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Introduction to R

Introduction to R

R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's...

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Manipulating and Basic Analysis of Tabular Metagenomics Datasets Using R.

Alex Coleman¹, Martin Callaghan²

¹Research Computing, IT Services, University of Leeds, Leeds, UK. A.Coleman1@leeds.ac.uk.

Methods in Molecular Biology (Clifton, N.J.)

|May 31, 2023

Summary

This summary is machine-generated.

This study introduces R programming for metagenomics data analysis, highlighting tools for efficient and reproducible tabular data manipulation and basic analysis in bioinformatics workflows.

Keywords:

Data analysis Data handling Data manipulation Metagenomics datasets R programming language Reproducible research

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Area of Science:

Bioinformatics and Computational Biology
Genomics and Molecular Biology

Background:

Metagenomics analysis relies heavily on handling tabular datasets.
Reproducible and efficient data workflows are crucial in modern biological research.

Purpose of the Study:

To provide an introduction to the R programming language for metagenomics data handling.
To showcase R tools for efficient and reproducible manipulation and analysis of tabular metagenomic data.

Main Methods:

Outlining fundamental R programming concepts.
Demonstrating specific R packages and functions for data manipulation.
Illustrating basic analytical approaches for metagenomic datasets using R.

Main Results:

R offers versatile tools for tabular data manipulation.
R facilitates the development of computationally efficient and reproducible metagenomics workflows.
Basic data analysis of metagenomics datasets can be effectively performed using R.

Conclusions:

R is a powerful and accessible tool for metagenomics data analysis.
Utilizing R enhances the efficiency and reproducibility of bioinformatics pipelines.
This guide serves as a starting point for researchers new to R in metagenomics.