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iglu

R package for Interpreting GLUcose data from CGMs (Continuous Glucose Monitors)

https://github.com/irinagain/iglu

Science Score: 49.0%

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Last synced: 7 months ago · JSON representation

Repository

R package for Interpreting GLUcose data from CGMs (Continuous Glucose Monitors)

Basic Info
Statistics
  • Stars: 27
  • Watchers: 2
  • Forks: 28
  • Open Issues: 7
  • Releases: 5
Created almost 7 years ago · Last pushed 9 months ago
Metadata Files
Readme Changelog License

README.Rmd 

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# iglu


[![CRAN\_Status\_Badge](https://www.r-pkg.org/badges/version/iglu)](https://cran.r-project.org/package=iglu)[![](https://cranlogs.r-pkg.org/badges/iglu)](https://CRAN.R-project.org/package=iglu)
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[![Codecov test coverage](https://codecov.io/gh/irinagain/iglu/branch/master/graph/badge.svg)](https://app.codecov.io/gh/irinagain/iglu?branch=master)


iglu: Interpreting data from Continuous Glucose Monitors (CGMs)
====================================================================

The R package 'iglu' provides functions for outputting relevant metrics for data collected from Continuous Glucose Monitors (CGM). For reference, see ["Interpretation of continuous glucose monitoring data: glycemic variability and quality of glycemic control." Rodbard  (2009)](https://doi.org/10.1089/dia.2008.0132). For more information on the package, see [package website](https://irinagain.github.io/iglu/). We also have a [GUI that requires no programming experience](https://irinagain.shinyapps.io/shiny_iglu/) and mirrored [Python version of iglu](https://github.com/IrinaStatsLab/iglu-py). 


For short **tutorial on how to use the package**, see [Video tutorial on working with CGM data in iglu](https://www.birs.ca/events/2025/5-day-workshops/25w5474/videos/watch/202502260906-Gaynanova.html) and corresponding [slides](https://irinagain.github.io/Resources/SlidesWorkshop.pdf).

iglu comes with two example datasets: example_data_1_subject and example_data_5_subject. These data are collected using Dexcom G4 CGM on subjects with Type II diabetes. Each dataset follows the structure iglu's functions are designed around. Note that the 1 subject data is a subset of the 5 subject data. See the examples below for loading and using the data. 

Attribution
------------

We are glad you found iglu useful. If you use it for computing CGM metrics or visualizing CGM data, please cite the package appropriately, depending on the features used.

**Original package paper**: most of the CGM metrics, time series and lasagna plots, AGP 

  * Broll S, Urbanek J, Buchanan D, Chun E, Muschelli J, Punjabi N and Gaynanova I (2021). [Interpreting blood glucose data with R package iglu.](https://doi.org/10.1371/journal.pone.0248560) *PLoS One*, Vol. 16, No. 4, e0248560.
  
**Updated package paper**: for episode (event) detection, postprandial metrics, built-in clustering and heatmap tools, new MAGE, and Python version
  
  * Chun E, Fernandes JN and Gaynanova I (2024) [An Update on the iglu Software for Interpreting Continuous Glucose Monitoring Data.](https://doi.org/10.1089/dia.2024.0154) *Diabetes Technology and Therapeutics,* Vol. 26, No. 12, 939-950.

**MAGE algorithm**: if you use MAGE, which is uniquely implemented and validated against manual computation, we ask that you additionally cite

 * Fernandes N, Nguyen N, Chun E, Punjabi N and Gaynanova I (2022) [Open-Source Algorithm to Calculate Mean Amplitude of Glycemic Excursions Using Short and Long Moving Averages.](https://doi.org/10.1177/19322968211061165) *Journal of Diabetes Science and Technology*, Vol. 16, No. 2, 576-577.

**Reuse or modification in another software:** 

iglu is free and released under the GNU General Public License v2 (GPL-2).  This permits use, modification, and distribution (including translations), under the following conditions:

- Any redistributed or modified version must also be licensed under GPL-2, even if iglu is used in only parts of the work.

- You must provide clear attribution to iglu for the appropriate parts of the code, describe your changes (if applicable), and identify yourself as the source of modifications.

We recommend referencing the specific version of iglu that was used/modified to help track divergence from future updates.


**Copyright and warranty:**

© 2020 Texas A&M University

© 2023 The Regents of the University of Michigan

Gaynanova Lab - 

iglu is free software released under GPL 2.0.  Use and modification are subject to the same license terms. It is distributed in the hope that it will be useful, but without any warranty. See the **LICENSE** file for full terms.



Installation
------------
The R package 'iglu' is available from CRAN, use the commands below to install the most recent Github version.

```{r, eval = FALSE}
# Plain installation
devtools::install_github("irinagain/iglu") # iglu package

# For installation with vignette
devtools::install_github("irinagain/iglu", build_vignettes = TRUE)
```

Example
-------

```{r}
library(iglu)
data(example_data_1_subject) # Load single subject data
## Plot data

# Use plot on dataframe with time and glucose values for time series plot
plot_glu(example_data_1_subject)

# Summary statistics and some metrics
summary_glu(example_data_1_subject)

in_range_percent(example_data_1_subject)

above_percent(example_data_1_subject, targets = c(80,140,200,250))

j_index(example_data_1_subject)

conga(example_data_1_subject)

# Load multiple subject data
data(example_data_5_subject)

plot_glu(example_data_5_subject, plottype = 'lasagna', datatype = 'average')

below_percent(example_data_5_subject, targets = c(80,170,260))

mage(example_data_5_subject)
```


Shiny App 
------------

Shiny App can be accessed locally via 
```{r, eval = FALSE}
library(iglu)
iglu_shiny()
```
or globally at [https://irinagain.shinyapps.io/shiny_iglu/](https://irinagain.shinyapps.io/shiny_iglu/). As new functionality gets added, local version will be slightly ahead of the global one.

Owner

  • Name: Irina Gaynanova
  • Login: irinagain
  • Kind: user
  • Company: University of Michigan

GitHub Events

Total
  • Create event: 1
  • Issues event: 6
  • Release event: 1
  • Watch event: 7
  • Issue comment event: 7
  • Push event: 18
  • Pull request review comment event: 4
  • Pull request review event: 6
  • Pull request event: 15
  • Fork event: 1
Last Year
  • Create event: 1
  • Issues event: 6
  • Release event: 1
  • Watch event: 7
  • Issue comment event: 7
  • Push event: 18
  • Pull request review comment event: 4
  • Pull request review event: 6
  • Pull request event: 15
  • Fork event: 1

Committers

Last synced: over 2 years ago

All Time
  • Total Commits: 743
  • Total Committers: 30
  • Avg Commits per committer: 24.767
  • Development Distribution Score (DDS): 0.649
Past Year
  • Commits: 38
  • Committers: 9
  • Avg Commits per committer: 4.222
  • Development Distribution Score (DDS): 0.737
Top Committers
Name Email Commits
stevebroll s****l@t****u 261
Irina Gaynanova i****g@s****u 148
LChun l****1@t****u 71
David Buchanan d****5@t****u 49
Jung Hoon Seo 5****1 30
Nathaniel n****4@t****u 29
muschellij2 m****2@g****m 24
David Buchanan d****5@g****m 24
Nhan Nguyen n****3@t****u 11
L Chun 7****n@g****m 10
John Schwenck j****2@g****m 10
Marielle Hicban m****c@t****u 10
stevebroll s****l@o****m 9
Mary Martin m****n@M****l 8
Irina Gaynanova i****n@g****m 8
Pratik Patel p****9@g****m 6
Irina Gaynanova i****n@s****u 6
Nhan Nguyen 6****3 5
Nathaniel J. Fernandes n****0@g****m 4
Irina Gaynanova i****g@c****u 4
Johnathan Shih j****s@t****u 3
Irina Gaynanova i****n@s****l 2
jseo0917 j****7@g****m 2
Ashok Meyyappan 6****2 2
marymartin16 6****6 2
jonathan dreyfuss j****f@g****m 1
stevebroll s****l@g****m 1
nate n****e@n****l 1
Nathaniel Fernandes 6****s 1
Irina Gaynanova i****g@c****u 1
Committer Domains (Top 20 + Academic)
tamu.edu: 7 client-10-228-46-152.tamulink.tamu.edu: 1 github.com: 1 client-10-229-191-164.tamulink.tamu.edu: 1 sph-230114-bio.ddns.sph.umich.edu: 1 stat.tamu.edu: 1

Issues and Pull Requests

Last synced: 7 months ago

All Time
  • Total issues: 14
  • Total pull requests: 142
  • Average time to close issues: 5 months
  • Average time to close pull requests: 4 days
  • Total issue authors: 11
  • Total pull request authors: 19
  • Average comments per issue: 1.86
  • Average comments per pull request: 0.15
  • Merged pull requests: 120
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 3
  • Pull requests: 7
  • Average time to close issues: N/A
  • Average time to close pull requests: 21 days
  • Issue authors: 2
  • Pull request authors: 4
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.0
  • Merged pull requests: 3
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • m-ajie (2)
  • miguelkennedy (2)
  • jo4hwcr (2)
  • seb-sauer (1)
  • staskh (1)
  • dhairya02 (1)
  • anupamsingh81 (1)
  • walterwilliamson (1)
  • steckhan (1)
  • David-A-Buchanan (1)
  • fieri (1)
  • mitchjduncan (1)
Pull Request Authors
  • LylChun (55)
  • Nathaniel-Fernandes (30)
  • David-A-Buchanan (15)
  • moshimor91 (8)
  • walterwilliamson (8)
  • neokok (8)
  • Nhannguyen993 (6)
  • marbhic (6)
  • stevebroll (5)
  • Toshihiko-tan (5)
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  • muschellij2 (3)
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Packages

  • Total packages: 1
  • Total downloads:
    • cran 745 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 13
  • Total maintainers: 1
cran.r-project.org: iglu

Interpreting Glucose Data from Continuous Glucose Monitors

  • Versions: 13
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 745 Last month
Rankings
Forks count: 4.1%
Stargazers count: 16.3%
Average: 24.4%
Dependent packages count: 29.8%
Dependent repos count: 35.5%
Downloads: 36.4%
Maintainers (1)
irinagn@umich.edu
Last synced: 8 months ago

Dependencies

DESCRIPTION cran
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  • knitr * suggests
  • rmarkdown * suggests
  • testthat >= 2.1.0 suggests
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