Introduction to Statistical Data Analysis using R

Introduction

Basic Syntax in R:

• Commands: R uses functions and commands written directly in the console or script files.
• Comments: Use # to write comments in your code (e.g., # This is a comment).
• Assignment: The assignment operator <- is commonly used to assign values (e.g., x <- 5).
• Printing: Use print() or simply type the variable name to display its value (e.g., print(x) or x).

#This is a comment
x <- 5
print(x)

## [1] 5

Common Data Types in R:

• Numeric: Numbers with decimals (e.g., 5.3, 2.0).
• Integer: Whole numbers, defined by appending L (e.g., 3L).
• Character: Strings or text (e.g., “Hello, R”).
• Logical: Boolean values (TRUE, FALSE).
• Complex: Complex numbers (e.g., 1 + 2i).

#Numeric
y <- c(5.8,10,0.1)
is.numeric(y)

## [1] TRUE

#character
z<- ('Hello')
is.character(z)

## [1] TRUE

#complex
a <-  1 + 2i
is.complex(a)

## [1] TRUE

log_var <- TRUE
is.logical(log_var)

## [1] TRUE

Basic Data Structures:

• Vector: A sequence of elements of the same type (e.g., c(1, 2, 3)).
• List: A collection that can hold different types (e.g., list(1, “text”, TRUE)).
• Matrix: A 2D array with elements of the same type (e.g., matrix(1:6, nrow = 2)).
• Data Frame: A table-like structure with columns of different types (e.g., data.frame(name = c(“A”, “B”), age = c(25, 30))).

# Vector
vec <- c(1, 2, 3, 4)
print(vec)    

## [1] 1 2 3 4

#list
lst <- list(1, "R", TRUE, 3 + 2i)
print(lst) 

## [[1]]
## [1] 1
## 
## [[2]]
## [1] "R"
## 
## [[3]]
## [1] TRUE
## 
## [[4]]
## [1] 3+2i

#matrix
mat <- matrix(1:6, nrow = 2)
print(mat)

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

#dataframe
df <- data.frame(name = c("Alice", "Bob"), 
                 age = c(25, 30))
print(df)

##    name age
## 1 Alice  25
## 2   Bob  30