R Online Editor

Let's start with R basics for data analysis:

# Basic output
print("Hello, R!")

# Variables and data types
name <- "R Programming"
number <- 42
is_true <- TRUE

# Vectors
numbers <- c(1, 2, 3, 4, 5)
names <- c("Alice", "Bob", "Charlie")

# Basic calculations
mean(numbers)
sum(numbers)
length(numbers)

Our editor provides real-time execution and statistical computing.

Learn R's fundamental data structure for data analysis:

# Create a data frame
df <- data.frame(
  name = c("Alice", "Bob", "Charlie", "Diana"),
  age = c(25, 30, 35, 28),
  score = c(85, 92, 78, 96)
)

# View the data
print(df)
str(df)  # Structure
summary(df)  # Summary statistics

# Access columns
df$name
df["age"]

# Filter data
df[df$age > 30, ]
subset(df, score > 85)

# Add new columns
df$grade <- ifelse(df$score >= 90, "A", "B")

Practice data manipulation in our interactive environment.

Our R editor runs R 4.4+ via WebR with specific constraints:

Version & Environment:

• R 4.4+ in WebAssembly (WebR)
• ~200 pre-installed packages
• Memory limit: ~2GB for datasets

Key Limitations:

• No graphics/plotting device (text output only)
• No file I/O (except URL imports)
• No parallel computing
• No external package installation
• No system calls

Available Packages:

# Check available packages
installed.packages()[, 'Package']

# Common packages available:
# base, stats, utils, methods, datasets
# dplyr (selected packages)

Focus on statistical computing and data manipulation.

Conduct statistical tests and analysis:

# Generate sample data
set.seed(123)  # For reproducibility
group1 <- rnorm(30, mean = 100, sd = 15)
group2 <- rnorm(30, mean = 110, sd = 15)

# Descriptive statistics
summary(group1)
sd(group1)  # Standard deviation
var(group1)  # Variance

# T-test
t.test(group1, group2)

# Correlation
x <- 1:20
y <- x * 2 + rnorm(20, 0, 3)
cor(x, y)
cor.test(x, y)

# Linear regression
model <- lm(y ~ x)
summary(model)
plot(x, y)
abline(model, col = "red")

Practice statistical analysis with comprehensive R functions.

Create custom functions for data analysis:

# Basic function
calculate_stats <- function(data) {
  list(
    mean = mean(data),
    median = median(data),
    sd = sd(data),
    min = min(data),
    max = max(data)
  )
}

# Function with default parameters
normalize_data <- function(data, method = "z-score") {
  if (method == "z-score") {
    return((data - mean(data)) / sd(data))
  } else if (method == "min-max") {
    return((data - min(data)) / (max(data) - min(data)))
  }
}

# Use the functions
sample_data <- rnorm(100, 50, 10)
stats <- calculate_stats(sample_data)
print(stats)

normalized <- normalize_data(sample_data)
plot(density(normalized))

Build reusable functions for your data analysis workflow.

Load and process data efficiently:

# Create sample dataset
data <- data.frame(
  id = 1:100,
  group = sample(c("A", "B", "C"), 100, replace = TRUE),
  value = rnorm(100, 50, 15),
  date = Sys.Date() + 1:100
)

# Data exploration
head(data)  # First 6 rows
tail(data)  # Last 6 rows
dim(data)   # Dimensions
colnames(data)  # Column names

# Data aggregation
aggregate(value ~ group, data, mean)
aggregate(value ~ group, data, function(x) c(mean=mean(x), sd=sd(x)))

# Data filtering and sorting
high_values <- data[data$value > 60, ]
sorted_data <- data[order(data$value, decreasing = TRUE), ]

# Handle missing values
# data$value[sample(100, 10)] <- NA  # Introduce NAs
# complete_cases <- data[complete.cases(data), ]

Master data processing techniques in our R environment.

Explore these official resources and learning materials:

Official Documentation:

• R Project - Official R language homepage
• R Manuals - Comprehensive R documentation
• R Language Definition - Language specification
• CRAN - Package repository and documentation

Learning Resources:

• R for Data Science - Comprehensive free book by Hadley Wickham
• Swirl - Interactive R learning in the console
• R Style Guide - Tidyverse coding conventions
• Quick-R - Practical R tutorials

Data Science & Statistics:

• Tidyverse - Modern R packages for data science
• ggplot2 Documentation - Data visualization
• R Markdown - Dynamic documents
• Shiny - Interactive web applications

These resources cover R from statistical computing basics to advanced data science!