Econ 265: Introduction to Econometrics

Lecture 2: Getting Started with R

Moshi Alam

Prerequisites

R and RStudio is installed
Update R packages regularly
Required packages for today:
- Base R (primary focus)

Today’s Agenda

Live coding
Focus on typing commands yourself
Avoid copy-paste to build muscle memory

Note

This is not a course in R!

RStudio Interface

Basic Operations

Basic Arithmetic

# Addition
1 + 2

# Subtraction
6 - 7

# Division
5 / 2

# Exponentiation
2^3

# Integer division
100 %/% 60  # How many whole hours in 100 minutes?

# Modulo (remainder)
100 %% 60   # How many minutes are left over?

Logic Operations

# Basic comparisons
1 > 2
1 > 2 & 1 > 0.5  # AND
1 > 2 | 1 > 0.5  # OR

# Truth testing
isTRUE(1 < 2)

Important Operators:

>, <: Greater/less than
>=, <=: Greater/less equal
==: Equality test
!=: Not equal
&: AND
|: OR

Logic: Important Details

Order of Precedence:

Logical operators (>, <, etc.)
Boolean operators (&, |)

# Be explicit!
1 > 0.5 & 1 > 2

# Not
1 > 0.5 & 2  # Can be confusing

Floating Point Numbers:

# This returns FALSE!
0.1 + 0.2 == 0.3

# Use instead:
all.equal(0.1 + 0.2, 0.3)

Value Matching

# Check if value is in a vector
4 %in% 1:10

# Create a "not in" operator
`%ni%` = Negate(`%in%`)
4 %ni% 5:10

Object-Oriented Programming

Everything is an Object

Common object types in R:

Vectors
Matrices
Data frames
Lists
Functions

# Create different objects
vec <- 1:5
mat <- matrix(1:9, nrow=3)
df <- data.frame(x=1:3, y=4:6)

Objects Have Classes

# Create data frame
d <- data.frame(
  x = 1:2,
  y = 3:4
)

# Check properties
class(d)
typeof(d)
str(d)

Understanding Objects:

class(): Object’s class
typeof(): Object’s type
str(): Object’s structure
View(): Interactive viewing

Global Environment

# Create data frame
d <- data.frame(
  x = 1:2,
  y = 3:4
)

# This fails:
lm(y ~ x)

# This works:
lm(y ~ x, data = d)

Key Points:

Objects live in the global environment
Must specify data source
Different from Stata’s single-dataset approach
Multiple objects can exist simultaneously

Assignment

Assignment Operators

Using Arrow (<-):

# Standard assignment
a <- 10 + 5

# Right assignment
10 + 5 -> a

Embodies the idea of assigned to

Using Equals (=):

# Alternative assignment
b = 10 + 10

# Must be on left with =
# This won't work:
# 10 + 10 = b

Assignment Choice

Most R users prefer <- for assignment
= has specific role in function evaluation
Personal choice, but be consistent
= is quicker to type and familiar from other languages

Programming Basics

Variables and Assignment

# Variable assignment
x <- 1
y <- "roses"
z <- function(x) { sqrt(x) }

# Using variables
z(9)

[1] 3

Control Flow: if/else

x <- 5
y <- 3

if (x > y) {
  print("x is larger than y")
} else {
  print("x is less than or equal to y")
}

[1] "x is larger than y"

Loops

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

[1] "Iteration 1"
[1] "Iteration 2"
[1] "Iteration 3"
[1] "Iteration 4"
[1] "Iteration 5"

# Loop with conditions
for(fruit in c("mangos","bananas","apples")) {
  print(paste("I love", fruit))
}

[1] "I love mangos"
[1] "I love bananas"
[1] "I love apples"

Functions

# Define function
greet <- function(name = "Lord Vader") {
  paste("Hello,", name)
}

# Use function
greet()

[1] "Hello, Lord Vader"

greet("Luke")

[1] "Hello, Luke"

Working with Variables

Creating Variables

# Price of a good
price <- 50

# Quantity sold
quantity <- 10

# Calculate revenue
revenue <- price * quantity
revenue

[1] 500

Multiple Assignments

# Assign same value to multiple variables
x <- y <- 5

# Check values
x

[1] 5

[1] 5

Data Types

Numeric Data

# GDP growth rate
gdp_growth <- 2.5
class(gdp_growth)

[1] "numeric"

# Population (whole number)
population <- 1000L  # L makes it integer
class(population)

[1] "integer"

Text and Logical Data

# Country name (character/string)
country <- "United States"
class(country)

[1] "character"

# Logical (TRUE/FALSE)
is_developed <- TRUE
class(is_developed)

[1] "logical"

Namespace Issues

Reserved Words

Strictly Reserved:

if
else
while
function
for
TRUE/FALSE
NULL
Inf
NA

Semi-Reserved:

c() (concatenate)
pi
Many function names

# Don't do this!
pi <- 2
c <- 4

Namespace Conflicts

library(dplyr)

# Shows conflicts:
# filter masked from 'package:stats'
# lag masked from 'package:stats'

Two solutions:

Use package::function()

stats::filter(1:10, rep(1, 2))

Assign permanently

filter <- stats::filter

Indexing

Using Square Brackets

Basic Indexing:

# Vector indexing
a <- 1:10
a[4]        # 4th element
a[c(4, 6)]  # 4th and 6th

# Matrix/dataframe
d[1, 1]     # First row & column

List Indexing:

my_list <- list(
  a = "hello",
  b = 1:3
)

my_list[[1]]     # First element
my_list[[2]][3]  # Third item of second element

Using Dollar Sign

# List example
my_list$a           # Access 'a' element
my_list$b[3]        # Third item of 'b'

# Data frame
starwars$name[1]    # First name

Cleaning Up

Removing Objects

# Remove specific objects
rm(a, b)

# Remove all objects (not recommended)
rm(list = ls())

# Detach package
detach(package:dplyr)

# Clear plots
dev.off()

Tip

Better to restart R session than use rm(list = ls())

Data Structures

Overview of Data Structures

R has several basic data structures:

Dimension	Homogeneous	Heterogeneous
1	Vector	List
2	Matrix	Data Frame
3+	Array	nested Lists

Vectors

Vectors are containers for objects of identical type:

# Create vectors with c()
x <- c(1, 3, 5, 7, 8, 9)
print(x)

[1] 1 3 5 7 8 9

# Create sequence
y <- 1:10
print(y)

 [1]  1  2  3  4  5  6  7  8  9 10

# Repeat values
rep("A", times = 5)

[1] "A" "A" "A" "A" "A"

Vector Operations

# Subsetting vectors
x <- c(1, 3, 5, 7, 8, 9)
x[1]      # First element

[1] 1

x[1:3]    # First three elements

[1] 1 3 5

x[c(1,3,4)] # Selected elements

[1] 1 5 7

Vector Logic

x <- c(1, 3, 5, 7, 8, 9)
# Logical operations
x > 3

[1] FALSE FALSE  TRUE  TRUE  TRUE  TRUE

x[x > 3]  # Subsetting with logic

[1] 5 7 8 9

sum(x > 3) # Count values > 3

[1] 4

Matrices

Two-dimensional arrays with same data type:

# Create matrix
X <- matrix(1:9, nrow = 3, ncol = 3)
print(X)

     [,1] [,2] [,3]
[1,]    1    4    7
[2,]    2    5    8
[3,]    3    6    9

# Matrix by rows
Y <- matrix(1:9, nrow = 3, ncol = 3, byrow = TRUE)
print(Y)

     [,1] [,2] [,3]
[1,]    1    2    3
[2,]    4    5    6
[3,]    7    8    9

Matrix Operations

# Matrix arithmetic
X + Y  # Element-wise addition

     [,1] [,2] [,3]
[1,]    2    6   10
[2,]    6   10   14
[3,]   10   14   18

X * Y  # Element-wise multiplication

     [,1] [,2] [,3]
[1,]    1    8   21
[2,]    8   25   48
[3,]   21   48   81

X %*% Y  # Matrix multiplication

     [,1] [,2] [,3]
[1,]   66   78   90
[2,]   78   93  108
[3,]   90  108  126

# Subsetting
X[1, 2]  # Element at row 1, column 2

[1] 4

X[1, ]   # First row

[1] 1 4 7

X[, 1]   # First column

[1] 1 2 3

Lists

Creating Lists

Lists can contain elements of different types:

# Create a list
ex_list <- list(
  a = c(1, 2, 3, 4),
  b = TRUE,
  c = "Hello!",
  d = matrix(1:4, 2, 2)
)
print(ex_list)

$a
[1] 1 2 3 4

$b
[1] TRUE

$c
[1] "Hello!"

$d
     [,1] [,2]
[1,]    1    3
[2,]    2    4

List Operations

# Access list elements
ex_list$a  # Using $

[1] 1 2 3 4

ex_list[[1]]  # Using [[]]

[1] 1 2 3 4

ex_list["a"]  # Using []

$a
[1] 1 2 3 4

# Add new elements
ex_list$e <- "New element"

Packages

Installing Packages

# Install a package
install.packages("tidyverse")

# Load the package
library(tidyverse)

Note

You only need to install a package once, but you need to load it each session

Help System

Getting Help

Basic Help:

# Full help
help(plot)

# Shorthand
?plot

# Examples
example(plot)

Package Help:

# Package vignettes
vignette("dplyr")

# List all vignettes
vignette(all = FALSE)

# Package demos
demo(package = "graphics")

Data Frames

Introduction to Data Frames

Data frames are table-like structures:

# Create a data frame
df <- data.frame(
  id = 1:3,
  name = c("John", "Jane", "Bob"),
  score = c(85, 92, 78)
)
print(df)

  id name score
1  1 John    85
2  2 Jane    92
3  3  Bob    78

Data Frame Operations

# Basic operations
names(df)  # Column names

[1] "id"    "name"  "score"

nrow(df)   # Number of rows

[1] 3

ncol(df)   # Number of columns

[1] 3

# Access columns
df$name

[1] "John" "Jane" "Bob"

df[["score"]]

[1] 85 92 78

Subsetting Data Frames

# Subset rows
df[df$score > 80, ]

  id name score
1  1 John    85
2  2 Jane    92

# Subset columns
df[, c("name", "score")]

  name score
1 John    85
2 Jane    92
3  Bob    78

# Using subset()
subset(df, score > 80, select = c("name", "score"))

  name score
1 John    85
2 Jane    92

Additional Resources

R Documentation: r-project.org
RStudio Cheatsheets: rstudio.com/resources/cheatsheets
Advanced R by Hadley Wickham: adv-r.hadley.nz
Stack Overflow R Tag: stackoverflow.com/questions/tagged/r

Practice on your own

Create vectors using different methods (c(), :, seq(), rep())
Practice logical operations and understand operator precedence
Create a list with different types of elements and practice indexing
Load a package and resolve a namespace conflict
Create and remove objects from your environment

Econ 265: Introduction to Econometrics

Prerequisites

Today’s Agenda

RStudio Interface

Basic Operations

Basic Arithmetic

Logic Operations

Logic: Important Details

Value Matching

Object-Oriented Programming

Everything is an Object

Objects Have Classes

Global Environment

Assignment

Assignment Operators

Assignment Choice

Programming Basics

Variables and Assignment

Control Flow: if/else

Loops

Functions

Working with Variables

Creating Variables

Multiple Assignments

Data Types

Numeric Data

Text and Logical Data

Namespace Issues

Reserved Words

Namespace Conflicts

Indexing

Using Square Brackets

Using Dollar Sign

Cleaning Up

Removing Objects

Data Structures

Overview of Data Structures

Vectors

Vector Operations

Vector Logic

Matrices

Matrix Operations

Lists

Creating Lists

List Operations

Packages

Installing Packages

Help System

Getting Help

Data Frames

Introduction to Data Frames

Data Frame Operations

Subsetting Data Frames

Additional Resources

Practice on your own

Questions?