# ============================================================================
# ECONOMETRICS CLASS 2: INTRODUCTION TO R
# ============================================================================

# ============================================================================
# PART 1: R BASICS
# ============================================================================

# Positron interface:
# - Console (bottom): where code runs
# - Editor (top): where you write scripts
# - Variables pane (right): shows your data and objects
# - Plots/Help tabs (right): displays visualizations and documentation

# R as a calculator
2 + 2
10 * 5
100 / 4
2^3
sqrt(25)
exp(2)
log(1)

# Creating objects (assignment)
x <- 5
y <- 10
z <- x + y
z

name <- "Ricardo" # An object of type string
name # print the object value in the console
Name # R is case sensitive

# Creating vectors
ages <- c(22, 23, 25, 21, 24)
ages

names <- c("Alice", "Bob", "Charlie", "Diana", "Eve")
names

# Basic operations on vectors
mean(ages)
median(ages)
sd(ages)
sum(ages)
length(ages)

# Accessing elements
ages[1] # First element
ages[1:3] # First three elements
ages[c(1, 5)] # First and fifth elements

# EXERCISE 1:
# Create a vector with the grades of 6 students: 85, 92, 78, 95, 88, 91
# Calculate the mean, median, and standard deviation
# YOUR CODE HERE:

# ============================================================================
# PART 2: DATA FRAMES & DATA EXPLORATION
# ============================================================================

# Instead of the built-in dataframes in R we will use tibbles
install.packages("tibble")
library("tibble")

# Creating a data frame
student_data <- tibble(
  name = c("Alice", "Bob", "Charlie", "Diana", "Eve"),
  age = c(22, 23, 25, 21, 24),
  grade = c(85, 92, 78, 95, 88),
  hours_studied = c(10, 15, 8, 18, 12)
)

# Alternatively
student_data <- tribble(
  ~name     , ~age , ~grade , ~hours_studied ,
  "Alice"   ,   22 ,     85 ,             10 ,
  "Bob"     ,   23 ,     92 ,             15 ,
  "Charlie" ,   25 ,     78 ,              8 ,
  "Diana"   ,   21 ,     95 ,             18 ,
  "Eve"     ,   24 ,     88 ,             12
)

# Viewing data
student_data
View(student_data) # Opens in a separate viewer

# Basic exploration
head(student_data) # First 6 rows
str(student_data) # Structure of the data
summary(student_data) # Summary statistics
dim(student_data) # Dimensions (rows, columns)
names(student_data) # Column names

# Accessing columns
student_data$age
student_data$grade

mean(student_data$grade)
max(student_data$hours_studied)

# Loading built-in datasets
data(mtcars)
head(mtcars)
?mtcars # Help file

# EXERCISE 2:
# Using the mtcars dataset:
# 1. How many rows and columns does it have?
# 2. What is the mean miles per gallon (mpg)?
# 3. What is the maximum horsepower (hp)?
# YOUR CODE HERE:

# ============================================================================
# KEY TAKEAWAYS
# ============================================================================
# - R uses <- for assignment
# - Vectors are created with c()
# - Data frames are like Excel spreadsheets
# - Access columns with $
# - summary() gives detailed regression output