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Essentials of R in Data Science

May 22, 2025

Introduction to R and Data Science

Overview

  • Introduction by Barton Paulson
  • Focus on the R programming language as a foundational tool in data science.
  • R is highlighted as the language of data science due to its extensive use in the field.

Why R?

  • Free and Open Source: Unlike many costly data software packages.
  • Optimized for Vector Operations: Allows processing of entire rows or tables without explicit loops.
  • Strong Community Support: Offers numerous examples and ongoing development.
  • Over 9000 Contributed Packages: Enables a wide range of functionalities.

Setting Up R

  • Installation: Download from R Project Homepage.
    • Use CRAN (Comprehensive R Archive Network) for mirror selection based on location.
  • Data Files for Course: Download necessary course files and scripts for practical exercises.
  • Basic R Interface: Source window for coding, console for output, and understanding commented lines.

Introduction to RStudio

  • Installation: Download RStudio for a more organized and user-friendly interface.
  • Benefits of RStudio:
    • Consistent keyboard commands across platforms.
    • Unified interface for coding, console output, environment variables, and plots.
    • Simplifies navigation and data management.

R Packages

  • Types of Packages:
    • Base Packages: Installed with R, not loaded by default.
    • Contributed Packages: Need to be downloaded and loaded separately.
  • Sources for Packages:
    • CRAN, CRANtastic, and GitHub.
  • Commonly Used Packages: dplyr, tidyr, stringr, lubridate, ggplot2, and others.
  • PacMan: A package manager to simplify loading and unloading other packages.

Basic R Commands and Data Handling

  • Plotting: Basic plots, histograms, scatterplots, and overlaying plots.
  • Basic Statistics: Using summary() and describe() (from psych package) functions for statistical summaries.
  • Selecting Cases: Subsetting data by categories or values.

Data Import and Export

  • Supported Formats: CSV, TXT, Excel, JSON files.
  • Using rio Package: Simplifies import operations with import() function.
  • Data Viewer: In RStudio, useful for examining datasets.

Modeling Data

  • Hierarchical Clustering: Understanding clustering of data based on similarity.
  • Principal Component Analysis (PCA): Dimensionality reduction to simplify data interpretation.
  • Regression Analysis: Using multiple variables to predict outcomes, including advanced techniques like Lasso.

Additional Resources and Next Steps

  • Further courses on R, Python, and data visualization at DataLab.cc.
  • Explore machine learning techniques.
  • Participate in R User Conferences and local user groups.
    • International Talk Like a Pirate Day as an unofficial R day.

This summary captures the key topics and instructions from the lecture, highlighting the main tools and methodologies related to R programming and data science.

Full transcript