{lemur} - Life expectancy monitor upscaled in R

R package and Shiny application

What is lemur?

lemur ("Life expectancy monitor upscaled in R") is an R package and Shiny application for scenario analysis of mortality. Given the cause-of-death distribution of a population, you ask "what if?" questions -- what would life expectancy be if cardiovascular mortality fell by 50%? -- and the package recomputes the life table, shows you the gains and losses at every age, and decomposes the change by age and cause of death. It also compares cause-of-death profiles and life tables between regions, sexes and time periods.

Everything runs on the bundled Global Burden of Disease 2023 (GBD 2023) estimates from IHME: 216 regions and the calendar-year-2023 data included -- a 1990 to 2023 span, three sex categories (male, female and the two sexes combined) and 18 broad cause groups, arranged in abridged life tables of 22 age groups (the terminal 95+ interval open).

The tool is hosted by the HASS Digital Research Hub (HDRH) at the Australian National University (ANU).

Installation

Once you have an R session open (in RStudio or the R console), install the package straight from GitHub:

lemur requires R version 4.3.0 or newer.

# 1. Install the pak package, if you do not have it yet
install.packages("pak")

# 2. Install lemur from GitHub (latest version on the main branch)
pak::pak("mpascariu/lemur")

The package bundles the GBD 2023 datasets (exposed as data_gbd_lt(), data_gbd_cod() and data_gbd_sdg()), covering deaths and life tables from 1990 to 2023, so you can run the examples on the help pages and the analysis functions without a database connection.

Run with Docker (no R required)

The app ships as a prebuilt image on the GitHub Container Registry (single-container use; the full compose stack builds its own image):

docker pull ghcr.io/mpascariu/lemur-shiny:latest

# quick start -- bundled data, local mode:
docker run -d --name lemur -p 3838:3838 ghcr.io/mpascariu/lemur-shiny:latest \
  R -e "options(shiny.port = 3838, shiny.host = '0.0.0.0'); lemur::run_app(lb = FALSE)"
# then open http://localhost:3838

For the full server deployment (PostgreSQL-backed, REST API included) see docs/docker_running_guide.md; building the images yourself is covered in docs/docker_building_guide.md.

Documentation

The full documentation -- a complete worked analysis of the GBD data with interactive figures -- is published as a web page:

https://mpascariu.github.io/lemur/

The same document ships with the package and opens straight from R: vignette("lemur-intro").

The Shiny application

Everything is also wrapped in an interactive dashboard, launched with a single call:

lemur::run_app()          # local data mode (bundled GBD data)

Five analysis modes -- scenario analysis within a region, region comparisons, sex comparisons, and two SDG target modes -- plus a data tab, a methods tab and an interactive map, all rendered natively in plotly.

References

• Every exported function has a worked example: ?modify_life_table, ?decompose_by_cod, ?plot_decompose, ?data_gbd_lt.
• The decomposition methods follow Andreev, Shkolnikov and Begun (2002), Algorithm for decomposition of differences between aggregate demographic measures, Demographic Research 7, 499-522.
• The data are the Global Burden of Disease Study 2023 (GBD 2023) results, Institute for Health Metrics and Evaluation (IHME) (https://vizhub.healthdata.org/gbd-results/). The calendar-year-2023 data is included; deaths and life tables span 1990-2023.
• Source code, issues and releases: https://github.com/mpascariu/lemur.