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 with R 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
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent