r.Rmd

# R and Reproducible Analysis {#r}

*Can everything be re-done easily if I change one data point in the inputs?*

At EHA **R** is our primary, though not exclusive, tool for analysis and
modeling work. R is not just a piece of software for statistics and data
manipulation but a computer language, meaning that our analyses are *scripted*.  This means they
thus can be automated, run again, built upon and extended.

## Install

-   [R itself](https://cran.r-project.org/)
-   [RStudio](https://www.rstudio.com/), the leading R development environment.

## Learn

Learning R is beyond the scope of this document, and you likely already have
some experience in it, but some good starting points are:

-   [The Software Carpentry Lessons](http://swcarpentry.github.io/r-novice-gapminder/)
-   [Swirl](http://swirlstats.com/), a set of interactive lessons run right
    in R.
-   [The JHU Coursera Series](https://www.coursera.org/specializations/jhu-data-science)
-   [R for Data Science by Hadley Wickam](http://r4ds.had.co.nz/) is a
    beginner/intermediate text that we highly recommend for getting up to speed
    with the particular workflows we recommend and the most recent packages that
    support them.
-   [Advanced R (Wickham)](http://adv-r.had.co.nz/) is very good for
    understanding how the language works.
-   [Efficient R by Colin Gillespie and Robin
    Lovelace](https://csgillespie.github.io/efficientR/) is helpful for
    imporving workflows and speeding up code.
-   [R Packages (Wickham)](http://r-pkgs.had.co.nz/) is good for package
    development.
-   [Cheatsheets](https://www.rstudio.com/resources/cheatsheets/) from RStudio
    are a useful references for a number of things.

[Dataquest courses](https://www.dataquest.io/path/data-analyst-r/) are also potentially useful.
If you feel they would match your learning style and needs, discuss EHA purchasing a subscription for you with your supervisor.

These resources are largely about the mechanics of programming in R, rather
than using it for statistical analyses.  This is a far larger subject, but see
the [Statistical Methods] section for a jumping-off point.  

## Additional Resources

**Trouble shooting your code**: [Getting Help]. 

**User groups/communities of practice**: [R Meetups](https://www.meetup.com/pro/r-user-groups)

**Specific domains**: [Training Plans]