unit 1 - intro to R

R Programming – Detailed Exam Notes

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1. Introduction to R

• Definition:
  R is a programming language and environment for statistical computing, data analysis, and graphics.
• History:
  Developed by Ross Ihaka and Robert Gentleman in 1993 at the University of Auckland.
• Key Features:
  1. Free and open-source.
  2. Large package repository (CRAN).
  3. Highly extensible for statistical methods and machine learning.
  4. Strong data visualization capabilities.
  5. Platform independent (Windows, Linux, macOS).
  6. Community-driven development.

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2. Getting R and Managing R

a) Installing R

• Download from CRAN: https://cran.r-project.org/.
• Install RStudio IDE for user-friendly interface.

b) Package Management

• Install a package:

  install.packages("dplyr")

• Load a package:

  library(dplyr)

• Remove a package:

  remove.packages("dplyr")

• List installed packages:

  installed.packages()

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3. Arithmetic and Matrix Operations

a) Arithmetic Operators

Operator	Description	Example (a=10, b=3)	Result
+	Addition	a+b	13
-	Subtraction	a-b	7
*	Multiplication	a*b	30
/	Division	a/b	3.33
b	1
%/%	Integer Division	a%/%b	3
^ or **	Power	a^b	1000

b) Matrix Creation

m1 <- matrix(1:6, nrow=2, ncol=3, byrow=TRUE)
print(m1)

Output:

     [,1] [,2] [,3]
[1,]    1    2    3
[2,]    4    5    6

c) Matrix Operations

m2 <- matrix(7:12, 2, 3)
m1 + m2       # Addition
m1 - m2       # Subtraction
m1 * m2       # Element-wise multiplication
t(m1)         # Transpose
m1 %*% t(m2)  # Matrix multiplication (2x3 %*% 3x2)

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4. Functions in R

a) Built-in Functions

Examples:

mean(c(1,2,3,4,5))   # Mean
sum(1:5)             # Sum
sqrt(16)             # Square root

b) User-defined Functions

# Function with arguments and return
power <- function(x, y) {
  result <- x^y
  return(result)
}
power(2, 3)  # 8

c) Default Parameters

greet <- function(name="Guest") {
  print(paste("Hello", name))
}
greet()         # Hello Guest
greet("R User") # Hello R User

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5. Control Structures

a) If-Else

x <- -2
if (x > 0) {
  print("Positive")
} else if (x < 0) {
  print("Negative")
} else {
  print("Zero")
}

b) For Loop

for (i in 1:5) {
  print(paste("Iteration:", i))
}

c) While Loop

count <- 1
while (count <= 3) {
  print(count)
  count <- count + 1
}

d) Repeat Loop (similar to do-while)

count <- 1
repeat {
  print(count)
  count <- count + 1
  if (count > 3) break
}

e) Switch Statement

choice <- "c"
result <- switch(choice,
                 "a" = "Apple",
                 "b" = "Banana",
                 "c" = "Cherry")
print(result)

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6. Working with Objects and Data

a) Types of Objects

Object Type	Description	Example
Vector	One-dimensional array of same type	c(1,2,3)
Matrix	2D array of same type	matrix(1:6,2,3)
List	Collection of elements (any type)	list(name="R", age=30)
Data Frame	Table-like structure (rows & columns)	data.frame(ID=1:3, Name=c("A","B","C"))
Factor	Stores categorical data	factor(c("M","F","M"))

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b) Manipulating Objects

x <- c(10, 20, 30)
x[2]       # Access (20)
x[1] <- 15 # Modify element
length(x)  # Length

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c) Constructing Data Objects

vec <- c(1,2,3,4)                        # Vector
mat <- matrix(1:9, 3, 3)                 # Matrix
lst <- list(name="R", year=1993)         # List
df  <- data.frame(ID=1:3, Score=c(90,85,88)) # Data Frame

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d) Types of Data Items

1. Numeric – x <- 3.14
2. Integer – y <- 5L
3. Character – z <- "Hello"
4. Logical – flag <- TRUE
5. Complex – c <- 2+3i

Check type:

typeof(x)
class(df)

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e) Structure of Data Items

str(df)
summary(df)

Output:

'data.frame': 3 obs. of  2 variables:
 $ ID   : int  1 2 3
 $ Score: num  90 85 88

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f) Reading and Getting Data

• CSV File:

  data <- read.csv("data.csv", header=TRUE)
  head(data)

• Text File:

  data <- read.table("data.txt", header=TRUE)

• Keyboard Input:

  x <- readline(prompt="Enter a number: ")
  as.numeric(x)

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g) Manipulating Data

df$Score[2] <- 95              # Modify value
subset(df, Score > 90)         # Filter rows
df$Grade <- c("A", "B", "A")   # Add new column
df[order(df$Score, decreasing=TRUE), ] # Sort

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h) Storing Data

save(df, file="data.RData")     # Save R object
load("data.RData")              # Load R object
write.csv(df, "output.csv")     # Export to CSV

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Quick Diagram: Objects in R (Hierarchy)

Object
│
├── Atomic Vectors (numeric, character, logical, complex)
├── Matrix (2D homogeneous)
├── Array (multi-dimensional homogeneous)
├── List (heterogeneous collection)
└── Data Frame (table, columns = vectors)