3.1 Get the data

Dataset: Global Burden of Disease (year, cause, sex, income, deaths)

The Global Burden of Disease dataset used in this chapter is more detailed than the one we used previously. For each year, the total number of deaths from the three broad disease categories are also separated into sex and World Bank income categories. This means that we have 24 rows for each year, and that the total number of deaths per year is the sum of these 24 rows:

library(tidyverse)
gbd_full <- read_csv("data/global_burden_disease_cause-year-sex-income.csv")

# Creating a single-year tibble for printing and simple examples:
gbd2017 <- gbd_full %>% 
  filter(year == 2017)
TABLE 3.1: Deaths per year (2017) from three broad disease categories, sex, and World Bank country-level income groups.
cause year sex income deaths_millions
Communicable diseases 2017 Female High 0.26
Communicable diseases 2017 Female Upper-Middle 0.55
Communicable diseases 2017 Female Lower-Middle 2.92
Communicable diseases 2017 Female Low 1.18
Communicable diseases 2017 Male High 0.29
Communicable diseases 2017 Male Upper-Middle 0.73
Communicable diseases 2017 Male Lower-Middle 3.10
Communicable diseases 2017 Male Low 1.35
Injuries 2017 Female High 0.21
Injuries 2017 Female Upper-Middle 0.43
Injuries 2017 Female Lower-Middle 0.66
Injuries 2017 Female Low 0.12
Injuries 2017 Male High 0.40
Injuries 2017 Male Upper-Middle 1.16
Injuries 2017 Male Lower-Middle 1.23
Injuries 2017 Male Low 0.26
Non-communicable diseases 2017 Female High 4.68
Non-communicable diseases 2017 Female Upper-Middle 7.28
Non-communicable diseases 2017 Female Lower-Middle 6.27
Non-communicable diseases 2017 Female Low 0.92
Non-communicable diseases 2017 Male High 4.65
Non-communicable diseases 2017 Male Upper-Middle 8.79
Non-communicable diseases 2017 Male Lower-Middle 7.30
Non-communicable diseases 2017 Male Low 1.00