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Knit directory: analysis-user-group/

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This 10-chapter workshop series is designed to introduce users to R programming with a focus on bioinformatics workflows and reproducibility. It emphasizes the mindset shift required to transition from manual tools like Excel to programmatic data analysis.

Estimated time: 1.5 hours

Start the Tutorial →

Introduction: Shifting mindsets to programming

Goals:
Introduce programming concepts and the mindset behind programmatic workflows.
  • Transition from manual tools (e.g. Excel & Prism) to programming-based workflows.
  • Understand the concept of handling data using code.
  • Learn to navigate directories and file paths programmatically.
  • Emphasize reproducibility and structured workflows.

Go to Chapter →

Chapter 1: Getting Started with R and RStudio

Goals:
Set up R and RStudio, and get familiar with the interface.
  • Understand what a programming language is.
  • Know the difference between R, RStudio and python.
  • Install R and RStudio.
  • Exploring the interface – Console, Environment, Scripts.
  • Installing and loading packages.
  • Start your first project.

Go to Chapter →

Chapter 2: Data Types and Structures

Goals:
Learn about data types and structures in R.
  • Basic data types: numeric, character, logical.
  • Data structures: vectors, matrices, data frames, lists.
  • Assessing and querying data types and structures.
  • Importing data (RDS, Excel, CSV files).

Go to Chapter →

Chapter 3: Basic Programming

Goals:
Understand programming logic for automating tasks.
  • Variables and assignments.
  • Conditional statements (if, else).
  • Loops (for, while).
  • Writing functions.

Go to Chapter →

Coming Soon

Chapter 4: Data Manipulation with tidyverse

Goals:
Introduce tidyverse for filtering, mutating, summarizing and pivoting data.
  • Filtering rows (filter()), selecting columns (select()).   • Adding new columns (mutate()).   • Grouping and summarizing (group_by() + summarize()).   • Pivot longer and Pivot wider

Go to Chapter →

Chapter 5: Visualization with ggplot2 – Part 1

Goals:
Create and customize visualizations using ggplot2.
  • Basics of ggplot2
  • Make a scatterplot, boxplot and column graph
  • Customise labels, themes, and colors.

Go to Chapter →

Chapter 6: Advanced Visualization with ggplot2 – Part 2

Goals:
Learn additional visualisation techniques.
  • Adjusting theme elements
  • Annotating the plots
  • facet_wrap() and grid_plot()
  • Savings plots

Go to Chapter →

Chapter 7: Statistical Analysis

Goals:
Apply simple statistic to the synthetic data.
  • Descriptive statistics: mean, median, mode.
  • Hypothesis testing (t-tests, ANOVA).
  • Correlation and regression.

Go to Chapter →

Chapter 8: Reproducible Reports with RMarkdown

Content coming soon!

Goals:
Build a dynamic and shareable report of your analysis.
  • Introduction to RMarkdown.
  • Combining text, code, and visuals.
  • Generate tables with knitr.
  • Editing the yaml for additional customisations.
  • Exporting to PDF and HTML.

Go to Chapter →

Conclusions

Go to Chapter →


sessionInfo()
R version 4.4.0 (2024-04-24)
Platform: aarch64-apple-darwin20
Running under: macOS Sonoma 14.3

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Australia/Sydney
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] workflowr_1.7.1

loaded via a namespace (and not attached):
 [1] vctrs_0.6.5       httr_1.4.7        cli_3.6.3         knitr_1.49       
 [5] rlang_1.1.4       xfun_0.50         stringi_1.8.4     processx_3.8.4   
 [9] promises_1.3.2    jsonlite_1.8.9    glue_1.8.0        rprojroot_2.0.4  
[13] git2r_0.35.0      htmltools_0.5.8.1 httpuv_1.6.15     ps_1.8.1         
[17] sass_0.4.9        fansi_1.0.6       rmarkdown_2.29    tibble_3.2.1     
[21] jquerylib_0.1.4   evaluate_1.0.1    fastmap_1.2.0     yaml_2.3.10      
[25] lifecycle_1.0.4   whisker_0.4.1     stringr_1.5.1     compiler_4.4.0   
[29] fs_1.6.5          pkgconfig_2.0.3   Rcpp_1.0.13-1     rstudioapi_0.17.1
[33] later_1.4.1       digest_0.6.37     R6_2.5.1          utf8_1.2.4       
[37] pillar_1.9.0      callr_3.7.6       magrittr_2.0.3    bslib_0.8.0      
[41] tools_4.4.0       cachem_1.1.0      getPass_0.2-4    
---
title: "Getting Started in R (2025)"
author: "Thomas R. O'Neil"
output:
  workflowr::wflow_html:
    toc: no
---

```{html, include=F}
<script>
document.addEventListener('DOMContentLoaded', function () {
  const checkboxes = document.querySelectorAll('input[type="checkbox"]');
  checkboxes.forEach((checkbox) => {
    checkbox.checked = localStorage.getItem(checkbox.id) === 'true';
    checkbox.addEventListener('change', () => {
      localStorage.setItem(checkbox.id, checkbox.checked);
    });
  });
});
</script>
```

This 10-chapter workshop series is designed to introduce users to R programming with a focus on bioinformatics workflows and reproducibility. It emphasizes the mindset shift required to transition from manual tools like Excel to programmatic data analysis.

<read-time><i>**Estimated time**: 1.5 hours</i></read-time><wimr>

[Start the Tutorial →](https://drthomasoneil.github.io/analysis-user-group/r-tutorial/_book/index.html)

<wimr>

## **Introduction: Shifting mindsets to programming**

<div class="hint-goals">    
**Goals:**    
Introduce programming concepts and the mindset behind programmatic workflows.   
&nbsp;&nbsp;&#8226; Transition from manual tools (e.g. Excel & Prism) to programming-based workflows.  
&nbsp;&nbsp;&#8226; Understand the concept of handling data using code.  
&nbsp;&nbsp;&#8226; Learn to navigate directories and file paths programmatically.  
&nbsp;&nbsp;&#8226; Emphasize reproducibility and structured workflows.    
</div>

[Go to Chapter →](https://drthomasoneil.github.io/analysis-user-group/r-tutorial/_book/introduction.html)

<wimr>

## **Chapter 1: Getting Started with R and RStudio**

<div class="hint-goals">  
**Goals:**  
Set up R and RStudio, and get familiar with the interface.    
&nbsp;&nbsp;&#8226; Understand what a programming language *is*.    
&nbsp;&nbsp;&#8226; Know the difference between R, RStudio and python.     
&nbsp;&nbsp;&#8226; Install R and RStudio.   
&nbsp;&nbsp;&#8226; Exploring the interface – Console, Environment, Scripts.    
&nbsp;&nbsp;&#8226; Installing and loading packages.    
&nbsp;&nbsp;&#8226; Start your first project.   
</div>

[Go to Chapter →](https://drthomasoneil.github.io/analysis-user-group/r-tutorial/_book/chapter-1-getting-started-in-r-and-rstudio.html)

<wimr>

## **Chapter 2: Data Types and Structures**

<div class="hint-goals">  
**Goals:**  
Learn about data types and structures in R.   
&nbsp;&nbsp;&#8226; Basic data types: numeric, character, logical.      
&nbsp;&nbsp;&#8226; Data structures: vectors, matrices, data frames, lists.   
&nbsp;&nbsp;&#8226; Assessing and querying data types and structures.   
&nbsp;&nbsp;&#8226; Importing data (RDS, Excel, CSV files).  
</div>

[Go to Chapter →](https://drthomasoneil.github.io/analysis-user-group/r-tutorial/_book/chapter-2-data-types-and-structures.html)

<wimr>

## **Chapter 3: Basic Programming**

<div class="hint-goals">      
**Goals:**     
Understand programming logic for automating tasks.   
&nbsp;&nbsp;&#8226; Variables and assignments.    
&nbsp;&nbsp;&#8226; Conditional statements (`if`, `else`).    
&nbsp;&nbsp;&#8226; Loops (`for`, `while`).   
&nbsp;&nbsp;&#8226; Writing functions.    
</div>

[Go to Chapter →](https://drthomasoneil.github.io/analysis-user-group/r-tutorial/_book/chapter-3-basic-programming.html)

<wimr>

<details><summary>**Coming Soon**</summary>

## **Chapter 4: Data Manipulation with `tidyverse`**

<div class="hint-goals">      
**Goals:**     
Introduce `tidyverse` for filtering, mutating, summarizing and pivoting data.    
&nbsp;&nbsp;&#8226; Filtering rows (`filter()`), selecting columns (`select()`). 
&nbsp;&nbsp;&#8226; Adding new columns (`mutate()`).
&nbsp;&nbsp;&#8226; Grouping and summarizing (`group_by()` + `summarize()`).
&nbsp;&nbsp;&#8226; Pivot longer and Pivot wider   
</div>

[Go to Chapter →](https://drthomasoneil.github.io/analysis-user-group/r-tutorial/_book/chapter-4-data-manipulation-with-tidyverse.html)

<wimr>

## **Chapter 5: Visualization with `ggplot2` – Part 1**

<div class="hint-goals">      
**Goals:**     
Create and customize visualizations using `ggplot2`.    
&nbsp;&nbsp;&#8226; Basics of `ggplot2`   
&nbsp;&nbsp;&#8226; Make a scatterplot, boxplot and column graph    
&nbsp;&nbsp;&#8226; Customise labels, themes, and colors.   
</div>

[Go to Chapter →](https://drthomasoneil.github.io/analysis-user-group/r-tutorial/_book/chapter-5-data-visualisation-with-ggplot2.html)

<wimr>

## **Chapter 6: Advanced Visualization with `ggplot2` – Part 2**

<div class="hint-goals">      
**Goals:**     
Learn additional visualisation techniques.    
&nbsp;&nbsp;&#8226; Adjusting theme elements    
&nbsp;&nbsp;&#8226; Annotating the plots     
&nbsp;&nbsp;&#8226; facet_wrap() and grid_plot()    
&nbsp;&nbsp;&#8226; Savings plots   
</div>

[Go to Chapter →](https://drthomasoneil.github.io/analysis-user-group/r-tutorial/_book/chapter-6-data-visualisation-with-ggplot2_2.html)

<wimr>

## **Chapter 7: Statistical Analysis**

<div class="hint-goals">      
**Goals:**     
Apply simple statistic to the synthetic data.    
&nbsp;&nbsp;&#8226; Descriptive statistics: mean, median, mode.     
&nbsp;&nbsp;&#8226; Hypothesis testing (t-tests, ANOVA).    
&nbsp;&nbsp;&#8226; Correlation and regression.       
</div>

[Go to Chapter →](https://drthomasoneil.github.io/analysis-user-group/r-tutorial/_book/chapter-7-statistical-analysis.html)

<wimr>

## **Chapter 8: Reproducible Reports with RMarkdown**

<hint-text><strong>*Content coming soon!*</strong></hint-text>

<div class="hint-goals">      
**Goals:**     
Build a dynamic and shareable report of your analysis.    
&nbsp;&nbsp;&#8226; Introduction to RMarkdown.   
&nbsp;&nbsp;&#8226; Combining text, code, and visuals.    
&nbsp;&nbsp;&#8226; Generate tables with knitr.       
&nbsp;&nbsp;&#8226; Editing the yaml for additional customisations.       
&nbsp;&nbsp;&#8226; Exporting to PDF and HTML.    
</div>

[Go to Chapter →](https://drthomasoneil.github.io/analysis-user-group/r-tutorial/_book/chapter-8-reproducible-reports-with-rmarkdown.html)

<wimr>

## **Conclusions**

[Go to Chapter →](https://drthomasoneil.github.io/analysis-user-group/r-tutorial/_book/conclusions.html)

</details>

<e>


