon(aes(fill = compound_interest), color = "black") +
scale_fill_gradient(name="compound interest", low="yellow", high = "red", na.value = "grey50")
view(mapdata)
map1
map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
geom_polygon(aes(fill = compound_interest), color = "black") +
scale_fill_gradient(name="compound interest", low="blue", high = "yellow", na.value = "grey50")
map1
map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
geom_polygon(aes(fill = compound_interest), color = "black") +
scale_fill_gradient(name="compound interest", low="blue", high = "red", na.value = "grey50")
map1
map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
geom_polygon(aes(fill = compound_interest), color = "black") +
scale_fill_gradient(name="compound interest", palette(colorRamps::matlab.like), na.value = "grey50")
map1
library(colorRamps)
install.packages("colorRamps")
library(colorRamps)
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
mapdata <- map_data("world")
mapdata <- left_join(mapdata, map_df, by="region", relation = "many-to-many")
# filter data to have both coordinates and value
mapdata <- map_data("world")
mapdata <- left_join(mapdata, map_df, by="region", relation = "many-to-many")
mapdata <- mapdata %>% filter(!is.na(mapdata$compound_interest))
map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
geom_polygon(aes(fill = compound_interest), color = "black") +
scale_fill_gradient(name="compound interest", palette(colorRamps::matlab.like), na.value = "grey50")
map1
view(mapdata)
library(dplyr)
library(tidyverse)
# install.packages("colorRamps")
library(ggplot2)
library(colorRamps)
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
mapdata <- map_data("world")
mapdata <- left_join(mapdata, map_df, by="region", relation = "many-to-many")
mapdata <- left_join(mapdata, map_df, by="region")
, relation = "many-to-many"
mapdata <- left_join(mapdata, map_df, by="region", relation = "many-to-many")
map1
library(dplyr)
library(tidyverse)
# install.packages("colorRamps")
library(ggplot2)
library(colorRamps)
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
mapdata <- map_data("world")
mapdata <- left_join(mapdata, map_df, by="region", relation = "many-to-many")
mapdata <- left_join(mapdata, map_df, by="region")
map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
geom_polygon(aes(fill = compound_interest), color = "black") +
scale_fill_gradient(name="compound interest", palette(colorRamps::matlab.like), na.value = "grey50")
map1
mapdata <- left_join(mapdata, map_df, by="region")
mapdata
mapdata <- mapdata %>% filter(!is.na(mapdata$compound_interest))
View(mapdata)
mapdata <- mapdata %>% filter(!is.na(mapdata$compound_interest.x))
map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
geom_polygon(aes(fill = compound_interest), color = "black") +
scale_fill_gradient(name="compound interest", palette(colorRamps::matlab.like), na.value = "grey50")
map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
geom_polygon(aes(fill = compound_interest.x), color = "black") +
scale_fill_gradient(name="compound interest", palette(colorRamps::matlab.like), na.value = "grey50")
map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
geom_polygon(aes(fill = compound_interest.x), color = "black")
map1
map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
geom_polygon(aes(fill = compound_interest.x), color = "black") +
scale_fill_gradient(name="compound interest", palette = matlab.like, na.value = "grey50")
map1
map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
geom_polygon(aes(fill = compound_interest.x), color = "black") +
scale_fill_gradient(name="compound interest", palette = "matlab.like", na.value = "grey50")
map1
map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
geom_polygon(aes(fill = compound_interest.x), color = "black") +
scale_fill_gradient(name="compound interest", low = "yellow", high = "red", na.value = "grey50")
map1
map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
geom_polygon(aes(fill = compound_interest.x), color = "black") +
scale_fill_gradient(name="compound interest", low = "white", high = "black", na.value = "grey50")
map1
# filter data to have both coordinates and value
geo_list <- c("Belarus", "Greece", "United Kingdom")
mapdata <- map_data("world")
mapdata <- left_join(mapdata, map_df, by="region")
mapdata <- mapdata %>% filter(!is.na(mapdata$compound_interest.x)| mapdata$region %in% geo_list)
# filter data to have both coordinates and value
geo_list <- c("Belarus", "Greece", "United Kingdom")
mapdata <- map_data("world")
mapdata <- left_join(mapdata, map_df, by="region")
mapdata <- mapdata %>% filter(!is.na(mapdata$compound_interest.x)| mapdata$region %in% geo_list)
mapdata <- map_data("world")
mapdata1 <- left_join(mapdata, map_df, by="region")
mapdata2 <- mapdata1 %>% filter(!is.na(mapdata1$compound_interest.x)| mapdata1$region %in% geo_list)
mapdata1
mapdata2 <- mapdata1 %>% filter(!is.na(mapdata1$compound_interest)| mapdata1$region %in% geo_list)
mapdata <- map_data("world")
mapdata1 <- left_join(mapdata, map_df, by="region")
mapdata2 <- mapdata1 %>% filter(!is.na(mapdata1$compound_interest)| mapdata1$region %in% geo_list)
map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
geom_polygon(aes(fill = compound_interest), color = "black") +
scale_fill_gradient(name="compound interest", low = "white", high = "black", na.value = "grey50")
map1
mapdata2
mapdata2
mapdata2
View(mapdata2)
mapdata2 <- mapdata1 %>% filter(mapdata1$region %in% geo_list)
#!is.na(mapdata1$compound_interest)|
mapdata2
View(mapdata2)
mapdata2 <- mapdata1 %>% filter(!is.na(mapdata1$compound_interest)| mapdata1$region %in% geo_list)
mapdata2
map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
geom_polygon(aes(fill = compound_interest), color = "black") +
scale_fill_gradient(name="compound interest", low = "white", high = "black", na.value = "grey50")
map1
map1 <- ggplot(mapdata2, aes(x = long, y = lat, group = group)) +
geom_polygon(aes(fill = compound_interest), color = "black") +
scale_fill_gradient(name="compound interest", low = "white", high = "black", na.value = "grey50")
map1
view(mapdata)
map1 <- ggplot(mapdata2, aes(x = long, y = lat, group = group)) +
geom_polygon(aes(fill = compound_interest), color = "black") +
scale_fill_gradient(name="compound interest", low = "white", high = "black", na.value = "yellow")
map1
# filter data to have both coordinates and value
geo_list <- c("Belarus", "Greece", "England")
mapdata <- map_data("world")
mapdata1 <- left_join(mapdata, map_df, by="region")
mapdata2 <- mapdata1 %>% filter(!is.na(mapdata1$compound_interest)| mapdata1$region %in% geo_list)
mapdata2
map1 <- ggplot(mapdata2, aes(x = long, y = lat, group = group)) +
geom_polygon(aes(fill = compound_interest), color = "black") +
scale_fill_gradient(name="compound interest", low = "white", high = "black", na.value = "yellow")
map1
# filter data to have both coordinates and value
geo_list <- c("Belarus", "Greece", "Latvia", "Albania",
"Switzerland", "Bosnia and Herzegowina", "Ukraine", "Scotland")
mapdata <- map_data("world")
mapdata1 <- left_join(mapdata, map_df, by="region")
mapdata2 <- mapdata1 %>% filter(!is.na(mapdata1$compound_interest)| mapdata1$region %in% geo_list)
mapdata2
map1 <- ggplot(mapdata2, aes(x = long, y = lat, group = group)) +
geom_polygon(aes(fill = compound_interest), color = "black") +
scale_fill_gradient(name="compound interest", low = "white", high = "black", na.value = "yellow")
map1
# filter data to have both coordinates and value
geo_list <- c("Belarus", "Greece", "Latvia", "Albania",
"Switzerland", "Bosnia and Herzegowina", "Ukraine", "Scotland", "Turkey")
mapdata <- map_data("world")
mapdata1 <- left_join(mapdata, map_df, by="region")
mapdata1 <- left_join(mapdata, map_df, by="region", relationship = "many-to-many")
mapdata2 <- mapdata1 %>% filter(!is.na(mapdata1$compound_interest)| mapdata1$region %in% geo_list)
mapdata2
map1 <- ggplot(mapdata2, aes(x = long, y = lat, group = group)) +
geom_polygon(aes(fill = compound_interest), color = "black") +
scale_fill_gradient(name="compound interest", low = "white", high = "black", na.value = "yellow")
map1
# filter data to have both coordinates and value
geo_list <- c("Belarus", "Greece", "Latvia", "Albania",
"Switzerland", "Bosnia and Herzegowina", "Ukraine",
"UK", "Turkey", "Serbia", "Macedonia", "North Macedonia")
mapdata <- map_data("world")
mapdata1 <- left_join(mapdata, map_df, by="region", relationship = "many-to-many")
mapdata2 <- mapdata1 %>% filter(!is.na(mapdata1$compound_interest)| mapdata1$region %in% geo_list)
mapdata2
map1 <- ggplot(mapdata2, aes(x = long, y = lat, group = group)) +
geom_polygon(aes(fill = compound_interest), color = "black") +
scale_fill_gradient(name="compound interest", low = "white", high = "black", na.value = "yellow")
map1
# filter data to have both coordinates and value
geo_list <- c("Belarus", "Greece", "Latvia", "Albania",
"Switzerland", "Bosnia and Herzegovina", "Ukraine",
"UK", "Turkey", "Serbia", "Kosovo" "Moldova", "North Macedonia",
mapdata <- map_data("world")
mapdata1 <- left_join(mapdata, map_df, by="region", relationship = "many-to-many")
mapdata2 <- mapdata1 %>% filter(!is.na(mapdata1$compound_interest)| mapdata1$region %in% geo_list)
mapdata2
map1 <- ggplot(mapdata2, aes(x = long, y = lat, group = group)) +
geom_polygon(aes(fill = compound_interest), color = "black") +
scale_fill_gradient(name="compound interest", low = "white", high = "black", na.value = "yellow")
map1
# filter data to have both coordinates and value
geo_list <- c("Belarus", "Greece", "Latvia", "Albania",
"Switzerland", "Bosnia and Herzegovina", "Ukraine",
"UK", "Turkey", "Serbia", "Kosovo", "Moldova", "North Macedonia",
"Montenegro")
mapdata <- map_data("world")
mapdata1 <- left_join(mapdata, map_df, by="region", relationship = "many-to-many")
mapdata2 <- mapdata1 %>% filter(!is.na(mapdata1$compound_interest)| mapdata1$region %in% geo_list)
mapdata2
map1 <- ggplot(mapdata2, aes(x = long, y = lat, group = group)) +
geom_polygon(aes(fill = compound_interest), color = "black") +
scale_fill_gradient(name="compound interest", low = "white", high = "black", na.value = "yellow")
map1
# filter data to have both coordinates and value
geo_list <- c("Belarus", "Greece", "Latvia", "Albania",
"Switzerland", "Bosnia and Herzegovina", "Ukraine",
"UK", "Turkey", "Serbia", "Kosovo", "Moldova", "North Macedonia",
"Montenegro", "cyprus", "Malta")
mapdata <- map_data("world")
mapdata1 <- left_join(mapdata, map_df, by="region", relationship = "many-to-many")
mapdata2 <- mapdata1 %>% filter(!is.na(mapdata1$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