R Programming - Class 1: Data Types and Data Structures

🎯 Objectives

By the end of this class, you will be able to:

• Understand basic data types in R.
• Identify and use common data structures in R.
• Perform simple operations with vectors, lists, matrices, and data frames.
• Inspect and manipulate R objects using functions like class(), typeof(), and str().

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🧭 Class Outline

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1. 🔰 Introduction to R and RStudio (10 min)

• What is R and why use it?
• Overview of RStudio:

  • Console

  • Source pane

  • Environment/History

  • Plots/Files/Packages

    

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2. 🔡 Data Types in R (20 min)

Data Type	Description	Example
numeric	Real numbers	3.14
integer	Whole numbers	5L
character	Text strings	"Hello"
logical	Boolean (TRUE/FALSE)	TRUE
complex	Complex numbers	1 + 2i

✅ Try it Out:

a <- 3.14
b <- 5L
c <- "Hello"
d <- TRUE
e <- 1 + 2i

class(a)
is.character(c)
is.logical(d)

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3. 🧱 Data Structures in R (45 min)

🟦 Vectors

• Collection of elements of the same class.
• Created using c() which stands for concatenate.
• Elements accessed using [], where [1] is the first element.
• Coercion: If mixed types are provided, R coerces to a common type.
• Vectors have only length, not dimensions.

v <- c(1, 2, 3)
names(v) <- c("One", "Two", "Three")

# check for dimension
dim(v)

NULL

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🟨 Matrices

• Matrices are vectors with a dimension attribute.

m <- matrix(1:6, nrow = 2, ncol = 3)
m

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

myrows <- c("A", "B", "C", "D")
mycol <- c("col1", "col2", "col3")

x <-  matrix(1:12, nrow = 4, ncol = 3, dimnames = list(myrows, mycol))

x

  col1 col2 col3
A    1    5    9
B    2    6   10
C    3    7   11
D    4    8   12

x[1:2, 2:3] #accessing elements in the matrix

  col2 col3
A    5    9
B    6   10

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🟩 Lists

• Lists can contain elements of different types.
• No coercion applied.
• Access using [ ], [[ ]], and $.

my_list <- list(name="Alice", age=25, scores=c(90, 95, 88))

# Accessing list elements
my_list[1]         # Returns a list

$name
[1] "Alice"

class(my_list[1])  # "list"

[1] "list"

my_list[[1]]       # Returns the element in its original type

[1] "Alice"

class(my_list[[1]]) # "character"

[1] "character"

my_list$name       # Returns "Alice"

[1] "Alice"

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🟧 Data Frames

• Like a table: rows and columns.
• Columns can be of different types.
• List of equal-length vectors

df <- data.frame(
  name = c("John", "Sara"),
  age = c(25, 30), 
  score = c(80, 95)
)

df

  name age score
1 John  25    80
2 Sara  30    95

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🟥 Factors

• Used for categorical data with levels.

g <- factor(c("male", "female", "female", "male"))
g

[1] male   female female male  
Levels: female male