513 lines
22 KiB
R
513 lines
22 KiB
R
colnames(df3)
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print(df3)
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year_country_gdp <- df3 %>% select( TIME_PERIOD, geo, OBS_VALUE)
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year_country_gdp <- na.omit(year_country_gdp)
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colnames(year_country_gdp)
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df3 %>% group_by(geo) %>% str()
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str(year_country_gdp)
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# Plot
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ggplot(year_country_gdp, aes(x = TIME_PERIOD, y = OBS_VALUE, color = geo, label = geo)) +
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geom_line() +
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geom_point() +
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geom_text(aes(label = geo), hjust = -0.1, size = 3)+
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labs(x = "Rok", y = 'PKB per capita w PPP [PPS_EU27_2020=100]') +
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scale_x_continuous(breaks=seq(2010,2024,2))
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year_country_gdp
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# ##################################################
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# House price index HPI
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df1
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house_price_index <- df1 %>% select( TIME_PERIOD, geo, OBS_VALUE)
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house_price_index <- na.omit(house_price_index)
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colnames(house_price_index)
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df1 %>% group_by(geo) %>% str()
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str(house_price_index)
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# Plot
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ggplot(house_price_index, aes(x = TIME_PERIOD, y = OBS_VALUE, color = geo, label = geo)) +
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geom_line() +
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geom_point() +
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geom_text(data = house_price_index %>%
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group_by(geo) %>%
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slice(n() - 1),
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aes(label = geo, hjust = -1, size = 4))+
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scale_x_continuous(breaks=seq(2010,2024,2))
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labs(x = "Rok", y = 'Indeks Cen nieruchomości [cena z 2015 roku = 100]')
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house_price_index
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# HICP - Harmonised Index for Consumer Prices
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df
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hicp_index <- df %>% select( TIME_PERIOD, geo, OBS_VALUE)
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hicp_index <- na.omit(hicp_index)
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colnames(hicp_index)
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df %>% group_by(geo) %>% str()
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str(hicp_index)
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# Plot
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ggplot(hicp_index, aes(x = TIME_PERIOD, y = OBS_VALUE, color = geo, label = geo)) +
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geom_line() +
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geom_point() +
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geom_text(data = hicp_index %>%
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group_by(geo) %>%
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slice(n()),
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aes(label = geo, hjust = -0.2, size = 4)) +
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labs(x = "Rok", y = 'Indeks inflacji konsumenckiej HICP [2015 = 100]') +
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scale_x_continuous(breaks=seq(2010,2024,2))
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hicp_index
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# Merge the two data frames using the 'country' and 'date' columns
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merged_df <- merge(house_price_index, hicp_index, by = c("geo", "TIME_PERIOD"))
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merged_df
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# Create a new column that divides 'value1' by 'value2'
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merged_df$house_prices_wo_hicp <- merged_df$OBS_VALUE.x - merged_df$OBS_VALUE.y
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merged_df$TIME_PERIOD
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merged_df$compound_growth <- 1 * (1 + 0.02) ^ (1:(merged_df$TIME_PERIOD-2015))
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# View the resulting merged data frame with the divided values
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merged_df
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merged_df <- na.omit(merged_df)
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colnames(merged_df)
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merged_df %>% group_by(geo) %>% str()
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str(merged_df)
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merged_df <- merged_df %>% filter(!geo %in% c("TR"))
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# Plot
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ggplot(merged_df, aes(x = TIME_PERIOD, y = house_prices_wo_hicp, color = geo)) +
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geom_line(linetype="dotted", size=1) +
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geom_point(aes(x=TIME_PERIOD, y=house_prices_wo_hicp)) +
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geom_text(data = merged_df %>%
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group_by(geo) %>%
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slice(n()),
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aes(label = geo, hjust = -0.2, size = 4)) +
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#stat_function(fun=function(x) 100*(1.04)^(x-2015), aes(colour = "4% Compounding")) +
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#stat_function(fun=function(x) 100*(1.07)^(x-2015), aes(colour = "7% Compounding")) +
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scale_x_continuous(breaks=seq(2010,2024,2)) +
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labs(x = "Year", y = 'Indeks cen nieruchomości zdyskontowany o wartość inflacji [2015 = 100]')
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map_df <- merged_df %>% select( geo, house_prices_wo_hicp, TIME_PERIOD)
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map_df <- filter(map_df, TIME_PERIOD == 2022)
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map_df
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merged_df
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# Plot
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ggplot(hicp_index, aes(x = TIME_PERIOD, y = OBS_VALUE, color = geo, label = geo)) +
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geom_line() +
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geom_point() +
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geom_text(data = hicp_index %>%
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group_by(geo) %>%
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slice(n()),
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aes(label = geo, hjust = -0.2, size = 4)) +
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labs(x = "Rok", y = 'Indeks inflacji konsumenckiej HICP [2015 = 100]') +
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scale_x_continuous(breaks=seq(2010,2024,2))
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write.csv(merged_df, ".//merged_df.csv")
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hicp_index
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write.csv(hicp_index, ".//hicp_index.csv")
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df = read.csv(".//data//compound_interest_housing")
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library(dplyr)
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# install.packages("ggplot2")
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library(ggplot2)
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df = read.csv(".//data//compound_interest_housing")
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df[,c("geo", "TIME_PERIOD", "OBS_VALUE")]
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library(dplyr)
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# install.packages("ggplot2")
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library(ggplot2)
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map_df = read.csv(".//data//compound_interest_housing")
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map_df = read.csv(".//data//compound_interest_housing.csv")
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map_df
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map_df = read.csv(".//data//compound_interest_housing_2.csv")
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map_df
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map_df = read.csv(".//data//compound_interest_housing_3.csv")
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map_df
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map_df = read.csv(".//data//compound_interest_housing.csv")
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map_df
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map_df = read.csv(".//data//compound_interest_housing.csv")
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map_df
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ggplot(map_df, aes(x = TIME_PERIOD, y = compound_interest, color = geo)) +
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geom_line(linetype="dotted", size=1) +
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geom_point(aes(x=TIME_PERIOD, y=compound_interest)) +
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geom_text(data = map_df %>%
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group_by(geo) %>%
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slice(n()),
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aes(label = geo, hjust = -0.2, size = 4)) +
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stat_function(fun=function(x) 100*(1.04)^(x-2015), aes(colour = "4% Compounding")) +
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stat_function(fun=function(x) 100*(1.07)^(x-2015), aes(colour = "7% Compounding")) +
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scale_x_continuous(breaks=seq(2010,2024,2)) +
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labs(x = "Year", y = 'Indeks cen nieruchomości zdyskontowany o wartość inflacji [2015 = 100]')
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ggplot(map_df, aes(x = TIME_PERIOD, y = compound_interest, color = geo)) +
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geom_line(linetype="dotted", size=1) +
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geom_point(aes(x=TIME_PERIOD, y=compound_interest)) +
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geom_text(data = map_df %>%
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group_by(geo) %>%
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slice(n()),
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aes(label = geo, hjust = -0.2, size = 4)) +
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scale_x_continuous(breaks=seq(2010,2024,2)) +
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labs(x = "Year", y = 'Indeks cen nieruchomości zdyskontowany o wartość inflacji [2015 = 100]')
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ggplot(map_df, aes(x = TIME_PERIOD, y = compound_interest, color = geo)) +
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geom_line(linetype="dotted", size=1) +
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geom_point(aes(x=TIME_PERIOD, y=compound_interest)) +
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geom_text(data = map_df %>%
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group_by(geo) %>%
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slice(n()),
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aes(label = geo, hjust = -0.2, size = 4)) +
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scale_x_continuous(breaks=seq(2010,2024,2)) +
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labs(x = "Year", y = 'wartość mieszkania jako % składany powyżej inflacji')
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ggplot(map_df, aes(x = TIME_PERIOD, y = compound_interest, color = geo)) +
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geom_point(aes(x=TIME_PERIOD, y=compound_interest)) +
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geom_text(data = map_df %>%
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group_by(geo) %>%
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slice(n()),
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aes(label = geo, hjust = -0.2, size = 4)) +
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scale_x_continuous(breaks=seq(2010,2024,2)) +
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labs(x = "Year", y = 'wartość mieszkania jako % składany powyżej inflacji')
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plot(map_df$compound_interest)
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plot(map_df$geo,map_df$compound_interest)
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barplot(map_df$geo, map_df$compound_interest)
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ggplot(map_df, aes(x = TIME_PERIOD, y = compound_interest, color = geo)) +
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geom_point(aes(x=TIME_PERIOD, y=compound_interest)) +
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geom_text(data = map_df %>%
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group_by(geo) %>%
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slice(n()),
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aes(label = geo, hjust = -0.2, size = 4)) +
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scale_x_continuous(breaks=seq(2010,2024,2)) +
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labs(x = "Year", y = 'wartość mieszkania jako % składany powyżej inflacji')
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# install.packages("tidyverse")
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library(ggplot2)
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install.packages("tidyverse")
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install.packages("tidyverse")
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library(tidyverse)
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library(tidyverse)
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install.packages("tidyverse")
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install.packages("tidyverse")
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library(tidyverse)
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install.packages("tidyverse")
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install.packages("tidyverse")
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library(tidyverse)
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# install.packages("tidyverse")
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library(ggplot2)
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view(mapdata)
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map_df = read.csv(".//data//compound_interest_housing.csv")
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map_df
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ggplot(map_df, aes(x = TIME_PERIOD, y = compound_interest, color = geo)) +
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geom_point(aes(x=TIME_PERIOD, y=compound_interest)) +
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geom_text(data = map_df %>%
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group_by(geo) %>%
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slice(n()),
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aes(label = geo, hjust = -0.2, size = 4)) +
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scale_x_continuous(breaks=seq(2010,2024,2)) +
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labs(x = "Year", y = 'wartość mieszkania jako % składany powyżej inflacji')
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view(mapdata)
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mapdata <- map_data("world")
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library(tidyverse)
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# install.packages("tidyverse")
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library(ggplot2)
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map_df = read.csv(".//data//compound_interest_housing.csv")
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map_df
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ggplot(map_df, aes(x = TIME_PERIOD, y = compound_interest, color = geo)) +
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geom_point(aes(x=TIME_PERIOD, y=compound_interest)) +
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geom_text(data = map_df %>%
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group_by(geo) %>%
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slice(n()),
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aes(label = geo, hjust = -0.2, size = 4)) +
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scale_x_continuous(breaks=seq(2010,2024,2)) +
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labs(x = "Year", y = 'wartość mieszkania jako % składany powyżej inflacji')
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mapdata <- map_data("world")
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ggplot(map_df, aes(x = TIME_PERIOD, y = compound_interest, color = geo)) +
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geom_point(aes(x=TIME_PERIOD, y=compound_interest)) +
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geom_text(data = map_df %>%
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group_by(geo),
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aes(label = geo, hjust = -0.2, size = 4)) +
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scale_x_continuous(breaks=seq(2010,2024,2)) +
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labs(x = "Year", y = 'wartość mieszkania jako % składany powyżej inflacji')
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library(dplyr)
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# install.packages("tidyverse")
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library(ggplot2)
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map_df = read.csv(".//data//compound_interest_housing.csv")
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map_df
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ggplot(map_df, aes(x = TIME_PERIOD, y = compound_interest, color = geo)) +
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geom_point(aes(x=TIME_PERIOD, y=compound_interest)) +
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geom_text(data = map_df %>%
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group_by(geo) %>%
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slice(n()),
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aes(label = geo, hjust = -0.2, size = 4)) +
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scale_x_continuous(breaks=seq(2010,2024,2)) +
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labs(x = "Year", y = 'wartość mieszkania jako % składany powyżej inflacji')
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mapdata <- map_data("world")
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view(mapdata)
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mapdata <- left_join(mapdata, map_df, by="region")
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test <- test %>% filter("Czech republic" )
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test
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test <- mapdata %>% filter("Czech republic" )
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test
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test <- mapdata %>% filter("Afghanistan" )
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test
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test <- filter(mapdata, region="Afghanistan" )
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test
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test <- filter(mapdata, region="Aruba" )
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test
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filter(mapdata, region="Aruba" )
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filter(mapdata, region="Aruba" )
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filter(mapdata, region=="Aruba" )
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filter(mapdata, region=="Czech republic" )
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filter(mapdata, region=="Czech Republic" )
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filter(mapdata, region=="Hungary
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" )
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write.csv(mapdata, "./mapdata.csv")
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map_df = read.csv(".//data//compound_interest_housing.csv")
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map_df
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ggplot(map_df, aes(x = TIME_PERIOD, y = compound_interest, color = geo)) +
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geom_point(aes(x=TIME_PERIOD, y=compound_interest)) +
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geom_text(data = map_df %>%
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group_by(geo) %>%
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slice(n()),
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aes(label = geo, hjust = -0.2, size = 4)) +
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scale_x_continuous(breaks=seq(2010,2024,2)) +
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labs(x = "Year", y = 'wartość mieszkania jako % składany powyżej inflacji')
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mapdata <- map_data("world")
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mapdata <- left_join(mapdata, map_df, by="region")
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mapdata <- left_join(mapdata, map_df, by="region", relation = "many-to-many")
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view(mapdata)
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mapdata <- mapdata %>% filter(!is.na(mapdata$compound_interest))
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view(mapdata)
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map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
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geom_polygon(aes(fill = compound_interest), color = "black")
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map1
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map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
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geom_polygon(aes(fill = compound_interest), color = "black") +
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scale_fill_gradient(name="compound interest", low="yellow", high = "red", na.value = "grey50")
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map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
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geom_polygon(aes(fill = compound_interest), color = "black") +
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scale_fill_gradient(name="compound interest", low="yellow", high = "red", na.value = "grey50")
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view(mapdata)
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map1
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map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
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geom_polygon(aes(fill = compound_interest), color = "black") +
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scale_fill_gradient(name="compound interest", low="blue", high = "yellow", na.value = "grey50")
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map1
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map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
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geom_polygon(aes(fill = compound_interest), color = "black") +
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scale_fill_gradient(name="compound interest", low="blue", high = "red", na.value = "grey50")
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map1
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map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
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geom_polygon(aes(fill = compound_interest), color = "black") +
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scale_fill_gradient(name="compound interest", palette(colorRamps::matlab.like), na.value = "grey50")
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map1
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library(colorRamps)
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install.packages("colorRamps")
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library(colorRamps)
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map_df = read.csv(".//data//compound_interest_housing.csv")
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map_df
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ggplot(map_df, aes(x = TIME_PERIOD, y = compound_interest, color = geo)) +
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geom_point(aes(x=TIME_PERIOD, y=compound_interest)) +
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geom_text(data = map_df %>%
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group_by(geo) %>%
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slice(n()),
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aes(label = geo, hjust = -0.2, size = 4)) +
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scale_x_continuous(breaks=seq(2010,2024,2)) +
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labs(x = "Year", y = 'wartość mieszkania jako % składany powyżej inflacji')
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# filter data to have both coordinates and value
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mapdata <- map_data("world")
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mapdata <- left_join(mapdata, map_df, by="region", relation = "many-to-many")
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# filter data to have both coordinates and value
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mapdata <- map_data("world")
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mapdata <- left_join(mapdata, map_df, by="region", relation = "many-to-many")
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mapdata <- mapdata %>% filter(!is.na(mapdata$compound_interest))
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map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
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geom_polygon(aes(fill = compound_interest), color = "black") +
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scale_fill_gradient(name="compound interest", palette(colorRamps::matlab.like), na.value = "grey50")
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map1
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view(mapdata)
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library(dplyr)
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library(tidyverse)
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# install.packages("colorRamps")
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library(ggplot2)
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library(colorRamps)
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map_df = read.csv(".//data//compound_interest_housing.csv")
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map_df
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ggplot(map_df, aes(x = TIME_PERIOD, y = compound_interest, color = geo)) +
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geom_point(aes(x=TIME_PERIOD, y=compound_interest)) +
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geom_text(data = map_df %>%
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group_by(geo) %>%
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slice(n()),
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aes(label = geo, hjust = -0.2, size = 4)) +
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scale_x_continuous(breaks=seq(2010,2024,2)) +
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labs(x = "Year", y = 'wartość mieszkania jako % składany powyżej inflacji')
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# filter data to have both coordinates and value
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mapdata <- map_data("world")
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mapdata <- left_join(mapdata, map_df, by="region", relation = "many-to-many")
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mapdata <- left_join(mapdata, map_df, by="region")
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, relation = "many-to-many"
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mapdata <- left_join(mapdata, map_df, by="region", relation = "many-to-many")
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map1
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library(dplyr)
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library(tidyverse)
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# install.packages("colorRamps")
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library(ggplot2)
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library(colorRamps)
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map_df = read.csv(".//data//compound_interest_housing.csv")
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map_df
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ggplot(map_df, aes(x = TIME_PERIOD, y = compound_interest, color = geo)) +
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geom_point(aes(x=TIME_PERIOD, y=compound_interest)) +
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geom_text(data = map_df %>%
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group_by(geo) %>%
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slice(n()),
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aes(label = geo, hjust = -0.2, size = 4)) +
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scale_x_continuous(breaks=seq(2010,2024,2)) +
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labs(x = "Year", y = 'wartość mieszkania jako % składany powyżej inflacji')
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# filter data to have both coordinates and value
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mapdata <- map_data("world")
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mapdata <- left_join(mapdata, map_df, by="region", relation = "many-to-many")
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mapdata <- left_join(mapdata, map_df, by="region")
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map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
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geom_polygon(aes(fill = compound_interest), color = "black") +
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scale_fill_gradient(name="compound interest", palette(colorRamps::matlab.like), na.value = "grey50")
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map1
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mapdata <- left_join(mapdata, map_df, by="region")
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mapdata
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mapdata <- mapdata %>% filter(!is.na(mapdata$compound_interest))
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View(mapdata)
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mapdata <- mapdata %>% filter(!is.na(mapdata$compound_interest.x))
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map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
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geom_polygon(aes(fill = compound_interest), color = "black") +
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scale_fill_gradient(name="compound interest", palette(colorRamps::matlab.like), na.value = "grey50")
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map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
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geom_polygon(aes(fill = compound_interest.x), color = "black") +
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scale_fill_gradient(name="compound interest", palette(colorRamps::matlab.like), na.value = "grey50")
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map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
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geom_polygon(aes(fill = compound_interest.x), color = "black")
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|
map1
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map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
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geom_polygon(aes(fill = compound_interest.x), color = "black") +
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|
scale_fill_gradient(name="compound interest", palette = matlab.like, na.value = "grey50")
|
|
map1
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map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
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geom_polygon(aes(fill = compound_interest.x), color = "black") +
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|
scale_fill_gradient(name="compound interest", palette = "matlab.like", na.value = "grey50")
|
|
map1
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map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
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geom_polygon(aes(fill = compound_interest.x), color = "black") +
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|
scale_fill_gradient(name="compound interest", low = "yellow", high = "red", na.value = "grey50")
|
|
map1
|
|
map1 <- ggplot(mapdata, aes(x = long, y = lat, group = group)) +
|
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geom_polygon(aes(fill = compound_interest.x), color = "black") +
|
|
scale_fill_gradient(name="compound interest", low = "white", high = "black", na.value = "grey50")
|
|
map1
|
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# filter data to have both coordinates and value
|
|
geo_list <- c("Belarus", "Greece", "United Kingdom")
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mapdata <- map_data("world")
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mapdata <- left_join(mapdata, map_df, by="region")
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|
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
|