10-community.Rmd

# An awesome community

::: {.goals}
-   How to engage with the R community
-   Great free learning resources
:::

We are almost at the end of this short introduction to R. We have barely scratched the
surface of what R has to offer, but I hope, this has helped you figure out whether R is
for you and whether you want to learn more about it. If you do, I think the best way is to
find other people to learn from.

## Get involved with the community

R has an incredibly friendly, diverse, and welcoming community that is very open to
beginners and loves to share knowledge, and because R is so widespread, there's always an
R nerd nearby who's willing to help. Don't be afraid to ask questions! Here are some ideas
how you could engage with the community:

-   The Twitter hashtag `#RStats` is a good way keep track of all the new things in the R
    world and "talk shop" with other R nerds.

-   R conferences and user groups are a good place to meet other R users. Jumping rivers
    is maintaining a list. (<https://jumpingrivers.github.io/meetingsR/index.html>). If
    there is non in your area, why not create your own?

-   There is also a *R for Data Science* learning community that organizes community
    learning events such as *Tidy Tuesday* -- a weekly data visualization challenge.
    Participate in [Tidy Tuesday](https://github.com/rfordatascience/tidytuesday) to
    sharpen your data wrangling and data visualization skills and see how other people are
    approaching the same data.

-   [Write your own package or contribute to
    packages.](https://www.rstudio.com/resources/rstudioglobal-2021/make-a-package-make-some-friends/)
    This is an excellent way to become a better R programmer.

## How to continue learning?

Another thing that is unique about the R community is that R is mainly used by (and taught
to) people who are not software engineers or computer scientists. As a result there is a
lot of learning material that is made for people without a technical background.
Amazingly, much of it is generously made available for free *and* really good! I will
present just a few personal recommendations. The [Awesome R Learning Resources
List](https://github.com/iamericfletcher/awesome-r-learning-resources) provides a more
comprehensive list of resources for learning R, and Flavio Azevedo has compiled [a list of
R Tutorials on
YouTube](http://flavioazevedo.com/stats-and-r-blog/2016/9/13/learning-r-on-youtube). R
user groups and conferences also often put their talks online. See, for example, the
[RStudio presentations](https://www.rstudio.com/resources/webinars/).

An excellent book to pick up next would be [*R for Data
Science*](https://r4ds.had.co.nz/). It provides a comprehensive overview of the Tidyverse.
If you are working in education, then also check out [*R for
Education*](https://rstudio4edu.github.io/rstudio4edu-book/) --- a handbook for teaching
and learning with R and RStudio.

Finally, the Carpentries are an organisation that teaches scientific computing skills to
researchers, including R courses for beginners. Many universities and research institutes
offer Carpentries courses for free to their students and employees. The material is
available online, e.g., [R for Social Sciences](https://datacarpentry.org/r-socialsci/)
and [R for Ecologists](https://datacarpentry.org/R-ecology-lesson/).