Welcome to the R Statistical Analysis tutorial! R is a powerful programming language and software environment for statistical computing and graphics. Whether you're a beginner or looking to deepen your skills, this guide will help you master essential statistical techniques using R. 🚀

Table of Contents

Getting Started with R

R is open-source and widely used in academia, research, and data science. To begin:

  1. Install R from https://cran.r-project.org/
  2. Use RStudio for an enhanced coding experience 📚
  3. Load datasets with read.csv() or read.table()
R_Logo

Basic Statistical Operations

R excels at performing calculations like mean, median, and standard deviation.

  • mean() computes the average of a dataset
  • median() finds the middle value
  • sd() calculates the standard deviation

Example: 

data <- c(1, 2, 3, 4, 5)
mean(data) # Output: 3
Statistical_Calculations

Data Visualization

Visualizing data is crucial for insights. Use packages like ggplot2 📈

  • Create histograms with ggplot() + geom_histogram()
  • Generate scatter plots using geom_point()
  • Customize themes with theme()
ggplot2_Charts

Advanced Analysis Techniques

Explore regression, hypothesis testing, and machine learning in R:

  • Linear regression: lm(y ~ x, data)
  • ANOVA: aov()
  • Clustering: kmeans()

For deeper learning, check out our R Programming Fundamentals guide! 🌐

Resources for Further Learning

Data_Visualization