Setup
# install.packages("devtools")
devtools::install_github("Fgazzelloni/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 with R book.
Load Sample Data
hmsidwR::sdi90_19 |>
head()
#> location year value
#> 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()
#> location sex age cause dx upper
#> 1 France male 10-14 Tracheal, bronchus, and lung cancer 0.3214538 0.4524740
#> 2 France female 10-14 Tracheal, bronchus, and lung cancer 0.4186573 0.7101662
#> 3 France both 10-14 Tracheal, bronchus, and lung cancer 0.7401111 1.1441821
#> 4 France male 15-19 Tracheal, bronchus, and lung cancer 1.0288445 1.4128852
#> 5 France female 15-19 Tracheal, bronchus, and lung cancer 1.1029335 1.8145559
#> 6 France both 15-19 Tracheal, bronchus, and lung cancer 2.1317780 2.9363421
#> lower
#> 1 0.2243492
#> 2 0.2275731
#> 3 0.4601763
#> 4 0.7247192
#> 5 0.6343383
#> 6 1.5632524Make 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)