Mortalité infantile par département et région - p2d_2019.fr

Data - INED

year

p2d_2019.fr %>%
  group_by(year) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

year	Nobs
1994	762
1995	762
1996	762
1997	762
1998	762
1999	762
2000	762
2001	762
2002	762
2003	762
2004	762
2005	762
2006	762
2007	762
2008	762
2009	762
2010	762
2011	762
2012	762
2013	762
1990	726
1991	726
1992	726
1993	726
2014	684
2015	684
2016	684
2017	684
2018	684
2019	684

dep_region

p2d_2019.fr %>%
  group_by(dep_region) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

variable

p2d_2019.fr %>%
  group_by(variable) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

variable	Nobs
deces_moins_de_28_jours	3708
deces_moins_de_7_jours	3708
deces_moins_un_an	3708
mortalite_infantile	3708
mortalite_neonatale	3708
nes_vivants	3708

Hauts de Seine, Paris, Seine-Saint-Denis

p2d_2019.fr %>%
  year_to_date2 %>%
  filter(variable %in% c("deces_moins_de_7_jours", "nes_vivants"),
         dep_region %in% c("Hauts-de-Seine", "Paris", "Seine-Saint-Denis")) %>%
  spread(variable, value) %>%
  mutate(value = 1000*deces_moins_de_7_jours/nes_vivants) %>%
  ggplot + geom_line(aes(x = date, y = value, color = dep_region)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  xlab("") + ylab("") + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%y")) +
  theme(legend.position = c(0.8, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0, 100, 0.2),
                     labels = dollar_format(a = .1, pre = "", su = "‰"))