death_rate_variation.R

library(tidyverse)
library(readxl)
library(janitor)
library(ggridges)

# let's download the table
download.file("https://www.istat.it/it/files//2020/03/Tavola-sintetica-decessi.xlsx",
              "Tavola-sintetica-decessi.xlsx")

# we prefer to work with clean names
deaths <- read_excel("Tavola-sintetica-decessi.xlsx",
  skip = 1) %>%
  clean_names() %>%
  rename(m_2019 = m_7,f_2019 = f_8,tot_2019 = m_f_9,
         m_2020 = m_10,f_2020 = f_11,tot_2020 = m_f_12)

# we define a pipe to calculate the change from 2019 to 2020
get_tots <- . %>%
  summarise(m_tot_2019 = sum(m_2019),
            f_tot_2019 = sum(f_2019),
            tot_tot_2019 = sum(tot_2019),
            m_tot_2020 = sum(m_2020),
            f_tot_2020 = sum(f_2020),
            tot_tot_2020 = sum(tot_2020))
            
# we define a pipe to calculate the total deaths
get_changes <- . %>%
  mutate(m_change = m_tot_2020 / m_tot_2019,
         f_change = f_tot_2020 / f_tot_2019,
         tot_change = tot_tot_2020 / tot_tot_2019) %>%
  select(starts_with("nome"), ends_with("change"))

# we create three ridges plot (one for municipalities, one for provincces, one for regions)

comune <- deaths %>%
  mutate(m_change = m_2020 / m_2019,
         f_change = f_2020 / f_2019,
         tot_change = tot_2020 / tot_2019) %>%
  select(starts_with("nome"), ends_with("change")) %>%
  pivot_longer(ends_with("change"), names_to = "Categoria", values_to = "Variazione") %>%
  filter(Variazione > 0, Variazione < Inf) %>%
  ggplot(aes(x = Variazione, y = Categoria)) +
  geom_density_ridges() +
  scale_x_log10() +
  theme_minimal() +
  labs(subtitle = "Per Comune",
       y = "Quanti comuni?",
       x = "Variazione")

provincia <- deaths_by_province %>%
  get_changes() %>%
  pivot_longer(ends_with("change"), names_to = "Categoria", values_to = "Variazione") %>%
  filter(Variazione < 100) %>%
  ggplot(aes(x = Variazione, y = Categoria)) +
  scale_x_log10() +
  geom_density_ridges() +
  theme_minimal() +
  labs(subtitle = "Per Provincia",
       y = "Quante provincie?",
       x = "Variazione")

regione <- deaths_by_region %>%
  get_changes() %>%
  pivot_longer(ends_with("change"), names_to = "Categoria", values_to = "Variazione") %>%
  filter(Variazione < 100) %>%
  ggplot(aes(x = Variazione, y = Categoria)) +
  scale_x_log10() +
  geom_density_ridges() +
  theme_minimal() +
  labs(subtitle = "Per Regione",
       y = "Quante regioni?",
       x = "Variazione")

# and we plot them together
library(patchwork)
(comune / provincia / regione)