Description
Health Metrics and the Spread of Infectious Diseases.
Description
A collection of datasets and supporting functions accompanying Health Metrics and the Spread of Infectious Diseases by Federica Gazzelloni (2024). This package provides data for health metrics calculations, including Disability-Adjusted Life Years (DALYs), Years of Life Lost (YLLs), and Years Lived with Disability (YLDs), as well as additional tools for analyzing and visualizing health data. Federica Gazzelloni (2024) <doi:10.5281/zenodo.10818338>.
README.md
hmsidwR - Health Metrics and the Spread of Infectious Diseases
The goal of {hmsidwR} is to provide the set of data used in the Health Metrics and the Spread of Infectious Diseases Machine Learning Applications and Spatial Modeling Analysis book.
Installation
install.packages("hmsidwR")
You can install the development version of hmsidwR from GitHub with:
# install.packages("devtools")
devtools::install_github("Fgazzelloni/hmsidwR")
Example
This is a basic example which shows you how to solve a common problem:
library(hmsidwR)
library(dplyr)
data(sdi90_19)
head(subset(sdi90_19, location == "Global"))
#> # A tibble: 6 × 3
#> location year value
#> <chr> <dbl> <dbl>
#> 1 Global 1990 0.511
#> 2 Global 1991 0.516
#> 3 Global 1992 0.521
#> 4 Global 1993 0.525
#> 5 Global 1994 0.529
#> 6 Global 1995 0.534
sdi_avg <- sdi90_19 |>
group_by(location) |>
reframe(sdi_avg = round(mean(value), 3))
head(sdi_avg)
#> # A tibble: 6 × 2
#> location sdi_avg
#> <chr> <dbl>
#> 1 Aceh 0.58
#> 2 Acre 0.465
#> 3 Afghanistan 0.238
#> 4 Aguascalientes 0.606
#> 5 Aichi 0.846
#> 6 Akita 0.792
sdi90_19 |>
filter(location %in% c("Global", "Italy", "France", "Germany")) |>
group_by(location) |>
reframe(sdi_avg = round(mean(value), 3)) |>
head()
#> # A tibble: 4 × 2
#> location sdi_avg
#> <chr> <dbl>
#> 1 France 0.79
#> 2 Germany 0.863
#> 3 Global 0.58
#> 4 Italy 0.763