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.
install.packages("hmsidwR")You can install the development version of hmsidwR from GitHub with:
# install.packages("devtools")
devtools::install_github("Fgazzelloni/hmsidwR")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 0.534sdi_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 0.792sdi90_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
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