Introduction to R

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.title[
# Introduction to R
]
.subtitle[
## R Foundations for Life Scientists
]
.author[
### Marcin Kierczak
]

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# Contents

* [About R](#about)
* [Timeline](#timeline)
* [Ideas behind R](#ideas)
* [Pros and cons of R](#pros_and_cons)
* [Ecosystem of packages](#num_packages)

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# Briefly about R

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# R is ...

* a programming language
* a programming platform (= environment + interpreter)
* a software project driven by the core team and the community
* a very powerful tool for statistical computing
* a very powerful computational tool in general
* a catalyst between an idea and its realization

]

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# R is not ...

* a tool to replace a statistician
* the very best programming language
* the most elegant programming solution
* the most efficient programming language

]

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# Yet ...

* it is very elegant
* it becomes more and more feature-rich

]

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![](data/common/r.png)
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# Timeline

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![](data/slide_intro/Ihaka_and_Gentleman.jpg)

* ca. 1992 &mdash; conceived by [Robert Gentleman](https://bit.ly/35kn99L) and [Ross Ihaka](https://en.wikipedia.org/wiki/Ross_Ihaka) (R&R) at the University of Auckland, NZ as a tool for **teaching statistics**

* 1994 &mdash; initial version
* 2000 &mdash; stable version

]

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![](data/slide_intro/jjallaire_siliconangle_com.jpg)

* 2011 &mdash; [RStudio](https://en.wikipedia.org/wiki/RStudio), first release by J.J. Allaire

![](data/slide_intro/hadley-wickham.jpg)

* ca. 2017 &mdash; Tidyverse by [Hadley Wickham](https://en.wikipedia.org/wiki/Hadley_Wickham)
]

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# Ideas behind R

* open-source solution &mdash; fast development

* based on the [S language](https://en.wikipedia.org/wiki/S_%28programming_language%29) created at the Bell Labs by [John Mc Kinley Chambers](https://bit.ly/2RhDqUx) to

> *turn ideas into software, quickly and faithfully*

* [lexical scope](https://en.wikipedia.org/wiki/Scope_%28computer_science%29%23Lexical_scoping) inspired by [Lisp](https://en.wikipedia.org/wiki/Lisp) syntax

* since 1997 developed by the R Development Core Team (ca. 20 experts, with Chambers onboard; 6 are active)

* overviewed by [The R Foundation for Statistical Computing](https://www.r-project.org/foundation/)

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# Packages

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<img src="data/slide_intro/packages.jpg" width="250pt" style="display: block; margin: auto;" />
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* developed by the community

* cover several very diverse areas of science/life

* uniformely structured and documented

* organised in repositiries:
    + [CRAN](https://cran.r-project.org)
    + [R-Forge](https://r-forge.r-project.org)
    + [Bioconductor](http://www.bioconductor.org)
    + [GitHub](https://github.com)

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# Pros and cons

steep learning curve

--
 uniform, clear and clean system of documentation and help

--
 difficulties due to a limited object-oriented programming capabilities, 
e.g. an agent-based simulation is a challenge

--
 good interconnectivity with compiled languages like Java or C

--
 cannot order a pizza for you (?)

--
 a very powerful ecosystem of packages

--
 free and open source, GNU GPL and GNU GPL 2.0

--
 easy to generate high quality graphics

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# Ecosystem of R packages

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# Thank you! Questions?

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Graphics from <img src="./assets/freepik.jpg" style="max-height:20px; vertical-align:middle;"> 
Created: 25-Oct-2023 • <a href="https://www.scilifelab.se/">SciLifeLab</a> • <a href="https://nbis.se/">NBIS</a> 

]