🗄️ Data Frames in R: Your Magic Spreadsheet

The Big Picture: What’s a Data Frame?

Imagine you have a magic notebook where you can organize anything—names, ages, favorite colors, test scores—all in neat rows and columns. That’s exactly what a Data Frame is in R!

Think of it like a classroom roster:

• Each row = one student
• Each column = one piece of info about that student (name, age, grade)

Data Frames are the heart of data analysis in R. Almost everything you do with real data uses them!

🎨 Our Analogy: The Classroom Roster

Throughout this guide, we’ll use a classroom roster as our example. Picture a teacher organizing information about students—that’s you building and working with Data Frames!

📝 Creating Data Frames

The Basic Recipe

Creating a Data Frame is like filling out a class roster. You list each column of information:

students <- data.frame(
  name = c("Emma", "Liam", "Ava"),
  age = c(10, 11, 10),
  grade = c("A", "B", "A")
)

What just happened?

• name, age, grade = column names (like headers in your roster)
• c() = combines values into a list
• Each student gets one value from each column

See Your Creation

print(students)

Output:

   name age grade
1  Emma  10     A
2  Liam  11     B
3   Ava  10     A

That’s it! You made a Data Frame with 3 students and 3 pieces of info each!

🔍 Data Frame Column Access

Grabbing One Column

Want just the names? Use the $ sign—like pointing at a column!

students$name
# Output: "Emma" "Liam" "Ava"

Using Square Brackets

You can also use brackets with the column name:

students["age"]
students[, "age"]   # Same thing!
students[, 2]       # Column 2 = age

Quick tip: The $ method is fastest to type!

graph TD
    A[Data Frame] --> B["$ dollar sign"]
    A --> C["[ ] brackets"]
    B --> D["students$name"]
    C --> E["students['name']"]
    C --> F["students[, 2]"]

👤 Data Frame Row Access

Getting One Row

Want all info about Emma (row 1)? Use brackets with row number:

students[1, ]
# Shows: Emma, 10, A

Getting Multiple Rows

Want Emma and Ava (rows 1 and 3)?

students[c(1, 3), ]

The Pattern

students[ROW, COLUMN]

• Leave ROW empty = all rows
• Leave COLUMN empty = all columns
• Fill both = specific cell!

students[2, 3]  # Row 2, Column 3 = "B"

🔎 Data Frame Inspection

Before working with data, you need to peek inside! Here are your detective tools:

Quick Look Commands

Example Inspection

str(students)

Output:

'data.frame':	3 obs. of 3 variables:
 $ name : chr  "Emma" "Liam" "Ava"
 $ age  : num  10 11 10
 $ grade: chr  "A" "B" "A"

This tells you: 3 students, 3 variables, and their data types!

🎯 Data Frame Filtering

Filtering = finding specific students that match what you’re looking for.

Find Students by Condition

Who is 10 years old?

students[students$age == 10, ]

Output:

   name age grade
1  Emma  10     A
3   Ava  10     A

How It Works

1. students$age == 10 creates TRUE/FALSE for each row
2. TRUE rows get selected
3. FALSE rows get ignored

More Filtering Examples

# Who got an A?
students[students$grade == "A", ]

# Who is older than 10?
students[students$age > 10, ]

# Who is 10 AND got an A?
students[students$age == 10 &
         students$grade == "A", ]

The subset() Shortcut

subset(students, age == 10)
# Same result, cleaner to read!

✏️ Data Frame Modification

Your roster isn’t set in stone! You can change, add, or remove things.

Change One Value

Emma turned 11:

students[1, "age"] <- 11

Add a New Column

Add a column for favorite subject:

students$subject <- c("Math", "Art", "Science")

Now your Data Frame has 4 columns!

Remove a Column

Changed your mind about that column?

students$subject <- NULL

Add a New Row

New student joined!

new_student <- data.frame(
  name = "Noah",
  age = 11,
  grade = "B"
)
students <- rbind(students, new_student)

rbind = row bind = glue rows together!

Change Column Names

names(students)[1] <- "student_name"
# Column 1 is now "student_name"

🔗 Data Frame Combining

Sometimes you have multiple rosters and need to merge them!

Stack Rows: rbind()

Two classes? Stack them vertically:

class_a <- data.frame(
  name = c("Emma", "Liam"),
  age = c(10, 11)
)
class_b <- data.frame(
  name = c("Ava", "Noah"),
  age = c(10, 11)
)

all_students <- rbind(class_a, class_b)

Result: 4 students, 2 columns

Add Columns: cbind()

Have extra info to add side-by-side?

grades <- data.frame(
  grade = c("A", "B", "A", "B")
)

full_roster <- cbind(all_students, grades)

Result: 4 students, 3 columns

Smart Merge: merge()

Combine based on matching values:

roster <- data.frame(
  id = c(1, 2, 3),
  name = c("Emma", "Liam", "Ava")
)

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

combined <- merge(roster, scores, by = "id")

Result:

  id name score
1  1 Emma    95
2  2 Liam    87
3  3  Ava    92

The id column matched up the right scores with the right students!

graph TD
    A[Combining Methods] --> B[rbind]
    A --> C[cbind]
    A --> D[merge]
    B --> E["Stack rows vertically"]
    C --> F["Add columns side-by-side"]
    D --> G["Join by matching column"]

🚀 Quick Reference Summary

🎉 You Did It!

You now understand the 7 core skills of Data Frames:

1. ✅ Creating them from scratch
2. ✅ Accessing columns with $ and []
3. ✅ Accessing rows with bracket notation
4. ✅ Inspecting with str(), summary(), and friends
5. ✅ Filtering to find exactly what you need
6. ✅ Modifying values, adding/removing columns and rows
7. ✅ Combining Data Frames with rbind, cbind, and merge

Data Frames are your superpower for working with real data in R. Now go organize some data! 🗂️