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 Modelling Analysis book.

Link to the online version of the Book: https://fgazzelloni.quarto.pub/hmsidr/

It also provides a set of functions to download data such as getunz(), and gbd_get_data() which allows the user to download data for the IHME SDG-API. With the theme_hmsid() is possible a customization of the ggplot2 theme, the string_search() function scan all folders and files to find a specific string. And, the kbfit() function fits a variogram models and then a set of kriging models to spatial data to select the best model based on metrics.

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