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. 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