This book introduces the open source R software language that can be
implemented in biostatistics for data organization, statistical
analysis, and graphical presentation. In the years since the authors'
2014 work Introduction to Data Analysis and Graphical Presentation in
Biostatistics with R, the R user community has grown exponentially and
the R language has increased in maturity and functionality. This updated
volume expands upon skill-sets useful for students and practitioners in
the biological sciences by describing how to work with data in an
efficient manner, how to engage in meaningful statistical analyses from
multiple perspectives, and how to generate high-quality graphics for
professional publication of their research.
A common theme for research in the diverse biological sciences is that
decision-making depends on the empirical use of data. Beginning with a
focus on data from a parametric perspective, the authors address topics
such as Student t-Tests for independent samples and matched pairs;
oneway and twoway analyses of variance; and correlation and linear
regression. The authors also demonstrate the importance of a
nonparametric perspective for quality assurance through chapters on the
Mann-Whitney U Test, Wilcoxon Matched-Pairs Signed-Ranks test,
Kruskal-Wallis H-Test for Oneway Analysis of Variance, and the Friedman
Twoway Analysis of Variance.
To address the element of data presentation, the book also provides an
extensive review of the many graphical functions available with R. There
are now perhaps more than 15,000 external packages available to the R
community. The authors place special emphasis on graphics using the
lattice package and the ggplot2 package, as well as less common, but
equally useful, figures such as bean plots, strip charts, and violin
plots.
A robust package of supplementary material, as well as an introduction
of the development of both R and the discipline of biostatistics, makes
this ideal for novice learners as well as more experienced
practitioners.
