Insights from data with R : an introduction for the life and environmental sciences / Owen L Petchey [and three others].

Format:

Book

Author/Creator:

Petchey, Owen L., author.

Series:

Oxford scholarship online.

Oxford scholarship online

Language:

English

Subjects (All):

Science--Data processing.

Science.

R (Computer program language).

Physical Description:

1 online resource (320 pages) : illustrations (colour).

Edition:

First edition.

Place of Publication:

Oxford, England ; New York, New York : Oxford University Press, [2021]

Summary:

This accessible and engaging text provides readers with the knowledge, experience, and confidence to work with raw data and unlock essential information (insights) from data summaries and visualisations.

Contents:

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!.

Notes:

Includes bibliographical references and index.

Description based on print version record.

Includes Index.

ISBN:

0-19-188435-9

0-19-258973-3