analysis.R

###  log icin degiskiliiikkk

library(dplyr)
library(ggplot2)
library(purrr)
library(tidyr)

#Let's load the datasets:

commune_level_data <- read.csv("datasets/commune_level_data.csv")
country_level_data <- read.csv("datasets/country_level_data.csv")

#Let's compute the Laspeyeres index for each commune:

commune_level_data <- commune_level_data %>%
  group_by(locality) %>%
  mutate(p0 = ifelse(year == "2010", average_price_nominal_euros, NA)) %>%
  fill(p0, .direction = "down") %>%
  mutate(p0_m2 = ifelse(year == "2010", average_price_m2_nominal_euros, NA)) %>%
  fill(p0_m2, .direction = "down") %>%
  ungroup() %>%
  mutate(pl = average_price_nominal_euros/p0*100,
         pl_m2 = average_price_m2_nominal_euros/p0_m2*100)


#Let's also compute it for the whole country:

country_level_data <- country_level_data %>%
  mutate(p0 = ifelse(year == "2010", average_price_nominal_euros, NA)) %>%
  fill(p0, .direction = "down") %>%
  mutate(p0_m2 = ifelse(year == "2010", average_price_m2_nominal_euros, NA)) %>%
  fill(p0_m2, .direction = "down") %>%
  mutate(pl = average_price_nominal_euros/p0*100,
         pl_m2 = average_price_m2_nominal_euros/p0_m2*100)


#We are going to create a plot for 5 communes and compare the price evolution in the communes
#to the national price evolution. Let's first list the communes:

communes <- c("Luxembourg",
              "Esch-sur-Alzette",
              "Mamer",
              "Schengen",
              "Wincrange")

# Luxembourg

filtered_data <- commune_level_data %>%
  filter(locality == communes[1])

data_to_plot <- bind_rows(
  country_level_data,
  filtered_data
)

lux_plot <- ggplot(data_to_plot) +
  geom_line(aes(y = pl_m2,
                x = year,
                group = locality,
                colour = locality))


# Esch sur Alzette

filtered_data <- commune_level_data %>%
  filter(locality == communes[2])

data_to_plot <- bind_rows(
  country_level_data,
  filtered_data
)

esch_plot <- ggplot(data_to_plot) +
  geom_line(aes(y = pl_m2,
                x = year,
                group = locality,
                colour = locality))

# Mamer

filtered_data <- commune_level_data %>%
  filter(locality == communes[3])

data_to_plot <- bind_rows(
  country_level_data,
  filtered_data
)

mamer_plot <- ggplot(data_to_plot) +
  geom_line(aes(y = pl_m2,
                x = year,
                group = locality,
                colour = locality))

# Schengen

filtered_data <- commune_level_data %>%
  filter(locality == communes[4])

data_to_plot <- bind_rows(
  country_level_data,
  filtered_data
)

schengen_plot <- ggplot(data_to_plot) +
  geom_line(aes(y = pl_m2,
                x = year,
                group = locality,
                colour = locality))

# Wincrange

filtered_data <- commune_level_data %>%
  filter(locality == communes[5])

data_to_plot <- bind_rows(
  country_level_data,
  filtered_data
)

wincrange_plot <- ggplot(data_to_plot) +
  geom_line(aes(y = pl_m2,
                x = year,
                group = locality,
                colour = locality))

# Let's save the plots
ggsave("plots/lux_plot.pdf", lux_plot)
ggsave("plots/esch_plot.pdf", esch_plot)
ggsave("plots/mamer_plot.pdf", mamer_plot)
ggsave("plots/schengen_plot.pdf", schengen_plot)
ggsave("plots/wincrange_plot.pdf", wincrange_plot)