covid_chart_italia.R

library(tidyverse)

df <- read_csv("ItalianCovidData/Daily Covis19 Italian Data Cumulative") %>%
  select(1:5) %>%
  gather(type, value, -Date, -Region) %>%
  group_by(Date, Region)

total <- df %>%
  dplyr::filter(Date == max(df$Date)) %>%
  group_by(
    Region
  ) %>%
  summarise(
    total = sum(value)
  ) %>%
  arrange(desc(total))

df_sel <- df %>%
  ungroup() %>%
  mutate(region_label = as.factor(Region) %>%
    fct_other(keep = c("Lombardia", "Emilia Romagna", "Veneto"), other_level = "resto d'Italia") %>%
    fct_relevel("resto d'Italia", "Veneto", "Emilia Romagna", "Lombardia")) %>%
  ungroup() %>%
  group_by(Date, Regione = region_label, type) %>%
  summarise(value = sum(value)) %>%
  mutate(
    type_italian = case_when(
      type == "Home Isolation" ~ "Isolamento domestico",
      type == "Hospitalised" ~ "In ospedale (esclusi T.I.)",
      type == "In ICU" ~ "In terapia intensiva (T.I.)"
    )
  )

g <- ggplot(df_sel, aes(x = Date, y = value, fill = Regione)) +
  geom_bar(stat = "identity") +
  facet_wrap(~type_italian, scales = "free", nrow = 1) +
  theme_light() +
  scale_fill_brewer(palette = "Set2") +
  labs(
    x = "", y = "Casi", title = "Italia Covid19",
    subtitle = "Dati Protezione Civile aggiornati al 3 Marzo 2020 ore 18:00",
    caption = "Fonte dati: https://github.com/DavideMagno/ItalianCovidData"
  ) +
  theme(
    strip.background = element_rect(fill = "grey30"),
    axis.text.x = element_text(angle = 45, hjust = 1)
  )
ggsave("situazione_italiana.png", width = 8, height = 6)