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…eking Help + Data Structures (#787)

* Added explanation of arguments in Intro so later lessons can build on this knowledge #773

* Added help file example to prime learners to write.csv() in next lesson #773

* Added explanation of why its handy to call arguments explicitly in Challenge 2 #773

* Added reminder to call arguments explicitly and remind learners about using help files to check default values #773

* Update _episodes_rmd/01-rstudio-intro.Rmd

Correction to grammar L241

Co-authored-by: Matthieu Bruneaux <matthieu.bruneaux@gmail.com>

* Update _episodes_rmd/03-seeking-help.Rmd

Additional info on default argument vales L43

Co-authored-by: Matthieu Bruneaux <matthieu.bruneaux@gmail.com>

* Update _episodes_rmd/03-seeking-help.Rmd

Some formatting improvements and changes in language to be more generalised

Co-authored-by: Matthieu Bruneaux <matthieu.bruneaux@gmail.com>

* Update _episodes_rmd/03-seeking-help.Rmd

Added more detail and explanation L59

Co-authored-by: Matthieu Bruneaux <matthieu.bruneaux@gmail.com>

* Update _episodes_rmd/03-seeking-help.Rmd

Formatting fix for challenges template L188

Co-authored-by: Matthieu Bruneaux <matthieu.bruneaux@gmail.com>

* Update _episodes_rmd/04-data-structures-part1.Rmd

Grammatical correction L44

Co-authored-by: Matthieu Bruneaux <matthieu.bruneaux@gmail.com>

* Update _episodes_rmd/03-seeking-help.Rmd

Grammatical correction L186

Co-authored-by: Matthieu Bruneaux <matthieu.bruneaux@gmail.com>

* Add `eval=FALSE` option to code chunks calling `?write.table`

I added the `eval=FALSE` option to two R code chunks calling `?write.table` and `?write.csv`.

This is to avoid the R markdown rendering process pausing in some situations, where it would open the corresponding help pages in the terminal while rendering the R markdown document and wait for user input to quit the help pages and continue with the R markdown rendering.

* Add one `eval=FALSE` to 01-rstudio-intro.Rmd

This was to avoid getting the output from `getwd()` in the rendered page, since it might be confusing for the reader to have something different on the lesson page and on their computer.

Co-authored-by: Matthieu Bruneaux <matthieu.bruneaux@gmail.com>
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R for Reproducible Scientific Analysis

An introduction to R for non-programmers using the Gapminder data. Please see https://swcarpentry.github.io/r-novice-gapminder for a rendered version of this material, the lesson template documentation for instructions on formatting, building, and submitting material, or run make in this directory for a list of helpful commands.

The goal of this lesson is to teach novice programmers to write modular code and best practices for using R for data analysis. R is commonly used in many scientific disciplines for statistical analysis and its array of third-party packages. We find that many scientists who come to Software Carpentry workshops use R and want to learn more. The emphasis of these materials is to give attendees a strong foundation in the fundamentals of R, and to teach best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation.

Note that this workshop focuses on the fundamentals of the programming language R, and not on statistical analysis.

The lesson contains more material than can be taught in a day. The [instructor notes page]({{ page.root }}/guide) has some suggested lesson plans suitable for a one or half day workshop.

A variety of third party packages are used throughout this workshop. These are not necessarily the best, nor are they comprehensive, but they are packages we find useful, and have been chosen primarily for their usability.

Maintainers: