basic-r-tutorial/index.html at main · bioinfodlsu/basic-r-tutorial · GitHub

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<!DOCTYPE html>

<html>
  <head>
    <title>Basic R Programming Tutorial</title>
    <meta name="viewport" content="width=device-width, initial-scale=1" />
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      href="https://cdn.jsdelivr.net/npm/bootstrap@5.3.3/dist/css/bootstrap.min.css"
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  <body class="p-lg-5 py-lg-3">
    <div class="container-fluid p-5">
      <h1 id="basics-of-r-programming">Basic R Programming Tutorial</h1>
      <p>
        <img
          src="https://img.shields.io/badge/r-%23276DC3.svg?style=flat&amp;logo=r&amp;logoColor=white"
          alt="badge"
        />
      </p>
      <p>
        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.
      </p>
      <p>
        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.
      </p>
      <p>
        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
        <a href="https://ggplot2.tidyverse.org/"><code>ggplot2</code></a>
        provide a grammar of graphics approach, enabling users to construct
        complex and customizable plots with ease.
      </p>
      <p>
        In recent years, R has gained popularity in the field of machine
        learning. Packages such as
        <a href="https://topepo.github.io/caret/"><code>caret</code></a
        >,
        <a
          href="https://www.rdocumentation.org/packages/randomForest/versions/4.7-1.2"
          ><code>randomForest</code></a
        >, and
        <a href="https://cran.r-project.org/web/packages/keras/vignettes/"
          ><code>keras</code></a
        >
        offer powerful tools for building and evaluating predictive models.
        R&#39;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.
      </p>
      <hr class="my-5" />
      <h2 id="-before-starting" class="mb-4">Before Starting</h2>
      <ol>
        <li>
          Download and install both <strong>R</strong> and
          <strong>RStudio</strong>:
          <a href="https://posit.co/download/rstudio-desktop/"
            >https://posit.co/download/rstudio-desktop/</a
          >
        </li>
        <li>
          This tutorial uses R markdown files. Kindly refer to this video on how
          to work with R markdown files on RStudio:
          <a href="https://www.youtube.com/watch?v=DNS7i2m4sB0"
            >https://www.youtube.com/watch?v=DNS7i2m4sB0</a
          >
        </li>
        <li>
          Download this
          <a
            href="https://github.com/bioinfodlsu/basic-r-tutorial/blob/main/phages.tsv"
            >file</a
          >
          (<code>phages.tsv</code>), and place it inside the same folder as the
          R markdown files that you will be creating in this tutorial.
        </li>
      </ol>
      <hr class="my-5" />
      <h2 id="-topic-outline" class="mb-4">Topic Outline</h2>
      <p>
        If you want to directly open and run the code on RStudio, refer to the
        <strong>R Markdown</strong> column.
      </p>
      <p>
        But, if you only want to view the contents of a tutorial (without having
        to download and open it on RStudio), refer to the
        <strong>HTML</strong> column.
      </p>
      <br />
      <table class="table table-striped">
        <thead class="table-dark">
          <tr>
            <th></th>
            <th>Topic</th>
            <th>R Markdown</th>
            <th>HTML</th>
          </tr>
        </thead>
        <tbody>
          <tr>
            <td>1</td>
            <td>Introduction to R Syntax</td>
            <td>
              <a
                href="https://github.com/bioinfodlsu/basic-r-tutorial/blob/main/1.%20Introduction%20to%20R%20Syntax.Rmd"
                >Link</a
              >
            </td>
            <td>
              <a href="1. Introduction to R Syntax.nb.html">Link</a>
            </td>
          </tr>
          <tr>
            <td>2</td>
            <td>Groups of Data: Vectors, Matrices &amp; Lists</td>
            <td>
              <a
                href="https://github.com/bioinfodlsu/basic-r-tutorial/blob/main/2.%20Groups%20of%20Data%20-%20Vectors%2C%20Matrices%20%26%20Lists.Rmd"
                >Link</a
              >
            </td>
            <td>
              <a href="2. Groups of Data - Vectors, Matrices & Lists.nb.html"
                >Link</a
              >
            </td>
          </tr>
          <tr>
            <td>3</td>
            <td>Data Frames</td>
            <td>
              <a
                href="https://github.com/bioinfodlsu/basic-r-tutorial/blob/main/3.%20Dataframes.Rmd"
                >Link</a
              >
            </td>
            <td>
              <a href="3. Dataframes.nb.html">Link</a>
            </td>
          </tr>
          <tr>
            <td>4</td>
            <td>Manipulating Data with <code>dplyr</code></td>
            <td>
              <a
                href="https://github.com/bioinfodlsu/basic-r-tutorial/blob/main/4.%20Manipulating%20Data%20with%20dplyr.Rmd"
                >Link</a
              >
            </td>
            <td>
              <a href="4. Manipulating Data with dplyr.nb.html">Link</a>
            </td>
          </tr>
          <tr>
            <td>5</td>
            <td>
              Fundamentals of Data Visualization with <code>ggplot2</code>
            </td>
            <td>
              <a
                href="https://github.com/bioinfodlsu/basic-r-tutorial/blob/main/5.%20Fundamentals%20of%20Data%20Visualization%20with%20ggplot2.Rmd"
                >Link</a
              >
            </td>
            <td>
              <a
                href="5. Fundamentals of Data Visualization with ggplot2.nb.html"
                >Link</a
              >
            </td>
          </tr>
          <tr>
            <td>6</td>
            <td>Descriptive Statistics</td>
            <td>
              <a
                href="https://github.com/bioinfodlsu/basic-r-tutorial/blob/main/6.%20Descriptive%20Statistics.Rmd"
                >Link</a
              >
            </td>
            <td>
              <a href="6. Descriptive Statistics.nb.html">Link</a>
            </td>
          </tr>
          <tr>
            <td>7</td>
            <td>Inferential Statistics</td>
            <td>
              <a
                href="https://github.com/bioinfodlsu/basic-r-tutorial/blob/main/7.%20Inferential%20Statistics.Rmd"
                >Link</a
              >
            </td>
            <td>
              <a href="7. Inferential Statistics.nb.html">Link</a>
            </td>
          </tr>
        </tbody>
      </table>
      <hr class="my-5" />
      <h2 id="-references" class="mb-4">References</h2>
      <p>This tutorial references the following resources:</p>
      <ul>
        <li>
          Hamel, G. (2020). Intro to R: Index. <em>Kaggle</em>.
          <a href="https://www.kaggle.com/code/hamelg/intro-to-r-index/notebook"
            >https://www.kaggle.com/code/hamelg/intro-to-r-index/notebook</a
          >
        </li>
        <li>
          Gatto, L. (2021). Chapter 5 Manipulating and analyzing data with
          dplyr. In
          <em
            >UCLouvain-CBIO/WSBIM1207: Introduction to bioinformatics (Version
            v2.0.0)</em
          >.
          <a href="https://uclouvain-cbio.github.io/WSBIM1207/sec-dplyr.html"
            >https://uclouvain-cbio.github.io/WSBIM1207/sec-dplyr.html</a
          >
        </li>
      </ul>
      <p>
        The dataset we use in this tutorial was downloaded using
        <a href="http://doi.org/10.1089/phage.2021.0007">INPHARED</a> last
        September 2022:
      </p>
      <ul>
        <li>
          Cook, R., Brown, N., Redgwell, T., Rihtman, B., Barnes, M., Clokie,
          M., Stekel, D. J., Hobman, J. L., Jones, M. A., &amp; Millard, A.
          (2021). INfrastructure for a PHAge REference Database: Identification
          of large-scale biases in the current collection of cultured phage
          genomes.
          <em>PHAGE, 2</em>(4), 214-223.
          <a href="http://doi.org/10.1089/phage.2021.0007"
            >http://doi.org/10.1089/phage.2021.0007</a
          >
        </li>
      </ul>
      <hr class="my-5" />
      <h2 id="-authors" class="mb-4">Authors</h2>
      <ul>
        <li class="mb-2">
          <strong>Daphne Janelyn L. Go</strong>
          <br />
          <a href="mailto:daphne_janelyn_go@dlsu.edu.ph"
            >daphne_janelyn_go@dlsu.edu.ph</a
          >
        </li>
        <li>
          <strong>Mark Edward M. Gonzales</strong> <br />
          <a href="mailto:gonzales.markedward@gmail.com"
            >gonzales.markedward@gmail.com</a
          >
        </li>
      </ul>
      <p>
        These materials were originally created for the
        <strong>Basic R Workshop</strong>, jointly organized by the
        <a href="https://bioinfodlsu.com/">Bioinformatics Lab</a> (College of
        Computer Studies) and the
        <a href="https://dlsu-scomb.github.io/"
          >Systems and Computational Biology Unit</a
        >
        (College of Science), De La Salle University, last July 12, 2023.
      </p>
    </div>
  </body>
</html>