Merge branch 'main' of https://github.com/bioinfodlsu/basic-r-tutorial

Author: memgonzales

.github/workflows/deploy-website.yml

@@ -0,0 +1,43 @@
+# Simple workflow for deploying static content to GitHub Pages
+name: Deploy static content to Pages
+
+on:
+  # Runs on pushes targeting the default branch
+  push:
+    branches: ["main"]
+
+  # Allows you to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+# Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages
+permissions:
+  contents: read
+  pages: write
+  id-token: write
+
+# Allow only one concurrent deployment, skipping runs queued between the run in-progress and latest queued.
+# However, do NOT cancel in-progress runs as we want to allow these production deployments to complete.
+concurrency:
+  group: "pages"
+  cancel-in-progress: false
+
+jobs:
+  # Single deploy job since we're just deploying
+  deploy:
+    environment:
+      name: github-pages
+      url: ${{ steps.deployment.outputs.page_url }}
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+      - name: Setup Pages
+        uses: actions/configure-pages@v5
+      - name: Upload artifact
+        uses: actions/upload-pages-artifact@v3
+        with:
+          # Upload entire repository
+          path: '.'
+      - name: Deploy to GitHub Pages
+        id: deployment
+        uses: actions/deploy-pages@v4

README.md

@@ -1,52 +1,16 @@
-# Basics of R Programming
+# Basic R Programming Tutorial
 
 ![badge][badge-r]
 
-R is a powerful programming language and software environment widely used for statistical analysis, data visualization, and machine learning. It provides a vast array of tools and libraries that make it a popular choice among data scientists, statisticians, and researchers.
+Repository for the Bioinformatics Lab's basic R programming tutorial
 
-R excels in statistical analysis and is equipped with a rich set of functions for descriptive statistics, hypothesis testing, regression analysis, time series analysis, and multivariate techniques. This makes it a preferred choice for researchers and analysts working with data from various fields, such as social sciences, finance, healthcare, and environmental studies.
+The tutorial materials can be viewed at [https://bioinfodlsu.com/basic-r-tutorial/](https://bioinfodlsu.com/basic-r-tutorial/)
 
-Moreover, R offers exceptional data visualization capabilities. Its default plotting system allows users to create a wide variety of static and interactive visualizations to explore and present data effectively. Additionally, packages like [`ggplot2`](https://ggplot2.tidyverse.org/) provide a grammar of graphics approach, enabling users to construct complex and customizable plots with ease.
-
-In recent years, R has gained popularity in the field of machine learning. Packages such as [`caret`](https://topepo.github.io/caret/), [`randomForest`](https://www.rdocumentation.org/packages/randomForest/versions/4.7-1.2), and [`keras`](https://cran.r-project.org/web/packages/keras/vignettes/) offer powerful tools for building and evaluating predictive models. R's integration with other languages, such as Python, allows users to leverage popular machine learning frameworks like TensorFlow and scikit-learn within their R workflow.
-
-## 📢 Before Starting
-
-1. Download and install both **R** and **RStudio**: https://posit.co/download/rstudio-desktop/
-1. This tutorial uses R markdown files. Kindly refer to this video on how to work with R markdown files on RStudio: https://www.youtube.com/watch?v=DNS7i2m4sB0
-1. Download this [file](https://github.com/bioinfodlsu/basic-r-tutorial/blob/main/phages.tsv) (`phages.tsv`), and place it inside the same folder as the R markdown files that you will be creating in this tutorial.
-
-## 📜 Topic Outline
-
-**R markdown** files (second column) can be directly opened and run on RStudio. However, when opened on GitHub, only the raw code is displayed and outputs (e.g., tables and plots) are stripped.
-
-**GitHub markdown** files (third column) cannot be directly opened and run on RStudio. However, GitHub displays them nicely while also preserving the outputs.
-
-| \#  | Topic                                             | R Markdown                                                                                                                              | GitHub Markdown |
-| --- | ------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------- | --------------- |
-| 1   | Introduction to R Syntax                          | [Link](https://github.com/bioinfodlsu/basic-r-tutorial/blob/main/1.%20Introduction%20to%20R%20Syntax.Rmd)                               |
-| 2   | Groups of Data: Vectors, Matrices & Lists         | [Link](https://github.com/bioinfodlsu/basic-r-tutorial/blob/main/2.%20Groups%20of%20Data%20-%20Vectors%2C%20Matrices%20%26%20Lists.Rmd) |
-| 3   | Data Frames                                       | [Link](https://github.com/bioinfodlsu/basic-r-tutorial/blob/main/3.%20Dataframes.Rmd)                                                   |
-| 4   | Manipulating Data with `dplyr`                    | [Link](https://github.com/bioinfodlsu/basic-r-tutorial/blob/main/4.%20Manipulating%20Data%20with%20dplyr.Rmd)                           |
-| 5   | Fundamentals of Data Visualization with `ggplot2` | [Link](https://github.com/bioinfodlsu/basic-r-tutorial/blob/main/5.%20Fundamentals%20of%20Data%20Visualization%20with%20ggplot2.Rmd)    |
-| 6   | Descriptive Statistics                            | [Link](https://github.com/bioinfodlsu/basic-r-tutorial/blob/main/6.%20Descriptive%20Statistics.Rmd)                                     |
-| 7   | Inferential Statistics                            | [Link](https://github.com/bioinfodlsu/basic-r-tutorial/blob/main/7.%20Inferential%20Statistics.Rmd)                                     |
-
-## 📚 References
-
-This tutorial references the following resources:
-
-- Hamel, G. (2020). Intro to R: Index. _Kaggle_. https://www.kaggle.com/code/hamelg/intro-to-r-index/notebook
-- Gatto, L. (2021). Chapter 5 Manipulating and analyzing data with dplyr. In _UCLouvain-CBIO/WSBIM1207: Introduction to bioinformatics (Version v2.0.0)_. https://uclouvain-cbio.github.io/WSBIM1207/sec-dplyr.html
-
-The dataset we use in this tutorial was downloaded using [INPHARED](http://doi.org/10.1089/phage.2021.0007) last September 2022:
-
-- Cook, R., Brown, N., Redgwell, T., Rihtman, B., Barnes, M., Clokie, M., Stekel, D. J., Hobman, J. L., Jones, M. A., & Millard, A. (2021). INfrastructure for a PHAge REference Database: Identification of large-scale biases in the current collection of cultured phage genomes. _PHAGE, 2_(4), 214-223. http://doi.org/10.1089/phage.2021.0007
-
-## 💻 Authors
+## Authors
 
 - **Daphne Janelyn L. Go** <br>
   daphne_janelyn_go@dlsu.edu.ph
+  
 - **Mark Edward M. Gonzales** <br>
   mark_gonzales@dlsu.edu.ph