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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
library(mctq)
library(lubridate)
library(hms)
```
# mctq <a href = "https://docs.ropensci.org/mctq/"><img src = "man/figures/logo.png" align="right" height="139" /></a>
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## Overview
`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 peoples' sleep behavior presented by Till Roenneberg, Anna Wirz-Justice, and Martha Merrow in [2003](https://doi.org/10.1177/0748730402239679). The aim of `mctq` is to facilitate the work of sleep and chronobiology scientists with MCTQ data and to improve reproducibility in research.
`mctq` adheres to the [tidyverse principles](https://tidyverse.tidyverse.org/articles/manifesto.html) and
integrates with the [tidyverse ecosystem](https://www.tidyverse.org/).
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](https://lubridate.tidyverse.org/) and [hms](https://hms.tidyverse.org/) packages from [tidyverse](https://www.tidyverse.org/). 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.
## Prerequisites
You need to have some familiarity with the [R programming language](https://www.r-project.org/) and with the [lubridate](https://lubridate.tidyverse.org/) and [hms](https://hms.tidyverse.org/) packages from [tidyverse](https://www.tidyverse.org/) 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](https://r4ds.had.co.nz/) and the Coursera course from John Hopkins University [Data Science: Foundations using R](https://www.coursera.org/specializations/data-science-foundations-r) (free for audit students).
Please refer to the [lubridate](https://lubridate.tidyverse.org/) and [hms](https://hms.tidyverse.org/) 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](https://r4ds.had.co.nz/dates-and-times.html) chapter from Wickham & Grolemund's book [R for Data Science](https://r4ds.had.co.nz/).
## Installation
You can install the released version of `mctq` from [CRAN](https://CRAN.R-project.org/package=mctq) with:
```{r, eval = FALSE}
install.packages("mctq")
```
And the development version from [GitHub](https://github.com/) with:
``` {r, eval = FALSE}
# install.packages("remotes")
remotes::install_github("ropensci/mctq")
```
## Usage
`mctq` makes use of the [lubridate](https://lubridate.tidyverse.org/) and [hms](https://hms.tidyverse.org/) packages from [tidyverse](https://www.tidyverse.org/), 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](https://youtu.be/eelVqfm8vVc), we strongly recommend that you use appropriate temporal objects while dealing with date/time in R. This 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 those used in the MCTQ publications. This 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).
Example:
```{r, message = FALSE}
# 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)
```
### 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 sleep-corrected local time of mid-sleep on work-free days.
* `sjl()` and `sjl_rel()`: compute MCTQ social jet lag.
* `sjl_sc()` and `sjl_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:
```{r, message = FALSE}
# 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)
```
See a quick tour of all MCTQ main functions [here](https://docs.ropensci.org/mctq/articles/mctq.html).
### Utilities
`mctq` is also equipped with some utility tools to help with your MCTQ data. The package also provides fictional datasets of the standard, micro, and shift MCTQ versions for testing and learning purposes.
All functions are properly documented, showing all the guidelines behind the computations. Click [here](https://docs.ropensci.org/mctq/reference/index.html) to see a list of them.
## Citation
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.
```{r}
citation("mctq")
```
## Contributing
We welcome contributions, including bug reports.
Take a moment to review our [Guidelines for Contributing](https://docs.ropensci.org/mctq/CONTRIBUTING.html).
## Acknowledgments
The initial development of `mctq` was supported by a scholarship provided by the [University of Sao Paulo (USP)](http://usp.br/) (❤️).
`mctq` hex logo was based on an illustration created by [hilda design matters Zurich](https://hilda.ch/) for the [Daylight Academy (DLA)](https://daylight.academy/).
<br>
Become an `mctq` supporter!
Click [here](https://github.com/sponsors/danielvartan) to make a donation. Please indicate the `mctq` package in your donation message.