mctq is an R package that provides a complete and consistent toolkit to process the Munich ChronoType Questionnaire (MCTQ), a quantitative and validated tool to assess chronotypes using peoples’ sleep behavior presented by Till Roenneberg, Anna Wirz-Justice, and Martha Merrow in 2003. The aim of mctq is to facilitate the work of sleep and chronobiology scientists with MCTQ data while also helping with research reproducibility.

Learn more about the MCTQ questionnaire at https://www.thewep.org/documentations/mctq.

Wait, an R package for a questionnaire?

Although it may look like a simple questionnaire, MCTQ requires a lot of date/time manipulation. This poses a challenge for many scientists, being that most people have difficulties with date/time data, especially when dealing with an extensive dataset. The mctq package comes to address this issue.

mctq can handle the processing tasks for the three MCTQ versions (standard, micro, and shift) with few dependencies, relying much of its applications on the lubridate and hms packages from tidyverse. We also designed mctq with the user experience in mind, by creating an interface that resembles the way the questionnaire data is shown in MCTQ publications, and by providing extensive and detailed documentation about each computation proposed by the MCTQ authors. The package also includes several utility tools, along with fictional datasets for testing and learning purposes.


You need to have some familiarity with the R programming language and with the lubridate and hms packages from tidyverse to use mctq main functions.

In case you don’t feel comfortable with R, we strongly recommend checking Hadley Wickham and Garrett Grolemund free and online book R for Data Science and the Coursera course from John Hopkins University Data Science: Foundations using R (free for audit students).

Please refer to the lubridate and hms package documentation to learn more about them. These two are essential packages to deal with date/time data in R. We also recommend that you read 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:

And the development version from GitHub with:

# install.packages("remotes")


mctq makes use of the lubridate and hms packages from tidyverse, which provide special objects to deal with date/time values in R. If your dataset does not conform to this structure, you first need to convert your data to it. Please refer to those package documentations to learn more about them.

Due to the circular nature of time, we strongly recommend that you use appropriate temporal objects while dealing with date/time in R. That can help you get rid of several computation mistakes while trying to adapt your data from a base 10 to a system rooted in a base 12 numerical system.

Workdays and work-free days variables

After your data is set to start, just use the mctq functions below to process it.

Note that the mctq functions uses a similar naming pattern to that used in the MCTQ publications. That makes it easy to find and apply any computation necessary.

  • 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).


# 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
#> 03:45:00

Combining workdays and work-free days variables

For 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 corrected local time of mid-sleep on work-free days.
  • sjl_rel() and sjl(): compute MCTQ social jet lag.
  • sjl_weighted(): compute MCTQ absolute social jetlag across all shifts (only for MCTQ Shift).


# 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)"

See a quick tour of all MCTQ main functions here.


mctq is also equipped with many utility functions. The package also provides fictional datasets of the standard, micro, and shift MCTQ versions for testing and learning purposes.

All functions are well documented, showing all the guidelines behind the computations. Click here to see a list of them.


If you use mctq in your research, please consider citing it. We put a lot of work to build and maintain a free and open-source R package. You can find the mctq citation below.

#> To cite {mctq} in publications use:
#>   Vartanian, D., Benedito-Silva, A. A., & Pedrazzoli, M. (2021).
#>   {mctq}: an R package for the Munich ChronoType Questionnaire.
#>   https://docs.ropensci.org/mctq/
#> A BibTeX entry for LaTeX users is
#>   @Unpublished{,
#>     title = {{mctq}: an R package for the Munich ChronoType Questionnaire},
#>     author = {Daniel Vartanian and Ana Amelia Benedito-Silva and Mario Pedrazzoli},
#>     year = {2021},
#>     url = {https://docs.ropensci.org/mctq/},
#>     note = {Lifecycle: maturing},
#>   }


mctq is a community project, everyone is welcome to contribute. Take a moment to review our Guidelines for Contributing.

Please note that this package is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

Support mctq


Working with science in Brazil is a daily challenge. There are few funding opportunities available and their value is not enough to live on. Added to this, every day Brazilian science suffers from deep cuts in funding, which requires researchers to always look for other sources of income.

If this package helps you in any way or you simply want to support the author’s work, please consider donating or even creating a membership subscription (if you can!). Your support will help with the author’s scientific pursuit and with the package maintenance.

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Thank you!