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Biological Data Science with R [NulledPremium]
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English | September 20, 2024 | ASIN: B0DHPCBJSL | 420 pages | EPUB | 3.83 Mb
"Biological Data Science with R" by Stephen D. Turner, Ph.D., is your essential guide to harnessing the power of R for biological data analysis. From foundational concepts to advanced data science techniques, this book is perfect for beginners and experts alike. With practical examples and hands-on exercises, you'll learn to manage, analyze, and visualize large-scale biological data, making your research more robust and reproducible.
Here's what you'll find inside:
Basics: Introduction to R and RStudio, covering basic operations and data structures to get you started with statistical computing in R.
Tibbles: Learn how to use tibbles (data frames) to handle tabular data efficiently, and explore techniques for importing and inspecting data.
Data Manipulation with dplyr: Master advanced data manipulation and analysis using the dplyr package, applying the split-apply-combine strategy to large datasets.
Tidy Data and Advanced Data Manipulation: Discover how to clean and transform data into a tidy format using the tidyr package, making it ready for analysis.
Data Visualization with ggplot2: Explore the ggplot2 package to create stunning visualizations of your data, including bivariate and univariate plots.
Refresher: Tidy Exploratory Data Analysis: Apply tidyverse tools for exploratory data analysis with real-world examples, preparing your data for deeper analysis.
Reproducible Reporting with RMarkdown: Learn the importance of reproducible research and how to create dynamic, reproducible reports using RMarkdown.
Essential Statistics: Get a concise overview of essential statistical methods in R, including t-tests, ANOVA, and regression models.
Survival Analysis: Delve into survival analysis using the Cox Proportional Hazards model and Kaplan-Meier plots for analyzing time-to-event data.
Predictive Analytics: Predicting and Forecasting Influenza: Explore predictive modeling techniques and forecasting methods using real-world influenza data.
Text Mining and NLP: Uncover the power of text mining and natural language processing (NLP) to analyze textual data, from sentiment analysis to topic modeling.
Count-Based Differential Expression Analysis of RNA-seq Data: Dive into RNA-seq data analysis using DESeq2, covering all steps from data import to visualization.
Visualizing and Annotating Phylogenetic Trees: Visualize and annotate phylogenetic trees with the ggtree package, integrating various data types for comprehensive insights.
Whether you're a student, researcher, or data enthusiast, "Biological Data Science with R" equips you with the skills and confidence to tackle biological data challenges with ease. Get ready to transform the way you work with biological data! |
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