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.