R Kernel Example¶

This notebook demonstrates using the R kernel (IRkernel) for statistical computing and data analysis.

Kernel Information¶

This notebook uses the R kernel. To use this kernel, you need to install:

install.packages('IRkernel')
IRkernel::installspec()

In [ ]:

# Display R version and information
R.version.string
sessionInfo()

# Display R version and information R.version.string sessionInfo()

R Features¶

R is excellent for:

• Statistical Analysis
• Data Visualization
• Machine Learning
• Bioinformatics
• Academic Research

In [ ]:

# Example: Basic data structures in R
# Create a data frame
languages <- data.frame(
  name = c("Python", "R", "Julia", "Ruby"),
  type = c("General", "Statistical", "Scientific", "General"),
  year_created = c(1991, 1993, 2012, 1995),
  data_science_score = c(9, 10, 8, 5),
  stringsAsFactors = FALSE
)

# Display the data frame
print("Programming Languages for Data Science:")
languages

# Example: Basic data structures in R # Create a data frame languages <- data.frame( name = c("Python", "R", "Julia", "Ruby"), type = c("General", "Statistical", "Scientific", "General"), year_created = c(1991, 1993, 2012, 1995), data_science_score = c(9, 10, 8, 5), stringsAsFactors = FALSE ) # Display the data frame print("Programming Languages for Data Science:") languages

In [ ]:

# Statistical summary
summary(languages$data_science_score)

# Calculate age of languages
current_year <- as.numeric(format(Sys.Date(), "%Y"))
languages$age <- current_year - languages$year_created

print("\nLanguages with age:")
languages[order(languages$age, decreasing = TRUE), ]

# Statistical summary summary(languages$data_science_score) # Calculate age of languages current_year <- as.numeric(format(Sys.Date(), "%Y")) languages$age <- current_year - languages$year_created print("\nLanguages with age:") languages[order(languages$age, decreasing = TRUE), ]

Data Visualization with Base R¶

In [ ]:

# Create visualizations
# Bar plot
barplot(languages$data_science_score, 
        names.arg = languages$name,
        main = "Data Science Scores by Language",
        ylab = "Score",
        col = c("#3776ab", "#276DC3", "#9558B2", "#CC342D"),
        ylim = c(0, 12))

# Add value labels
text(x = 1:4 * 1.2 - 0.5, 
     y = languages$data_science_score + 0.5, 
     labels = languages$data_science_score)

# Create visualizations # Bar plot barplot(languages$data_science_score, names.arg = languages$name, main = "Data Science Scores by Language", ylab = "Score", col = c("#3776ab", "#276DC3", "#9558B2", "#CC342D"), ylim = c(0, 12)) # Add value labels text(x = 1:4 * 1.2 - 0.5, y = languages$data_science_score + 0.5, labels = languages$data_science_score)

In [ ]:

# Statistical analysis example
# Generate sample data
set.seed(42)
n <- 100
x <- rnorm(n, mean = 50, sd = 10)
y <- 2 * x + rnorm(n, mean = 0, sd = 5)

# Create scatter plot with regression line
plot(x, y, 
     main = "Linear Regression Example",
     xlab = "X Variable",
     ylab = "Y Variable",
     pch = 19,
     col = rgb(0, 0, 1, 0.5))

# Fit linear model
model <- lm(y ~ x)
abline(model, col = "red", lwd = 2)

# Add R-squared to plot
r_squared <- round(summary(model)$r.squared, 3)
text(40, 120, paste("R² =", r_squared), pos = 4)

# Statistical analysis example # Generate sample data set.seed(42) n <- 100 x <- rnorm(n, mean = 50, sd = 10) y <- 2 * x + rnorm(n, mean = 0, sd = 5) # Create scatter plot with regression line plot(x, y, main = "Linear Regression Example", xlab = "X Variable", ylab = "Y Variable", pch = 19, col = rgb(0, 0, 1, 0.5)) # Fit linear model model <- lm(y ~ x) abline(model, col = "red", lwd = 2) # Add R-squared to plot r_squared <- round(summary(model)$r.squared, 3) text(40, 120, paste("R² =", r_squared), pos = 4)

Advanced Statistical Analysis¶

In [ ]:

# ANOVA example
# Create sample data for three groups
set.seed(123)
group_a <- rnorm(30, mean = 100, sd = 15)
group_b <- rnorm(30, mean = 110, sd = 15)
group_c <- rnorm(30, mean = 105, sd = 15)

# Combine into a data frame
experiment_data <- data.frame(
  value = c(group_a, group_b, group_c),
  group = factor(rep(c("A", "B", "C"), each = 30))
)

# Perform ANOVA
anova_result <- aov(value ~ group, data = experiment_data)
summary(anova_result)

# Box plot
boxplot(value ~ group, data = experiment_data,
        main = "Group Comparison",
        xlab = "Group",
        ylab = "Value",
        col = c("lightblue", "lightgreen", "lightcoral"))

# ANOVA example # Create sample data for three groups set.seed(123) group_a <- rnorm(30, mean = 100, sd = 15) group_b <- rnorm(30, mean = 110, sd = 15) group_c <- rnorm(30, mean = 105, sd = 15) # Combine into a data frame experiment_data <- data.frame( value = c(group_a, group_b, group_c), group = factor(rep(c("A", "B", "C"), each = 30)) ) # Perform ANOVA anova_result <- aov(value ~ group, data = experiment_data) summary(anova_result) # Box plot boxplot(value ~ group, data = experiment_data, main = "Group Comparison", xlab = "Group", ylab = "Value", col = c("lightblue", "lightgreen", "lightcoral"))

In [ ]:

# Time series analysis example
# Generate time series data
dates <- seq(as.Date("2023-01-01"), by = "day", length.out = 365)
trend <- seq(100, 150, length.out = 365)
seasonal <- 10 * sin(2 * pi * (1:365) / 365)
noise <- rnorm(365, mean = 0, sd = 5)
values <- trend + seasonal + noise

# Create time series plot
plot(dates, values, type = "l",
     main = "Time Series Analysis",
     xlab = "Date",
     ylab = "Value",
     col = "blue")

# Add trend line
lines(dates, trend, col = "red", lwd = 2)

# Add legend
legend("topleft", 
       legend = c("Observed", "Trend"),
       col = c("blue", "red"),
       lty = 1,
       lwd = c(1, 2))

# Time series analysis example # Generate time series data dates <- seq(as.Date("2023-01-01"), by = "day", length.out = 365) trend <- seq(100, 150, length.out = 365) seasonal <- 10 * sin(2 * pi * (1:365) / 365) noise <- rnorm(365, mean = 0, sd = 5) values <- trend + seasonal + noise # Create time series plot plot(dates, values, type = "l", main = "Time Series Analysis", xlab = "Date", ylab = "Value", col = "blue") # Add trend line lines(dates, trend, col = "red", lwd = 2) # Add legend legend("topleft", legend = c("Observed", "Trend"), col = c("blue", "red"), lty = 1, lwd = c(1, 2))

R Packages and Functions¶

In [ ]:

# Custom functions
calculate_stats <- function(x) {
  stats <- list(
    mean = mean(x, na.rm = TRUE),
    median = median(x, na.rm = TRUE),
    sd = sd(x, na.rm = TRUE),
    min = min(x, na.rm = TRUE),
    max = max(x, na.rm = TRUE),
    n = length(x[!is.na(x)])
  )
  return(stats)
}

# Test the function
test_data <- c(23, 45, 67, 89, 12, 34, 56, 78, 90, NA, 43)
results <- calculate_stats(test_data)

print("Statistical Summary:")
for (name in names(results)) {
  cat(sprintf("%-10s: %.2f\n", name, results[[name]]))
}

# Custom functions calculate_stats <- function(x) { stats <- list( mean = mean(x, na.rm = TRUE), median = median(x, na.rm = TRUE), sd = sd(x, na.rm = TRUE), min = min(x, na.rm = TRUE), max = max(x, na.rm = TRUE), n = length(x[!is.na(x)]) ) return(stats) } # Test the function test_data <- c(23, 45, 67, 89, 12, 34, 56, 78, 90, NA, 43) results <- calculate_stats(test_data) print("Statistical Summary:") for (name in names(results)) { cat(sprintf("%-10s: %.2f\n", name, results[[name]])) }

Installing R Packages¶

You can install R packages directly from a notebook cell:

In [ ]:

# Example of installing packages (commented out to avoid actual installation)
# install.packages("ggplot2")
# install.packages("dplyr")
# install.packages("tidyr")
# install.packages("caret")

print("Use install.packages('package_name') to install R packages")
print("Popular R packages for data science:")
print("- ggplot2: Advanced data visualization")
print("- dplyr: Data manipulation")
print("- tidyr: Data tidying")
print("- caret: Machine learning")

# Example of installing packages (commented out to avoid actual installation) # install.packages("ggplot2") # install.packages("dplyr") # install.packages("tidyr") # install.packages("caret") print("Use install.packages('package_name') to install R packages") print("Popular R packages for data science:") print("- ggplot2: Advanced data visualization") print("- dplyr: Data manipulation") print("- tidyr: Data tidying") print("- caret: Machine learning")

Summary¶

The R kernel (IRkernel) provides:

• Comprehensive statistical computing capabilities
• Excellent built-in visualization functions
• Extensive package ecosystem (CRAN)
• Specialized tools for various domains
• Strong academic and research community

R in Jupyter is ideal for:

• Statistical analysis and modeling
• Data visualization and exploration
• Academic research and publication
• Bioinformatics and genomics
• Time series analysis