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Paweł Lewicki 2023-05-30 14:42:23 +02:00
parent e9dc172402
commit 9cd7ec6d23
2 changed files with 183 additions and 181 deletions

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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]')

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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