Title: Health Metrics and the Spread of Infectious Diseases
Version: 1.1.2
Description: A collection of datasets and supporting functions accompanying Health Metrics and the Spread of Infectious Diseases by Federica Gazzelloni (2024). This package provides data for health metrics calculations, including Disability-Adjusted Life Years (DALYs), Years of Life Lost (YLLs), and Years Lived with Disability (YLDs), as well as additional tools for analyzing and visualizing health data. Federica Gazzelloni (2024) <doi:10.5281/zenodo.10818338>.
License: MIT + file LICENSE
URL: https://github.com/Fgazzelloni/hmsidwR, https://fgazzelloni.github.io/hmsidwR/
BugReports: https://github.com/Fgazzelloni/hmsidwR/issues
Depends: R (≥ 2.10)
Imports: ggplot2, gstat, purrr, showtext, sysfonts, tibble
Suggests: devtools, dplyr, geomtextpath, ggthemes, httr, janitor, knitr, lubridate, maps, pkgdown, plotly, readr, readxl, rmarkdown, sessioninfo, sf, stats, testthat (≥ 3.0.0), tidyr, tidyverse
Config/testthat/edition: 3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.2
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2024-11-12 17:20:52 UTC; federicagazzelloni
Author: Federica Gazzelloni ORCID iD [aut, cre]
Maintainer: Federica Gazzelloni <fede.gazzelloni@gmail.com>
Repository: CRAN
Date/Publication: 2024-11-13 15:00:02 UTC

hmsidwR: Health Metrics and the Spread of Infectious Diseases

Description

logo

A collection of datasets and supporting functions accompanying Health Metrics and the Spread of Infectious Diseases by Federica Gazzelloni (2024). This package provides data for health metrics calculations, including Disability-Adjusted Life Years (DALYs), Years of Life Lost (YLLs), and Years Lived with Disability (YLDs), as well as additional tools for analyzing and visualizing health data. Federica Gazzelloni (2024) doi:10.5281/zenodo.10818338.

Author(s)

Maintainer: Federica Gazzelloni fede.gazzelloni@gmail.com (ORCID)

See Also

Useful links:

Dataset: Health Metrics Data - Number of Deaths Due to 9 Causes in 2019

Description

A dataset containing the number of Deaths due to 9 causes in 6 regions for 2019.

Usage

data(deaths2019)

Format

A dataframe with 2754 rows and 7 variables:

The variables are as follows:

location

character, France, Germany, Global, Italy, United Kingdom, United States of America

sex

character, Female, Male, Both

age

character, age groups from <1 to 85+ each 5 years

cause

character, Alzheimer's disease and other dementias, Breast cancer, Chronic obstructive pulmonary disease, Colon and rectum cancer, Diabetes and kidney diseases, Lower respiratory infections, Road injuries, Stroke, Tracheal, bronchus, and lung cancer

val

numeric, deaths number estimation

upper

numeric, upper value estimation

lower

numeric, lower value estimation

Source

2019 data from the IHME website

Examples

data(deaths2019)
head(deaths2019)

Health Metrics Data - Number of Deaths Due to 9 Causes in 6 Locations for the Years 2011 and 2021.

Description

Health Metrics Data - Number of Deaths Due to 9 Causes in 6 Locations for the Years 2011 and 2021.

Usage

data(deaths9)

Format

A dataframe with 5112 rows and 7 variables:

The variables are as follows:

location

character, France, Germany, Global, Italy, UK, USA

iso2

character, country code

sex

character, female, male, both

age

character, 5-year age groups from <5 to 85+

cause

character, Alzheimer's disease and other dementias, Breast cancer, Chronic obstructive pulmonary disease, Colon and rectum cancer, Diabetes and kidney diseases, Lower respiratory infections, Road injuries, Stroke, Tracheal, bronchus, and lung cancer

year

integer, years 2011 and 2019

dx

numeric, deaths number estimation

Source

2021 data from the IHME website

Examples

data(deaths9)
head(deaths9)

Dataset: Health Metrics Data - Disability Weights and Severity in 2019 and 2021

Description

A dataset containing the Disability Weights estimates, upper and lower values, and the Severity level for Stroke, Tuberculosis, and HIV for all countries.

Usage

disweights

Format

A dataframe with 463 rows and 9 variables:

The variables are as follows:

sequela

character, disease sequela

specification

character, diesase specification

cause1

character, first cause of disease - morbidity

cause2

character, second cause of disease - morbidity

severity

character, mild, moderate, severe, mean

dw

numeric, disability weights estimation

upper

numeric, upper value estimation

lower

numeric, lower value estimation

Source

Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2019 and 2021 Disability Weights. Seattle, United States of America: Institute for Health Metrics and Evaluation (IHME), 2024.

Dataset: Health Metrics Data - G7 Countries

Description

A subset of data from the IHME GBD on Deaths, Disability-Adjusted Life Years (DALYs), Years of Life Lost (YLLs), and Years Lived with Disability (YLDs), Incidence and Prevalence, age standardized for all causes and respiratory infections and tuberculosis. For years 2010, 2019 and 2021.

Usage

g7_hmetrics

Format

A dataframe with 3402 rows and 9 variables:

The variables are as follows:

measure

character, metric name

location

character, country

sex

character, Female, Male, Both

cause

character, all causes, and respiratory infections and tuberculosis

year

integer, year

val

numeric, estimated values

upper

numeric, estimated upper values

lower

numeric, estimated lower values

Details

Locations available are Global, Canada, France, Germany, Italy, Japan, UK, and US.

Source

https://vizhub.healthdata.org/gbd-results/

Dataset: Health Metrics Data - Germany lungcancer Deaths 2019

Description

A dataset containing deaths number due to lungcancer in Germany 2019.

Usage

germany_lungc

Format

A dataframe with 48 rows and 8 variables:

The variables are as follows:

age

character, age groups from 10-14 to 85+ each 5 years

sex

character, both, male, female

prevalence

numeric, prevalence rate estimation due to lungcancer

prev_upper

numeric, upper value estimation

prev_lower

numeric, lower value estimation

dx

numeric, deaths rate estimation due to lungcancer

dx_upper

numeric, upper value estimation

dx_lower

numeric, lower value estimation

Source

2019 data from the IHME website

Download, Unzip and Read Data: getunz

Description

Download, Unzip and Read Data: getunz

Usage

getunz(url)

Arguments

url

A url string for a .zip file.

Value

A dataframe object from a zipped file. Particulary useful For downloading data from IHME GBD Results: "https://vizhub.healthdata.org/gbd-results/". The function takes the url, creates a temp directory, unzip the file, if more than one csv files is available, it lists the files, and reads them.

Select a dataset from the IHME GBD results and download it. You will receive an email with a url. Use the url to download the data.

Examples

## Not run: 
# This is a dontrun example because it requires a valid url.
url <- "https://www.healthdata.org/.../some-file.zip"
getunz(url)

## End(Not run)

Dataset: Global Health Observatory (GHO) - Countries Life Expectancy and Healthy Life Expectancy(HALE) 2000-2019

Description

A dataset containing World countries Life Expectancy and HALE from 2000 to 2019.

Usage

gho_le_hale

Format

A dataframe with 8784 rows and 6 variables:

The variables are as follows:

indicator

character, Healthy life expectancy (HALE) at age 60 (years),
Healthy life expectancy (HALE) at birth (years),
Life expectancy at age 60 (years),
Life expectancy at birth (years)

year

numeric, from 2000 to 2019

region

character, 6 World regions: Africa, Americas, Eastern Mediterranean, Europe, South-East Asia, and Western Pacific

country

character, 183 World countries

sex

character, both, male, female

value

numeric, value of the indicator

Source

WHO

Dataset: Global Health Observatory (GHO) Life tables: WHO Global Life table values

Description

A dataset containing the Global region Life tables from 2000 to 2019.

Usage

gho_lifetables

Format

A dataframe with 1995 rows and 5 variables:

The variables are as follows:

indicator

character, Tx - person-years lived above age x,
ex - expectation of life at age x,
lx - number of people left alive at age x,
nLx - person-years lived between ages x and x+n,
nMx - age-specific death rate between ages x and x+n,
ndx - number of people dying between ages x and x+n,
nqx - probability of dying between ages x and x+n

year

numeric, from 2000 to 2019

age

character, from <1 to 85+ each 5 years

sex

character, both, male, female

value

numeric, value of the tables

Source

WHO

Dataset: Health Metrics Data - Simple Feature Collection Average Disability-Adjusted Life Years (DALYs) per 100,000 population from 1990 to 2021

Description

Dataset: Health Metrics Data - Simple Feature Collection Average Disability-Adjusted Life Years (DALYs) per 100,000 population from 1990 to 2021

Usage

idDALY_map_data

Format

A Simple feature collection with 1402 rows and 4 variables:

group

double, country's polygon

location_name

character, 200 Countries affected by Infectious Diseases

DALYs

double, Average DALYs per 100,000 population from 1990 to 2021

geometry

POLYGON

Source

2021 data from the IHME website

Dataset: Health Metrics Data - Infectious Diseases 1980-2021

Description

A dataset containing average values for deaths rates, Disability-Adjusted Life Years (DALYs), Years of Life Lost (YLLs), and Years Lived with Disability (YLDs) due to 37 infectious diseases form 1980 to 2012 for all countries.

Usage

id_affected_countries

Format

A dataframe with 3066 rows and 6 variables:

The variables are as follows:

location_name

character, list of countries

year

numeric, from 1980 to 2021

DALYs

numeric, DALYs for 100 000

YLLs

numeric, YLLs for 100 000

YLDs

numeric, YLDs for 100 000

Deaths

numeric, deaths rate

Source

IHME website

Global Region Health Metrics Data - Incidence and Prevalence for Stroke 2019 and 2021 Numbers - 5-year age groups from <1 to 85+ and both Location available Global

Description

Global Region Health Metrics Data - Incidence and Prevalence for Stroke 2019 and 2021 Numbers - 5-year age groups from <1 to 85+ and both Location available Global

Usage

incprev_stroke

Format

A dataframe with 228 rows and 7 variables:

The variables are as follows:

measure

character, metric name

sex

character, female, male, both

age

character, age groups from <1 to 85+ each 5 years

year

integer, years 2019 and 2021

val

numeric, estimated values

upper

numeric, estimated upper values

lower

numeric, estimated lower values

Source

https://vizhub.healthdata.org/gbd-results/

Dataset: Health Metrics Data - Infectious Diseases 1980-2021

Description

A dataset containing Deaths rates, Disability-Adjusted Life Years (DALYs), Years of Life Lost (YLLs), and Years Lived with Disability (YLDs), Prevalence and Incidence due to Infectious Diseases form 1980 to 2021 for Lesotho, Eswatini, Malawi, Central African Republic, and Zambia.

Usage

infectious_diseases

Format

A dataframe with 7470 rows and 10 variables:

The variables are as follows:

year

numeric, from 1980 to 2021

location_name

character, list of countries

location_id

numeric, list of countries by id

cause_name

character, type of infectious disease

Deaths

numeric, deaths rate

DALYs

numeric, DALYs for 100 000

YLDs

numeric, YLDs for 100 000

YLLs

numeric, YLLs for 100 000

Prevalence

numeric, prevalence rate

Incidence

numeric, incidence rate

val

numeric, estimated values

Source

IHME website

Kriging Best Fit: kbfit - Fit variogram models and kriging models to spatial data and select the best model based on the metrics values

Description

Kriging Best Fit: kbfit - Fit variogram models and kriging models to spatial data and select the best model based on the metrics values

Usage

kbfit(response, formula, data, models, initial_values)

Arguments

response

A character string specifying the response variable

formula

A formula object specifying the model to fit: response ~ predictors

data

A simple feature object containing the variables in the formula

models

A list of characters vector specifying the variogram models to fit

initial_values

A list of named numeric vectors specifying the initial values for the variogram models: psill, range, nugget

Value

A list with two elements: all_models and best_model

Examples

## Not run: 
# This is a dontrun example because it requires a spatial data object(data_sf).
# Try different initial values for fitting the variogram models
initial_values <- list(
  list(psill = 1, range = 100000, nugget = 10),
  list(psill = 0.5, range = 50000, nugget = 5),
  list(psill = 2, range = 150000, nugget = 15)
)

# Set some models to fit
models <- c("Sph", "Exp", "Gau")

# Select Best: Fit variogram models and kriging models
result <- hmsidwR::kbfit(response = "response",
                   formula = response ~ predictor1 + predictor2,
                   data = data_sf,
                   models = c("Sph", "Exp", "Gau", "Mat"),
                   initial_values = initial_values)

result$all_models
result$best_model

## End(Not run)

Dataset: Health Metrics Data - Rabies Deaths and DALYs from 1980 to 2021

Description

A subset of data from the IHME GBD on Disability-Adjusted Life Years (DALYs) and Deaths due to All Causes and Rabies. Locations available are Global Region and Asia.

Usage

rabies

Format

A dataframe with 296 rows and 7 variables:

The variables are as follows:

measure

character, metric name

location

character, country

cause

character, cause

year

integer, year

val

numeric, estimated values

upper

numeric, estimated upper values

lower

numeric, estimated lower values

Source

https://www.healthdata.org/

Dataset: Health Metrics Data - Socio-Demographic Index (SDI) for 1990 and 2019

Description

A subset of data from the IHME GBD containing location, year and estimated values of the SDI for the years 1990 and 2019.

Usage

sdi90_19

Format

A dataframe with 20010 rows and 3 variables:

The variables are as follows:

location

character, country

year

integer, year

val

numeric, estimated values

Source

<healthdata.org>

Health Metrics Data - Disability-Adjusted Life Years (DALYs) Estimations for 204 countries in 2021 with spatial information.

Description

Health Metrics Data - Disability-Adjusted Life Years (DALYs) Estimations for 204 countries in 2021 with spatial information.

Usage

data(spatialdalys2021)

Format

A dataframe with 92862 rows and 7 variables:

The variables are as follows:

location

character, France, Germany, Global, Italy, UK, USA, ...

value

double, DALYs number estimation

lower_bound

double, DALYs number estimation lower bound

upper_bound

double, DALYs number estimation upper bound

long

double, longitude

lat

double, latitude

group

double, polygons' group

Source

2021 data from the IHME website

Examples

data(spatialdalys2021)
head(spatialdalys2021)

Description

Scan all folders and files to find a string: string_search

Usage

string_search(path = ".", pattern, string)

Arguments

path

If NULL, the current directory is used

pattern

A regular expression pattern such as '\.R$'

string

A string such as 'metric'

Value

A character vector with the names of the files that contain the string

Examples

string_search(path=".","\\.R$","metric")

# function string_search

Custom ggplot2 theme function

Description

Custom ggplot2 theme function

Usage

theme_hmsid(
  base_size,
  text_size,
  subtitle_size,
  subtitle_margin,
  plot_title_size,
  plot_title_margin,
  ...
)

Arguments

base_size

base font size

text_size

plot text size

subtitle_size, subtitle_margin

plot subtitle size and margin

plot_title_size, plot_title_margin

plot title size and margin

...

Other arguments passed to theme_hmsid

Value

A customized theme for a ggplot object.

Examples

library(ggplot2)
dat <- data.frame(
  x = seq_along(1:5),
  y = rnorm(n = 5, mean = 0.5, sd = 1)
)
dat |>
  ggplot(aes(x = x, y = y)) +
  geom_line() +
  hmsidwR::theme_hmsid()