R Programming Examples

Usin R environment on this website https://kapitals-pi.blogspot.com/p/learning-r-programming.html

Explore a variety of R code snippets showcasing data analysis, visualization, and more.

Basic R Operations & Visualizations

# Basic arithmetic and vector operations
numbers <- c(1, 2, 3, 4, 5)
mean_numbers <- mean(numbers)
print(paste("The mean of", toString(numbers), "is", mean_numbers))

# Simple histogram with base R
hist(iris$Sepal.Length, 
     main="Histogram of Sepal Length", 
     xlab="Sepal Length", 
     col="lightblue", 
     border="black")

# Using dplyr for data manipulation
library(dplyr)
mtcars %>%
  group_by(cyl) %>%
  summarise(avg_mpg = mean(mpg), 
            count = n()) %>%
  print()

# Advanced ggplot2 visualization
library(ggplot2)
ggplot(data = diamonds, aes(x = carat, y = price, color = cut)) +
  geom_point() +
  theme_minimal() +
  labs(title="Diamond Price vs Carat by Cut", 
       x="Carat", 
       y="Price")

# Linear regression example
model <- lm(mpg ~ wt + hp, data = mtcars)
summary(model)

# Creating a simple function
fibonacci <- function(n) {
  if(n <= 1) return(n)
  else return(fibonacci(n-1) + fibonacci(n-2))
}
print(sapply(1:10, fibonacci))

Demonstrates basic calculations, histogram plotting, data manipulation with dplyr, advanced plotting with ggplot2, statistical modeling, and custom function creation.

Advanced R Use Cases

# Creating a boxplot with base R
boxplot(mpg ~ cyl, data = mtcars,
        main="MPG by Number of Cylinders",
        xlab="Cylinders", ylab="Miles Per Gallon",
        col="lightgreen", border="darkblue")

# Using tidyr and dplyr for data reshaping and analysis
library(tidyr)
library(dplyr)
airquality %>%
  pivot_longer(cols = c(Ozone, Solar.R, Wind, Temp), 
               names_to = "Variable", 
               values_to = "Value") %>%
  group_by(Variable) %>%
  summarise(mean = mean(Value, na.rm = TRUE),
            sd = sd(Value, na.rm = TRUE)) %>%
  print()

# Interactive plot with plotly
library(plotly)
p <- plot_ly(data = iris, 
             x = ~Sepal.Length, 
             y = ~Sepal.Width, 
             color = ~Species, 
             type = "scatter", 
             mode = "markers") %>%
  layout(title = "Iris Sepal Dimensions by Species")
p

# Time series analysis with ts and forecast
library(forecast)
airpass <- ts(AirPassengers, frequency = 12)
model <- auto.arima(airpass)
forecast <- forecast(model, h = 12)
plot(forecast, main="12-Month Forecast of Air Passengers")

# Creating a simple Shiny app
library(shiny)
ui <- fluidPage(
  sliderInput("n", "Number of points", 10, 100, 50),
  plotOutput("scatter")
)
server <- function(input, output) {
  output$scatter <- renderPlot({
    plot(rnorm(input$n), rnorm(input$n), 
         main="Random Scatter Plot", 
         xlab="X", ylab="Y", col="purple")
  })
}
# Uncomment to run: shinyApp(ui, server)

# Matrix operations
A <- matrix(c(1, 2, 3, 4), nrow=2)
B <- matrix(c(5, 6, 7, 8), nrow=2)
matrix_product <- A %*% B
print("Matrix A:")
print(A)
print("Matrix B:")
print(B)
print("Matrix Product A*B:")
print(matrix_product)

Covers boxplot visualization, data reshaping with tidyr/dplyr, interactive plotting with plotly, time series forecasting, Shiny app structure, and matrix operations.