R Programming Tutorial - Learn the Basics of Statistical Computing

Introduction

This tutorial provides a hands-on overview of the R programming language, a critical tool in statistical computing and data science. Whether you're a beginner or looking to refresh your skills, this guide will walk you through the essentials of R, including installation, basic plotting, data manipulation, and more.

Step 1: Install R

• Download R from the Comprehensive R Archive Network (CRAN) at CRAN R Project.
• Choose the appropriate version for your operating system (Windows, Mac, or Linux).
• Follow the installation prompts to complete the setup.

Step 2: Install RStudio

• Download RStudio, a powerful IDE for R, from RStudio's website.
• Install RStudio following the on-screen instructions.
• Open RStudio after installation to start coding in R.

Step 3: Install R Packages

• R has numerous packages that extend its functionality. To install packages, use the following command in the R console:

  install.packages("package_name")
  

• Replace "package_name" with the name of the package you wish to install.

Step 4: Basic Plotting with plot()

• To create a simple plot, use the plot() function. For example:

  x <- 1:10
  y <- x^2
  plot(x, y)
  

• This will create a scatter plot of y against x.

Step 5: Create Bar Charts

• Use the barplot() function to create bar charts:

  counts <- table(c("A", "B", "A", "C", "B", "A", "C"))
  barplot(counts)
  

• This code generates a bar chart based on the frequency of each category.

Step 6: Create Histograms

• Histograms visualize the distribution of numerical data:

  data <- rnorm(100)
  hist(data)
  

• This code generates a histogram of 100 random numbers from a normal distribution.

Step 7: Create Scatterplots

• Scatterplots can be created similarly to basic plots:

  plot(x, y)
  

• This shows the relationship between two continuous variables.

Step 8: Overlaying Plots

• To overlay multiple plots, use the par() function:

  par(new = TRUE)
  plot(x, y)
  plot(x, y + 10, col = "red")
  

• This allows you to visualize multiple datasets on the same graph.

Step 9: Summary Statistics with summary()

• Use the summary() function to get descriptive statistics of your data:

  summary(data)
  

• This provides insights into the minimum, maximum, mean, and quartiles.

Step 10: Descriptive Statistics with describe()

• The describe() function from the psych package gives detailed statistics:

  library(psych)
  describe(data)
  

Step 11: Selecting Cases

• To filter or select specific cases in a dataset, use indexing:

  selected_data <- data[data > threshold]
  

Step 12: Understanding Data Formats

• R supports various data formats, including data frames and matrices. Use str() to inspect data structure:

  str(data_frame)
  

Step 13: Working with Factors

• Factors are used to handle categorical data in R:

  factor_variable <- factor(c("A", "B", "A", "C"))
  

Step 14: Entering Data

• Manually enter data using the data.frame() function:

  my_data <- data.frame(name = c("A", "B"), value = c(1, 2))
  

Step 15: Importing Data

• Import datasets from CSV files using:

  my_data <- read.csv("path/to/your/file.csv")
  

Step 16: Hierarchical Clustering

• Use the hclust() function for clustering analysis:

  dist_matrix <- dist(data)
  clusters <- hclust(dist_matrix)
  plot(clusters)
  

Step 17: Principal Component Analysis

• Perform PCA using the prcomp() function:

  pca_result <- prcomp(data, center = TRUE, scale = TRUE)
  summary(pca_result)
  

Step 18: Regression Analysis

• Conduct linear regression with the lm() function:

  model <- lm(y ~ x, data = my_data)
  summary(model)
  

Conclusion

This tutorial covers the foundational skills needed to get started with R programming. From installation to basic statistical analysis, you now have the tools to explore data science further. Consider exploring more advanced topics or specific packages to expand your R capabilities. Happy coding!