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1.1. Introduction to R

1.1.2. Key Concepts in SAS vs R Programming

1. Understanding Package Terminology

**Packages**

  • Collections of functions, data, documentation, and sometimes underlying code (e.g., C++) packaged together to extend R's functionality

  • Your R installation includes several base packages that provide essential functionality

  • There are over 18,000 community-contributed packages spanning virtually all domains of analysis

  • Each package typically focuses on specific tasks or methods (visualization, modeling, etc.)

**Functions**

  • Similar to SAS macros, functions are reusable blocks of code that perform specific tasks

  • Functions accept input arguments and return outputs in standardized ways

  • Unlike SAS macros, R functions are first-class objects that can be passed as arguments

  • R functions follow consistent naming and documentation conventions

**Repositories**

  • Central locations where packages are stored and distributed:

  • CRAN (Comprehensive R Archive Network): Most common, carefully curated

  • Bioconductor: Specialized in bioinformatics and genomics

  • GitHub: Cutting-edge packages with less formal review process

  • R-Forge: Collaborative development platform for R packages

**Libraries**

  • Directories on your machine where packages are installed for access and reuse

  • The term "library" refers to the location where packages are stored

  • R maintains separate library paths for system and user-installed packages

  • You must explicitly load packages from libraries before using their functions

2. Components of Statistical Software

Statistical software platforms like SAS and R all contain five fundamental components:

  1. Data Input and Management: Systems for reading, transforming, and organizing data
  2. Statistical and Graphical Procedures: Tools for analyzing data and creating visualizations
  3. Output Management System: Frameworks for extracting and customizing output (ODS in SAS)
  4. Macro Language: Methods to reuse and automate sets of commands
  5. Matrix Language: Capabilities for implementing algorithms through matrix operations

Key Differences in Implementation:

  • SAS implements these five areas as separate systems with different rules and syntax
  • Most SAS users only learn the first two components
  • R integrates all five components into one consistent language system
  • This integration gives R significant advantages in flexibility and power

Output Management Contrast:

  • SAS procedures print all output at once
    /* SAS prints all descriptive statistics */
PROC MEANS DATA=mydata;
RUN;
  • R stores results as objects that can be selectively accessed
    # R stores results in an object
result <- summary(mydata)
# Access specific parts as needed
result$min
result$max
  • R's approach makes output management easier but produces less publication-ready results by default
  • SAS produces more polished output but it's harder to use for further analysis

Five fundamental components of statistical software platforms

This fundamental architectural difference explains why R has become the preferred language for developing new statistical methods and why the transition from SAS to R requires adapting to a different analytical mindset.

3. Programming Conventions

Understanding programming conventions is essential when transitioning from SAS to R:

Data Management Practices

  • Practice Data: R offers multiple ways to generate practice data and includes example datasets
  • Location Management: Example programs typically look for data files in a specified directory (e.g., "myRfolder")
  • Self-Contained Code: Each example loads its data independently to prevent interference from previous operations
  • Reproducibility: This approach ensures that each program can run on its own without dependencies

Program Structure and Documentation

  • Header Comments: Every program begins with comments stating its purpose and filename
    # R Program for Calculating BMI
# BMI_Calculator.R
# Author: Jane Smith
# Date: 2023-06-22
# Purpose: Calculate BMI from height and weight data
  • File Naming: Consistent naming conventions across languages (e.g., SelectingVars.sas, SelectingVars.sps, SelectingVars.R)
  • Parallel Examples: Programs performing the same task in different languages use identical base filenames
  • Comments: Detailed annotations explain functionality and decision points

Data Storage Conventions

  • File Format: R data objects are stored in .Rdata files with matching names (e.g., mydata stored in mydata.Rdata)
    # Saving an R data object
save(mydata, file = "myRfolder/mydata.Rdata")

# Loading an R data object
load("myRfolder/mydata.Rdata")
  • Workspace Management: Complete workspaces including data objects and functions can be stored
    # Save entire workspace
save.image("myRfolder/myWorkspace.Rdata")

# Load entire workspace
load("myRfolder/myWorkspace.Rdata")
  • Object Permanence: Saving objects allows consistent retrieval across sessions
  • Cross-Platform Compatibility: .Rdata files maintain consistent format across operating systems

Resource Access

  • Online Repository: Example files can be downloaded from dedicated websites
  • Documentation: Clear documentation of where to find supporting materials
  • Organization: Related files are grouped together for easier navigation
  • Modification Guidance: Examples are structured to be easily adaptable to different environments

These conventions facilitate learning by providing consistent examples that can be modified and extended as you develop proficiency in R programming.

4. Typographic Conventions

Understanding typographic conventions is crucial for learning programming languages effectively:

Font Styling

  • Code Formatting: All programming code, R package names, and function names appear in courier font
  • Document References: Names of other documents and menus appear in italic font
  • Menu Navigation: Menu paths are shown in the format File > Save as, indicating "choose Save as from the File menu"

Case Conventions

  • SAS: Commands and statements appear in UPPERCASE to distinguish them from user-defined names
  • R Language: Uses exact case as required since R is case-sensitive
  • Naming Convention: User-defined objects in examples typically include the prefix "my" (e.g., mydata, mySubset) to distinguish them from built-in functions

Input and Output Distinction

  • Prompt Symbols: R console examples retain the input prompt symbols:

    • > indicates the start of a new command
    • + indicates continuation lines of the same command

  • Example Format: Input lines are clearly separated from output lines R-console Input and Output Distinction

  • Spacing: Additional spacing is sometimes used to improve legibility of code examples

  • Output Markers: R prefixes output lines with indicators like [1] which shows the index of the first element displayed

Educational Approach

  • Consistent Style: These typographic conventions are maintained throughout documentation
  • Visual Distinction: Helps learners differentiate between commands, variables, and output
  • Reduced Confusion: Clear visual separation prevents confusion when transitioning between languages
  • Learning Aid: Consistent formatting supports more efficient comprehension of concepts
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