R Programming Documentation

Introduction to R

R is a programming language and free software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. It is widely used among statisticians and data miners for developing statistical software and data analysis.

Key features of R include:

Effective data handling and storage facility
A suite of operators for calculations on arrays, in particular matrices
A large, coherent, integrated collection of intermediate tools for data analysis
Graphical facilities for data analysis and display
A well-developed, simple and effective programming language

R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is now developed by the R Development Core Team.

print("Hello, World!")

Basic Syntax

R is an interpreted language which means that commands are executed directly without needing to compile a program.

Basic R syntax includes:

Assignment operators: <- or =
Comments start with #
Function calls use parentheses

Here are some basic examples:

x <- 5  # Assignment
y = 10 # Alternative assignment
x + y  # Addition

R is case-sensitive, so 'Variable' and 'variable' would be two different objects.

# This is a comment
my_var <- 42
print(my_var)

Data Types

R has several basic data types:

Numeric: decimal numbers (default type for numbers)
Integer: whole numbers
Logical: TRUE or FALSE
Character: strings
Complex: complex numbers

You can check the type of a variable using the class() function:

x <- 10.5
class(x)  # "numeric"

y <- 10L
class(y)  # "integer"

z <- TRUE
class(z)  # "logical"

R also has special values:

NA    # Not Available/Not Applicable
NULL  # Null object
Inf   # Infinity
NaN   # Not a Number

Control Structures

Control structures allow you to control the flow of execution of the program. R provides standard control structures similar to other languages:

Conditional statements:

# if statement
x <- 5
if (x > 0) {
    print("Positive number")
} else if (x < 0) {
    print("Negative number")
} else {
    print("Zero")
}

Loops:

# for loop
for (i in 1:5) {
    print(i)
}

# while loop
count <- 1
while (count <= 5) {
    print(count)
    count <- count + 1
}

R also has vectorized operations that often eliminate the need for explicit loops:

x <- 1:5
y <- x * 2  # Vectorized operation

Functions

Functions are fundamental building blocks in R. You can create your own functions using the function() function:

# Function definition
square <- function(x) {
    return(x * x)
}

# Function call
square(5)  # Returns 25

Functions can have default arguments:

power <- function(x, exponent = 2) {
    return(x ^ exponent)
}

power(3)     # Returns 9 (3^2)
power(3, 3)  # Returns 27 (3^3)

R has many built-in functions for statistical operations:

mean(c(1, 2, 3, 4, 5))  # Returns 3
sd(c(1, 2, 3, 4, 5))    # Standard deviation
summary(c(1, 2, 3, 4, 5)) # Statistical summary

Anonymous functions (lambdas) are also supported:

sapply(1:5, function(x) x^2) # Returns c(1, 4, 9, 16, 25)

Data Frames

Data frames are the most important data structure in R for data analysis. They are similar to tables in a database or Excel spreadsheets.

Creating a data frame:

# Create a data frame
df <- data.frame(
    name = c("Alice", "Bob", "Charlie"),
    age = c(25, 30, 35),
    score = c(88.5, 92.0, 85.5)
)

# View the data frame
print(df)

Accessing data frame elements:

df$name       # Access column
df[1, ]      # First row
df[, 2]      # Second column
df[2, "age"] # Specific cell

Common operations on data frames:

head(df)      # View first few rows
summary(df)  # Summary statistics
dim(df)      # Dimensions (rows, columns)
names(df)    # Column names

R's dplyr package provides a powerful grammar for data manipulation:

library(dplyr)
df %>% filter(age > 25) %>% arrange(desc(score))