R Introduction and Advantages

• R is free coding language used in scientific areas (ecology, climate sciences)
• Key advantages:
  • Free of charge, downloadable with internet connection
  • Collaborative - code sharing through R platform and alternative sources
  • Repeatable/shareable - scripts can be published with research
  • Comprehensive workflow - handles data cleaning, statistical processes, GIS mapping
  • Visualization capabilities - produces quality plots for research communication

RStudio Interface Overview

• Four main panels in RStudio interface:
  • Script panel: where code is written and orders are listed
  • Console panel: displays executed commands and results
  • Environment panel (top right): stores created data and objects
  • Files/Plots/Packages/Help panel (bottom right): file navigation, plot display, package management, help documentation
• Interface customization available (background colors, fonts)
• Help function: FN + F1 to access function documentation

Basic R Operations and Variable Assignment

• R functions as "big calculator"
• Assignment operator: arrow sign (<-)
• Variable creation: x <- 2
• Code execution methods:
  • Point at line + Command + Return (Mac)
  • Highlight code + click Run button
• Comments: created using # symbol (appear in gray, not executed)
• Variable overwriting: reassigning values replaces previous values
• Mathematical operations: addition, exponential functions (exp())
• Variable removal: rm() function

Logical Operations

• Comparison operators:
  • Greater than: >
  • Less than: <
  • Equal to: == (two equal signs)
  • Not equal to: !=
• Returns TRUE/FALSE boolean values

Data Types

• Numeric data types: numbers with decimals
• Integer data types: whole numbers without decimals
• Character data types: text/words (must use quotes and c() function)
• Class identification: class() function
• Data type checking: is.integer() function
• Type conversion: as.integer() function forces numeric to integer

Vectors

• Multiple values assigned to single variable using c() function
• Creation syntax: vector_name <- c(value1, value2, value3)
• Numeric vectors: c(1, 2, 3)
• Character vectors: c("Monday", "Tuesday", "Wednesday")
• Vector operations:
  • rep() function: repeats values (rep(22, 20) repeats 22 twenty times)
  • Sequence creation: 1:100 creates numbers 1 to 100
  • seq() function: creates sequences with specified intervals (seq(1, 100, by=2))
• Vector indexing:
  • Single element: vector[20] accesses 20th position
  • Multiple elements: vector[c(10, 12)] accesses positions 10 and 12
  • Element removal: vector[-3] removes 3rd element
• Random data generation:
  • sample() function: random selection from range
  • rnorm() function: normal distribution random data
• Vector analysis functions:
  • length(): returns vector length
  • summary(): provides min, max, quartiles, median, mean
  • sum(): adds all vector elements
  • min(), max(): minimum and maximum values
• Visualization: hist() creates histograms

Matrices

• Two-dimensional data structure (rows and columns)
• Only numerical variables allowed (no characters)
• Creation: matrix() function specifying data, rows, columns
• Example: matrix(vector, nrow=10, ncol=5)
• Data must fit matrix dimensions (warnings for mismatched sizes)
• Column naming: colnames() function assigns names
• Matrix navigation:
  • [row, column] indexing system
  • [4, 3]: specific cell (row 4, column 3)
  • [4, ]: entire row 4
  • [, 3]: entire column 3
• Column selection: matrix[, c(1, 3)] selects columns 1 and 3

Data Frames

• Evolution of matrices allowing mixed data types (numbers and characters)
• Built-in datasets available in R (mtcars example)
• Data frame operations:
  • length(): number of columns
  • nrow(): number of rows
  • colnames(): column names
  • Same indexing as matrices [row, column]
• Column access using $ operator: dataframe$columnname
• Auto-complete feature for column names after $ symbol

Lists

• Master data structure containing all other data types
• Can store vectors, matrices, data frames together
• Creation: list() function with multiple data structures
• Hierarchical storage with expandable view in RStudio
• Element access using [[index]] notation
• Ultimate flexibility for mixed data storage

Practice Exercises

• Exercises provided to practice concepts covered
• Additional internet resources recommended for further learning
• R community provides extensive online documentation and tutorials