Insights from Data with R : an Introduction for the Life and Environmental Sciences.

Includes index.

Includes bibliographical references and index.

Machine generated contents note: ch. 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 -- ch. 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 -- ch. 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 file choose -- 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 -- ch. 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 -- 4.4.5.Communication (beautifying the graphs) -- 4.4.6.Beautifying the wingspan, age, sex graph -- 4.5.Another view of the question and data -- 4.5.1.Before you continue... -- 4.5.2.A prey-centric view -- 4.6.A caveat -- 4.7.Summing up and looking forward -- 4.8.A small reward, if you like dogs -- ch. 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 mutate function -- 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 -- ch. 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.Dates 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 -- ch. 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 -- ch. 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 -- 8.6.Summing up and looking forward -- 8.7.A cat-related reward -- ch. 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 numeric variables: 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 more variables -- 9.5.Summing up and looking forward -- ch. 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!.