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🏃♀️ Actigraphy Data Analysis in R
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actverse is an R package that provides a complete toolkit to process,analyze and visualize actigraphy data. Its aim is to facilitate the workof sleep and chronobiology scientists with actigraphy data and toimprove reproducibility in research.
actverse adheres to thetidyverseprinciples andintegrates with thetidyverse ecosystem.
You need to have some familiarity with theR programminglanguage and with thetsibble package to useactverse main functions.
If you don’t feel comfortable with R, we strongly recommend checkingHadley Wickham and Garrett Grolemund’s free and online bookR for DataScience and the Coursera course from JohnHopkins UniversityData Science: Foundations usingR(free for audit students).
Please refer to thetsibble packagedocumentation to learn more about it.tsibble is an essential packageto deal with time series in R. We also recommend that you read theDates and times chapterfrom Wickham & Grolemund’s bookR for DataScience and thetsibbleobjects subchapter from Rob J.Hyndman & George Athanasopoulos’ bookForecasting: Principles andPractice.
You can installactverse with:
# install.packages("remotes")remotes::install_github("giperbio/actverse")
The R ecosystem has avastnumber of time seriesstandards and we had to choose one of them while developingactverse.A standard for time objects is a must, becausetime can have manyrepresentations and can be rooted indifferent numerical systems. We believe that the best time seriesstandard available for packages that adheres to thetidyverseprinciples isthetsibble. As the namesuggests,tsibble is an adaptation for time series of thetidyversetibble object.
Mostactverse functions will require that your data be in thetsibble standard. Adaptingyour data is a simple process and can make a big difference when dealingwith time series in R. Please refer totsibble documentation tolearn how to do this.
We also recommend seeing thetsbox package,an R package that propose to be an “universal translator” (🖖) for Rtime series standards.
read_acttrust(): Read, tidy, and validate anActTrust file.write_acttrust(): Adapt and write atsibbleto a readableActTrust file.
Example:
file<- get_from_zenodo(doi="10.5281/zenodo.4898822",path= tempdir(),file="processed.txt")data<- read_acttrust(file,tz="America/Sao_Paulo")data#> # A tsibble: 51,806 x 17 [1m] <America/Sao_Paulo>#> timestamp pim tat zcm orienta…¹ wrist…² exter…³ light ambie…⁴#> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>#> 1 2021-04-24 04:14:00 7815 608 228 0 26.9 24.6 3.58 1.45#> 2 2021-04-24 04:15:00 2661 160 64 0 27.2 25.1 5.23 2.12#> 3 2021-04-24 04:16:00 3402 243 80 0 27.7 25.5 3.93 1.59#> 4 2021-04-24 04:17:00 4580 317 125 0 27.9 25.8 4.14 1.68#> 5 2021-04-24 04:18:00 2624 255 33 0 28.0 25.9 3.16 1.28#> 6 2021-04-24 04:19:00 3929 246 105 0 28.1 26.1 3.63 1.47#> 7 2021-04-24 04:20:00 5812 369 171 0 28.2 26.4 11.5 4.67#> 8 2021-04-24 04:21:00 3182 270 54 0 28.4 26.7 2.4 0.97#> 9 2021-04-24 04:22:00 6362 373 189 0 28.6 26.9 3.28 1.33#> 10 2021-04-24 04:23:00 2621 159 64 0 28.7 27.1 2.97 1.2#> # … with 51,796 more rows, 8 more variables: red_light <dbl>,#> # green_light <dbl>, blue_light <dbl>, ir_light <dbl>, uva_light <dbl>,#> # uvb_light <dbl>, event <dbl>, state <dbl>, and abbreviated variable names#> # ¹orientation, ²wrist_temperature, ³external_temperature, ⁴ambient_light
periodogram(): Compute Sokolove & Bushell’s$\chi^{2}$ periodogram.spectrogram(): Compute a spectrogram based on Sokolove & Bushell’speriodogram.
Example:
per<- periodogram(data,"pim")
spec<- spectrogram(data,"pim")
na_approx()na_locf()na_overall_mean()na_overall_median()na_overall_mode()na_spline()na_weekly_mean()na_zero()na_plot(): ReplaceNAby interpolation.
Example:
x<- c(NA,1,5,10,NA,5,10,1,NA,10,1,5,NA,NA)index<- seq(as.Date("2020-01-01"), as.Date("2020-01-14"),by="day")na_approx(x,index,fill_na_tips=TRUE)#> [1] 1.0 1.0 5.0 10.0 7.5 5.0 10.0 1.0 5.5 10.0 1.0 5.0 5.0 5.0na_plot(x,index, na_approx(x,index,fill_na_tips=TRUE))
get_from_zenodo(): Get data from a Zenodo record.get_sun_stats(): Get sun related statistics from different APIs.
Example:
get_sun_stats(lat=-23.5489,lon=-46.6388,tz="America/Sao_Paulo") %>%dplyr::as_tibble() %>% t()#> [,1]#> date "2023-02-23"#> lat "-23.5489"#> lon "-46.6388"#> tz "America/Sao_Paulo"#> sunrise_start "06:00:15"#> sunrise_end "06:02:37"#> golden_hour_end "06:30:30"#> solar_noon "12:21:26"#> golden_hour_start "18:12:22"#> sunset_start "18:40:15"#> sunset_end "18:42:37"#> dusk "19:05:42"#> nautical_dusk "19:32:49"#> night_start "20:00:22"#> nadir "00:21:26"#> night_end "04:42:30"#> nautical_dawn "05:10:04"#> dawn "05:37:10"
actverse also comes with many utility functions and provides freeactigraphy datasets for testing and learning purposes.
All functions are properly documented, showing all the guidelines behindthe computations. Clickhere tosee a list of them.
Example:
# Find the epochs/periodicities in a 'tsibble'read_acttrust(file,regularize=FALSE) %>% find_epoch()#> $best_match#> [1] 60#>#> $prevalence#> # A tibble: 4 × 2#> epoch proportion#> <dbl> <dbl>#> 1 60 1.00#> 2 94 0.0000193#> 3 86 0.0000193#> 4 101 0.0000193
If you useactverse in your research, please consider citing it. Weput a lot of work to build and maintain a free and open-source Rpackage. You can find the citation below.
citation("actverse")#>#> To cite {actverse} in publications use:#>#> Vartanian, D., Matias, V. A., Serrano, C. A. M., & Benedito-Silva, A.#> A. (2023). {actverse}: tools for actigraphy data analysis. R package#> version 0.0.0.9000. https://giperbio.github.io/actverse/#>#> A BibTeX entry for LaTeX users is#>#> @Unpublished{,#> title = {{actverse}: tools for actigraphy data analysis},#> author = {Daniel Vartanian and Vinicius {Alves Matias} and Cassio {Almeida Mattos Serrano} and Ana Amelia Benedito-Silva},#> year = {2023},#> url = {https://giperbio.github.io/actverse/},#> note = {R package version 0.0.0.9000},#> }
We welcome contributions, including bug reports.
Take a moment to review ourGuidelines forContributing.
The initial development ofactverse was supported by threescholarships provided by theUniversity of Sao Paulo(USP) (❤️).
Become anactverse supporter!
Clickhere to make adonation. Please indicate theactverse package in your donationmessage.
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🏃♀️ Actigraphy Data Analysis in R