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🧾 R Data Frames – Tabular Data Handling in R Programming

🧲 Introduction – What Are Data Frames in R?

A data frame in R is a two-dimensional, tabular data structure—similar to a spreadsheet or SQL table—where each column can have a different data type (numeric, character, logical, etc.), but all columns must have the same length.

Data frames are central to data analysis in R. Most real-world data (from CSVs, databases, APIs) is loaded into R as a data frame.

🎯 In this guide, you’ll learn:

How to create, access, and modify data frames
Use column filtering, row subsetting, and data transformation
Apply useful built-in functions for summary and structure
Handle missing values and combine multiple data frames

🛠️ Creating a Data Frame

students <- data.frame(
  Name = c("Alice", "Bob", "Charlie"),
  Age = c(22, 25, 23),
  Passed = c(TRUE, TRUE, FALSE)
)

✅ Data types:

Name: character
Age: numeric
Passed: logical

Use str(students) to inspect structure.

🔍 Accessing Data Frame Elements

📌 By Column Name

students$Name         # Returns column as vector
students[["Age"]]     # Also returns column

🔢 By Index

students[1, ]         # First row
students[, 2]         # Second column
students[2, 1]        # Row 2, Col 1

🔎 Subsetting with Conditions

students[students$Passed == TRUE, ]   # Only those who passed

✏️ Modifying Data Frames

🔄 Add Column

students$Score <- c(90, 85, 70)

🧹 Remove Column

students$Score <- NULL

🔁 Add Row (with `rbind()`)

new_row <- data.frame(Name = "David", Age = 24, Passed = TRUE)
students <- rbind(students, new_row)

🔁 Combining Data Frames

📍 Row Bind

df1 <- data.frame(A = 1:2, B = c("x", "y"))
df2 <- data.frame(A = 3:4, B = c("z", "w"))
rbind(df1, df2)

📍 Column Bind

df3 <- data.frame(C = c(TRUE, FALSE))
cbind(df1, df3)

🧠 Useful Data Frame Functions

Function	Description
`str()`	Structure of data frame
`summary()`	Summary statistics
`nrow()` / `ncol()`	Number of rows / columns
`names()`	Column names
`rownames()`	Row labels
`head()` / `tail()`	Preview top or bottom rows
`subset()`	Subset rows using condition

⚠️ Handling Missing Values (NA)

df <- data.frame(x = c(1, NA, 3))
is.na(df)            # Identify missing
na.omit(df)          # Remove rows with NA

📊 Converting Between Data Types

Convert From	To	Function
Matrix	Data Frame	`as.data.frame()`
List	Data Frame	`as.data.frame()`
Data Frame	Matrix	`as.matrix()`

📌 Summary – Recap & Next Steps

Data frames are the most practical and widely-used data structure for structured tabular data in R. Mastering their creation, manipulation, and filtering is key for data science workflows.

🔍 Key Takeaways:

Create with data.frame() using named columns
Access using $, [row, col], or logical conditions
Add/remove rows and columns with rbind() / cbind()
Use summary(), str(), and head() for inspection
Handle missing data with is.na() and na.omit()

⚙️ Real-World Relevance:
Used in nearly every R project: from importing Excel/CSV data, transforming datasets, modeling results, to exporting reports.

❓ FAQs – R Data Frames

❓ What is the difference between a data frame and a matrix in R?
✅ A matrix holds only one data type; a data frame allows mixed types in different columns.

❓ How can I filter rows in a data frame?
✅ Use logical conditions:

df[df$Age > 25, ]

❓ How do I add a new column to a data frame?
✅ Assign a vector directly:

df$NewCol <- c(1, 2, 3)

❓ How do I remove rows with missing values?
✅ Use:

na.omit(df)

❓ How can I preview the top 5 rows?
✅ Use:

head(df, 5)

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