Data Processing

🍳 R Data Processing: Your Kitchen Adventure!

Imagine you’re a master chef in a magical kitchen. Your ingredients? Data! Your cooking tools? R functions! Let’s learn how to slice, dice, mix, and serve beautiful data dishes.

🥄 The Kitchen Metaphor

Think of your data like a big basket of ingredients. Sometimes you need to:

• Pick only the tomatoes (Subset)
• Chop them into pieces (Transform)
• Work inside the basket easily (with/within)
• Mix two baskets together (Merge)
• Find what’s common or different (Set Operations)
• Count how many of each (Contingency Tables)
• Rearrange your table nicely (Table Manipulation)

Let’s cook! 🍽️

1️⃣ Subset Function: Picking Your Ingredients

What is it?

subset() helps you pick only the data you want — like reaching into a fruit basket and grabbing only the apples!

The Magic Words

subset(data, condition)
subset(data, condition, select = columns)

🍎 Simple Example

# Our fruit basket
fruits <- data.frame(
  name = c("apple", "banana", "cherry"),
  color = c("red", "yellow", "red"),
  price = c(2, 1, 3)
)

# Pick only red fruits
red_fruits <- subset(fruits,
                     color == "red")

# Result: apple and cherry!

🎯 Pro Tips

• Use select to pick specific columns:

# Get names of cheap fruits
subset(fruits,
       price < 2,
       select = name)

Why Kids Love It 🧒

It’s like having a magic wand that says “Give me only the toys that are blue!” and poof — you get exactly what you asked for!

2️⃣ Transform Function: Cooking Your Data

What is it?

transform() lets you add new columns or change existing ones — like adding seasoning to your dish!

The Magic Words

transform(data, new_column = calculation)

🧮 Simple Example

# Our students
students <- data.frame(
  name = c("Amy", "Bob"),
  math = c(80, 90),
  science = c(70, 85)
)

# Add total score
students <- transform(students,
  total = math + science,
  average = (math + science) / 2
)

🎨 What Happens?

  name  math  science  total  average
1  Amy    80       70    150     75.0
2  Bob    90       85    175     87.5

Why Kids Love It 🧒

It’s like putting stickers on your notebook — you’re adding new stuff without throwing anything away!

3️⃣ With and Within Functions: Working Inside the Box

The Problem

Typing data$column everywhere is tiring! Like saying “the red box’s apple, the red box’s banana, the red box’s cherry…”

The Solution: with() and within()

graph TD
    A["Your Data Frame"] --> B{What do you want?}
    B -->|Just calculate something| C["with"]
    B -->|Change the data| D["within"]
    C --> E["Returns a result"]
    D --> F["Returns modified data"]

🎪 with() Example

# Calculate without $ signs
with(students, {
  total <- math + science
  print(mean(total))
})
# Just shows the answer!

🔧 within() Example

# Modify the data itself
students <- within(students, {
  grade <- ifelse(average >= 80,
                  "A", "B")
})
# Now students HAS a grade column!

The Difference 🤔

4️⃣ Merge Function: Mixing Two Baskets

What is it?

merge() is like having two puzzle pieces that snap together! It combines data from different tables.

The Magic Words

merge(table1, table2, by = "matching_column")

🧩 Simple Example

# Student names
names_df <- data.frame(
  id = c(1, 2, 3),
  name = c("Amy", "Bob", "Cat")
)

# Student scores
scores_df <- data.frame(
  id = c(1, 2, 3),
  score = c(95, 87, 92)
)

# Snap them together!
complete <- merge(names_df,
                  scores_df,
                  by = "id")

🎨 Result

  id name score
1  1  Amy    95
2  2  Bob    87
3  3  Cat    92

🔮 Different Types of Merge

graph TD
    A["Merge Types"] --> B["Inner: Only matching"]
    A --> C["Left: Keep all left"]
    A --> D["Right: Keep all right"]
    A --> E["Full: Keep everything"]

# Keep everyone from left table
merge(x, y, by="id", all.x=TRUE)

# Keep everyone from both
merge(x, y, by="id", all=TRUE)

5️⃣ Set Operations: Finding Friends & Strangers

What is it?

Like comparing two groups of friends:

• Who’s in BOTH groups? (intersect)
• Who’s in EITHER group? (union)
• Who’s ONLY in group A? (setdiff)

🎭 Simple Examples

group_a <- c("Amy", "Bob", "Cat")
group_b <- c("Bob", "Cat", "Dan")

# Friends in BOTH
intersect(group_a, group_b)
# "Bob" "Cat"

# ALL friends combined
union(group_a, group_b)
# "Amy" "Bob" "Cat" "Dan"

# Only in group A
setdiff(group_a, group_b)
# "Amy"

# Only in group B
setdiff(group_b, group_a)
# "Dan"

🎪 Visual Summary

Group A: 🔴 Amy | 🟡 Bob | 🟢 Cat
Group B:        | 🟡 Bob | 🟢 Cat | 🔵 Dan

intersect: 🟡🟢 (Bob, Cat)
union:     🔴🟡🟢🔵 (All four)
setdiff A-B: 🔴 (Just Amy)
setdiff B-A: 🔵 (Just Dan)

6️⃣ Contingency Tables: Counting Your Stickers

What is it?

table() counts how many of each thing you have — like organizing your sticker collection by color and shape!

🎨 Simple Example

# Our pets
pets <- data.frame(
  animal = c("cat", "dog", "cat",
             "dog", "cat"),
  color = c("white", "brown", "brown",
            "white", "white")
)

# Count by animal type
table(pets$animal)
# cat: 3, dog: 2

# Two-way table
table(pets$animal, pets$color)

🎯 Two-Way Result

     brown white
cat     1     2
dog     1     1

🔧 Add Margins (Totals)

my_table <- table(pets$animal,
                  pets$color)
addmargins(my_table)

      brown white Sum
cat      1     2   3
dog      1     1   2
Sum      2     3   5

7️⃣ Table Manipulation: Arranging Your Display

Key Functions

📊 Proportion Tables

my_table <- table(pets$animal,
                  pets$color)

# Overall percentages
prop.table(my_table)
# Each cell / total

# Row percentages
prop.table(my_table, 1)
# Each row adds to 1

# Column percentages
prop.table(my_table, 2)
# Each column adds to 1

🎪 Row Percentages Example

       brown white
cat    0.33  0.67   (1 brown, 2 white)
dog    0.50  0.50   (1 brown, 1 white)

🧊 Flatten Complex Tables

# 3-way table
t3 <- table(survey$gender,
            survey$age,
            survey$vote)

# Make it readable
ftable(t3)

🎓 The Complete Recipe

graph TD
    A["📦 Raw Data"] --> B["🔍 subset"]
    B --> C["🔧 transform"]
    C --> D["📝 with/within"]
    D --> E{Need to combine?}
    E -->|Yes| F["🔗 merge"]
    E -->|No| G["Compare sets?"]
    G -->|Yes| H["⚙️ Set Operations"]
    G -->|No| I["Count things?"]
    F --> I
    H --> I
    I -->|Yes| J["📊 table"]
    J --> K["🎨 Table Manipulation"]
    K --> L["✨ Beautiful Results!"]

🚀 Quick Reference Card

🎉 You Did It!

You’ve just learned how to:

• ✅ Pick exactly what you need (subset)
• ✅ Transform and enhance data (transform)
• ✅ Work efficiently inside data (with/within)
• ✅ Combine data sources (merge)
• ✅ Compare groups (set operations)
• ✅ Count and summarize (contingency tables)
• ✅ Display beautifully (table manipulation)

You’re now a Data Processing Chef! 👨‍🍳

“Data processing in R is like cooking — once you know your tools, you can create anything!”