Statistical Analysis using R PDF
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Summary
This document provides an introduction to using R for statistical analysis. It discusses the key features of R, such as its extensive libraries for statistical analysis and visualization capabilities. Data preparation, including importing and cleaning, is also covered. The document targets an audience interested in learning statistical methods implemented in R.
Full Transcript
Module 4 Statistical analysis using R Introduction to R for Statistical Analysis: R is an open-source statistical programming language that provides a robust data analysis, statistical modeling, and data visualization environment. It's widely used in academia, re...
Module 4 Statistical analysis using R Introduction to R for Statistical Analysis: R is an open-source statistical programming language that provides a robust data analysis, statistical modeling, and data visualization environment. It's widely used in academia, research, and industry because of its ability to handle complex statistical calculations. Key features of R include: Wide variety of statistical packages: Over 18,000 packages are available on CRAN (Comprehensive R Archive Network) for specialized statistical analysis. Rich data visualization: Packages like ggplot2 and lattice offer powerful tools for creating high-quality visualizations. Handling various data formats: R can manage different data structures such as vectors, matrices, data frames, and lists. Preparing Data for Statistical Analysis in R: Data preparation is a crucial step before conducting any statistical analysis. This involves cleaning, transforming, and organizing the data. Common data preparation tasks in R include: Data Import: You can import data from various formats such as CSV, Excel, databases, and even directly from web APIs. # Importing a CSV file data
