W9.R

# Week 9: Advanced Data Manipulation with dplyr and Exploratory Data Analysis

# This week builds on prior data wrangling concepts, 
# introducing advanced functions from dplyr to filter, 
# group, summarize, and arrange data efficiently. Additionally, 
# students will explore the basics of exploratory data analysis (EDA) techniques.
# 

# Exercise 1: Filtering Data in iris

# Load the iris dataset.

# Filter rows where Petal.Length is greater than 4.

# Extract only columns Species, Sepal.Length, and Petal.Length for those rows.


# Exercise 2: Grouping and Summarizing Data

# Group the iris dataset by Species.

# Calculate the mean and standard deviation of Sepal.Width for each species.

# Save the results to a new data frame called iris_summary.


# Exercise 3: Arranging Data by Multiple Columns

# Arrange the rows of iris by Species (ascending) and Sepal.Length (descending).

# Show the first 10 rows of the sorted dataset.


# Exercise 4: Exploring Summary Statistics for EDA

# Compute the following summary statistics for Sepal.Length:
#   Minimum, Maximum, Mean, Median, and Variance.

# Use the base R function summary() and compare the results.


# Exercise 5: Using dplyr's mutate() for Derived Columns

# Add a new column to iris called Petal.Ratio, calculated as Petal.Length / Petal.Width.

# Filter rows where Petal.Ratio is greater than 2.


# Show & Tell: Try Something New
# Explore a function from dplyr or another R package 
# that wasn't covered in these exercises. 
# Share your findings and demonstrate how you applied it to your dataset.