R-Ticulate : A Beginner's Guide to Data Analysis for Natural Scientists / Martin Bader and Sebastian Leuzinger.

Format:

Book

Author/Creator:

Bader, Martin (Professor), author.

Leuzinger, Sebastian, author.

Language:

English

Subjects (All):

R (Computer program language).

Science--Data processing.

Science.

Science--Statistical methods.

Physical Description:

1 online resource (222 pages)

Edition:

First edition.

Place of Publication:

Hoboken, New Jersey : John Wiley & Sons, Inc., [2024]

Summary:

An accessible learning resource that develops data analysis skills for natural science students in an efficient style using the R programming language R-ticulate: A Beginner's Guide to Data Analysis for Natural Scientists is a compact, example-based, and user-friendly statistics textbook without unnecessary frills, but instead filled with engaging, relatable examples, practical tips, online exercises, resources, and references to extensions, all on a level that follows contemporary curricula taught in large parts of the world. The content structure is unique in the sense that statistical skills are introduced at the same time as software (programming) skills in R. This is by far the best way of teaching from the authors' experience. Readers of this introductory text will find: * Explanations of statistical concepts in simple, easy-to-understand language * A variety of approaches to problem solving using both base R and tidyverse * Boxes dedicated to specific topics and margin text that summarizes key points * A clearly outlined schedule organized into 12 chapters corresponding to the 12 semester weeks of most universities While at its core a traditional printed book, R-ticulate: A Beginner's Guide to Data Analysis for Natural Scientists comes with a wealth of online teaching material, making it an ideal and efficient reference for students who wish to gain a thorough understanding of the subject, as well as for instructors teaching related courses.

Contents:

Cover

Title Page

Copyright

Contents

Foreword

Preface

About the Companion Website

Chapter 1 Hypotheses, Variables, Data

1.1 Occam's Razor

1.2 Scientific Hypotheses

1.3 The Choice of a Software

1.3.1 First Steps in R

1.4 Variables

1.4.1 Variable Names and Values

1.4.2 Types of Variables

1.4.3 Predictor and Response Variables

1.5 Data Processing and Data Formats

1.5.1 The Long vs. the Wide Format

1.5.2 Choice of Variable, Dataset, and File Names

1.5.3 Adding, Removing, and Subsetting Variables and Data Frames

1.5.4 Aggregating Data

1.5.5 Working with Time and Strings

Chapter 2 Measuring Variation

2.1 What Is Variation?

2.2 Treatment vs. Control

2.3 Systematic and Unsystematic Variation

2.4 The Signal‐to‐Noise Ratio

2.5 Measuring Variation Graphically

2.6 Measuring Variation Using Metrics

2.7 The Standard Error

2.8 Population vs. Sample

Chapter 3 Distributions and Probabilities

3.1 Probability Distributions

3.2 Finding the Best Fitting Distribution for Sample Data

3.2.1 Graphical Tools

3.2.2 Goodness‐of‐Fit Tests

3.3 Quantiles

3.4 Probabilities

3.4.1 Density Functions (dnorm, dbinom,...)

3.4.2 Probability Distribution Functions (pnorm, pbinom,...)

3.4.3 Quantile Functions (qnorm, qbinom,...)

3.4.4 Random Sampling Functions (rnorm, rbinom,...)

3.5 The Normal Distribution

3.6 Central Limit Theorem

3.7 Test Statistics

3.7.1 Null and Alternative Hypotheses

3.7.2 The Alpha Threshold and Significance Levels

3.7.3 Type I and Type II Errors

References

Chapter 4 Replication and Randomisation

4.1 Replication

4.2 Statistical Independence

4.3 Randomisation

4.4 Randomisation in R

4.5 Spatial Replication and Randomisation in Observational Studies

Chapter 5 Two‐Sample and One‐Sample Tests.

5.1 The t‐Statistic

5.2 Two Sample Tests: Comparing Two Groups

5.2.1 Student's t‐Test

5.2.1.1 Testing for Normality

5.2.1.2 What to Write in a Report or Paper and How to Visualise the Results of a t‐Test

5.2.1.3 Two‐Tailed vs. One‐Tailed t‐Tests

5.2.2 Rank‐Based Two‐Sample Tests

5.3 One‐Sample Tests

5.4 Power Analyses and Sample Size Determination

Chapter 6 Communicating Quantitative Information Using Visuals

6.1 The Fundamentals of Scientific Plotting

6.2 Scatter Plots

6.3 Line Plots

6.4 Box Plots and Bar Plots

6.5 Multipanel Plots and Plotting Regions

6.6 Adding Text, Formulae, and Colour

6.7 Interaction Plots

6.8 Images, Colour Contour Plots, and 3D Plots

6.8.1 Adding Images to Plots

6.8.2 Colour Contour Plots

Chapter 7 Working with Categorical Data

7.1 Tabling and Visualising Categorical Data

7.2 Contingency Tables

7.3 The Chi‐squared Test

7.4 Decision Trees

7.5 Optimising Decision Trees

Chapter 8 Working with Continuous Data

8.1 Covariance

8.2 Correlation Coefficient

8.3 Transformations

8.4 Plotting Correlations

8.5 Correlation Tests

Chapter 9 Linear Regression

9.1 Basics and Simple Linear Regression

9.1.1 Making Sense of the summary Output for Regression Models Fitted with lm

9.1.2 Model Diagnostics

9.1.3 Model Predictions and Visualisation

9.1.4 What to Write in a Report or Paper?

9.1.4.1 Material and Methods

9.1.4.2 Results

9.1.5 Dealing with Variance Heterogeneity

9.2 Multiple Linear Regression

9.2.1 Multicollinearity in Multiple Regression Models

9.2.2 Testing Interactions Among Predictors

9.2.3 Model Selection and Comparison

9.2.4 Variable Importance

9.2.5 Visualising Multiple Linear Regression Results

References.

Chapter 10 One or More Categorical Predictors - Analysis of Variance

10.1 Comparing Groups

10.2 Comparing Groups Numerically

10.3 One‐way ANOVA Using R

10.4 Checking for the Model Assumptions

10.5 Post Hoc Comparisons

10.6 Two‐way ANOVA and Interactions

10.7 What If the Model Assumptions Are Violated?

Reference

Chapter 11 Analysis of Covariance (ANCOVA)

11.1 Interpreting ANCOVA Results

11.2 Post Hoc Test for ANCOVA

Chapter 12 Some of What Lies Ahead

12.1 Generalised Linear Models

12.2 Nonlinear Regression

12.2.1 Initial Parameter Estimates (Starting Values)

12.2.2 Nonlinear Model Fitting and Visualisation

12.3 Generalised Additive Models

12.4 Modern Approaches to Dealing with Heteroscedasticity

12.4.1 Variance Modelling Using Generalised Least‐squares Estimation

12.4.2 Robust, Heteroscedasticity‐Consistent Covariance Matrix Estimation

Index

EULA.

Notes:

Description based on publisher supplied metadata and other sources.

Description based on print version record.

Includes bibliographical references and index.

ISBN:

9781119717980

1119717981

9781119718000

1119718007