The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

Welcome to hmsidwR!

Setup

# install.packages("devtools")
devtools::install_github("Fgazzelloni/hmsidwR")
library(hmsidwR)

This package provides the set of data used in the Health Metrics and the Spread of Infectious Diseases Machine Learning Applications and Spatial Modeling Analysis book.

Load Sample Data

hmsidwR::sdi90_19 |>
  head()
#> # 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
hmsidwR::deaths2019 |>
  head()
#> # A tibble: 6 × 7
#>   location sex    age   cause                           dx upper  lower
#>   <chr>    <chr>  <ord> <chr>                        <dbl> <dbl>  <dbl>
#> 1 UK       male   <1    Lower respiratory infections 39.7  51.0  29.5  
#> 2 UK       female <1    Lower respiratory infections 30.0  38.0  22.6  
#> 3 UK       both   <1    Lower respiratory infections 69.7  88.3  53.3  
#> 4 UK       male   <1    Stroke                        1.33  2.41  0.850
#> 5 UK       female <1    Stroke                        1.04  1.84  0.669
#> 6 UK       both   <1    Stroke                        2.38  4.21  1.55

Make a Plot

library(tidyverse)
id <- hmsidwR::id_affected_countries %>%
  ggplot(aes(
    x = year,
    group = location_name
  )) +
  geom_line(aes(y = YLLs),
    linewidth = 0.2,
    color = "grey"
  ) +
  geom_line(
    data = id_affected_countries %>%
      filter(location_name %in% c(
        "Lesotho",
        "Eswatini",
        "Malawi",
        "Central African Republic",
        "Zambia"
      )),
    aes(y = YLLs, color = location_name)
  ) +
  theme_minimal() +
  theme(legend.position = "none") +
  labs(
    title = "Countries with highest AVG YLLs",
    subtitle = "due to infectious diseases from 1990 to 2021",
    caption = "DataSource: IHME GBD Results for infectious diseases deaths and YLLs 1980 to 1999",
    x = "Year", y = "DALYs"
  )
# add a plotly version
library(plotly)
plotly::ggplotly(id)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.