Note: Cover -- Insights from Data with R: An Introduction for the Life and Environmental Sciences -- Copyright -- Preface -- Overview -- The learning 'curve' -- Untidy and dirty data -- No statistical tests or models -- Exploratory data analysis -- Zen and the art of 'data science' -- Open-science trends -- Intended readers -- How is the book organized? -- Online companion material -- Boxes -- Some ideas for instructors using this book -- Relationship with Getting Started with R (GSwR), second edition, Beckerman, Childs, and Petchey (2017) -- Acknowledgements -- Contents -- Chapter 1: Introduction -- 1.1 What are insights? -- 1.1.1 Dictionary -- 1.1.2 The business perspective -- 1.1.3 Our definition -- 1.1.4 Our ecology example . . . we love fruit -- 1.2 Question, question, question (how are data born?) -- 1.3 But what exactly are data? -- 1.4 Response and predictor variables -- 1.5 Some key features of datasets -- 1.6 Demonstrations of getting insights from data -- 1.7 The general Insights workflow -- 1.8 Summing up and looking forward -- Chapter 2: Getting acquainted -- 2.1 Getting acquainted with R and RStudio -- 2.1.1 Why r? -- 2.1.2 Why rstudio? -- 2.1.3 Getting and installing r -- 2.1.4 Getting and installing rstudio -- 2.1.5 A brief tour of rstudio -- 2.2 Your first R command! -- 2.2.1 Getting to know r a little better -- 2.2.2 Storing and reusing results -- 2.2.3 What names should i use? -- 2.3 Writing scripts -- 2.3.1 Comments in your scripts -- 2.3.2 Save and keep safe your script file -- 2.3.3 Running your scripts -- 2.4 When things go wrong… -- 2.4.1 Errors -- 2.4.2 Warnings -- 2.4.3 The dreaded + -- 2.5 Functions -- 2.5.1 Functions, the sequel -- 2.6 Add-on packages -- 2.6.1 Finding add-on packages -- 2.6.2 Installing (downloading) packages -- 2.6.3 Loading packages -- 2.6.4 An analogy -- 2.6.5 Updating r, rstudio, and your packages. , 2.7 Getting help -- 2.7.1 R help system and files -- 2.7.2 Navigating help files -- 2.7.3 Vignettes -- 2.7.4 Cheat sheets -- 2.7.5 Other sources of help -- 2.7.6 Asking for help from others -- 2.8 Common pitfalls -- 2.9 Summing up and looking forward -- Chapter 3: Workflow Demonstration part 1: Preparation -- 3.1 What is the question? -- 3.1.1 The three response variables -- 3.1.2 The hypotheses -- 3.2 Design of the study -- 3.3 Preparing your data -- 3.3.1 Acquire the dataset -- 3.4 Preparing your computer -- 3.4.1 Making the project folder for the bat data -- 3.4.2 Projects in rstudio -- 3.4.3 create a new r script and load packages -- 3.5 Get the data into R -- 3.5.1 View and refine the import -- 3.6 Getting going with data management -- 3.6.1 How the data are stored in r -- 3.7 Clean and tidy the data -- 3.7.1 Tidying the data -- 3.7.2 Cleaning the data -- 3.7.3 Refine the variable names -- 3.7.4 Fix the dates -- 3.7.5 Rename some values in a variable -- 3.7.6 Check for duplicates -- 3.7.7 Check for implausible and invalid values -- 3.7.8 What about those nas? -- 3.8 Stop that! Don't even think about it! -- 3.8.1 Don't mess with the 'working directory' -- 3.8.2 Don't use the data import tool or -- 3.8.3 Don't even think about using the attach function -- 3.8.4 Avoid using square brackets or dollar signs -- 3.9 Summing up and looking forward -- Chapter 4: Workflow Demonstration part 2: Getting insights -- 4.1 Initial insights 1: Numbers and counting -- 4.1.1 Our first insights: the number, sex, and age of bats -- 4.2 Initial insights 2: Distributions -- 4.2.1 Insights . . . . you've done it! -- 4.3 Transform the data -- 4.4 Insights about our questions -- 4.4.1 Distribution of number of prey -- 4.4.2 Shapes: mean wingspan -- 4.4.3 Shapes: proportion migratory -- 4.4.4 relationships -- Dietary sex differences -- Age-sex interactions. , 4.4.5 Communication (beautifying the graphs) -- 4.4.6 Beautifying the wingspan, age and sex graph -- 4.5 Another view of the question and data -- 4.5.1 Before you continue… -- 4.5.2 A prey-centric view -- Transform the data -- Visualizing the proportions -- Odds and odds ratios -- 4.6 A caveat -- 4.7 Summing up and looking forward -- 4.8 A small reward, if you like dogs -- Chapter 5: Dealing with data 1: Digging into dplyr -- 5.1 Introducing dplyr -- 5.1.1 Selecting variables with the select function -- 5.1.2 Renaming variables with select and rename -- 5.1.3 Creating new variables with the mutatefunction -- 5.1.4 Getting particular observations with filter -- 5.1.5 Ordering observations with arrange -- 5.2 Grouping and summarizing data with dplyr -- 5.2.1 Summarizing data-the nitty-gritty -- 5.2.2 Grouped summaries using group_by magic -- 5.2.3 More than one grouping variable -- 5.2.4 Using group_by with other verbs -- 5.2.5 Removing grouping information -- 5.3 Summing up and looking forward -- Chapter 6: Dealing with data 2: Expanding your toolkit -- 6.1 Pipes and pipelines -- 6.1.1 Why do we need pipes? -- 6.1.2 On why you shouldn't nest functions -- 6.2 Subduing the pesky string -- 6.3 Elegantly managing dates and times -- 6.3.1 Date/time formats -- 6.3.2 Dtes in the bat project data -- 6.3.3 Why parse dates? -- 6.3.4 More about parsing dates/times -- 6.3.5 Calculations with dates/times -- 6.4 Changing between wider and longer data arrangements -- 6.4.1 Going longer -- 6.4.2 Going wider -- 6.5 Summing up and looking forward -- Chapter 7: Getting to grips with ggplot2 -- 7.1 Anatomy of a ggplot -- 7.1.1 Layers -- 7.1.2 Scales -- 7.1.3 Coordinate system -- 7.1.4 Fantastic faceting -- 7.2 Putting it into practice -- 7.2.1 Inheriting data and aesthetics from ggplot -- 7.3 Beautifying plots -- 7.3.1 Working with layer-specific geom properties. , 7.3.2 Adding titles and labels -- 7.3.3 Themes -- 7.4 Summing up and looking forward -- Chapter 8: Making deeper insights part 1: Working with single variables -- 8.1 Variables and data -- 8.1.1 Numeric versus categorical variables -- 8.1.2 Ratio versus interval scales -- 8.2 Samples and distributions -- 8.2.1 Understanding numerical variables -- 8.3 Graphical summaries of numeric variables -- 8.3.1 Making some insights about wingspan -- 8.3.2 Descriptive statistics for numeric variables -- 8.3.3 Measuring central tendency -- 8.3.4 Measuring dispersion -- 8.3.5 Mapping measures of central tendency and dispersion to a figure -- 8.3.6 Combining histograms and boxplots -- 8.4 A moment with missing values in numeric variables (NAs) -- 8.5 Exploring a categorical variable -- 8.5.1 Understanding categorical variables -- Numerical summaries -- Graphical summaries of categorical variables -- 8.6 Summing up and looking forward -- 8.7 A cat-related reward -- Chapter 9: Making deeper insights part 2: Relationships among (many) variables -- 9.1 Associations between two numeric variables -- 9.1.1 Descriptive statistics: correlations -- 9.1.2 Other measures of correlation -- 9.1.3 Graphical summaries between two numericvariables: the scatterplot -- 9.2 Associations between two categorical variables -- 9.2.1 Numerical summaries -- 9.2.2 Graphical summaries -- 9.2.3 An alternative, and perhaps more valuable -- 9.3 Categorical-numerical associations -- 9.3.1 Numerical summaries -- 9.3.2 Graphical summaries for numerical versus categorical data -- 9.3.3 Alternatives to box-and-whisker plots -- 9.4 Building in complexity: Relationships among three or morevariables -- 9.5 Summing up and looking forward -- Chapter 10: Looking back and looking forward -- 10.1 Next learning steps -- 10.2 Reproducibility: What, why, and how?. , 10.2.1 Why should you try and make your work reproducible? -- 10.2.2 How can you make your work more reproducible? -- 10.3 Congratulations! -- Index.

Note: Cover -- Contents -- Preface -- Introduction to the second edition -- What this book is about -- How the book is organized -- Why R? -- Updates -- Acknowledgements -- 1 Getting and Getting Acquainted with R -- 1.1 Getting started -- 1.2 Getting R -- 1.3 Getting RStudio -- 1.4 Let's play -- 1.5 Using R as a giant calculator (the size of your computer) -- 1.6 Your first script -- 1.7 Intermezzo remarks -- 1.8 Important functionality: packages -- 1.9 Getting help -- 1.10 A mini-practical-some in-depth play -- 1.11 Some more top tips and hints for a successful first (and more) R experience -- Appendix 1a Mini-tutorial solutions -- Appendix 1b File extensions and operating systems -- 2 Getting Your Data into R -- 2.1 Getting data ready for R -- 2.2 Getting your data into R -- 2.3 Checking that your data are your data -- 2.4 Basic troubleshooting while importing data -- 2.5 Summing up -- Appendix Advanced activity: dealing with untidy data -- 3 Data Management, Manipulation, and Exploration with dplyr -- 3.1 Summary statistics for each variable -- 3.2 dplyr verbs -- 3.3 Subsetting -- 3.4 Transforming -- 3.5 Sorting -- 3.6 Mini-summary and two top tips -- 3.7 Calculating summary statistics about groups of your data -- 3.8 What have you learned …lots -- Appendix 3a Comparing classic methods and dplyr -- Appendix 3b Advanced dplyr -- 4 Visualizing Your Data -- 4.1 The first step in every data analysis-making a picture -- 4.2 ggplot2: a grammar for graphics -- 4.3 Box-and-whisker plots -- 4.4 Distributions: making histograms of numeric variables -- 4.5 Saving your graphs for presentation, documents, etc. -- 4.6 Closing remarks -- 5 Introducing Statistics in R -- 5.1 Getting started doing statistics in R -- 5.2 χ2 contingency table analysis -- 5.3 Two-sample t-test -- 5.4 Introducing... linear models -- 5.5 Simple linear regression. , 5.6 Analysis of variance: the one-way ANOVA -- 5.7 Wrapping up -- Appendix Getting packages not on CRAN -- 6 Advancing Your Statistics in R -- 6.1 Getting started with more advanced statistics -- 6.2 The two-way ANOVA -- 6.3 Analysis of covariance (ANCOVA) -- 6.4 Overview: an analysis workflow -- 7 Getting Started with Generalized Linear Models -- 7.1 Introduction -- 7.2 Counts and rates-Poisson GLMs -- 7.3 Doing it wrong -- 7.4 Doing it right-the Poisson GLM -- 7.5 When a Poisson GLM isn't good for counts -- 7.6 Summary, and beyond simple Poisson regression -- 8 Pimping Your Plots: Scales and Themes in ggplot2 -- 8.1 What you already know about graphs -- 8.2 Preparation -- 8.3 What you may want to customize -- 8.4 Axis labels, axis limits, and annotation -- 8.5 Scales -- 8.6 The theme -- 8.7 Summing up -- 9 Closing Remarks: Final Comments andEncouragement -- General Appendices -- Appendix 1 Data Sources -- Appendix 2 Further Reading -- Appendix 3 R Markdown -- Index.