These notes are an introduction to using the statistical software package R for an introductory statistics course. They are meant to accompany an introductory statistics book such as Kitchens “Exploring Statistics”. The goals are not to show all the features of R, or to replace a standard textbook, but rather to be used with a textbook to illustrate the features of R that can be learned in a one-semester, introductory statistics course.

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Chapter from "Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan (Second Edition)" by John K. Kruschke

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Author: Marco Martinez

This material is intended as an introductory guide to data analysis with the R system, to assist in statistical computing training for life science researchers.  It was produced as companion material for a seminar (R Tutorial for Life Sciences) given at The University of Tennessee in the spring of 2009, sponsored by the National Institute for Mathematical and Biological Synthesis (NIMBioS).

The principal aim is to provide a step-by-step guide on the use of R to carry out statistical analysis and techniques widely used in the life sciences.  In each section, there is a detailed explanation of a command in R, followed by a biological example with all the instructions (in red) needed to run the test and with the corresponding output in R (in blue).  There is assumed previous knowledge in statistics and experimental design, essentially corresponding to a basic undergraduate introductory statistic course.

Author: Andy Hector

    " He currently convenes and teaches statistics on the Quantitative Methods for Biologsits course for undergraduates at the Universtiy of Oxford.  He has contributed to several publications on eccologal analysis."

Published: March 15, 2015 by Oxford University Press

Summary: This book provides a contemporary introduction to the classical techniques and modern ectensions of linear model analysis.  It emphasizes on estimation-based approach that accounts for recent criticisms of over-use of probability values and introduces the alternative approach using information criteria.  It is based on the use of the open-source R programming language for statistics and graphics that is rapidly becoming the lingua franca in many areas of science.  Statistics is introduced through worked analyses preformed in R using interesting data sets from ecology, evoluntionary biology, and environmental science.  Data sets and R scripts are available as supporting material.

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Author: Gregg Hartvigsen

    "Taught at a workshop on network analysis using R at the national Institute for mathematical and Biological Synthesis at the University of Tennessee, Knoxville."

Published: February 18, 2014 by Columbia University Press

Summary: This book guides readers through the processes of entering data into R, working with data in R, and using R to visualize data using histograms, boxplots, barplots, scatterplots, and other common graph types.  Hartvigsen covers testing data for normality, defining and identifying outliers, and working with non-normal data.  Students are introduced to common one- and two-sample tests as well as one- and two-analysis of variance (ANOVA), correlation, and linear and non-linear regression analyses. Also included is a secion on advanced procedures and a chapter inroducing algorithms and the art of programming using R.

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Author: Peter Dalgaard

    "Has been a key member of the R Core Team since August 1997 and is well known among R users for his activity on R"

Published: January 9, 2004 by Springer

Summary: This book provides an elementary-level introduction to R targeting both non-statistician scientists in various fields and students of statistics.    The main mode of presentation is via code examples with liberal commenting of the code and output, from the computational as well as the statistical viewpoint.  Supplementary R package can be downloaded that contains the data sets.

The book includes examples of statistical standard deviations, one- and two- sample tests with continuous data, regression analysis, one- and two- way analysis of variance, regression analysis, analysis of tabular data and sample size calculations.  In the last chapters includes  multiple linear regression analysis, linear models in general, logistic regression, survival analysis, Poisson regression and nonlinear regression.

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Author: Andrew P. Beckerman, Owen L. Petchey

    "Evolutionary Ecologists with over 20 years of combined experience using R for data analysis and visualization"

Published: July 22, 2012 by Oxford University Press

This book provides a fundamental introduction for biologists new to R.  While teaching how to import, explore, graph and analyze data, it keeps readers focussed on their ultimate goals-communicating their data in oral presentations, posters, papers, and reports.  It also provides a consistent workflow for using R that is simple, efficient, reliable, accurate, and reproducible.  The material in the book reproduces the engaging and sometimes humorous nature of the three-day course on which it is based.

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