R Online Compiler

Run R programs online with our statistical computing environment. Perfect for data analysis, statistics, machine learning, and research.

r Online Compiler

# Hello World in R
print("Hello, World!")
cat("Welcome to R Online Compiler\n")

# Variables and data types
language <- "R"
version <- 4.3
is_statistical <- TRUE

cat("Language:", language, "\n")
cat("Version:", version, "\n")
cat("Statistical:", is_statistical, "\n")

# Vectors
numbers <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
cat("Numbers:", numbers, "\n")

# Basic statistics
cat("Mean:", mean(numbers), "\n")
cat("Median:", median(numbers), "\n")
cat("Standard deviation:", sd(numbers), "\n")

# Data frame
students <- data.frame(
  name = c("Alice", "Bob", "Charlie", "Diana"),
  age = c(20, 22, 21, 23),
  grade = c(85, 92, 78, 96)
)

print("Student data:")
print(students)

# Summary statistics
cat("\nGrade statistics:\n")
print(summary(students$grade))

# Simple plot data (values only)
x <- 1:10
y <- x^2
cat("\nPlot data (x, y):\n")
for(i in 1:length(x)) {
  cat("(", x[i], ",", y[i], ")\n")
}

# Functions
calculate_bmi <- function(weight, height) {
  bmi <- weight / (height^2)
  return(bmi)
}

bmi_result <- calculate_bmi(70, 1.75)
cat("BMI for 70kg, 1.75m:", round(bmi_result, 2), "\n")

# Conditional logic
if (bmi_result < 18.5) {
  cat("Underweight\n")
} else if (bmi_result < 25) {
  cat("Normal weight\n")
} else if (bmi_result < 30) {
  cat("Overweight\n")
} else {
  cat("Obese\n")
}

Output

Click "Run" to execute your code and see the output here...

# Hello World in R
print("Hello, World!")
cat("Welcome to R Online Compiler\n")

# Variables and data types
language <- "R"
version <- 4.3
is_statistical <- TRUE

cat("Language:", language, "\n")
cat("Version:", version, "\n")
cat("Statistical:", is_statistical, "\n")

# Vectors
numbers <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
cat("Numbers:", numbers, "\n")

# Basic statistics
cat("Mean:", mean(numbers), "\n")
cat("Median:", median(numbers), "\n")
cat("Standard deviation:", sd(numbers), "\n")

# Data frame
students <- data.frame(
  name = c("Alice", "Bob", "Charlie", "Diana"),
  age = c(20, 22, 21, 23),
  grade = c(85, 92, 78, 96)
)

print("Student data:")
print(students)

# Summary statistics
cat("\nGrade statistics:\n")
print(summary(students$grade))

# Simple plot data (values only)
x <- 1:10
y <- x^2
cat("\nPlot data (x, y):\n")
for(i in 1:length(x)) {
  cat("(", x[i], ",", y[i], ")\n")
}

# Functions
calculate_bmi <- function(weight, height) {
  bmi <- weight / (height^2)
  return(bmi)
}

bmi_result <- calculate_bmi(70, 1.75)
cat("BMI for 70kg, 1.75m:", round(bmi_result, 2), "\n")

R Compiler Features

R 4.3 with latest packages
Statistical functions and analysis
Data frames and vectors
Built-in statistical tests
Data visualization capabilities

About R Programming

R is a programming language and environment specifically designed for statistical computing and graphics. It's widely used by statisticians, data scientists, and researchers worldwide.

R provides a wide variety of statistical and graphical techniques, including linear and nonlinear modeling, time-series analysis, classification, clustering, and more.

Use our R online compiler to perform statistical analysis, create data visualizations, or learn R programming for data science without installing R locally.