Upload files to ''
This commit is contained in:
parent
9cd7ec6d23
commit
9d98bb75f1
@ -1,3 +1,148 @@
|
||||
<<<<<<< HEAD
|
||||
library(dplyr)
|
||||
# install.packages("lifecycle")
|
||||
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)
|
||||
@ -141,3 +286,4 @@ ggplot(merged_df, aes(x = TIME_PERIOD, y = house_prices_wo_hicp, color = geo)) +
|
||||
scale_x_continuous(breaks=seq(2010,2024,2)) +
|
||||
labs(x = "Year", y = 'Indeks cen nieruchomości zdyskontowany o wartość inflacji [2015 = 100]')
|
||||
|
||||
>>>>>>> 9cd7ec6d23058242fa1b48bf76dd80c927b5ef48
|
||||
|
@ -1,3 +1,46 @@
|
||||
<<<<<<< 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")
|
||||
@ -38,3 +81,4 @@ map1 <- ggplot(mapdata2, aes(x = long, y = lat, group = group)) +
|
||||
labs(title = "World Map with Compound Interest") # Set plot title
|
||||
map1
|
||||
|
||||
>>>>>>> 9cd7ec6d23058242fa1b48bf76dd80c927b5ef48
|
||||
|
62
projekt_2_second attempt.R
Normal file
62
projekt_2_second attempt.R
Normal file
@ -0,0 +1,62 @@
|
||||
library(tidyverse)
|
||||
library(eurostat)
|
||||
# install.packages("ggthemes")
|
||||
library(leaflet)
|
||||
library(sf)
|
||||
library(scales)
|
||||
library(cowplot)
|
||||
library(ggthemes)
|
||||
library(RColorBrewer)
|
||||
|
||||
map_df = read.csv(".//data//compound_interest_housing.csv")
|
||||
map_df
|
||||
|
||||
SHP_0 <- get_eurostat_geospatial(resolution = 10,
|
||||
nuts_level = 0,
|
||||
year = 2016)
|
||||
SHP_0 %>%
|
||||
ggplot() +
|
||||
geom_sf()
|
||||
|
||||
EU28 <- eu_countries %>%
|
||||
select(geo = code, name)
|
||||
|
||||
SHP_28 <- SHP_0 %>%
|
||||
select(geo = NUTS_ID, geometry) %>%
|
||||
inner_join(EU28, by = "geo") %>%
|
||||
arrange(geo) %>%
|
||||
st_as_sf()
|
||||
|
||||
SHP_28 %>%
|
||||
ggplot() +
|
||||
geom_sf() +
|
||||
scale_x_continuous(limits = c(-10, 35)) +
|
||||
scale_y_continuous(limits = c(35, 65))
|
||||
|
||||
mapdata_new <- left_join(SHP_28, map_df, by="geo", relationship = "many-to-many")
|
||||
|
||||
|
||||
|
||||
# Create a continuous palette function
|
||||
pal <- colorQuantile("Blues", mapdata_new$substr_house_prices_wo_hicp, n = 9)
|
||||
|
||||
popup_content <- paste0( "Country_information_box: "
|
||||
, mapdata_new$name
|
||||
, "<br>"
|
||||
, "<a href='"
|
||||
, mapdata_new$name
|
||||
, "' target='_blank'>"
|
||||
, "Click Here</a>")
|
||||
|
||||
leaflet() %>%
|
||||
addProviderTiles(providers$CartoDB.Positron) %>%
|
||||
addPolygons(data=mapdata_new,
|
||||
fillOpacity = 0.6, # Przezroczystość
|
||||
stroke = TRUE, # Borders visible
|
||||
color = "grey", # Border color
|
||||
weight = 1,
|
||||
|
||||
fillColor = ~pal(mapdata_new$substr_house_prices_wo_hicp),
|
||||
popup = popup_content,
|
||||
popupOptions = popupOptions(maxWidth ="600px", closeOnClick = TRUE),
|
||||
)
|
Loading…
Reference in New Issue
Block a user