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index.qmd
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# Welcome! {.unnumbered}
[](https://creativecommons.org/licenses/by/4.0/)
[](https://doi.org/10.5281/zenodo.4061900)
```{r write-packages-to-bib, include=FALSE}
# automatically create a bib database for R packages
knitr::write_bib(
unique(desc::desc_get_deps()$package[-1]),
here::here("includes/packages.bib")
)
```
Reproducibility and open scientific practices are increasingly demanded
of, and needed by, scientists and researchers in our modern research
environments. As we our tools for generating data become more
sophisticated and powerful, we also need to start using more
sophisticated and powerful tools for processing it. Training on how to
use these tools and build modern data analysis skills is lacking for
researchers, even though this work is highly time-consuming and
technical. As a consequence of this unawareness of the need for these
skills, how *exactly* data is processed is poorly, if at all, described
in scientific studies. This hidden aspect of research could have major
impacts on the reproducibility of studies. Therefore, this course was
created specifically to start addressing these types of problems.
The course is designed as a series of participatory live-coding lessons,
where the instructor and learner code together, and is interspersed with
hands-on exercises and group work using real-world datasets. This
website contains all of the material for the course, from reading
material to exercises to images. It is structured as a book, with
"chapters" as lessons, given in order of appearance. We make heavy use
of the website throughout the course where code-along sessions follow
the material on the website nearly exactly (with slight modifications
for time or more detailed explanations).
The course material was created using [Quarto](https://quarto.org) to
write the lessons and create the book format,
[GitHub](https://github.com/) to host the [Git](https://git-scm.com/)
repository of the material, and [GitHub
Actions](https://github.com/features/actions) with
[Netlify](https://www.netlify.com/) to build and host the website. The
original source material for this course is found on the
[`r-cubed-intermediate`](https://github.com/rostools/r-cubed-intermediate)
GitHub repository.
Want to contribute to this course? Check out the
[README](https://github.com/rostools/r-cubed-intermediate/blob/main/README.md)
file as well as the
[CONTRIBUTING](https://github.com/rostools/r-cubed-intermediate/blob/main/CONTRIBUTING.md)
file on the GitLab repository for more details. The main way to
contribute is by using [GitHub](https://github.com/) and creating a [new
Issue](https://github.com/rostools/r-cubed-intermediate/issues/new) to
make comments and give feedback for the material.
## Target audiences
This website and its content are targeted to three groups:
1. For the **learners** to use during the course, both to follow along
in case they get lost and also to use as a reference after the
course ends. The learner is someone who is currently or will soon
actively be doing research (e.g. a PhD or postdoc), who is likely in
biomedical research, and who has no or little knowledge on coding
in R. A more detailed description of who the learner is can be found
in @sec-is-it-for-you.
2. For the **instructors** to use as a guide for when they do the
code-along sessions and lectures.
3. For those who are **interested in teaching**, who may not have much
experience or may not know where to start, to use this website as a
guide to running and instructing their own workshops.
## Re-use and licensing {#licensing}
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/"><img src="https://i.creativecommons.org/l/by/4.0/80x15.png" alt="Creative Commons License" style="border-width:0"/></a>
The course is licensed under the [Creative Commons Attribution 4.0
International License](https://creativecommons.org/licenses/by/4.0/) so
the material can be used, re-used, and modified, as long as there is
attribution to this source.
## Acknowledgements
The course material draws inspiration from these excellent resources:
- [R for Data Science](https://r4ds.had.co.nz/)
- [Advanced R](https://adv-r.hadley.nz/)
- [R Packages](https://r-pkgs.org/)
- [UofTCoders Reproducible Quantitative Methods for
EEB](https://uoftcoders.github.io/rcourse/)
- [Software and Data Carpentry](https://carpentries.org/) workshop
material
The [Danish Diabetes and Endocrinology
Academy](https://www.ddeacademy.dk/) hosted, organized, and sponsored
this course. A huge thanks to them for their involvement, support, and
sponsorship! [Steno Diabetes Center Aarhus](https://www.stenoaarhus.dk/)
and [Aarhus University](https://international.au.dk/) employs Luke, who
is the lead instructor and curriculum developer.
{width="90px"}
{width="55px"}