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🏃♀️ A Tidyverse-Style Toolbox for Actigraphy Data Analysis
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danielvartan/actverse
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actverse is an R package that offers a comprehensive toolkit forprocessing, analyzing, and visualizingactigraphy data. It isdesigned to support sleep and chronobiology researchers by streamliningworkflows and enhancing reproducibility in actigraphy research.
The package is built ontidyverseprinciples andintegrates seamlessly with thetidyverseecosystem, ensuring a consistent anduser-friendly experience for data manipulation and analysis.
If you find this project useful, please consider giving it a star!
You can installactverse using theremotes package:
remotes::install_github("danielvartan/actverse")
The R ecosystem offers awidevariety of time seriesstandards, and selecting the right one was an important decision indevelopingactverse. A consistent standard for time objects isessential, astime can be represented in manyways and may be based on differentnumerical systems. For packages that followtidyverseprinciples, webelieve thetsibblepackage provides the best time series standard. As its name suggests,tsibble extends thetidyversetibble object for time series data.
Mostactverse functions require your data to be in thetsibble format. Convertingyour data is straightforward and can significantly improve yourexperience working with time series in R. Please refer to thetsibble documentation forguidance on adapting your data.
read_acttrust():Read, tidy, and validate anActTrustfile.write_acttrust():Write atsibbleto a readableActTrustfile.
Example:
file<- get_from_zenodo(doi="10.5281/zenodo.4898822",dir= tempdir(),file="processed.txt")
data<-file|> read_acttrust(tz="America/Sao_Paulo")
library(dplyr)data|> glimpse()#> Rows: 51,806#> Columns: 17#> $ timestamp <dttm> 2021-04-24 04:14:00, 2021-04-24 04:15:00, 202…#> $ pim <dbl> 7815, 2661, 3402, 4580, 2624, 3929, 5812, 3182…#> $ tat <dbl> 608, 160, 243, 317, 255, 246, 369, 270, 373, 1…#> $ zcm <dbl> 228, 64, 80, 125, 33, 105, 171, 54, 189, 64, 6…#> $ orientation <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ wrist_temperature <dbl> 26.87, 27.18, 27.68, 27.86, 28.04, 28.13, 28.2…#> $ external_temperature <dbl> 24.62, 25.06, 25.50, 25.75, 25.94, 26.06, 26.3…#> $ light <dbl> 3.58, 5.23, 3.93, 4.14, 3.16, 3.63, 11.53, 2.4…#> $ ambient_light <dbl> 1.45, 2.12, 1.59, 1.68, 1.28, 1.47, 4.67, 0.97…#> $ red_light <dbl> 0.57, 0.86, 0.64, 0.67, 0.51, 0.56, 3.22, 0.37…#> $ green_light <dbl> 0.66, 0.95, 0.71, 0.75, 0.57, 0.68, 3.49, 0.44…#> $ blue_light <dbl> 0.24, 0.36, 0.26, 0.28, 0.21, 0.30, 1.48, 0.20…#> $ ir_light <dbl> 0.17, 0.25, 0.20, 0.20, 0.16, 0.18, 1.00, 0.13…#> $ uva_light <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ uvb_light <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ event <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ state <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
sri():Compute Phillips et al. Sleep Regularity Index(SRI).state_prop():Compute the proportion of time spent in a specific state (e.g.,Sleeping, Awake).
Example:
Compare the results with the actogram plot shown in the followingsections.
sri_data<-data|> sri()sri_data#> # A tsibble: 1,440 x 5 [1m]#> time state previous_state agreement sri#> <time> <list> <list> <list> <dbl>#> 1 00'00" <fct [36]> <fct [36]> <lgl [36]> 77.1#> 2 01'00" <fct [36]> <fct [36]> <lgl [36]> 77.1#> 3 02'00" <fct [36]> <fct [36]> <lgl [36]> 77.1#> 4 03'00" <fct [36]> <fct [36]> <lgl [36]> 77.1#> 5 04'00" <fct [36]> <fct [36]> <lgl [36]> 77.1#> 6 05'00" <fct [36]> <fct [36]> <lgl [36]> 77.1#> # ℹ 1,434 more rows
library(rutils)# github.com/danielvartan/rutilssri_data|>rutils:::stats_summary("sri",threshold=NULL)|> print(n=Inf)#> # A tibble: 15 × 2#> name value#> <chr> <chr>#> 1 n 1440#> 2 n_rm_na 1440#> 3 n_na 0#> 4 mean 58.7795701029482#> 5 var 785.141504008496#> 6 sd 28.0203765857723#> 7 min -25#> 8 q_1 37.1428571428571#> 9 median 63.6363636363637#> 10 q_3 86.6666666666667#> 11 max 100#> 12 iqr 49.5238095238095#> 13 range 125#> 14 skewness -0.401815749365758#> 15 kurtosis 2.10073487543524
library(ggplot2)library(scales)sri_data|> ggplot(ggplot2::aes(x=time,y=sri))+ geom_smooth(color="#FC2913")+ labs(x="Time of day (Hour)",y="Sleep Regularity Index (SRI)" )+ scale_x_time(breaks= breaks_width("6 hours"),labels= label_time("%-H")# Use "%#H" for Windows )+ scale_y_continuous(limits= c(0,NA))+actverse:::get_actverse_theme()
periodogram():Compute Sokolove & Bushell’s$\chi^{2}$ periodogram.spectrogram():Create a spectrogram plot based on Sokolove & Bushell’s$\chi^{2}$ periodogram.
Example:
data|> periodogram("pim")
data|> spectrogram("pim")
We strongly recommend using theraggpackage as your backend graphics device for complex data visualizations.It is faster and produces higher quality images than R default graphicsdevice.
actogram():Create an actogram plot from actigraphy data.
data|> actogram(col="pim",days=-1,latitude=-23.55065,longitude=-46.63338,double_plot=TRUE )
na_approx()na_locf()na_overall_mean()na_overall_median()na_overall_mode()na_spline()na_weekly_mean()na_zero()na_plot(): Interpolate missing values in a numeric vector.
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 5.0
na_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:
library(rutils)# github.com/danielvartan/rutilsget_sun_stats(latitude=-23.5489,longitude=-46.6388,tz="America/Sao_Paulo")|>rutils:::list_as_tibble()|> print(n=Inf)#> # A tibble: 18 × 2#> name value#> <chr> <chr>#> 1 date 2025-06-05#> 2 latitude -23.5489#> 3 longitude -46.6388#> 4 tz America/Sao_Paulo#> 5 sunrise_start 06:44:16#> 6 sunrise_end 06:46:50#> 7 golden_hour_end 07:17:28#> 8 solar_noon 12:06:31#> 9 golden_hour_start 16:55:34#> 10 sunset_start 17:26:13#> 11 sunset_end 17:28:46#> 12 dusk 17:53:17#> 13 nautical_dusk 18:21:18#> 14 night_start 18:48:54#> 15 nadir 00:06:31#> 16 night_end 05:24:08#> 17 nautical_dawn 05:51:44#> 18 dawn 06:19:45
actverse also comes with many utility functions and providesactigraphy datasets for testing and learning purposes.
All functions are thoroughly documented, with detailed explanations ofthe underlying methodologies and computational guidelines. For acomplete list of available functions and their documentation, clickhere.
Example:
file|> read_acttrust(regularize=FALSE)|> find_epoch()#> $best_match#> [1] 60#>#> $prevalence#> # A tibble: 4 × 2#> epoch proportion#> <dbl> <dbl>#> 1 60 1.00#> 2 86 0.0000193#> 3 94 0.0000193#> 4 101 0.0000193
citation("actverse")#> To cite {actverse} in publications use:#>#> Vartanian, D., Matias, V. A., Serrano, C. A. M., & Benedito-Silva,#> A. A. (2025). {actverse}: A Tidyverse-style toolbox for actigraphy#> data analysis [Computer software, R package].#> https://danielvartan.github.io/actverse/#>#> A BibTeX entry for LaTeX users is#>#> @Misc{,#> title = {{actverse}: A Tidyverse-style toolbox for actigraphy data analysis},#> author = {Daniel Vartanian and Vinicius Alves Matias and Cassio Almeida Mattos Serrano and Ana Amélia Benedito-Silva},#> year = {2025},#> url = {https://danielvartan.github.io/actverse/},#> note = {R package},#> }
actverse code is released under theMITlicense. This means you can use,modify, and distribute the code freely, as long as you include theoriginal license and copyright notice in any copies or substantialportions of the software.
Contributions are welcome, including bug reports. Take a moment toreview ourGuidelines forContributing.
You can also support the development ofactverse by becoming asponsor. Clickhere to makea donation. Please mentionactverse in your donation message.
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