Online Books
Statistical Inference via Data Science (A ModernDive into R and the Tidyverse)
- Introduction to R, RStudio and the Tidyverse for visualizing, wrangling and analyzing your data.
Applied Statistics for Experimental Biology by Jeffrey A. Walker
- (Primary text we follow for BIO 5100/L) Takes a statistical modeling approach, focusing on estimates of effects and uncertainty instead of traditional hypothesis testing.
R for Data Science (2nd Edition) by Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund
- Mostly covers details of using the tidyverse to import, wrangle and get your data ready to analyze.
Spatial Statistics for Data Science: Theory and Practice with R by Paula Moraga
- Describes statistical methods, modeling approaches, and visualization techniques to analyze spatial data using R. Includes an overview of types of spatial data, relevant R packages for analyzing and visualizing spatial data, explanations of spatial statistics, and fully reproducible examples analyzing areal, geostatistical, and point pattern data.
Guide to Effect Sizes and Confidence Intervals
- Collaborative guide aims to provide academics, students and researchers with hands-on, step-by-step instructions for calculating effect sizes and confidence intervals for common statistical procedures.
Fundamentals of Data Visualization (using R) by Claus O. Wilke
A guide to making visualizations that accurately reflect the data, tell a story, and look professional.
The R code for the book can be found on GitHub, at https://github.com/clauswilke/dataviz
Analysis of community ecology data in R by David Zelený
- Multivariate analysis including ordination (e.g., PCA, nMDS plots), cluster analysis and diversity analysis
Applied Biostats by Yaniv Brandvain
- A “book” written to accompany his biostats course, lots of good guidance and resources.
Statistical Thinking for the 21st Century by Russell A. Poldrack
- Modern approach to introductory statistics, good sections on plotting and summarizing data.
Introduction to Modern Statistics by Mine Çetinkaya-Rundel & Johanna Hardin
- Modern take on introductory statistics in an R based format.
Modern Statistics for Modern Biology by Susan Holmes, Wolfgang Huber
Covers many modern approaches to data analysis, and many different types of data including RNA-Seq, flow-cytometry, taxa abundances, imaging data and single cell measurements.
Note some of the R code relies on Base R instead of Tidyverse functions.
Beyond Multiple Linear Regression: Generalized Linear Models and Multilevel Models By Julie Legler and Paul Roback
- More advanced stats topics including Generalized Linear Models likelihood theory, zero-inflated Poisson, and parametric bootstrapping
Biological Statistics by John H. McDonald
- Traditional statistics text, covering details of specific hypothesis tests (not R based, but potentially useful if your adviser makes you to use old school statistics)
The Big Book of R (links to online textbooks)
- A maintained list of of online R textbook resources