mctq is an R package that provides a complete toolkit to process the
Munich ChronoType Questionnaire (MCTQ), a quantitative and validated
tool to assess chronotypes using individuals’ sleep behavior, as
presented by Till Roenneberg, Anna Wirz-Justice, and Martha Merrow in
2003. Its aim is to
facilitate the work of sleep and chronobiology scientists with MCTQ data
and improve reproducibility in research.
mctq adheres to the tidyverse
principles and
integrates with the tidyverse ecosystem.
Learn more about the MCTQ questionnaire at https://www.thewep.org/documentations/mctq.
Although it may seem like a simple questionnaire, MCTQ requires
extensive date/time manipulation, which poses challenges for many
scientists. The mctq package addresses this issue by providing tools
to handle the processing tasks for the three MCTQ versions (standard,
micro, and shift) with few dependencies, relying mainly on the
lubridate and
hms packages from
tidyverse.
We designed mctq with user experience in mind, creating an interface
that resembles the questionnaire data as shown in MCTQ publications and
providing extensive documentation about each computation proposed by the
MCTQ authors. The package also includes fictional datasets for testing
and learning purposes.
You need some familiarity with the R programming
language and the
lubridate and
hms packages from
tidyverse to use mctq’s main functions.
If you are new to R, we recommend Hadley Wickham and Garrett Grolemund’s free online book R for Data Science and the Coursera course from Johns Hopkins University Data Science: Foundations using R (free for audit students).
Please refer to the lubridate and hms documentation to learn more about handling date/time data in R. We also recommend reading the Dates and times chapter from Wickham & Grolemund’s book R for Data Science.
You can install the released version of mctq from
CRAN with:
install.packages("mctq")And the development version from GitHub with:
# install.packages("remotes")
remotes::install_github("ropensci/mctq")mctq uses the lubridate and
hms packages, which provide special
objects to handle date/time values in R. Ensure your dataset conforms to
this structure before using mctq. Refer to the respective package
documentation for more details.
Because of the circular nature of time, using appropriate temporal objects is crucial to avoid computation mistakes while adapting data from a base 10 to a base 12 numerical system.
For detailed usage instructions, visit our Get started guide.
After preparing your data, use the following mctq functions to process
it. The function names follow the patterns used in MCTQ publications,
making it easy to apply the necessary computations:
fd(): compute MCTQ work-free days.so(): compute MCTQ local time of sleep onset.gu(): compute MCTQ local time of getting out of bed.sdu(): compute MCTQ sleep duration.tbt(): compute MCTQ total time in bed.msl(): compute MCTQ local time of mid-sleep.napd(): compute MCTQ nap duration (only for MCTQ Shift).sd24(): compute MCTQ 24 hours sleep duration (only for MCTQ Shift).
Example:
# Local time of preparing to sleep on workdays
sprep_w <- c(hms::parse_hm("23:45"), hms::parse_hm("02:15"))
# Sleep latency or time to fall asleep after preparing to sleep on workdays
slat_w <- c(lubridate::dminutes(30), lubridate::dminutes(90))
# Local time of sleep onset on workdays
so(sprep_w, slat_w)
#> 00:15:00
#> 03:45:00For computations combining workdays and work-free days, use:
sd_week(): compute MCTQ average weekly sleep duration.sd_overall(): compute MCTQ overall sleep duration (only for MCTQ Shift).sloss_week(): compute MCTQ weekly sleep loss.le_week(): compute MCTQ average weekly light exposure.msf_sc(): compute MCTQ chronotype or sleep-corrected local time of mid-sleep on work-free days.sjl()andsjl_rel(): compute MCTQ social jet lag.sjl_sc()andsjl_sc_rel(): compute Jankowski’s MCTQ sleep-corrected social jetlag.sjl_weighted(): compute MCTQ absolute social jetlag across all shifts (only for MCTQ Shift).
Example:
# Local time of mid-sleep on workdays
msw <- c(hms::parse_hm("02:05"), hms::parse_hm("04:05"))
# Local time of mid-sleep on work-free days
msf <- c(hms::parse_hm("23:05"), hms::parse_hm("08:30"))
# Relative social jetlag
sjl_rel(msw, msf)
#> [1] "-10800s (~-3 hours)" "15900s (~4.42 hours)"mctq includes utility tools to help with your MCTQ data and provides
fictional datasets for the standard, micro, and shift MCTQ versions for
testing and learning purposes.
All functions are documented with guidelines behind the computations. Click here to see the full list.
If you use mctq in your research, please consider citing it. We put
significant effort into building and maintaining this free and
open-source R package. Find the citation below.
citation("mctq")
#> To cite {mctq} in publications use:
#>
#> Vartanian, D. (2024). {mctq}: Munich ChronoType Questionnaire tools
#> (Version 0.3.2.9001) [Computer software - R package]. CRAN; rOpenSci.
#> https://doi.org/10.32614/CRAN.package.mctq
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Misc{,
#> title = {{mctq}: Munich ChronoType Questionnaire tools},
#> author = {Daniel Vartanian},
#> year = {2024},
#> publisher = {CRAN; rOpenSci},
#> doi = {10.32614/CRAN.package.mctq},
#> note = {R package version 0.3.2.9001},
#> }We welcome contributions, including bug reports. Take a moment to review our Guidelines for Contributing.
The initial development of mctq was supported by a scholarship from
the University of Sao Paulo (USP) (❤️).
The mctq hex logo is based on an illustration by hilda design matters
Zurich for the Daylight Academy
(DLA).
Become an mctq supporter!
Click here to make a
donation. Please indicate the mctq package in your donation message.
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