data.frame()

data.frame(..., row.names = NULL, check.rows = FALSE, check.names = TRUE, stringsAsFactors = FALSE)

A data frame is R’s primary structure for storing rectangular data — like a spreadsheet or SQL table. Each column can hold a different data type (numeric, character, factor, etc.), but all columns must have the same length.

Syntax

data.frame(
  col1 = values1,
  col2 = values2,
  ...
)

Parameters

Parameter	Type	Default	Description
...	any	—	Column name = value pairs
row.names	NULL or character	NULL	Row names (NULL generates automatic names)
check.rows	logical	FALSE	If TRUE, check that rows are same length
check.names	logical	TRUE	If TRUE, check that column names are valid
stringsAsFactors	logical	FALSE	If TRUE, convert character columns to factors

Examples

Creating a basic data frame

# Create a simple data frame
df <- data.frame(
  name = c("Alice", "Bob", "Charlie"),
  age = c(25, 30, 35),
  score = c(85.5, 92.0, 78.5)
)

df
#      name age score
# 1    Alice  25  85.5
# 2      Bob  30  92.0
# 3  Charlie  35  78.5

# Check structure
str(df)
# 'data.frame': 3 obs. of 3 variables:
#  $ name : chr  "Alice" "Bob" "Charlie"
#  $ age : num  25 30 35
#  $ score: num  85.5 92 78.5

Accessing columns and rows

# Access column by name
df$name
# [1] "Alice"   "Bob"     "Charlie"

# Access by position
df[[1]]
# [1] "Alice"   "Bob"     "Charlie"

# Subset rows
df[1, ]
#   name age score
# 1 Alice  25  85.5

# Subset columns
df[, "age"]
# [1] 25 30 35

Adding and modifying columns

# Add a new column
df$city <- c("London", "Paris", "Berlin")
df
#      name age score    city
# 1    Alice  25  85.5  London
# 2      Bob  30  92.0   Paris
# 3  Charlie  35  78.5  Berlin

# Modify existing column
df$age <- df$age + 1
df
#      name age score    city
# 1    Alice  26  85.5  London
# 2      Bob  31  92.0   Paris
# 3  Charlie  36  78.5  Berlin

Common Patterns

Converting to tibble (tidyverse)

library(tibble)
as_tibble(df)
# # A tibble: 3 × 4
#   name      age score city   
#   <chr>   <dbl> <dbl> <chr>  
# 1 Alice      26  85.5 London 
# 2 Bob        31  92   Paris  
# 3 Charlie    36  78.5 Berlin

Reading from CSV

# Read CSV into data frame
df <- read.csv("data.csv", stringsAsFactors = FALSE)

# Same with readr
library(readr)
df <- read_csv("data.csv")

See Also

• tibble::tibble
• base::matrix
• base::list