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This package provides the data used in the Health Metrics and the Spread of Infectious Diseases with R book
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Fgazzelloni/hmsidwR
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The goal of{hmsidwR} is to provide the set of data used in theHealth Metrics and the Spread of Infectious Diseases Machine LearningApplications and Spatial Modelling Analysis book. It also provides aset of functions to download data such asgetunz(), andgbd_get_data() which allows the user to download data for the IHMESDG-API. With thetheme_hmsid() is possible a customization of theggplot2 theme, thestring_search() function scan all folders and filesto find a specific string. And, thekbfit() function fits a variogrammodel and then a set of kriging models to spatial data to select thebest model based on metrics.
install.packages("hmsidwR")You can install the development version ofhmsidwR, which might bealways a bit more updated than the CRAN version, fromGitHub 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 0.534
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
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This package provides the data used in the Health Metrics and the Spread of Infectious Diseases with R book
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