Welcome to Week 3! This week, we explore data manipulation using dplyr and the tidyverse ecosystem - essential tools for organizing, cleaning, and visualizing agricultural data. Learn to filter, select, arrange, and transform data efficiently!

🔧 What You'll Learn This Week

🔍 Data Subsetting - Using brackets and logical conditions

🔗 Pipes (%>%) - Chain operations for readable workflows

📋 filter() - Subset rows based on conditions

✂️ select() - Choose specific columns efficiently

🔄 mutate() - Create new variables and transformations

📊 group_by() + summarize() - Calculate group statistics

🚀 Getting Started: Step-by-Step Guide

Step 1: Launch Week 3 Binder Environment 🌐

Click the "Launch Week 3" button above to start your R environment. This will take 2-5 minutes to load with all necessary packages for data manipulation.

Step 2: Navigate to Class Activity 📚

Once Binder loads, you'll see the Jupyter Notebook interface. In the left panel, you'll see:

assignment/ - Assignment 3 on data visualization and analysis
class_activity/ - Week 3 lab tutorial
Various files (README.md, runtime.txt, etc.)

Click on the class_activity folder to access this week's content.

Step 3: Open the Week 3 Lab Notebook 📖

Inside the class_activity folder, double-click on Week3_Data_Manipulation.ipynb to open the interactive lab notebook.

Step 4: Work with Multiple Datasets 📊

This week we'll use multiple datasets including the iris dataset and real-world data! The notebook will guide you through:

Loading tidyverse and dplyr packages
Basic data subsetting with brackets
Filtering and slicing data efficiently
Advanced column selection techniques
Data transformation and visualization

🎯 Interactive Learning Tools

Practice with Data Manipulation Tools

Use these interactive tools to understand data manipulation concepts before working with R code:

💡 Tip: Use these tools to visualize data manipulation concepts before applying them in your R notebook!

🧮 Key R Functions This Week

Data Manipulation

            filter(data, condition)     # Subset rows

            select(data, columns)       # Choose columns

            slice(data, rows)           # Select by position

            arrange(data, variable)     # Sort data

            mutate(data, new_var = ...)  # Create variables

            group_by(data, variable)    # Group for analysis

Column Selection Helpers

            starts_with("Sepal")        # Columns starting with "Sepal"

            ends_with("Length")         # Columns ending with "Length"

            contains("Petal")           # Columns containing "Petal"

            matches(".*Width")          # Regular expression matching

Data Cleaning

            str_replace_all(text, pattern, replacement)  # Clean text

            na.omit(data)               # Remove missing values

            as.integer(vector)          # Convert data types

📝 Assignment 3: Data Visualization and Analysis

Step 1: Access Assignment Folder 📋

From the main directory, click on the assignment folder to access Assignment 3.

Step 2: Open Assignment 3 Notebook 📄

Double-click on Assignment3.ipynb to open your assignment on data visualization and analysis.

Assignment Overview (20 points total)

📊

Part 1: LA Data Analysis (6 points)

Filter data by gender and create comparative boxplots

🧹

Part 2: SAT Dataset Processing (9 points)

Import, clean, and subset real-world data

📈

Part 3: Distribution Analysis (5 points)

Create stem-and-leaf plots and analyze patterns

Step 3: Work with Real-World Data 🌍

The assignment uses multiple datasets:

LA Data - Victim demographics and statistics
SAT Dataset 2012 - School performance data requiring cleaning

Learn to handle messy real-world data with non-numeric values and missing information!

🌾 Why This Matters in Agriculture

🌱 Crop Yield Analysis - Filter by variety, location, season

🧪 Soil Sample Processing - Clean mixed numeric/text data

🌧️ Weather Data Analysis - Group by month, calculate averages

🐄 Livestock Performance - Compare treatments, identify outliers

✅ Quality Control - Monitor product consistency over time

Data Manipulation Skills Help You:

Clean and organize messy agricultural datasets
Filter data by treatment groups or conditions
Create new variables for analysis (ratios, categories)
Summarize data by groups (variety, location, time)
Prepare data for statistical analysis and visualization

💾 Saving Your Work

⚠️ Important: Binder environments are temporary! Always save your work locally.

Download Your Notebook 📥

When you're done working, save your progress:

Save your notebook: File → Save
Download .ipynb file: File → Download
Export HTML/PDF: File → Save and Export Notebook As → HTML

Continue Your Progress Later 🔄

To resume your work:

Launch Binder again
Click Upload button
Upload your saved .ipynb file
Continue where you left off!

📤 Submission Requirements

For Assignment 3, submit TWO files to UC Davis Canvas:

📄

HTML/PDF Report

Your completed assignment with all outputs and analysis

💾

.ipynb File

Your notebook code as backup

Due Date: Check Canvas for assignment deadline

🎯 Learning Objectives

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

✅ Master data subsetting with brackets and logical conditions

✅ Apply dplyr functions for efficient data manipulation

✅ Chain operations using pipes for readable workflows

✅ Select columns using helper functions and patterns

✅ Clean real-world data with mixed data types

✅ Create visualizations to understand data patterns

❓ Need Help?

📧 Contact Information

Mohammadreza Narimani
📧 mnarimani@ucdavis.edu
🏫 Department of Biological and Agricultural Engineering, UC Davis

🔧 Common Issues

Binder won't load? Try refreshing or clearing browser cache
Package error? Make sure to run the library loading cell first
Data not found? Check that you're in the correct directory
Pipe not working? Ensure you've loaded dplyr or tidyverse

📚 Additional Resources

dplyr Cheat Sheet: Available in RStudio Help menu
Tidyverse Documentation: ?function_name for help
Practice: Try different combinations of functions

🌟 Tips for Success

💡 Best Practices

Start simple - Master basic functions before combining them
Use pipes wisely - Chain operations for readable code
Check your data - Use head(), str(), and summary() frequently
Practice filtering - Try different conditions and combinations
Experiment - The best way to learn is by doing!

⚡ Keyboard Shortcuts

Shift + Enter Run current cell and move to next

Ctrl + Enter Run current cell and stay in place

Tab Auto-complete function names

Ctrl + Shift + M Insert pipe operator (%>%)

🎉 Ready to Start?

Click the Binder badge below to launch Week 3!

Launch Week 3 Interactive Environment

Happy data wrangling! 🔧🌾

Last updated: October 2025 | PLS 120 - Applied Statistics in Agriculture | UC Davis
Week 3: Data Manipulation with dplyr

🌾 Week 3: Data Manipulation with dplyr