Introduction to R

.title[
# Introduction to R
]
.subtitle[
## R Foundations for Data Analysis
]
.author[
### Marcin Kierczak and Nima Rafati
]

---

---
name: content
class: spaced

# Contents

* [Why R](#whyR)
* [About R](#about)
* [Pros and cons of R](#pros_and_cons)
* [Ecosystem of packages](#num_packages)
* [Programming language](#programming_language)
* [Packages](#packages)
* [Package installation](#pkg_cran_inst)

---
name: whyR

# WHY R?
.pull-left-50[

![](data/slide_intro/Lab_data_no_sense.png)

]

# With the power of R, your project can ARISE:

**A**utomative: Streamline your analysis

**R**eproducible: Reproduce the results

**I**nterpretable: Make sense

**S**hareable: Share with colleagues/mentors (FAIR)

**E**xplainable: Easy to explain

---
name: about

# Briefly about R

# 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

]

# R is not ...

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

]

# Yet ...

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

]

---
name: timeline

---
name: pros_and_cons
class: spaced

# 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

---
name: programming_language

# Programing Language

--
> Programming is a process of instructing a computer to perform a specific task. We write these instructions by **programming language**. It can be as simple as calculation (like a calculator) or complex applications.

* flow of _data_ 
--

* Data is collected information which qualitatively and/or quantitatively describe an entity.  
--

* Data is collected from quite diverse sources (data types).  
--

* Data processing.  
--

* Data cleaning.

<img src="data/slide_programming/Data_Information_Knowledge.png" width="75%" style="display: block; margin: auto;" />
---
# Programing Language cted.

* from one _function_ to another
--

* Function is a **reusable** chunk of code that performs a task. It takes **inputs** as well as **arguments** to process. 
--

* each function does something to the data and return output(s)
--
   
   * For example `mean()`, `min()`   
--
---
# Three things to think about

* what *types* of data can I process?

* how do I *write* what I want?

* when does it *mean* anything?

---
# Data type

<img src="data/slide_programming/Data_classification.png" width="75%" style="display: block; margin: auto;" />
---

# Three components of a language

* what *types* of data can I process &mdash; *type system*

* int &mdash; 1 2 5 9
  * double &mdash; 1.23 -5.74
  * char &mdash; a b test 7 9
  * logical &mdash; TRUE/FALSE (T/F)

* how do I *write* what I want &mdash; *syntax* defined by a language *grammar*

`2 * 1 + 1` vs. `(+ (* 2 1) 1)`

* when does it *mean* anything &mdash; *semantics*

* *Colorful yellow train sleeps on a crazy wave.* &mdash; has no generally accepted meaning
  * *There is $500 on his empty bank acount.* &mdash; internal contradiction

---
name: topic2

# Where to start?

*Divide et impera* &mdash; divide and rule.

**Top-down approach:** define the big problem and split it into smaller ones. Assume you have solution to the small problems and continue &mdash; push the responsibility down.
Wishful thinking!

---

# Packages

.pull-right-50[
<img src="data/slide_intro/packages.jpg" width="250pt" style="display: block; margin: auto;" />
]

* developed by the community

* cover several very diverse areas of science/life

* uniformly 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)

---
name: num_packages
# Ecosystem of R packages

---
name: work_with_packages

# Working with packages

Packages are organised in repositories. The three main repositories are:

* [CRAN](https://cran.r-project.org)
* [R-Forge](http://r-forge.r-project.org)
* [Bioconductor](http://www.bioconductor.org)

We also have [GitHub](https://github.com).

--
# Working with packages -- CRAN example.

---
name: pkg_cran_inst

# Working with packages -- installation

Only a few packages are pre-installed:

``` r
library(modelr)
```

In order to install a package from command line, use:

``` r
install.packages("ggplot2",dependencies=TRUE)
```

---
name: work_pkg_details

# Working with packages -- details

It may happen that you want to also specify the repository, e.g. because it is geographically closer to you or because your default mirror is down:

``` r
install.packages('ggplot2',dependencies=TRUE,repos="http://cran.se.r-project.org")
```

But, sometimes, this does not work either because the package is not available for your platform. In such case, you need to *compile* it from its *source code*.

---
name: work_pkg_details2

# Working with packages -- details cted.
<img src="data/slide_r_environment/ggplot2_CRAN.png" width="150%" style="display: block; margin: auto;" />

---
name: source_pkg_inst

# Working with packages -- installing from source.

- Download the source file, in our example *ggplot2_3.4.3.tar.gz*.
- Install it:

``` r
install.packages("path/to/ggplot2_3.4.3.tar.gz",
                 repos=NULL,
                 type='source',
                 dependencies=TRUE)
```

- Load it:

``` r
library('ggplot2') # always forces reloading
require('ggplot2') # load only if not already loaded
```

- Enjoy!

---
name: pkg_github

# Packages -- GitHub

Nowadays, more and more developers distribute their packages via GitHub. The easiest way to install packages from the GitHub is via the *devtools* package:

- Install the *devtools* package.
- Load it.
- Install.
- Enjoy!

``` r
install.packages('devtools',dependencies=TRUE)
library('devtools')
install_github('talgalili/installr')
```

---
name: pkg_bioconductor

# Packages -- Bioconductor

First install Bioconductor Manager:

``` r
if (!requireNamespace("BiocManager",quietly = TRUE))
    install.packages("BiocManager")
```

---
name: pkg_bioconductor2

# Packages -- Bioconductor cted.

Now, you can install particular packages from Bioconductor:

``` r
BiocManager::install("GenomicRanges")
```

For more info, visit [Bioconductor website](http://www.bioconductor.org/install/).

---
# One package to rule them all -- the magic of `renv`

- first time do `renv::activate()` and `renv::init()`
- while working: `renv::hydrate()` and `renv::snapshot()`

Now, send `renv.lock` to your friend to share the environment and she can:

- restore the environment `renv::restore()`

**Pure magic!**

---
name: end_slide
class: end-slide, middle
count: false

# Thank you! Questions?

.end-text[

Graphics from <img src="./assets/freepik.jpg" style="max-height:20px; vertical-align:middle;"> 
Created: 31-Oct-2024 • <a href="https://www.scilifelab.se/">SciLifeLab</a> • <a href="https://nbis.se/">NBIS</a> 

]