Claisse Lab @ Cal Poly Pomona
Online R & Stats Textbooks
(Includes R code unless otherwise noted)
Statistical Inference via Data Science (A ModernDive into R and the Tidyverse)
by Chester Ismay and Albert Y. Kim
Introduction to R, RStudio and the Tidyverse for visualizing, wrangling and analyzing your data.
A guide to making visualizations that accurately reflect the data, tell a story, and look professional. And the R code for the book can be found on GitHub, at https://github.com/clauswilke/dataviz
Takes a statistical modeling approach, focusing on estimates of effects and uncertainty.
A "book" written to accompany his biostats course, lots of good guidance and resources.
Modern approach to introductory statistics, good sections on plotting and summarizing data.
Mostly covers details of using the tidyverse to import, wrangle and get your data ready to analyze.
Modern take on introductory statistics in an R based format.
More traditional statistics text, covering details of specific hypothesis tests (not R based, but useful background)
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. (Although some of the R code relies on Base R instead of Tidyverse functions).
Multivariate analysis including ordination (e.g., PCA, nMDS plots), cluster analysis and diversity analysis
Broadening Your Statistical Horizons: 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
A maintained list of of online R textbook resources
Everything you want to know about RMarkdown (no stats)