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---
editor:
markdown:
wrap: 72
---
## Introduction
This workshop is designed to provide beginners with foundational
understanding of R programming language. Through a combination of
theoretical explanations, hands-on coding exercises, and practical
applications, participants will learn how to leverage R for data
analysis, manipulation and visualization cancer biology datasets.
The workshop will cover essential programming concepts and gradually
introduce more advanced topics, with a focus on using the tidyverse
package suite for efficient data handling, analysis and visualization.
The aim of this workshop is to improve the reproducibility and
efficiency of scientific research by teaching powerful tools in data
analysis and creating informative plots.
## Learning Objectives
Participants will gain the following skills:
- Proficiency in using R and RStudio for data analysis.
- Basic R programming skills.
- Reading, tidying, and joining datasets using `readr` and `tidyr`
packages.
- Data manipulation and transformation using `dplyr` package.
- Creating various types of plots using `ggplot2` package.
## Prerequisites
Before starting this course you will need to ensure that your computer
is set up with the required software. If you have any difficulty
installing any of this software then please contact one of the trainers
for help.
### Installing R and RStudio
**R** and **RStudio** are separate downloads and installations.
**R** is the underlying statistical computing environment. The base R
system and a very large collection of packages that give you access to a
huge range of statistical and analytical functionality are available
from [CRAN](https://cran.r-project.org), the Comprehensive R Archive
Network.
However, using R alone is no fun. **RStudio** is a graphical integrated
development environment (IDE) that makes using R much easier and more
interactive.
#### Option 1: Local Installation
You need to install R before you install RStudio.
```{=html}
<details>
<summary style="font-weight: 600;" >
Windows
</summary>
```
- **If you already have R and RStudio installed:**
- Open RStudio, and click on "Help" \> "Check for updates". If a
new version is available, quit RStudio, and download the latest
version for RStudio.
- To check which version of R you are using, start RStudio and the
first thing that appears in the console indicates the version of
R you are running. Alternatively, you can type `sessionInfo()`,
which will also display which version of R you are running. Go
on the [CRAN
website](https://cran.r-project.org/bin/windows/base/) and check
whether a more recent version is available. If so, please
download and install it. You can [check
here](https://cran.r-project.org/bin/windows/base/rw-FAQ.html#How-do-I-UNinstall-R_003f)
for more information on how to remove old versions from your
system if you wish to do so.
- **If you don't have R and RStudio installed:**
- Download R from the [CRAN
website](https://cran.r-project.org/bin/windows/base/release.htm).
- Run the `.exe` file that was just downloaded
- Go to the [RStudio download
page](https://www.rstudio.com/products/rstudio/download/#download)
- Under *Installers* select **RStudio x.yy.zzz - Windows 10/8/7**
where x, y, and z represent version numbers)
- Double click the file to install it
- Once it's installed, open RStudio to make sure it works and you
don't get any error messages.
</details>
```{=html}
<details>
<summary style="font-weight: 600;" >macOS</summary>
```
- **If you already have R and RStudio installed:**
- Open RStudio, and click on "Help" \> "Check for updates". If a
new version is available, quit RStudio, and download the latest
version for RStudio.
- To check the version of R you are using, start RStudio and the
first thing that appears on the terminal indicates the version
of R you are running. Alternatively, you can type
`sessionInfo()`, which will also display which version of R you
are running. Go on the [CRAN
website](https://cran.r-project.org/bin/macosx/) and check
whether a more recent version is available. If so, please
download and install it.
- **If you don't have R and RStudio installed:**
- Download R from the [CRAN
website](https://cran.r-project.org/bin/macosx/).
- Select the `.pkg` file for the latest R version
- Double click on the downloaded file to install R
- It is also a good idea to install
[XQuartz](https://www.xquartz.org/) (needed by some packages)
- Go to the [RStudio download
page](https://www.rstudio.com/products/rstudio/download/#download)
- Under *Installers* select **RStudio x.yy.zzz - Mac OS X 10.6+
(64-bit)** (where x, y, and z represent version numbers)
- Double click the file to install RStudio
- Once it's installed, open RStudio to make sure it works and you
don't get any error messages.
</details>
```{=html}
<details>
<summary style="font-weight: 600;" >
Linux
</summary>
```
- Follow the instructions for your distribution from
[CRAN](https://cloud.r-project.org/bin/linux), they provide
information to get the most recent version of R for common
distributions. For most distributions, you could use your package
manager (e.g., for Debian/Ubuntu run `sudo apt-get install r-base`,
and for Fedora `sudo yum install R`), but we don't recommend this
approach as the versions provided by this are usually out of date.
In any case, make sure you have at least R 4.3.2.
- Go to the [RStudio download
page](https://www.rstudio.com/products/rstudio/download/#download)
- Under *Installers* select the version that matches your
distribution, and install it with your preferred method (e.g., with
Debian/Ubuntu `sudo dpkg -i rstudio-x.yy.zzz-amd64.deb` at the
terminal).
- Once it's installed, open RStudio to make sure it works and you
don't get any error messages.
</details>
#### Option 2: Using Open On Demand on Peter Mac Cluster
```{=html}
<details>
<summary style="font-weight: 600;" >
Requesting Access to Peter Mac Cluster
</summary>
```
To request access to the Cluster
1. Login to [SNOW](https://rchauprod.service-now.com/snow?id=pmcc_home)
and create an IT Service Desk Request as shown
[here](https://confluence.petermac.org.au/pages/viewpage.action?pageId=161024525).
2. Complete the required fields, including the following information.
- **Request Item:** (Other)
- **Service:** Research Computing Cluster
- **Subject:** HPC Cluster Account for \$first_name \$last_name
- **Description:** Please include the name of the person who needs the
account, their lab group, their manager or supervisor and what
experience they have with using a HPC cluster (e.g. none, some,
experienced).
</details>
```{=html}
<details>
<summary style="font-weight: 600;" >
Using Open On Demand
</summary>
```
**Open OnDemand** is a web portal that provides access to PeterMac file
systems and cluster resources. It allows to view, edit, upload and
download files, create, edit, submit and monitor jobs, run GUI
applications, and connect via SSH, all via a web browser and with a
minimal knowledge of Linux and scheduler commands. It enables running
RStudio on the cluster giving you access to more memory than with our
RStudio Pro server. Future work will enable it to use Jupyter notebooks
on the cluster.
**How to login**
Requirements:
- Must have a cluster account.
- If want to use remotely:
- Must have remote access to Citrix or VPN.
- With Citrix must use Peter Mac Virtual Desktop and the Firefox
inside that.
- Username: your short cluster username not your Peter Mac computer
username (i.e., first_letter_of_first_namelast_name ex: srajapaksa
NOT last_namefirst_name ex: rajapaksasandun)
- Password: Peter Mac cluster password.
In a browser navigate to <https://research-cluster.petermac.org.au> and
enter your login, as in:
{fig-align="center" width="500"}
If you see the following Unauthorized error instead of the login prompt,
you will need to use the Citrix Edge Browser.
{fig-align="center" width="500"}
You should then see in the Open OnDemand Dashboard as in:
{fig-align="center" width="500"}
</details>
### Installing R Packages
On this course we will be making use of a brilliant collection of
packages designed for data science called the **`tidyverse`** that make
it much easier and more fun to work with your data. After installing R
and RStudio, follow the instructions below to install the `tidyverse`
package suite.
- After starting RStudio, at the console type:
`install.packages("tidyverse")` (look for the 'Console' tab and type
at the `>` prompt)
- You can also do this by going to Tools -\> Install Packages and
typing the names of the packages separated by a comma.
## Data
The Metabric study characterized the genomic mutations and gene
expression profiles for 2509 primary breast tumours. In addition to the
gene expression data generated using microarrays, genome-wide copy
number profiles were obtained using SNP microarrays. Targeted sequencing
was performed for 2509 primary breast tumours, along with 548 matched
normals, using a panel of 173 of the most frequently mutated breast
cancer genes as part of the Metabric study.
**Refrences:**
- [Curtis *et al.*, Nature 486:346-52,
2012](https://pubmed.ncbi.nlm.nih.gov/22522925)
- [Pereira *et al.*, Nature Communications 7:11479,
2016](https://www.ncbi.nlm.nih.gov/pubmed/27161491)
Both the clinical data and the gene expression values were downloaded
from
[cBioPortal](https://www.cbioportal.org/study/summary?id=brca_metabric).
We excluded observations for patient tumor samples lacking expression
data, resulting in a data set with fewer rows.
## Into the tidyverse
The core tidyverse includes the packages that you're likely to use in
everyday data analyses. Therefore, this workshop offers an introduction
to these core packages. As of tidyverse 1.3.0, the following packages
are included in the core tidyverse:
::: {#fig-datatypes}
{fig-align="center"}
Hex logos for the eight core tidyverse packages and their primary
purposes. Image
source:<https://education.rstudio.com/blog/2020/07/teaching-the-tidyverse-in-2020-part-1-getting-started/>
:::
- [**ggplot2**](https://ggplot2.tidyverse.org/): *Grammar of
Graphics.* Enables the creation of graphics in a declarative manner.
- [**dplyr**](https://dplyr.tidyverse.org/): *Grammar for data
manipulation*. Presents a set of verbs to address common challenges
in data manipulation.
- [**tidyr**](https://tidyr.tidyverse.org/): Provides a collection of
functions for achieving tidy data.
- [**readr**](https://readr.tidyverse.org/): Facilitates the rapid and
user-friendly reading of rectangular data (e.g., csv, tsv, and fwf).
- [**purrr**](https://purrr.tidyverse.org/): *Functional programming
toolkit.* Offers a set of tools for efficient work with functions
and vectors.
- [**tibble**](https://tibble.tidyverse.org/): *Tibbles*, a modern
re-imagining of the data frame, offering a more user-friendly and
efficient approach to handling tabular data.
- [**stringr**](https://stringr.tidyverse.org/): Provides a set of
functions designed to simplify and enhance string manipulations.
- [**forcats**](https://forcats.tidyverse.org/): Provides a suite of
useful tools for handling and manipulating categorical variables
(*factors*).
## Credits and Acknowledgement
These content were adapted from the following course materials:
- [R for Data Science book](https://r4ds.had.co.nz/index.html)
- [OHI Data Science
Training](http://ohi-science.org/data-science-training/index.html)
- [Data Carpentry](https://datacarpentry.org)
- [WEHI tidyr
coursebook](https://bookdown.org/ansellbr/WEHI_tidyR_course_book/)
by Brendan R. E. Ansell
- content developed by Maria Doyle.
------------------------------------------------------------------------