Excess mortality

The tweet

pacman::p_load(tidyverse, janitor, here)

The dataset

nordic <- read_csv("HEALTH_MORTALITY_22122022103454710.csv")

Source: https://stats.oecd.org/index.aspx?queryid=104676# Query: Dec 22, 2022, 9.54 UK Time

head(nordic) %>% 
  knitr::kable()

COUNTRY	Country	WEEK	Week number	GENDER	Gender	AGE	Age	VARIABLE	Variable	YEAR	Year	Value	Flag Codes	Flags
SWE	Sweden	37	37	FEMALE	Females	Y0T44	0 to 44	EXCESSNB	Excess deaths (number)	2020	2020	3.4	NA	NA
SWE	Sweden	37	37	FEMALE	Females	Y0T44	0 to 44	EXCESSNB	Excess deaths (number)	2021	2021	1.4	NA	NA
SWE	Sweden	37	37	FEMALE	Females	Y0T44	0 to 44	EXCESSNB	Excess deaths (number)	2022	2022	-1.6	NA	NA
FIN	Finland	17	17	FEMALE	Females	Y_GE65	65 and over	EXCESSNB	Excess deaths (number)	2020	2020	115.8	NA	NA
FIN	Finland	17	17	FEMALE	Females	Y_GE65	65 and over	EXCESSNB	Excess deaths (number)	2021	2021	-6.2	NA	NA
FIN	Finland	17	17	FEMALE	Females	Y_GE65	65 and over	EXCESSNB	Excess deaths (number)	2022	2022	99.8	NA	NA

Select only relevant variables

nordic <- nordic %>% 
  select(Country, `Week number`, Gender, Age, Variable, Year, Value)

Plot

Plot the excess deaths

nordic %>%
  filter(Gender == "Total" &
           Variable == "Excess deaths (% change from average)" &
           Year == "2022") %>%
  ggplot(aes(
    x = `Week number`,
    y = Value,
    color = Country,
    group = Country
  ))   +
  geom_line() +
  # ylim(-30, 30) + 
  coord_cartesian(ylim = c(-30, 30)) + 
  # scale_y_continuous(limits = c(-30, 30)) + 
  facet_grid(Country ~ Age  ) +
  theme_minimal() + 
  geom_hline(yintercept = 0, color = "dark grey") + 
  labs(title = "Excess deaths (% change from average)", 
       subtitle = "Source: OECD, https://stats.oecd.org/index.aspx?queryid=104676#", 
       y = "%")